From 22ccb4b83fa4686b22a0a18039816f7c109e933a Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Mon, 17 Nov 2025 16:00:39 +0800 Subject: [PATCH 01/39] docs(readme): update star request message styling --- reme_ai/config/default.yaml | 4 + reme_ai/context/file_system_tool/__init__.py | 31 ++ reme_ai/context/file_system_tool/edit_op.py | 153 +++++++++ .../context/file_system_tool/edit_prompt.yaml | 23 ++ reme_ai/context/file_system_tool/glob_op.py | 295 ++++++++++++++++++ .../context/file_system_tool/glob_prompt.yaml | 11 + reme_ai/context/file_system_tool/grep_op.py | 176 +++++++++++ .../context/file_system_tool/grep_prompt.yaml | 15 + .../context/file_system_tool/read_file_op.py | 120 +++++++ .../file_system_tool/read_file_prompt.yaml | 12 + .../context/file_system_tool/rip_grep_op.py | 160 ++++++++++ .../file_system_tool/rip_grep_prompt.yaml | 15 + .../context/file_system_tool/write_file_op.py | 89 ++++++ .../file_system_tool/write_file_prompt.yaml | 9 + .../file_system_tool/write_todos_op.py | 102 ++++++ .../file_system_tool/write_todos_prompt.yaml | 35 +++ reme_ai/context/offload/__init__.py | 0 reme_ai/context/offload/context_compact.py | 20 ++ 18 files changed, 1270 insertions(+) create mode 100644 reme_ai/context/file_system_tool/__init__.py create mode 100644 reme_ai/context/file_system_tool/edit_op.py create mode 100644 reme_ai/context/file_system_tool/edit_prompt.yaml create mode 100644 reme_ai/context/file_system_tool/glob_op.py create mode 100644 reme_ai/context/file_system_tool/glob_prompt.yaml create mode 100644 reme_ai/context/file_system_tool/grep_op.py create mode 100644 reme_ai/context/file_system_tool/grep_prompt.yaml create mode 100644 reme_ai/context/file_system_tool/read_file_op.py create mode 100644 reme_ai/context/file_system_tool/read_file_prompt.yaml create mode 100644 reme_ai/context/file_system_tool/rip_grep_op.py create mode 100644 reme_ai/context/file_system_tool/rip_grep_prompt.yaml create mode 100644 reme_ai/context/file_system_tool/write_file_op.py create mode 100644 reme_ai/context/file_system_tool/write_file_prompt.yaml create mode 100644 reme_ai/context/file_system_tool/write_todos_op.py create mode 100644 reme_ai/context/file_system_tool/write_todos_prompt.yaml create mode 100644 reme_ai/context/offload/__init__.py create mode 100644 reme_ai/context/offload/context_compact.py diff --git a/reme_ai/config/default.yaml b/reme_ai/config/default.yaml index 7d1dcee0..15dfc89a 100644 --- a/reme_ai/config/default.yaml +++ b/reme_ai/config/default.yaml @@ -156,6 +156,10 @@ flow: description: "user query" required: true + compact: + flow_content: ContextCompactOp() >> WriteFile() + .... + llm: default: backend: openai_compatible diff --git a/reme_ai/context/file_system_tool/__init__.py b/reme_ai/context/file_system_tool/__init__.py new file mode 100644 index 00000000..ee9b2214 --- /dev/null +++ b/reme_ai/context/file_system_tool/__init__.py @@ -0,0 +1,31 @@ +"""File system tool package. + +This package provides file-related operations that can be used in LLM-powered flows. +It includes ready-to-use operations for: + +- EditOp: File editing operation for replacing text within files +- GlobOp: File search operation for finding files matching glob patterns +- GrepOp: Text search operation for finding patterns in file contents +- ReadFileOp: File reading operation for reading file contents +- RipGrepOp: Text search operation using ripgrep for efficient pattern matching +- WriteFileOp: File writing operation for writing content to files +- WriteTodosOp: To-do list management operation for tracking subtasks +""" + +from .edit_op import EditOp +from .glob_op import GlobOp +from .grep_op import GrepOp +from .read_file_op import ReadFileOp +from .rip_grep_op import RipGrepOp +from .write_file_op import WriteFileOp +from .write_todos_op import WriteTodosOp + +__all__ = [ + "EditOp", + "GlobOp", + "GrepOp", + "ReadFileOp", + "RipGrepOp", + "WriteFileOp", + "WriteTodosOp", +] diff --git a/reme_ai/context/file_system_tool/edit_op.py b/reme_ai/context/file_system_tool/edit_op.py new file mode 100644 index 00000000..eb8d844b --- /dev/null +++ b/reme_ai/context/file_system_tool/edit_op.py @@ -0,0 +1,153 @@ +"""File edit operation module. + +This module provides a tool operation for editing files by replacing text. +It supports creating new files, editing existing files, and replacing multiple occurrences. +""" + +from pathlib import Path +from typing import Optional + +from flowllm.core.context import C +from flowllm.core.op import BaseAsyncToolOp +from flowllm.core.schema import ToolCall + + +@C.register_op() +class EditOp(BaseAsyncToolOp): + """File edit operation. + + This operation replaces text within a file. By default, replaces a single + occurrence, but can replace multiple occurrences when expected_replacements + is specified. Supports creating new files when old_string is empty. + """ + + file_path = __file__ + + def __init__(self, **kwargs): + kwargs.setdefault("raise_exception", False) + super().__init__(**kwargs) + + def build_tool_call(self) -> ToolCall: + """Build and return the tool call schema for this operator.""" + tool_params = { + "name": "Edit", + "description": self.get_prompt("tool_desc"), + "input_schema": { + "file_path": { + "type": "string", + "description": self.get_prompt("file_path"), + "required": True, + }, + "old_string": { + "type": "string", + "description": self.get_prompt("old_string"), + "required": True, + }, + "new_string": { + "type": "string", + "description": self.get_prompt("new_string"), + "required": True, + }, + "expected_replacements": { + "type": "number", + "description": self.get_prompt("expected_replacements"), + "required": False, + }, + }, + } + + return ToolCall(**tool_params) + + async def async_execute(self): + """Execute the file edit operation.""" + file_path: str = self.input_dict.get("file_path", "").strip() + old_string: str = self.input_dict.get("old_string", "") + new_string: str = self.input_dict.get("new_string", "") + expected_replacements: Optional[int] = self.input_dict.get("expected_replacements") + + # Validate inputs + if not file_path: + raise ValueError("The 'file_path' parameter cannot be empty.") + + if expected_replacements is None: + expected_replacements = 1 + + if expected_replacements < 1: + raise ValueError("The 'expected_replacements' parameter must be at least 1.") + + # Resolve file path + file_path_obj = Path(file_path).expanduser().resolve() + + # Check if file exists + file_exists = file_path_obj.exists() and file_path_obj.is_file() + + # Handle new file creation + if not old_string and not file_exists: + # Create new file + file_path_obj.parent.mkdir(parents=True, exist_ok=True) + file_path_obj.write_text(new_string, encoding="utf-8") + self.set_output(f"Created new file: {file_path_obj}") + return + + # File must exist for editing + if not file_exists: + raise FileNotFoundError( + f"File not found: {file_path_obj}. Use an empty old_string to create a new file.", + ) + + # Cannot create file that already exists + if not old_string and file_exists: + raise ValueError( + f"Failed to edit. Attempted to create a file that already exists: {file_path_obj}", + ) + + # Read current content + current_content = file_path_obj.read_text(encoding="utf-8") + current_content = current_content.replace("\r\n", "\n") + + # Count occurrences + occurrences = current_content.count(old_string) + + # Validate occurrences + if occurrences == 0: + raise ValueError( + f"Failed to edit, could not find the string to replace. " + f"0 occurrences found for old_string in {file_path_obj}.", + ) + + if occurrences != expected_replacements: + occurrence_term = "occurrence" if expected_replacements == 1 else "occurrences" + raise ValueError( + f"Failed to edit, expected {expected_replacements} {occurrence_term} " + f"but found {occurrences} for old_string in {file_path_obj}.", + ) + + # Check if old_string and new_string are identical + if old_string == new_string: + raise ValueError( + f"No changes to apply. The old_string and new_string are identical in {file_path_obj}.", + ) + + # Perform replacement + new_content = current_content.replace(old_string, new_string, occurrences) + + # Check if content actually changed + if current_content == new_content: + raise ValueError( + f"No changes to apply. The new content is identical to the current content in {file_path_obj}.", + ) + + # Write new content + file_path_obj.write_text(new_content, encoding="utf-8") + + # Set output + result_msg = f"Successfully modified file: {file_path_obj} ({occurrences} replacements)." + self.set_output(result_msg) + + async def async_default_execute(self, e: Exception = None, **kwargs): + """Fill outputs with a default failure message when execution fails.""" + file_path: str = self.input_dict.get("file_path", "").strip() + error_msg = f'Failed to edit file "{file_path}"' + if e: + error_msg += f": {str(e)}" + self.set_output(error_msg) diff --git a/reme_ai/context/file_system_tool/edit_prompt.yaml b/reme_ai/context/file_system_tool/edit_prompt.yaml new file mode 100644 index 00000000..970363c3 --- /dev/null +++ b/reme_ai/context/file_system_tool/edit_prompt.yaml @@ -0,0 +1,23 @@ +tool_desc: | + Replaces text within a file. By default, replaces a single occurrence, but can replace multiple occurrences when \`expected_replacements\` is specified. This tool requires providing significant context around the change to ensure precise targeting. Always use the ${READ_FILE_TOOL_NAME} tool to examine the file's current content before attempting a text replacement. + The user has the ability to modify the \`new_string\` content. If modified, this will be stated in the response. + Expectation for required parameters: + 1. \`file_path\` is the path to the file to modify. + 2. \`old_string\` MUST be the exact literal text to replace (including all whitespace, indentation, newlines, and surrounding code etc.). + 3. \`new_string\` MUST be the exact literal text to replace \`old_string\` with (also including all whitespace, indentation, newlines, and surrounding code etc.). Ensure the resulting code is correct and idiomatic. + 4. NEVER escape \`old_string\` or \`new_string\`, that would break the exact literal text requirement. + **Important:** If ANY of the above are not satisfied, the tool will fail. CRITICAL for \`old_string\`: Must uniquely identify the single instance to change. Include at least 3 lines of context BEFORE and AFTER the target text, matching whitespace and indentation precisely. If this string matches multiple locations, or does not match exactly, the tool will fail. + **Multiple replacements:** Set \`expected_replacements\` to the number of occurrences you want to replace. The tool will replace ALL occurrences that match \`old_string\` exactly. Ensure the number of replacements matches your expectation. + +file_path: | + The path to the file to modify. + +old_string: | + The exact literal text to replace, preferably unescaped. For single replacements (default), include at least 3 lines of context BEFORE and AFTER the target text, matching whitespace and indentation precisely. For multiple replacements, specify expected_replacements parameter. If this string is not the exact literal text (i.e. you escaped it) or does not match exactly, the tool will fail. + +new_string: | + The exact literal text to replace `old_string` with, preferably unescaped. Provide the EXACT text. Ensure the resulting code is correct and idiomatic. + +expected_replacements: | + Optional: Number of replacements expected. Defaults to 1 if not specified. Use when you want to replace multiple occurrences. + diff --git a/reme_ai/context/file_system_tool/glob_op.py b/reme_ai/context/file_system_tool/glob_op.py new file mode 100644 index 00000000..1f98124d --- /dev/null +++ b/reme_ai/context/file_system_tool/glob_op.py @@ -0,0 +1,295 @@ +"""Glob file search operation module. + +This module provides a tool operation for finding files matching glob patterns. +It enables efficient file discovery based on name or path structure, especially +in large codebases. Files are sorted by modification time (newest first). +""" + +import fnmatch +import time +from pathlib import Path +from typing import List, Optional + +from flowllm.core.context import C +from flowllm.core.op import BaseAsyncToolOp +from flowllm.core.schema import ToolCall +from loguru import logger +from pathspec import PathSpec +from pathspec.patterns.gitwildmatch import GitIgnorePattern + + +@C.register_op() +class GlobOp(BaseAsyncToolOp): + """Glob file search operation. + + This operation efficiently finds files matching specific glob patterns, + returning absolute paths sorted by modification time (newest first). + Supports gitignore patterns for filtering files. + """ + + file_path = __file__ + + def __init__(self, gitignore_patterns: List[str] = None, **kwargs): + kwargs.setdefault("raise_exception", False) + super().__init__(**kwargs) + self.gitignore_patterns = gitignore_patterns + + def build_tool_call(self) -> ToolCall: + """Build and return the tool call schema for this operator.""" + tool_params = { + "name": "FindFiles", + "description": self.get_prompt("tool_desc"), + "input_schema": { + "pattern": { + "type": "string", + "description": self.get_prompt("pattern"), + "required": True, + }, + "dir_path": { + "type": "string", + "description": self.get_prompt("dir_path"), + "required": False, + }, + "case_sensitive": { + "type": "boolean", + "description": self.get_prompt("case_sensitive"), + "required": False, + }, + }, + } + + return ToolCall(**tool_params) + + def _should_ignore_file( + self, + file_path: Path, + root_dir: Path, + ) -> bool: + """Check if a file should be ignored based on ignore patterns. + + Args: + file_path: Path to the file to check. + root_dir: Root directory for resolving relative paths. + + Returns: + True if the file should be ignored, False otherwise. + """ + if not self.gitignore_patterns: + return False + + ignore_spec = PathSpec.from_lines(GitIgnorePattern, self.gitignore_patterns) + + # Get relative path from root_dir + relative_path = file_path.relative_to(root_dir) + + # Check if file matches ignore patterns + return ignore_spec.match_file(str(relative_path)) + + async def async_execute(self): + """Execute the glob search operation.""" + pattern: str = self.input_dict.get("pattern", "").strip() + dir_path: Optional[str] = self.input_dict.get("dir_path") + case_sensitive: bool = self.input_dict.get("case_sensitive", False) + + # Validate pattern + if not pattern: + error_msg = "The 'pattern' parameter cannot be empty." + logger.error(f"{self.name}: {error_msg}") + self.set_output(error_msg) + return + + # Determine search directory + if dir_path: + search_dir = Path(dir_path).expanduser().resolve() + if not search_dir.exists(): + error_msg = f"Search path does not exist: {search_dir}" + logger.error(f"{self.name}: {error_msg}") + self.set_output(error_msg) + return + if not search_dir.is_dir(): + error_msg = f"Search path is not a directory: {search_dir}" + logger.error(f"{self.name}: {error_msg}") + self.set_output(error_msg) + return + else: + search_dir = Path.cwd() + + # Collect matching files + all_entries: List[Path] = [] + ignored_count = 0 + + # Check if pattern is an exact file path + full_path = search_dir / pattern + if full_path.exists() and full_path.is_file(): + # Use exact match + if not self._should_ignore_file( + full_path, + search_dir, + ): + all_entries.append(full_path) + else: + # Use glob pattern matching + matching_files = self._glob_match( + search_dir, + pattern, + case_sensitive=case_sensitive, + ) + + # Filter by ignore patterns + for file_path in matching_files: + if self._should_ignore_file( + file_path, + search_dir, + ): + ignored_count += 1 + else: + all_entries.append(file_path) + + # Check if any files found + if not all_entries: + message = f'No files found matching pattern "{pattern}"' + if dir_path: + message += f" within {search_dir}" + if ignored_count > 0: + message += f" ({ignored_count} files were ignored)" + self.set_output(message) + return + + # Sort files by modification time + now_timestamp = time.time() + # recency_threshold_days: Number of days to consider a file "recent" for sorting (default: 1). + recency_threshold_ms = self.op_params.get("recency_threshold_days", 1) * 24 * 60 * 60 * 1000 + + def get_sort_key(path: Path) -> tuple: + mtime = path.stat().st_mtime + mtime_ms = mtime * 1000 + is_recent = (now_timestamp * 1000) - mtime_ms < recency_threshold_ms + + if is_recent: + # Recent files: sort by mtime descending (newest first) + return 0, -mtime_ms + else: + # Old files: sort alphabetically + return 1, str(path) + + sorted_entries = sorted(all_entries, key=get_sort_key) + + # Format results + sorted_absolute_paths = [str(entry.resolve()) for entry in sorted_entries] + file_list_description = "\n".join(sorted_absolute_paths) + file_count = len(sorted_absolute_paths) + + result_message = f'Found {file_count} file(s) matching "{pattern}"' + if dir_path: + result_message += f" within {search_dir}" + if ignored_count > 0: + result_message += f" ({ignored_count} additional files were ignored)" + result_message += ", sorted by modification time (newest first):\n" + result_message += file_list_description + + self.set_output(result_message) + + async def async_default_execute(self, e: Exception = None, **kwargs): + """Fill outputs with a default failure message when execution fails.""" + pattern: str = self.input_dict.get("pattern", "").strip() + error_msg = f'Failed to search files matching pattern "{pattern}"' + if e: + error_msg += f": {str(e)}" + self.set_output(error_msg) + + def _glob_match( + self, + root_dir: Path, + pattern: str, + case_sensitive: bool = False, + ) -> List[Path]: + """Match files using glob pattern. + + Args: + root_dir: Root directory to search in. + pattern: Glob pattern to match. + case_sensitive: Whether matching should be case-sensitive. + + Returns: + List of matching file paths. + """ + matching_files: List[Path] = [] + + # Handle ** pattern (recursive match) + if "**" in pattern: + # Split pattern into parts + parts = pattern.split("**", 1) + prefix = parts[0].rstrip("/") + suffix = parts[1] if len(parts) > 1 else "" + + # Walk through directory tree + for path in root_dir.rglob("*"): + if not path.is_file(): + continue + + rel_path = path.relative_to(root_dir) + rel_str = str(rel_path).replace("\\", "/") + + # Check if path matches pattern + if self._matches_glob_pattern( + rel_str, + prefix, + suffix, + case_sensitive, + ): + matching_files.append(path) + else: + # For patterns without **, use fnmatch for better compatibility + # Walk through directory tree and match manually + pattern_normalized = pattern.replace("\\", "/") + for path in root_dir.rglob("*"): + if not path.is_file(): + continue + + rel_path = path.relative_to(root_dir) + rel_str = str(rel_path).replace("\\", "/") + + # Match using fnmatch + if case_sensitive: + if fnmatch.fnmatch(rel_str, pattern_normalized): + matching_files.append(path) + else: + if fnmatch.fnmatch(rel_str.lower(), pattern_normalized.lower()): + matching_files.append(path) + + return matching_files + + @staticmethod + def _matches_glob_pattern( + path_str: str, + prefix: str, + suffix: str, + case_sensitive: bool, + ) -> bool: + """Check if a path matches a glob pattern with **. + + Args: + path_str: Path string to check (relative to root). + prefix: Prefix pattern before **. + suffix: Suffix pattern after **. + case_sensitive: Whether matching should be case-sensitive. + + Returns: + True if path matches pattern, False otherwise. + """ + if not case_sensitive: + path_str = path_str.lower() + prefix = prefix.lower() + suffix = suffix.lower() + + # Check prefix + if prefix: + if not path_str.startswith(prefix): + return False + + # Check suffix + if suffix: + if not fnmatch.fnmatch(path_str, f"*{suffix}"): + return False + + return True diff --git a/reme_ai/context/file_system_tool/glob_prompt.yaml b/reme_ai/context/file_system_tool/glob_prompt.yaml new file mode 100644 index 00000000..5912bdcb --- /dev/null +++ b/reme_ai/context/file_system_tool/glob_prompt.yaml @@ -0,0 +1,11 @@ +tool_desc: | + Efficiently finds files matching specific glob patterns (e.g., `src/**/*.ts`, `**/*.md`), returning absolute paths sorted by modification time (newest first). Ideal for quickly locating files based on their name or path structure, especially in large codebases. + +pattern: | + The glob pattern to match against (e.g., '**/*.py', 'docs/*.md'). + +dir_path: | + Optional: The absolute path to the directory to search within. If omitted, searches the current working directory. + +case_sensitive: | + Optional: Whether the search should be case-sensitive. Defaults to false. \ No newline at end of file diff --git a/reme_ai/context/file_system_tool/grep_op.py b/reme_ai/context/file_system_tool/grep_op.py new file mode 100644 index 00000000..ee808796 --- /dev/null +++ b/reme_ai/context/file_system_tool/grep_op.py @@ -0,0 +1,176 @@ +"""Grep text search operation module. + +This module provides a tool operation for searching text patterns in files. +It enables efficient content-based search using regular expressions, with support +for glob pattern filtering and result limiting. +""" + +import fnmatch +import re +from pathlib import Path +from typing import List, Optional + +from flowllm.core.context import C +from flowllm.core.op import BaseAsyncToolOp +from flowllm.core.schema import ToolCall +from loguru import logger + + +@C.register_op() +class GrepOp(BaseAsyncToolOp): + """Grep text search operation. + + This operation searches for text patterns in files using regular expressions. + Supports glob pattern filtering and result limiting. + """ + + file_path = __file__ + + def __init__(self, **kwargs): + kwargs.setdefault("raise_exception", False) + super().__init__(**kwargs) + + def build_tool_call(self) -> ToolCall: + """Build and return the tool call schema for this operator.""" + tool_params = { + "name": "Grep", + "description": self.get_prompt("tool_desc"), + "input_schema": { + "pattern": { + "type": "string", + "description": self.get_prompt("pattern"), + "required": True, + }, + "path": { + "type": "string", + "description": self.get_prompt("path"), + "required": False, + }, + "glob": { + "type": "string", + "description": self.get_prompt("glob"), + "required": False, + }, + "limit": { + "type": "number", + "description": self.get_prompt("limit"), + "required": False, + }, + }, + } + + return ToolCall(**tool_params) + + async def async_execute(self): + """Execute the grep search operation.""" + pattern: str = self.input_dict.get("pattern", "").strip() + path: Optional[str] = self.input_dict.get("path") + glob_pattern: Optional[str] = self.input_dict.get("glob") + limit: Optional[int] = self.input_dict.get("limit") + + # Validate pattern + if not pattern: + raise ValueError("The 'pattern' parameter cannot be empty.") + + # Validate regex pattern + try: + regex = re.compile(pattern, re.IGNORECASE) + except re.error as e: + raise ValueError(f"Invalid regular expression pattern: {pattern}. Error: {str(e)}") + + # Determine search directory + if path: + search_dir = Path(path).expanduser().resolve() + if not search_dir.exists(): + raise ValueError(f"Search path does not exist: {search_dir}") + if not search_dir.is_dir(): + raise ValueError(f"Search path is not a directory: {search_dir}") + else: + search_dir = Path.cwd() + + # Collect matching files based on glob pattern + files_to_search: List[Path] = [] + if glob_pattern: + # Use glob pattern to filter files + glob_normalized = glob_pattern.replace("\\", "/") + for file_path in search_dir.rglob("*"): + if not file_path.is_file(): + continue + rel_path = file_path.relative_to(search_dir) + rel_str = str(rel_path).replace("\\", "/") + if fnmatch.fnmatch(rel_str.lower(), glob_normalized.lower()): + files_to_search.append(file_path) + else: + # Search all files recursively + files_to_search = [f for f in search_dir.rglob("*") if f.is_file()] + + # Search for matches + matches: List[dict] = [] + for file_path in files_to_search: + try: + content = file_path.read_text(encoding="utf-8", errors="ignore") + lines = content.split("\n") + for line_num, line in enumerate(lines, start=1): + if regex.search(line): + try: + relative_path = file_path.relative_to(search_dir) + except ValueError: + relative_path = file_path.name + matches.append( + { + "file_path": str(relative_path), + "line_number": line_num, + "line": line, + } + ) + if limit and len(matches) >= limit: + break + if limit and len(matches) >= limit: + break + except Exception as e: + logger.debug(f"Could not read {file_path}: {str(e)}") + continue + + # Format results + if not matches: + search_location = f'in path "{path}"' if path else "in the workspace directory" + filter_desc = f' (filter: "{glob_pattern}")' if glob_pattern else "" + result_msg = f'No matches found for pattern "{pattern}" {search_location}{filter_desc}.' + self.set_output(result_msg) + return + + # Group matches by file + matches_by_file = {} + for match in matches: + file_key = match["file_path"] + if file_key not in matches_by_file: + matches_by_file[file_key] = [] + matches_by_file[file_key].append(match) + + # Build output + total_matches = len(matches) + match_term = "match" if total_matches == 1 else "matches" + search_location = f'in path "{path}"' if path else "in the workspace directory" + filter_desc = f' (filter: "{glob_pattern}")' if glob_pattern else "" + + output_lines = [ + f'Found {total_matches} {match_term} for pattern "{pattern}" {search_location}{filter_desc}:\n---', + ] + + for file_path in sorted(matches_by_file.keys()): + output_lines.append(f"File: {file_path}") + for match in sorted(matches_by_file[file_path], key=lambda x: x["line_number"]): + trimmed_line = match["line"].strip() + output_lines.append(f"L{match['line_number']}: {trimmed_line}") + output_lines.append("---") + + result_msg = "\n".join(output_lines) + self.set_output(result_msg) + + async def async_default_execute(self, e: Exception = None, **kwargs): + """Fill outputs with a default failure message when execution fails.""" + pattern: str = self.input_dict.get("pattern", "").strip() + error_msg = f'Failed to search for pattern "{pattern}"' + if e: + error_msg += f": {str(e)}" + self.set_output(error_msg) diff --git a/reme_ai/context/file_system_tool/grep_prompt.yaml b/reme_ai/context/file_system_tool/grep_prompt.yaml new file mode 100644 index 00000000..0c9a256b --- /dev/null +++ b/reme_ai/context/file_system_tool/grep_prompt.yaml @@ -0,0 +1,15 @@ +tool_desc: | + A powerful search tool for finding patterns in files using regular expressions. Supports full regex syntax (e.g., "log.*Error", "function\\s+\\w+"), glob pattern filtering, and result limiting. Ideal for searching code or text content across multiple files. + +pattern: | + The regular expression pattern to search for in file contents. + +path: | + Optional: The directory to search in. Defaults to current working directory. + +glob: | + Optional: Glob pattern to filter files (e.g., "*.js", "*.{ts,tsx}"). + +limit: | + Optional: Maximum number of matching lines to return. Shows all matches if not specified. + diff --git a/reme_ai/context/file_system_tool/read_file_op.py b/reme_ai/context/file_system_tool/read_file_op.py new file mode 100644 index 00000000..92d24cc5 --- /dev/null +++ b/reme_ai/context/file_system_tool/read_file_op.py @@ -0,0 +1,120 @@ +"""Read file operation module. + +This module provides a tool operation for reading file contents. +It supports reading entire files or specific line ranges for large files. +""" + +from pathlib import Path +from typing import Optional + +from flowllm.core.context import C +from flowllm.core.op import BaseAsyncToolOp +from flowllm.core.schema import ToolCall + + +@C.register_op() +class ReadFileOp(BaseAsyncToolOp): + """Read file operation. + + This operation reads and returns the content of a specified file. + For text files, it can read specific line ranges using offset and limit. + """ + + file_path = __file__ + + def __init__(self, **kwargs): + kwargs.setdefault("raise_exception", False) + super().__init__(**kwargs) + + def build_tool_call(self) -> ToolCall: + """Build and return the tool call schema for this operator.""" + tool_params = { + "name": "ReadFile", + "description": self.get_prompt("tool_desc"), + "input_schema": { + "absolute_path": { + "type": "string", + "description": self.get_prompt("absolute_path"), + "required": True, + }, + "offset": { + "type": "number", + "description": self.get_prompt("offset"), + "required": False, + }, + "limit": { + "type": "number", + "description": self.get_prompt("limit"), + "required": False, + }, + }, + } + + return ToolCall(**tool_params) + + async def async_execute(self): + """Execute the read file operation.""" + absolute_path: str = self.input_dict.get("absolute_path", "").strip() + offset: Optional[int] = self.input_dict.get("offset") + limit: Optional[int] = self.input_dict.get("limit") + + # Validate absolute_path + if not absolute_path: + raise ValueError("The 'absolute_path' parameter cannot be empty.") + + # Resolve file path + file_path_obj = Path(absolute_path).expanduser().resolve() + + # Check if file exists + if not file_path_obj.exists(): + raise FileNotFoundError(f"File not found: {file_path_obj}") + + if not file_path_obj.is_file(): + raise ValueError(f"Path is not a file: {file_path_obj}") + + # Read file content + content = file_path_obj.read_text(encoding="utf-8") + lines = content.split("\n") + + # Handle line range if specified + if offset is not None or limit is not None: + if offset is None: + offset = 0 + if limit is None: + limit = len(lines) + + # Validate offset and limit + if offset < 0: + raise ValueError("Offset must be a non-negative number") + if limit <= 0: + raise ValueError("Limit must be a positive number") + + total_lines = len(lines) + start = offset + end = min(offset + limit, total_lines) + + if start >= total_lines: + raise ValueError( + f"Offset {offset} is beyond file length ({total_lines} lines)", + ) + + selected_lines = lines[start:end] + result_content = "\n".join(selected_lines) + + # Format output with range information + if end < total_lines: + result = f"Showing lines {start}-{end-1} of {total_lines} total lines.\n\n---\n\n{result_content}" + else: + result = result_content + else: + result = content + + self.set_output(result) + + async def async_default_execute(self, e: Exception = None, **kwargs): + """Fill outputs with a default failure message when execution fails.""" + absolute_path: str = self.input_dict.get("absolute_path", "").strip() + error_msg = f'Failed to read file "{absolute_path}"' + if e: + error_msg += f": {str(e)}" + self.set_output(error_msg) diff --git a/reme_ai/context/file_system_tool/read_file_prompt.yaml b/reme_ai/context/file_system_tool/read_file_prompt.yaml new file mode 100644 index 00000000..52f6a466 --- /dev/null +++ b/reme_ai/context/file_system_tool/read_file_prompt.yaml @@ -0,0 +1,12 @@ +tool_desc: | + Reads and returns the content of a specified file. For text files, it can read specific line ranges using the 'offset' and 'limit' parameters. Use offset and limit to paginate through large files. + +absolute_path: | + The absolute path to the file to read (e.g., '/home/user/project/file.txt'). Relative paths are not supported. You must provide an absolute path. + +offset: | + Optional: For text files, the 0-based line number to start reading from. Requires 'limit' to be set. Use for paginating through large files. + +limit: | + Optional: For text files, maximum number of lines to read. Use with 'offset' to paginate through large files. If omitted when offset is provided, reads from offset to the end of the file. + diff --git a/reme_ai/context/file_system_tool/rip_grep_op.py b/reme_ai/context/file_system_tool/rip_grep_op.py new file mode 100644 index 00000000..8bc3ca11 --- /dev/null +++ b/reme_ai/context/file_system_tool/rip_grep_op.py @@ -0,0 +1,160 @@ +"""Ripgrep text search operation module. + +This module provides a tool operation for searching text patterns in files +using ripgrep (rg). It enables efficient content-based search using regular +expressions with support for glob pattern filtering and result limiting. +""" + +import asyncio +from pathlib import Path +from typing import Optional + +from flowllm.core.context import C +from flowllm.core.op import BaseAsyncToolOp +from flowllm.core.schema import ToolCall +from loguru import logger + + +@C.register_op() +class RipGrepOp(BaseAsyncToolOp): + """Ripgrep text search operation. + + This operation searches for text patterns in files using ripgrep (rg). + Supports glob pattern filtering and result limiting. + """ + + file_path = __file__ + + def __init__(self, **kwargs): + kwargs.setdefault("raise_exception", False) + super().__init__(**kwargs) + + def build_tool_call(self) -> ToolCall: + """Build and return the tool call schema for this operator.""" + tool_params = { + "name": "RipGrep", + "description": self.get_prompt("tool_desc"), + "input_schema": { + "pattern": { + "type": "string", + "description": self.get_prompt("pattern"), + "required": True, + }, + "path": { + "type": "string", + "description": self.get_prompt("path"), + "required": False, + }, + "glob": { + "type": "string", + "description": self.get_prompt("glob"), + "required": False, + }, + "limit": { + "type": "number", + "description": self.get_prompt("limit"), + "required": False, + }, + }, + } + + return ToolCall(**tool_params) + + async def async_execute(self): + """Execute the ripgrep search operation.""" + pattern: str = self.input_dict.get("pattern", "").strip() + path: Optional[str] = self.input_dict.get("path") + glob_pattern: Optional[str] = self.input_dict.get("glob") + limit: Optional[int] = self.input_dict.get("limit") + + # Validate pattern + if not pattern: + raise ValueError("The 'pattern' parameter cannot be empty.") + + # Determine search path + if path: + search_path = Path(path).expanduser().resolve() + if not search_path.exists(): + raise ValueError(f"Search path does not exist: {search_path}") + else: + search_path = Path.cwd() + + # Build ripgrep command + rg_args = [ + "rg", + "--line-number", + "--no-heading", + "--with-filename", + "--ignore-case", + "--regexp", + pattern, + ] + + # Add glob pattern if provided + if glob_pattern: + rg_args.extend(["--glob", glob_pattern]) + + # Add search path + rg_args.append(str(search_path)) + + # Execute ripgrep + try: + process = await asyncio.create_subprocess_exec( + *rg_args, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE, + ) + stdout, stderr = await process.communicate() + except FileNotFoundError: + raise ValueError("ripgrep (rg) is not installed or not in PATH") + + # Handle ripgrep exit codes + if process.returncode == 0: + raw_output = stdout.decode("utf-8").strip() + elif process.returncode == 1: + # No matches found + raw_output = "" + else: + error_msg = stderr.decode("utf-8").strip() + raise ValueError(f"ripgrep exited with code {process.returncode}: {error_msg}") + + # Build search description + search_location = f'in path "{path}"' if path else "in the workspace directory" + filter_desc = f' (filter: "{glob_pattern}")' if glob_pattern else "" + + # Check if we have any matches + if not raw_output: + result_msg = f'No matches found for pattern "{pattern}" {search_location}{filter_desc}.' + self.set_output(result_msg) + return + + # Split into lines and apply limit + all_lines = [line for line in raw_output.split("\n") if line.strip()] + total_matches = len(all_lines) + match_term = "match" if total_matches == 1 else "matches" + + # Apply limit if specified + lines_to_include = all_lines + truncated = False + if limit and len(all_lines) > limit: + lines_to_include = all_lines[:limit] + truncated = True + + # Build output + header = f'Found {total_matches} {match_term} for pattern "{pattern}" {search_location}{filter_desc}:\n---\n' + grep_output = "\n".join(lines_to_include) + + result_msg = header + grep_output + if truncated: + omitted = total_matches - len(lines_to_include) + result_msg += f"\n---\n[{omitted} {'line' if omitted == 1 else 'lines'} truncated] ..." + + self.set_output(result_msg) + + async def async_default_execute(self, e: Exception = None, **kwargs): + """Fill outputs with a default failure message when execution fails.""" + pattern: str = self.input_dict.get("pattern", "").strip() + error_msg = f'Failed to search for pattern "{pattern}"' + if e: + error_msg += f": {str(e)}" + self.set_output(error_msg) diff --git a/reme_ai/context/file_system_tool/rip_grep_prompt.yaml b/reme_ai/context/file_system_tool/rip_grep_prompt.yaml new file mode 100644 index 00000000..6faef042 --- /dev/null +++ b/reme_ai/context/file_system_tool/rip_grep_prompt.yaml @@ -0,0 +1,15 @@ +tool_desc: | + A powerful search tool built on ripgrep for finding patterns in files using regular expressions. Supports full regex syntax (e.g., "log.*Error", "function\\s+\\w+"), glob pattern filtering, and result limiting. Ideal for efficient searching across large codebases. + +pattern: | + The regular expression pattern to search for in file contents. + +path: | + Optional: The file or directory to search in. Defaults to current working directory. + +glob: | + Optional: Glob pattern to filter files (e.g., "*.js", "*.{ts,tsx}"). + +limit: | + Optional: Maximum number of matching lines to return. Shows all matches if not specified. + diff --git a/reme_ai/context/file_system_tool/write_file_op.py b/reme_ai/context/file_system_tool/write_file_op.py new file mode 100644 index 00000000..b8f604d3 --- /dev/null +++ b/reme_ai/context/file_system_tool/write_file_op.py @@ -0,0 +1,89 @@ +"""Write file operation module. + +This module provides a tool operation for writing content to files. +It supports creating new files or overwriting existing files, and automatically +creates parent directories if they don't exist. +""" + +from pathlib import Path + +from flowllm.core.context import C +from flowllm.core.op import BaseAsyncToolOp +from flowllm.core.schema import ToolCall + + +@C.register_op() +class WriteFileOp(BaseAsyncToolOp): + """Write file operation. + + This operation writes content to a specified file. If the file doesn't exist, + it will be created. If parent directories don't exist, they will be created automatically. + """ + + file_path = __file__ + + def __init__(self, **kwargs): + kwargs.setdefault("raise_exception", False) + super().__init__(**kwargs) + + def build_tool_call(self) -> ToolCall: + """Build and return the tool call schema for this operator.""" + tool_params = { + "name": "WriteFile", + "description": self.get_prompt("tool_desc"), + "input_schema": { + "file_path": { + "type": "string", + "description": self.get_prompt("file_path"), + "required": True, + }, + "content": { + "type": "string", + "description": self.get_prompt("content"), + "required": True, + }, + }, + } + + return ToolCall(**tool_params) + + async def async_execute(self): + """Execute the write file operation.""" + file_path: str = self.input_dict.get("file_path", "").strip() + content: str = self.input_dict.get("content", "") + + # Validate file_path + if not file_path: + raise ValueError("The 'file_path' parameter cannot be empty.") + + # Resolve file path + file_path_obj = Path(file_path).expanduser().resolve() + + # Check if path is a directory + if file_path_obj.exists() and file_path_obj.is_dir(): + raise ValueError(f"Path is a directory, not a file: {file_path_obj}") + + # Create parent directories if they don't exist + file_path_obj.parent.mkdir(parents=True, exist_ok=True) + + # Check if file exists + file_exists = file_path_obj.exists() and file_path_obj.is_file() + + # Write content to file + file_path_obj.write_text(content, encoding="utf-8") + + # Format success message + if file_exists: + result = f"Successfully overwrote file: {file_path_obj}" + else: + result = f"Successfully created and wrote to new file: {file_path_obj}" + + self.set_output(result) + + async def async_default_execute(self, e: Exception = None, **kwargs): + """Fill outputs with a default failure message when execution fails.""" + file_path: str = self.input_dict.get("file_path", "").strip() + error_msg = f'Failed to write file "{file_path}"' + if e: + error_msg += f": {str(e)}" + self.set_output(error_msg) diff --git a/reme_ai/context/file_system_tool/write_file_prompt.yaml b/reme_ai/context/file_system_tool/write_file_prompt.yaml new file mode 100644 index 00000000..02740d04 --- /dev/null +++ b/reme_ai/context/file_system_tool/write_file_prompt.yaml @@ -0,0 +1,9 @@ +tool_desc: | + Writes content to a specified file in the local filesystem. If the file doesn't exist, it will be created. If parent directories don't exist, they will be created automatically. If the file already exists, it will be overwritten with the new content. + +file_path: | + The absolute path to the file to write to (e.g., '/home/user/project/file.txt'). Relative paths are not supported. You must provide an absolute path. + +content: | + The content to write to the file. + diff --git a/reme_ai/context/file_system_tool/write_todos_op.py b/reme_ai/context/file_system_tool/write_todos_op.py new file mode 100644 index 00000000..997a1d75 --- /dev/null +++ b/reme_ai/context/file_system_tool/write_todos_op.py @@ -0,0 +1,102 @@ +"""Write todos operation module. + +This module provides a tool operation for managing todo lists. +It enables tracking subtasks with status (pending, in_progress, completed, cancelled). +""" + +from typing import List + +from flowllm.core.context import C +from flowllm.core.op import BaseAsyncToolOp +from flowllm.core.schema import ToolCall + +TODO_STATUSES = ["pending", "in_progress", "completed", "cancelled"] + + +@C.register_op() +class WriteTodosOp(BaseAsyncToolOp): + """Write todos operation. + + This operation manages a todo list with subtasks that can be tracked + through different statuses: pending, in_progress, completed, cancelled. + """ + + file_path = __file__ + + def build_tool_call(self) -> ToolCall: + """Build and return the tool call schema for this operator.""" + tool_params = { + "name": "WriteTodos", + "description": self.get_prompt("tool_desc"), + "input_schema": { + "todos": { + "type": "array", + "description": self.get_prompt("todos"), + "required": True, + "items": { + "type": "object", + "properties": { + "description": { + "type": "string", + "description": self.get_prompt("todo_description"), + }, + "status": { + "type": "string", + "description": self.get_prompt("todo_status"), + "enum": TODO_STATUSES, + }, + }, + "required": ["description", "status"], + }, + }, + }, + } + + return ToolCall(**tool_params) + + async def async_execute(self): + """Execute the write todos operation.""" + todos: List[dict] = self.input_dict.get("todos", []) + + # Validate todos + if not isinstance(todos, list): + raise ValueError("The 'todos' parameter must be an array") + + # Validate each todo item + for i, todo in enumerate(todos): + if not isinstance(todo, dict): + raise ValueError(f"Todo item at index {i} must be an object") + + if "description" not in todo or not isinstance(todo["description"], str): + raise ValueError(f"Todo item at index {i} must have a non-empty description string") + + if not todo["description"].strip(): + raise ValueError(f"Todo item at index {i} must have a non-empty description string") + + if "status" not in todo or todo["status"] not in TODO_STATUSES: + raise ValueError( + f"Todo item at index {i} must have a valid status ({', '.join(TODO_STATUSES)})", + ) + + # Validate only one in_progress task + in_progress_count = sum(1 for todo in todos if todo.get("status") == "in_progress") + if in_progress_count > 1: + raise ValueError("Only one task can be 'in_progress' at a time") + + # Format todo list + if not todos: + result_message = "Successfully cleared the todo list." + else: + todo_list_string = "\n".join( + f"{i + 1}. [{todo['status']}] {todo['description']}" for i, todo in enumerate(todos) + ) + result_message = f"Successfully updated the todo list. The current list is now:\n{todo_list_string}" + + self.set_output(result_message) + + async def async_default_execute(self, e: Exception = None, **kwargs): + """Fill outputs with a default failure message when execution fails.""" + error_msg = "Failed to update the todo list" + if e: + error_msg += f": {str(e)}" + self.set_output(error_msg) diff --git a/reme_ai/context/file_system_tool/write_todos_prompt.yaml b/reme_ai/context/file_system_tool/write_todos_prompt.yaml new file mode 100644 index 00000000..b7f6ea5c --- /dev/null +++ b/reme_ai/context/file_system_tool/write_todos_prompt.yaml @@ -0,0 +1,35 @@ +tool_desc: | + This tool can help you list out the current subtasks that are required to be completed for a given user request. The list of subtasks helps you keep track of the current task, organize complex queries and help ensure that you don't miss any steps. With this list, the user can also see the current progress you are making in executing a given task. + + Depending on the task complexity, you should first divide a given task into subtasks and then use this tool to list out the subtasks that are required to be completed for a given user request. + Each of the subtasks should be clear and distinct. + + Use this tool for complex queries that require multiple steps. If you find that the request is actually complex after you have started executing the user task, create a todo list and use it. If execution of the user task requires multiple steps, planning and generally is higher complexity than a simple Q&A, use this tool. + + DO NOT use this tool for simple tasks that can be completed in less than 2 steps. If the user query is simple and straightforward, do not use the tool. If you can respond with an answer in a single turn then this tool is not required. + + ## Task state definitions + + - pending: Work has not begun on a given subtask. + - in_progress: Marked just prior to beginning work on a given subtask. You should only have one subtask as in_progress at a time. + - completed: Subtask was successfully completed with no errors or issues. If the subtask required more steps to complete, update the todo list with the subtasks. All steps should be identified as completed only when they are completed. + - cancelled: As you update the todo list, some tasks are not required anymore due to the dynamic nature of the task. In this case, mark the subtasks as cancelled. + + ## Methodology for using this tool + 1. Use this todo list list as soon as you receive a user request based on the complexity of the task. + 2. Keep track of every subtask that you update the list with. + 3. Mark a subtask as in_progress before you begin working on it. You should only have one subtask as in_progress at a time. + 4. Update the subtask list as you proceed in executing the task. The subtask list is not static and should reflect your progress and current plans, which may evolve as you acquire new information. + 5. Mark a subtask as completed when you have completed it. + 6. Mark a subtask as cancelled if the subtask is no longer needed. + 7. You must update the todo list as soon as you start, stop or cancel a subtask. Don't batch or wait to update the todo list. + +todos: | + The complete list of todo items. This will replace the existing list. + +todo_description: | + The description of the task. + +todo_status: | + The current status of the task. Must be one of: pending, in_progress, completed, cancelled. + diff --git a/reme_ai/context/offload/__init__.py b/reme_ai/context/offload/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/reme_ai/context/offload/context_compact.py b/reme_ai/context/offload/context_compact.py new file mode 100644 index 00000000..e670b7fa --- /dev/null +++ b/reme_ai/context/offload/context_compact.py @@ -0,0 +1,20 @@ +from flowllm.core.context import C +from flowllm.core.op import BaseAsyncOp +from flowllm.core.schema import ToolCall + + +@C.register_op() +class ContextCompactOp(BaseAsyncOp): + + def __init__(self, ratio: 0.3, **kwargs): + super().__init__(**kwargs) + self.ratio: float = ratio + + def async_execute(self): + messages = self.context.messages + + self.llm.achat(messages=messages, tools=None) + self.token_count() + + self.context["messages"] = messages + self.context["offloaded_data"] = {"f": "ddd"} From 7fe953a8d50f6992c4784644d0eb262888f24035 Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Mon, 17 Nov 2025 16:14:32 +0800 Subject: [PATCH 02/39] docs(context): add documentation for --- reme_ai/context/offload/context_compact.py | 24 ++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/reme_ai/context/offload/context_compact.py b/reme_ai/context/offload/context_compact.py index e670b7fa..4529ca2a 100644 --- a/reme_ai/context/offload/context_compact.py +++ b/reme_ai/context/offload/context_compact.py @@ -18,3 +18,27 @@ class ContextCompactOp(BaseAsyncOp): self.context["messages"] = messages self.context["offloaded_data"] = {"f": "ddd"} + + +""" +1. token counter 计数，最长上下文X20%=20K + 1. openai tiktoken / hagggingface / modelscope / rule-based + self.token_counter.count() +2. tool: + 1. dump_tool(write_file) + 2. grep / rip_grep / read_file +3. compact: + if > threashold(20K): + tool_call -> write_file + 写一个引用：/xx/xxx/xxx.txt。是否保留前面的token（） +4. compress: + 1. prompt + 2. context -> write_file + + +1. case study: +2. messages + 1. agentscope: reme + 2. http远程 reme + +""" \ No newline at end of file From a093a9428d9a04256fd70ff48ea0b3a117563f9e Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Tue, 18 Nov 2025 15:36:20 +0800 Subject: [PATCH 03/39] refactor(context): restructure file system operations and context compaction --- pyproject.toml | 19 +- reme_ai/config/default.yaml | 4 - reme_ai/context/file_system_tool/__init__.py | 31 -- reme_ai/context/file_system_tool/edit_op.py | 153 --------- .../context/file_system_tool/edit_prompt.yaml | 23 -- reme_ai/context/file_system_tool/glob_op.py | 295 ------------------ .../context/file_system_tool/glob_prompt.yaml | 11 - reme_ai/context/file_system_tool/grep_op.py | 176 ----------- .../context/file_system_tool/read_file_op.py | 120 ------- .../context/file_system_tool/rip_grep_op.py | 160 ---------- .../file_system_tool/rip_grep_prompt.yaml | 15 - .../context/file_system_tool/write_file_op.py | 89 ------ .../file_system_tool/write_file_prompt.yaml | 9 - .../file_system_tool/write_todos_op.py | 102 ------ .../file_system_tool/write_todos_prompt.yaml | 35 --- reme_ai/context/file_tool/__init__.py | 19 ++ .../context/file_tool/batch_write_file_op.py | 45 +++ reme_ai/context/file_tool/grep_op.py | 52 +++ .../grep_prompt.yaml | 0 reme_ai/context/file_tool/read_file_op.py | 46 +++ .../read_file_prompt.yaml | 0 reme_ai/context/offload/context_compact.py | 157 ++++++++-- 22 files changed, 307 insertions(+), 1254 deletions(-) delete mode 100644 reme_ai/context/file_system_tool/__init__.py delete mode 100644 reme_ai/context/file_system_tool/edit_op.py delete mode 100644 reme_ai/context/file_system_tool/edit_prompt.yaml delete mode 100644 reme_ai/context/file_system_tool/glob_op.py delete mode 100644 reme_ai/context/file_system_tool/glob_prompt.yaml delete mode 100644 reme_ai/context/file_system_tool/grep_op.py delete mode 100644 reme_ai/context/file_system_tool/read_file_op.py delete mode 100644 reme_ai/context/file_system_tool/rip_grep_op.py delete mode 100644 reme_ai/context/file_system_tool/rip_grep_prompt.yaml delete mode 100644 reme_ai/context/file_system_tool/write_file_op.py delete mode 100644 reme_ai/context/file_system_tool/write_file_prompt.yaml delete mode 100644 reme_ai/context/file_system_tool/write_todos_op.py delete mode 100644 reme_ai/context/file_system_tool/write_todos_prompt.yaml create mode 100644 reme_ai/context/file_tool/__init__.py create mode 100644 reme_ai/context/file_tool/batch_write_file_op.py create mode 100644 reme_ai/context/file_tool/grep_op.py rename reme_ai/context/{file_system_tool => file_tool}/grep_prompt.yaml (100%) create mode 100644 reme_ai/context/file_tool/read_file_op.py rename reme_ai/context/{file_system_tool => file_tool}/read_file_prompt.yaml (100%) diff --git a/pyproject.toml b/pyproject.toml index a29ded0e..804e7f46 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,13 +32,26 @@ classifiers = [ keywords = ["llm", "memory", "experience", "memoryscope", "ai", "mcp", "http"] dependencies = [ - "flowllm[reme]>=0.2.0.0", + "flowllm[reme]>=0.2.0.3", ] [project.optional-dependencies] -dev = ["jupyter-book", "ghp-import", "myst-nb", "sphinxcontrib-bibtex", "furo", "sphinxcontrib-mermaid"] +dev = [ + "jupyter-book", + "ghp-import", + "myst-nb", + "sphinxcontrib-bibtex", + "furo", + "sphinxcontrib-mermaid" +] -full = ["reme_ai[dev]"] +token = [ + "flowllm[token]>=0.2.0.3" +] + +full = [ + "reme_ai[dev,token]" +] [tool.setuptools.packages.find] where = ["."] diff --git a/reme_ai/config/default.yaml b/reme_ai/config/default.yaml index 15dfc89a..7d1dcee0 100644 --- a/reme_ai/config/default.yaml +++ b/reme_ai/config/default.yaml @@ -156,10 +156,6 @@ flow: description: "user query" required: true - compact: - flow_content: ContextCompactOp() >> WriteFile() - .... - llm: default: backend: openai_compatible diff --git a/reme_ai/context/file_system_tool/__init__.py b/reme_ai/context/file_system_tool/__init__.py deleted file mode 100644 index ee9b2214..00000000 --- a/reme_ai/context/file_system_tool/__init__.py +++ /dev/null @@ -1,31 +0,0 @@ -"""File system tool package. - -This package provides file-related operations that can be used in LLM-powered flows. -It includes ready-to-use operations for: - -- EditOp: File editing operation for replacing text within files -- GlobOp: File search operation for finding files matching glob patterns -- GrepOp: Text search operation for finding patterns in file contents -- ReadFileOp: File reading operation for reading file contents -- RipGrepOp: Text search operation using ripgrep for efficient pattern matching -- WriteFileOp: File writing operation for writing content to files -- WriteTodosOp: To-do list management operation for tracking subtasks -""" - -from .edit_op import EditOp -from .glob_op import GlobOp -from .grep_op import GrepOp -from .read_file_op import ReadFileOp -from .rip_grep_op import RipGrepOp -from .write_file_op import WriteFileOp -from .write_todos_op import WriteTodosOp - -__all__ = [ - "EditOp", - "GlobOp", - "GrepOp", - "ReadFileOp", - "RipGrepOp", - "WriteFileOp", - "WriteTodosOp", -] diff --git a/reme_ai/context/file_system_tool/edit_op.py b/reme_ai/context/file_system_tool/edit_op.py deleted file mode 100644 index eb8d844b..00000000 --- a/reme_ai/context/file_system_tool/edit_op.py +++ /dev/null @@ -1,153 +0,0 @@ -"""File edit operation module. - -This module provides a tool operation for editing files by replacing text. -It supports creating new files, editing existing files, and replacing multiple occurrences. -""" - -from pathlib import Path -from typing import Optional - -from flowllm.core.context import C -from flowllm.core.op import BaseAsyncToolOp -from flowllm.core.schema import ToolCall - - -@C.register_op() -class EditOp(BaseAsyncToolOp): - """File edit operation. - - This operation replaces text within a file. By default, replaces a single - occurrence, but can replace multiple occurrences when expected_replacements - is specified. Supports creating new files when old_string is empty. - """ - - file_path = __file__ - - def __init__(self, **kwargs): - kwargs.setdefault("raise_exception", False) - super().__init__(**kwargs) - - def build_tool_call(self) -> ToolCall: - """Build and return the tool call schema for this operator.""" - tool_params = { - "name": "Edit", - "description": self.get_prompt("tool_desc"), - "input_schema": { - "file_path": { - "type": "string", - "description": self.get_prompt("file_path"), - "required": True, - }, - "old_string": { - "type": "string", - "description": self.get_prompt("old_string"), - "required": True, - }, - "new_string": { - "type": "string", - "description": self.get_prompt("new_string"), - "required": True, - }, - "expected_replacements": { - "type": "number", - "description": self.get_prompt("expected_replacements"), - "required": False, - }, - }, - } - - return ToolCall(**tool_params) - - async def async_execute(self): - """Execute the file edit operation.""" - file_path: str = self.input_dict.get("file_path", "").strip() - old_string: str = self.input_dict.get("old_string", "") - new_string: str = self.input_dict.get("new_string", "") - expected_replacements: Optional[int] = self.input_dict.get("expected_replacements") - - # Validate inputs - if not file_path: - raise ValueError("The 'file_path' parameter cannot be empty.") - - if expected_replacements is None: - expected_replacements = 1 - - if expected_replacements < 1: - raise ValueError("The 'expected_replacements' parameter must be at least 1.") - - # Resolve file path - file_path_obj = Path(file_path).expanduser().resolve() - - # Check if file exists - file_exists = file_path_obj.exists() and file_path_obj.is_file() - - # Handle new file creation - if not old_string and not file_exists: - # Create new file - file_path_obj.parent.mkdir(parents=True, exist_ok=True) - file_path_obj.write_text(new_string, encoding="utf-8") - self.set_output(f"Created new file: {file_path_obj}") - return - - # File must exist for editing - if not file_exists: - raise FileNotFoundError( - f"File not found: {file_path_obj}. Use an empty old_string to create a new file.", - ) - - # Cannot create file that already exists - if not old_string and file_exists: - raise ValueError( - f"Failed to edit. Attempted to create a file that already exists: {file_path_obj}", - ) - - # Read current content - current_content = file_path_obj.read_text(encoding="utf-8") - current_content = current_content.replace("\r\n", "\n") - - # Count occurrences - occurrences = current_content.count(old_string) - - # Validate occurrences - if occurrences == 0: - raise ValueError( - f"Failed to edit, could not find the string to replace. " - f"0 occurrences found for old_string in {file_path_obj}.", - ) - - if occurrences != expected_replacements: - occurrence_term = "occurrence" if expected_replacements == 1 else "occurrences" - raise ValueError( - f"Failed to edit, expected {expected_replacements} {occurrence_term} " - f"but found {occurrences} for old_string in {file_path_obj}.", - ) - - # Check if old_string and new_string are identical - if old_string == new_string: - raise ValueError( - f"No changes to apply. The old_string and new_string are identical in {file_path_obj}.", - ) - - # Perform replacement - new_content = current_content.replace(old_string, new_string, occurrences) - - # Check if content actually changed - if current_content == new_content: - raise ValueError( - f"No changes to apply. The new content is identical to the current content in {file_path_obj}.", - ) - - # Write new content - file_path_obj.write_text(new_content, encoding="utf-8") - - # Set output - result_msg = f"Successfully modified file: {file_path_obj} ({occurrences} replacements)." - self.set_output(result_msg) - - async def async_default_execute(self, e: Exception = None, **kwargs): - """Fill outputs with a default failure message when execution fails.""" - file_path: str = self.input_dict.get("file_path", "").strip() - error_msg = f'Failed to edit file "{file_path}"' - if e: - error_msg += f": {str(e)}" - self.set_output(error_msg) diff --git a/reme_ai/context/file_system_tool/edit_prompt.yaml b/reme_ai/context/file_system_tool/edit_prompt.yaml deleted file mode 100644 index 970363c3..00000000 --- a/reme_ai/context/file_system_tool/edit_prompt.yaml +++ /dev/null @@ -1,23 +0,0 @@ -tool_desc: | - Replaces text within a file. By default, replaces a single occurrence, but can replace multiple occurrences when \`expected_replacements\` is specified. This tool requires providing significant context around the change to ensure precise targeting. Always use the ${READ_FILE_TOOL_NAME} tool to examine the file's current content before attempting a text replacement. - The user has the ability to modify the \`new_string\` content. If modified, this will be stated in the response. - Expectation for required parameters: - 1. \`file_path\` is the path to the file to modify. - 2. \`old_string\` MUST be the exact literal text to replace (including all whitespace, indentation, newlines, and surrounding code etc.). - 3. \`new_string\` MUST be the exact literal text to replace \`old_string\` with (also including all whitespace, indentation, newlines, and surrounding code etc.). Ensure the resulting code is correct and idiomatic. - 4. NEVER escape \`old_string\` or \`new_string\`, that would break the exact literal text requirement. - **Important:** If ANY of the above are not satisfied, the tool will fail. CRITICAL for \`old_string\`: Must uniquely identify the single instance to change. Include at least 3 lines of context BEFORE and AFTER the target text, matching whitespace and indentation precisely. If this string matches multiple locations, or does not match exactly, the tool will fail. - **Multiple replacements:** Set \`expected_replacements\` to the number of occurrences you want to replace. The tool will replace ALL occurrences that match \`old_string\` exactly. Ensure the number of replacements matches your expectation. - -file_path: | - The path to the file to modify. - -old_string: | - The exact literal text to replace, preferably unescaped. For single replacements (default), include at least 3 lines of context BEFORE and AFTER the target text, matching whitespace and indentation precisely. For multiple replacements, specify expected_replacements parameter. If this string is not the exact literal text (i.e. you escaped it) or does not match exactly, the tool will fail. - -new_string: | - The exact literal text to replace `old_string` with, preferably unescaped. Provide the EXACT text. Ensure the resulting code is correct and idiomatic. - -expected_replacements: | - Optional: Number of replacements expected. Defaults to 1 if not specified. Use when you want to replace multiple occurrences. - diff --git a/reme_ai/context/file_system_tool/glob_op.py b/reme_ai/context/file_system_tool/glob_op.py deleted file mode 100644 index 1f98124d..00000000 --- a/reme_ai/context/file_system_tool/glob_op.py +++ /dev/null @@ -1,295 +0,0 @@ -"""Glob file search operation module. - -This module provides a tool operation for finding files matching glob patterns. -It enables efficient file discovery based on name or path structure, especially -in large codebases. Files are sorted by modification time (newest first). -""" - -import fnmatch -import time -from pathlib import Path -from typing import List, Optional - -from flowllm.core.context import C -from flowllm.core.op import BaseAsyncToolOp -from flowllm.core.schema import ToolCall -from loguru import logger -from pathspec import PathSpec -from pathspec.patterns.gitwildmatch import GitIgnorePattern - - -@C.register_op() -class GlobOp(BaseAsyncToolOp): - """Glob file search operation. - - This operation efficiently finds files matching specific glob patterns, - returning absolute paths sorted by modification time (newest first). - Supports gitignore patterns for filtering files. - """ - - file_path = __file__ - - def __init__(self, gitignore_patterns: List[str] = None, **kwargs): - kwargs.setdefault("raise_exception", False) - super().__init__(**kwargs) - self.gitignore_patterns = gitignore_patterns - - def build_tool_call(self) -> ToolCall: - """Build and return the tool call schema for this operator.""" - tool_params = { - "name": "FindFiles", - "description": self.get_prompt("tool_desc"), - "input_schema": { - "pattern": { - "type": "string", - "description": self.get_prompt("pattern"), - "required": True, - }, - "dir_path": { - "type": "string", - "description": self.get_prompt("dir_path"), - "required": False, - }, - "case_sensitive": { - "type": "boolean", - "description": self.get_prompt("case_sensitive"), - "required": False, - }, - }, - } - - return ToolCall(**tool_params) - - def _should_ignore_file( - self, - file_path: Path, - root_dir: Path, - ) -> bool: - """Check if a file should be ignored based on ignore patterns. - - Args: - file_path: Path to the file to check. - root_dir: Root directory for resolving relative paths. - - Returns: - True if the file should be ignored, False otherwise. - """ - if not self.gitignore_patterns: - return False - - ignore_spec = PathSpec.from_lines(GitIgnorePattern, self.gitignore_patterns) - - # Get relative path from root_dir - relative_path = file_path.relative_to(root_dir) - - # Check if file matches ignore patterns - return ignore_spec.match_file(str(relative_path)) - - async def async_execute(self): - """Execute the glob search operation.""" - pattern: str = self.input_dict.get("pattern", "").strip() - dir_path: Optional[str] = self.input_dict.get("dir_path") - case_sensitive: bool = self.input_dict.get("case_sensitive", False) - - # Validate pattern - if not pattern: - error_msg = "The 'pattern' parameter cannot be empty." - logger.error(f"{self.name}: {error_msg}") - self.set_output(error_msg) - return - - # Determine search directory - if dir_path: - search_dir = Path(dir_path).expanduser().resolve() - if not search_dir.exists(): - error_msg = f"Search path does not exist: {search_dir}" - logger.error(f"{self.name}: {error_msg}") - self.set_output(error_msg) - return - if not search_dir.is_dir(): - error_msg = f"Search path is not a directory: {search_dir}" - logger.error(f"{self.name}: {error_msg}") - self.set_output(error_msg) - return - else: - search_dir = Path.cwd() - - # Collect matching files - all_entries: List[Path] = [] - ignored_count = 0 - - # Check if pattern is an exact file path - full_path = search_dir / pattern - if full_path.exists() and full_path.is_file(): - # Use exact match - if not self._should_ignore_file( - full_path, - search_dir, - ): - all_entries.append(full_path) - else: - # Use glob pattern matching - matching_files = self._glob_match( - search_dir, - pattern, - case_sensitive=case_sensitive, - ) - - # Filter by ignore patterns - for file_path in matching_files: - if self._should_ignore_file( - file_path, - search_dir, - ): - ignored_count += 1 - else: - all_entries.append(file_path) - - # Check if any files found - if not all_entries: - message = f'No files found matching pattern "{pattern}"' - if dir_path: - message += f" within {search_dir}" - if ignored_count > 0: - message += f" ({ignored_count} files were ignored)" - self.set_output(message) - return - - # Sort files by modification time - now_timestamp = time.time() - # recency_threshold_days: Number of days to consider a file "recent" for sorting (default: 1). - recency_threshold_ms = self.op_params.get("recency_threshold_days", 1) * 24 * 60 * 60 * 1000 - - def get_sort_key(path: Path) -> tuple: - mtime = path.stat().st_mtime - mtime_ms = mtime * 1000 - is_recent = (now_timestamp * 1000) - mtime_ms < recency_threshold_ms - - if is_recent: - # Recent files: sort by mtime descending (newest first) - return 0, -mtime_ms - else: - # Old files: sort alphabetically - return 1, str(path) - - sorted_entries = sorted(all_entries, key=get_sort_key) - - # Format results - sorted_absolute_paths = [str(entry.resolve()) for entry in sorted_entries] - file_list_description = "\n".join(sorted_absolute_paths) - file_count = len(sorted_absolute_paths) - - result_message = f'Found {file_count} file(s) matching "{pattern}"' - if dir_path: - result_message += f" within {search_dir}" - if ignored_count > 0: - result_message += f" ({ignored_count} additional files were ignored)" - result_message += ", sorted by modification time (newest first):\n" - result_message += file_list_description - - self.set_output(result_message) - - async def async_default_execute(self, e: Exception = None, **kwargs): - """Fill outputs with a default failure message when execution fails.""" - pattern: str = self.input_dict.get("pattern", "").strip() - error_msg = f'Failed to search files matching pattern "{pattern}"' - if e: - error_msg += f": {str(e)}" - self.set_output(error_msg) - - def _glob_match( - self, - root_dir: Path, - pattern: str, - case_sensitive: bool = False, - ) -> List[Path]: - """Match files using glob pattern. - - Args: - root_dir: Root directory to search in. - pattern: Glob pattern to match. - case_sensitive: Whether matching should be case-sensitive. - - Returns: - List of matching file paths. - """ - matching_files: List[Path] = [] - - # Handle ** pattern (recursive match) - if "**" in pattern: - # Split pattern into parts - parts = pattern.split("**", 1) - prefix = parts[0].rstrip("/") - suffix = parts[1] if len(parts) > 1 else "" - - # Walk through directory tree - for path in root_dir.rglob("*"): - if not path.is_file(): - continue - - rel_path = path.relative_to(root_dir) - rel_str = str(rel_path).replace("\\", "/") - - # Check if path matches pattern - if self._matches_glob_pattern( - rel_str, - prefix, - suffix, - case_sensitive, - ): - matching_files.append(path) - else: - # For patterns without **, use fnmatch for better compatibility - # Walk through directory tree and match manually - pattern_normalized = pattern.replace("\\", "/") - for path in root_dir.rglob("*"): - if not path.is_file(): - continue - - rel_path = path.relative_to(root_dir) - rel_str = str(rel_path).replace("\\", "/") - - # Match using fnmatch - if case_sensitive: - if fnmatch.fnmatch(rel_str, pattern_normalized): - matching_files.append(path) - else: - if fnmatch.fnmatch(rel_str.lower(), pattern_normalized.lower()): - matching_files.append(path) - - return matching_files - - @staticmethod - def _matches_glob_pattern( - path_str: str, - prefix: str, - suffix: str, - case_sensitive: bool, - ) -> bool: - """Check if a path matches a glob pattern with **. - - Args: - path_str: Path string to check (relative to root). - prefix: Prefix pattern before **. - suffix: Suffix pattern after **. - case_sensitive: Whether matching should be case-sensitive. - - Returns: - True if path matches pattern, False otherwise. - """ - if not case_sensitive: - path_str = path_str.lower() - prefix = prefix.lower() - suffix = suffix.lower() - - # Check prefix - if prefix: - if not path_str.startswith(prefix): - return False - - # Check suffix - if suffix: - if not fnmatch.fnmatch(path_str, f"*{suffix}"): - return False - - return True diff --git a/reme_ai/context/file_system_tool/glob_prompt.yaml b/reme_ai/context/file_system_tool/glob_prompt.yaml deleted file mode 100644 index 5912bdcb..00000000 --- a/reme_ai/context/file_system_tool/glob_prompt.yaml +++ /dev/null @@ -1,11 +0,0 @@ -tool_desc: | - Efficiently finds files matching specific glob patterns (e.g., `src/**/*.ts`, `**/*.md`), returning absolute paths sorted by modification time (newest first). Ideal for quickly locating files based on their name or path structure, especially in large codebases. - -pattern: | - The glob pattern to match against (e.g., '**/*.py', 'docs/*.md'). - -dir_path: | - Optional: The absolute path to the directory to search within. If omitted, searches the current working directory. - -case_sensitive: | - Optional: Whether the search should be case-sensitive. Defaults to false. \ No newline at end of file diff --git a/reme_ai/context/file_system_tool/grep_op.py b/reme_ai/context/file_system_tool/grep_op.py deleted file mode 100644 index ee808796..00000000 --- a/reme_ai/context/file_system_tool/grep_op.py +++ /dev/null @@ -1,176 +0,0 @@ -"""Grep text search operation module. - -This module provides a tool operation for searching text patterns in files. -It enables efficient content-based search using regular expressions, with support -for glob pattern filtering and result limiting. -""" - -import fnmatch -import re -from pathlib import Path -from typing import List, Optional - -from flowllm.core.context import C -from flowllm.core.op import BaseAsyncToolOp -from flowllm.core.schema import ToolCall -from loguru import logger - - -@C.register_op() -class GrepOp(BaseAsyncToolOp): - """Grep text search operation. - - This operation searches for text patterns in files using regular expressions. - Supports glob pattern filtering and result limiting. - """ - - file_path = __file__ - - def __init__(self, **kwargs): - kwargs.setdefault("raise_exception", False) - super().__init__(**kwargs) - - def build_tool_call(self) -> ToolCall: - """Build and return the tool call schema for this operator.""" - tool_params = { - "name": "Grep", - "description": self.get_prompt("tool_desc"), - "input_schema": { - "pattern": { - "type": "string", - "description": self.get_prompt("pattern"), - "required": True, - }, - "path": { - "type": "string", - "description": self.get_prompt("path"), - "required": False, - }, - "glob": { - "type": "string", - "description": self.get_prompt("glob"), - "required": False, - }, - "limit": { - "type": "number", - "description": self.get_prompt("limit"), - "required": False, - }, - }, - } - - return ToolCall(**tool_params) - - async def async_execute(self): - """Execute the grep search operation.""" - pattern: str = self.input_dict.get("pattern", "").strip() - path: Optional[str] = self.input_dict.get("path") - glob_pattern: Optional[str] = self.input_dict.get("glob") - limit: Optional[int] = self.input_dict.get("limit") - - # Validate pattern - if not pattern: - raise ValueError("The 'pattern' parameter cannot be empty.") - - # Validate regex pattern - try: - regex = re.compile(pattern, re.IGNORECASE) - except re.error as e: - raise ValueError(f"Invalid regular expression pattern: {pattern}. Error: {str(e)}") - - # Determine search directory - if path: - search_dir = Path(path).expanduser().resolve() - if not search_dir.exists(): - raise ValueError(f"Search path does not exist: {search_dir}") - if not search_dir.is_dir(): - raise ValueError(f"Search path is not a directory: {search_dir}") - else: - search_dir = Path.cwd() - - # Collect matching files based on glob pattern - files_to_search: List[Path] = [] - if glob_pattern: - # Use glob pattern to filter files - glob_normalized = glob_pattern.replace("\\", "/") - for file_path in search_dir.rglob("*"): - if not file_path.is_file(): - continue - rel_path = file_path.relative_to(search_dir) - rel_str = str(rel_path).replace("\\", "/") - if fnmatch.fnmatch(rel_str.lower(), glob_normalized.lower()): - files_to_search.append(file_path) - else: - # Search all files recursively - files_to_search = [f for f in search_dir.rglob("*") if f.is_file()] - - # Search for matches - matches: List[dict] = [] - for file_path in files_to_search: - try: - content = file_path.read_text(encoding="utf-8", errors="ignore") - lines = content.split("\n") - for line_num, line in enumerate(lines, start=1): - if regex.search(line): - try: - relative_path = file_path.relative_to(search_dir) - except ValueError: - relative_path = file_path.name - matches.append( - { - "file_path": str(relative_path), - "line_number": line_num, - "line": line, - } - ) - if limit and len(matches) >= limit: - break - if limit and len(matches) >= limit: - break - except Exception as e: - logger.debug(f"Could not read {file_path}: {str(e)}") - continue - - # Format results - if not matches: - search_location = f'in path "{path}"' if path else "in the workspace directory" - filter_desc = f' (filter: "{glob_pattern}")' if glob_pattern else "" - result_msg = f'No matches found for pattern "{pattern}" {search_location}{filter_desc}.' - self.set_output(result_msg) - return - - # Group matches by file - matches_by_file = {} - for match in matches: - file_key = match["file_path"] - if file_key not in matches_by_file: - matches_by_file[file_key] = [] - matches_by_file[file_key].append(match) - - # Build output - total_matches = len(matches) - match_term = "match" if total_matches == 1 else "matches" - search_location = f'in path "{path}"' if path else "in the workspace directory" - filter_desc = f' (filter: "{glob_pattern}")' if glob_pattern else "" - - output_lines = [ - f'Found {total_matches} {match_term} for pattern "{pattern}" {search_location}{filter_desc}:\n---', - ] - - for file_path in sorted(matches_by_file.keys()): - output_lines.append(f"File: {file_path}") - for match in sorted(matches_by_file[file_path], key=lambda x: x["line_number"]): - trimmed_line = match["line"].strip() - output_lines.append(f"L{match['line_number']}: {trimmed_line}") - output_lines.append("---") - - result_msg = "\n".join(output_lines) - self.set_output(result_msg) - - async def async_default_execute(self, e: Exception = None, **kwargs): - """Fill outputs with a default failure message when execution fails.""" - pattern: str = self.input_dict.get("pattern", "").strip() - error_msg = f'Failed to search for pattern "{pattern}"' - if e: - error_msg += f": {str(e)}" - self.set_output(error_msg) diff --git a/reme_ai/context/file_system_tool/read_file_op.py b/reme_ai/context/file_system_tool/read_file_op.py deleted file mode 100644 index 92d24cc5..00000000 --- a/reme_ai/context/file_system_tool/read_file_op.py +++ /dev/null @@ -1,120 +0,0 @@ -"""Read file operation module. - -This module provides a tool operation for reading file contents. -It supports reading entire files or specific line ranges for large files. -""" - -from pathlib import Path -from typing import Optional - -from flowllm.core.context import C -from flowllm.core.op import BaseAsyncToolOp -from flowllm.core.schema import ToolCall - - -@C.register_op() -class ReadFileOp(BaseAsyncToolOp): - """Read file operation. - - This operation reads and returns the content of a specified file. - For text files, it can read specific line ranges using offset and limit. - """ - - file_path = __file__ - - def __init__(self, **kwargs): - kwargs.setdefault("raise_exception", False) - super().__init__(**kwargs) - - def build_tool_call(self) -> ToolCall: - """Build and return the tool call schema for this operator.""" - tool_params = { - "name": "ReadFile", - "description": self.get_prompt("tool_desc"), - "input_schema": { - "absolute_path": { - "type": "string", - "description": self.get_prompt("absolute_path"), - "required": True, - }, - "offset": { - "type": "number", - "description": self.get_prompt("offset"), - "required": False, - }, - "limit": { - "type": "number", - "description": self.get_prompt("limit"), - "required": False, - }, - }, - } - - return ToolCall(**tool_params) - - async def async_execute(self): - """Execute the read file operation.""" - absolute_path: str = self.input_dict.get("absolute_path", "").strip() - offset: Optional[int] = self.input_dict.get("offset") - limit: Optional[int] = self.input_dict.get("limit") - - # Validate absolute_path - if not absolute_path: - raise ValueError("The 'absolute_path' parameter cannot be empty.") - - # Resolve file path - file_path_obj = Path(absolute_path).expanduser().resolve() - - # Check if file exists - if not file_path_obj.exists(): - raise FileNotFoundError(f"File not found: {file_path_obj}") - - if not file_path_obj.is_file(): - raise ValueError(f"Path is not a file: {file_path_obj}") - - # Read file content - content = file_path_obj.read_text(encoding="utf-8") - lines = content.split("\n") - - # Handle line range if specified - if offset is not None or limit is not None: - if offset is None: - offset = 0 - if limit is None: - limit = len(lines) - - # Validate offset and limit - if offset < 0: - raise ValueError("Offset must be a non-negative number") - if limit <= 0: - raise ValueError("Limit must be a positive number") - - total_lines = len(lines) - start = offset - end = min(offset + limit, total_lines) - - if start >= total_lines: - raise ValueError( - f"Offset {offset} is beyond file length ({total_lines} lines)", - ) - - selected_lines = lines[start:end] - result_content = "\n".join(selected_lines) - - # Format output with range information - if end < total_lines: - result = f"Showing lines {start}-{end-1} of {total_lines} total lines.\n\n---\n\n{result_content}" - else: - result = result_content - else: - result = content - - self.set_output(result) - - async def async_default_execute(self, e: Exception = None, **kwargs): - """Fill outputs with a default failure message when execution fails.""" - absolute_path: str = self.input_dict.get("absolute_path", "").strip() - error_msg = f'Failed to read file "{absolute_path}"' - if e: - error_msg += f": {str(e)}" - self.set_output(error_msg) diff --git a/reme_ai/context/file_system_tool/rip_grep_op.py b/reme_ai/context/file_system_tool/rip_grep_op.py deleted file mode 100644 index 8bc3ca11..00000000 --- a/reme_ai/context/file_system_tool/rip_grep_op.py +++ /dev/null @@ -1,160 +0,0 @@ -"""Ripgrep text search operation module. - -This module provides a tool operation for searching text patterns in files -using ripgrep (rg). It enables efficient content-based search using regular -expressions with support for glob pattern filtering and result limiting. -""" - -import asyncio -from pathlib import Path -from typing import Optional - -from flowllm.core.context import C -from flowllm.core.op import BaseAsyncToolOp -from flowllm.core.schema import ToolCall -from loguru import logger - - -@C.register_op() -class RipGrepOp(BaseAsyncToolOp): - """Ripgrep text search operation. - - This operation searches for text patterns in files using ripgrep (rg). - Supports glob pattern filtering and result limiting. - """ - - file_path = __file__ - - def __init__(self, **kwargs): - kwargs.setdefault("raise_exception", False) - super().__init__(**kwargs) - - def build_tool_call(self) -> ToolCall: - """Build and return the tool call schema for this operator.""" - tool_params = { - "name": "RipGrep", - "description": self.get_prompt("tool_desc"), - "input_schema": { - "pattern": { - "type": "string", - "description": self.get_prompt("pattern"), - "required": True, - }, - "path": { - "type": "string", - "description": self.get_prompt("path"), - "required": False, - }, - "glob": { - "type": "string", - "description": self.get_prompt("glob"), - "required": False, - }, - "limit": { - "type": "number", - "description": self.get_prompt("limit"), - "required": False, - }, - }, - } - - return ToolCall(**tool_params) - - async def async_execute(self): - """Execute the ripgrep search operation.""" - pattern: str = self.input_dict.get("pattern", "").strip() - path: Optional[str] = self.input_dict.get("path") - glob_pattern: Optional[str] = self.input_dict.get("glob") - limit: Optional[int] = self.input_dict.get("limit") - - # Validate pattern - if not pattern: - raise ValueError("The 'pattern' parameter cannot be empty.") - - # Determine search path - if path: - search_path = Path(path).expanduser().resolve() - if not search_path.exists(): - raise ValueError(f"Search path does not exist: {search_path}") - else: - search_path = Path.cwd() - - # Build ripgrep command - rg_args = [ - "rg", - "--line-number", - "--no-heading", - "--with-filename", - "--ignore-case", - "--regexp", - pattern, - ] - - # Add glob pattern if provided - if glob_pattern: - rg_args.extend(["--glob", glob_pattern]) - - # Add search path - rg_args.append(str(search_path)) - - # Execute ripgrep - try: - process = await asyncio.create_subprocess_exec( - *rg_args, - stdout=asyncio.subprocess.PIPE, - stderr=asyncio.subprocess.PIPE, - ) - stdout, stderr = await process.communicate() - except FileNotFoundError: - raise ValueError("ripgrep (rg) is not installed or not in PATH") - - # Handle ripgrep exit codes - if process.returncode == 0: - raw_output = stdout.decode("utf-8").strip() - elif process.returncode == 1: - # No matches found - raw_output = "" - else: - error_msg = stderr.decode("utf-8").strip() - raise ValueError(f"ripgrep exited with code {process.returncode}: {error_msg}") - - # Build search description - search_location = f'in path "{path}"' if path else "in the workspace directory" - filter_desc = f' (filter: "{glob_pattern}")' if glob_pattern else "" - - # Check if we have any matches - if not raw_output: - result_msg = f'No matches found for pattern "{pattern}" {search_location}{filter_desc}.' - self.set_output(result_msg) - return - - # Split into lines and apply limit - all_lines = [line for line in raw_output.split("\n") if line.strip()] - total_matches = len(all_lines) - match_term = "match" if total_matches == 1 else "matches" - - # Apply limit if specified - lines_to_include = all_lines - truncated = False - if limit and len(all_lines) > limit: - lines_to_include = all_lines[:limit] - truncated = True - - # Build output - header = f'Found {total_matches} {match_term} for pattern "{pattern}" {search_location}{filter_desc}:\n---\n' - grep_output = "\n".join(lines_to_include) - - result_msg = header + grep_output - if truncated: - omitted = total_matches - len(lines_to_include) - result_msg += f"\n---\n[{omitted} {'line' if omitted == 1 else 'lines'} truncated] ..." - - self.set_output(result_msg) - - async def async_default_execute(self, e: Exception = None, **kwargs): - """Fill outputs with a default failure message when execution fails.""" - pattern: str = self.input_dict.get("pattern", "").strip() - error_msg = f'Failed to search for pattern "{pattern}"' - if e: - error_msg += f": {str(e)}" - self.set_output(error_msg) diff --git a/reme_ai/context/file_system_tool/rip_grep_prompt.yaml b/reme_ai/context/file_system_tool/rip_grep_prompt.yaml deleted file mode 100644 index 6faef042..00000000 --- a/reme_ai/context/file_system_tool/rip_grep_prompt.yaml +++ /dev/null @@ -1,15 +0,0 @@ -tool_desc: | - A powerful search tool built on ripgrep for finding patterns in files using regular expressions. Supports full regex syntax (e.g., "log.*Error", "function\\s+\\w+"), glob pattern filtering, and result limiting. Ideal for efficient searching across large codebases. - -pattern: | - The regular expression pattern to search for in file contents. - -path: | - Optional: The file or directory to search in. Defaults to current working directory. - -glob: | - Optional: Glob pattern to filter files (e.g., "*.js", "*.{ts,tsx}"). - -limit: | - Optional: Maximum number of matching lines to return. Shows all matches if not specified. - diff --git a/reme_ai/context/file_system_tool/write_file_op.py b/reme_ai/context/file_system_tool/write_file_op.py deleted file mode 100644 index b8f604d3..00000000 --- a/reme_ai/context/file_system_tool/write_file_op.py +++ /dev/null @@ -1,89 +0,0 @@ -"""Write file operation module. - -This module provides a tool operation for writing content to files. -It supports creating new files or overwriting existing files, and automatically -creates parent directories if they don't exist. -""" - -from pathlib import Path - -from flowllm.core.context import C -from flowllm.core.op import BaseAsyncToolOp -from flowllm.core.schema import ToolCall - - -@C.register_op() -class WriteFileOp(BaseAsyncToolOp): - """Write file operation. - - This operation writes content to a specified file. If the file doesn't exist, - it will be created. If parent directories don't exist, they will be created automatically. - """ - - file_path = __file__ - - def __init__(self, **kwargs): - kwargs.setdefault("raise_exception", False) - super().__init__(**kwargs) - - def build_tool_call(self) -> ToolCall: - """Build and return the tool call schema for this operator.""" - tool_params = { - "name": "WriteFile", - "description": self.get_prompt("tool_desc"), - "input_schema": { - "file_path": { - "type": "string", - "description": self.get_prompt("file_path"), - "required": True, - }, - "content": { - "type": "string", - "description": self.get_prompt("content"), - "required": True, - }, - }, - } - - return ToolCall(**tool_params) - - async def async_execute(self): - """Execute the write file operation.""" - file_path: str = self.input_dict.get("file_path", "").strip() - content: str = self.input_dict.get("content", "") - - # Validate file_path - if not file_path: - raise ValueError("The 'file_path' parameter cannot be empty.") - - # Resolve file path - file_path_obj = Path(file_path).expanduser().resolve() - - # Check if path is a directory - if file_path_obj.exists() and file_path_obj.is_dir(): - raise ValueError(f"Path is a directory, not a file: {file_path_obj}") - - # Create parent directories if they don't exist - file_path_obj.parent.mkdir(parents=True, exist_ok=True) - - # Check if file exists - file_exists = file_path_obj.exists() and file_path_obj.is_file() - - # Write content to file - file_path_obj.write_text(content, encoding="utf-8") - - # Format success message - if file_exists: - result = f"Successfully overwrote file: {file_path_obj}" - else: - result = f"Successfully created and wrote to new file: {file_path_obj}" - - self.set_output(result) - - async def async_default_execute(self, e: Exception = None, **kwargs): - """Fill outputs with a default failure message when execution fails.""" - file_path: str = self.input_dict.get("file_path", "").strip() - error_msg = f'Failed to write file "{file_path}"' - if e: - error_msg += f": {str(e)}" - self.set_output(error_msg) diff --git a/reme_ai/context/file_system_tool/write_file_prompt.yaml b/reme_ai/context/file_system_tool/write_file_prompt.yaml deleted file mode 100644 index 02740d04..00000000 --- a/reme_ai/context/file_system_tool/write_file_prompt.yaml +++ /dev/null @@ -1,9 +0,0 @@ -tool_desc: | - Writes content to a specified file in the local filesystem. If the file doesn't exist, it will be created. If parent directories don't exist, they will be created automatically. If the file already exists, it will be overwritten with the new content. - -file_path: | - The absolute path to the file to write to (e.g., '/home/user/project/file.txt'). Relative paths are not supported. You must provide an absolute path. - -content: | - The content to write to the file. - diff --git a/reme_ai/context/file_system_tool/write_todos_op.py b/reme_ai/context/file_system_tool/write_todos_op.py deleted file mode 100644 index 997a1d75..00000000 --- a/reme_ai/context/file_system_tool/write_todos_op.py +++ /dev/null @@ -1,102 +0,0 @@ -"""Write todos operation module. - -This module provides a tool operation for managing todo lists. -It enables tracking subtasks with status (pending, in_progress, completed, cancelled). -""" - -from typing import List - -from flowllm.core.context import C -from flowllm.core.op import BaseAsyncToolOp -from flowllm.core.schema import ToolCall - -TODO_STATUSES = ["pending", "in_progress", "completed", "cancelled"] - - -@C.register_op() -class WriteTodosOp(BaseAsyncToolOp): - """Write todos operation. - - This operation manages a todo list with subtasks that can be tracked - through different statuses: pending, in_progress, completed, cancelled. - """ - - file_path = __file__ - - def build_tool_call(self) -> ToolCall: - """Build and return the tool call schema for this operator.""" - tool_params = { - "name": "WriteTodos", - "description": self.get_prompt("tool_desc"), - "input_schema": { - "todos": { - "type": "array", - "description": self.get_prompt("todos"), - "required": True, - "items": { - "type": "object", - "properties": { - "description": { - "type": "string", - "description": self.get_prompt("todo_description"), - }, - "status": { - "type": "string", - "description": self.get_prompt("todo_status"), - "enum": TODO_STATUSES, - }, - }, - "required": ["description", "status"], - }, - }, - }, - } - - return ToolCall(**tool_params) - - async def async_execute(self): - """Execute the write todos operation.""" - todos: List[dict] = self.input_dict.get("todos", []) - - # Validate todos - if not isinstance(todos, list): - raise ValueError("The 'todos' parameter must be an array") - - # Validate each todo item - for i, todo in enumerate(todos): - if not isinstance(todo, dict): - raise ValueError(f"Todo item at index {i} must be an object") - - if "description" not in todo or not isinstance(todo["description"], str): - raise ValueError(f"Todo item at index {i} must have a non-empty description string") - - if not todo["description"].strip(): - raise ValueError(f"Todo item at index {i} must have a non-empty description string") - - if "status" not in todo or todo["status"] not in TODO_STATUSES: - raise ValueError( - f"Todo item at index {i} must have a valid status ({', '.join(TODO_STATUSES)})", - ) - - # Validate only one in_progress task - in_progress_count = sum(1 for todo in todos if todo.get("status") == "in_progress") - if in_progress_count > 1: - raise ValueError("Only one task can be 'in_progress' at a time") - - # Format todo list - if not todos: - result_message = "Successfully cleared the todo list." - else: - todo_list_string = "\n".join( - f"{i + 1}. [{todo['status']}] {todo['description']}" for i, todo in enumerate(todos) - ) - result_message = f"Successfully updated the todo list. The current list is now:\n{todo_list_string}" - - self.set_output(result_message) - - async def async_default_execute(self, e: Exception = None, **kwargs): - """Fill outputs with a default failure message when execution fails.""" - error_msg = "Failed to update the todo list" - if e: - error_msg += f": {str(e)}" - self.set_output(error_msg) diff --git a/reme_ai/context/file_system_tool/write_todos_prompt.yaml b/reme_ai/context/file_system_tool/write_todos_prompt.yaml deleted file mode 100644 index b7f6ea5c..00000000 --- a/reme_ai/context/file_system_tool/write_todos_prompt.yaml +++ /dev/null @@ -1,35 +0,0 @@ -tool_desc: | - This tool can help you list out the current subtasks that are required to be completed for a given user request. The list of subtasks helps you keep track of the current task, organize complex queries and help ensure that you don't miss any steps. With this list, the user can also see the current progress you are making in executing a given task. - - Depending on the task complexity, you should first divide a given task into subtasks and then use this tool to list out the subtasks that are required to be completed for a given user request. - Each of the subtasks should be clear and distinct. - - Use this tool for complex queries that require multiple steps. If you find that the request is actually complex after you have started executing the user task, create a todo list and use it. If execution of the user task requires multiple steps, planning and generally is higher complexity than a simple Q&A, use this tool. - - DO NOT use this tool for simple tasks that can be completed in less than 2 steps. If the user query is simple and straightforward, do not use the tool. If you can respond with an answer in a single turn then this tool is not required. - - ## Task state definitions - - - pending: Work has not begun on a given subtask. - - in_progress: Marked just prior to beginning work on a given subtask. You should only have one subtask as in_progress at a time. - - completed: Subtask was successfully completed with no errors or issues. If the subtask required more steps to complete, update the todo list with the subtasks. All steps should be identified as completed only when they are completed. - - cancelled: As you update the todo list, some tasks are not required anymore due to the dynamic nature of the task. In this case, mark the subtasks as cancelled. - - ## Methodology for using this tool - 1. Use this todo list list as soon as you receive a user request based on the complexity of the task. - 2. Keep track of every subtask that you update the list with. - 3. Mark a subtask as in_progress before you begin working on it. You should only have one subtask as in_progress at a time. - 4. Update the subtask list as you proceed in executing the task. The subtask list is not static and should reflect your progress and current plans, which may evolve as you acquire new information. - 5. Mark a subtask as completed when you have completed it. - 6. Mark a subtask as cancelled if the subtask is no longer needed. - 7. You must update the todo list as soon as you start, stop or cancel a subtask. Don't batch or wait to update the todo list. - -todos: | - The complete list of todo items. This will replace the existing list. - -todo_description: | - The description of the task. - -todo_status: | - The current status of the task. Must be one of: pending, in_progress, completed, cancelled. - diff --git a/reme_ai/context/file_tool/__init__.py b/reme_ai/context/file_tool/__init__.py new file mode 100644 index 00000000..d9d5cfb9 --- /dev/null +++ b/reme_ai/context/file_tool/__init__.py @@ -0,0 +1,19 @@ +"""File tool package for ReMe framework. + +This package provides file-related operations that can be used in LLM-powered flows. +It includes ready-to-use operations for: + +- BatchWriteFileOp: Batch write multiple files operation +- GrepOp: Text search operation for finding patterns in files +- ReadFileOp: Read single file operation +""" + +from .batch_write_file_op import BatchWriteFileOp +from .grep_op import GrepOp +from .read_file_op import ReadFileOp + +__all__ = [ + "BatchWriteFileOp", + "GrepOp", + "ReadFileOp", +] diff --git a/reme_ai/context/file_tool/batch_write_file_op.py b/reme_ai/context/file_tool/batch_write_file_op.py new file mode 100644 index 00000000..ae2b2a5c --- /dev/null +++ b/reme_ai/context/file_tool/batch_write_file_op.py @@ -0,0 +1,45 @@ +"""Batch write file operation module. + +This module provides a tool operation for batch writing multiple files at once. +It processes a dictionary of file paths and contents, writing each file sequentially +and returning a combined result of all write operations. +""" + +from flowllm.core.context import C +from flowllm.core.op import BaseAsyncOp +from flowllm.extensions.file_tool import WriteFileOp +from loguru import logger + + +@C.register_op() +class BatchWriteFileOp(BaseAsyncOp): + """Batch write file operation. + + This operation writes multiple files in a single batch. It takes a dictionary + of file paths and contents from the context, and writes each file using + WriteFileOp. Returns a combined result of all write operations. + """ + + async def async_execute(self): + """Execute the batch write file operation. + + Reads write_file_dict from context, which should be a dictionary mapping + file paths to file contents. Writes each file sequentially and collects + the results. + """ + # Get write file dictionary from context + write_file_dict: dict = self.context.get("write_file_dict", {}) + if not write_file_dict: + self.context.response.answer = "No write file task." + logger.info("No write file task.") + return + + # Process each file in the dictionary + result = [] + for file_path, content in write_file_dict.items(): + write_op = WriteFileOp() + await write_op.async_call(file_path=file_path, content=content) + result.append(write_op.output) + + # Combine all results into a single response + self.context.response.answer = "\n".join(result) diff --git a/reme_ai/context/file_tool/grep_op.py b/reme_ai/context/file_tool/grep_op.py new file mode 100644 index 00000000..df25bf86 --- /dev/null +++ b/reme_ai/context/file_tool/grep_op.py @@ -0,0 +1,52 @@ +"""Grep text search operation module. + +This module provides a tool operation for searching text patterns in files. +It enables efficient content-based search using regular expressions, with support +for glob pattern filtering and result limiting. +""" + +from flowllm.core.context import C +from flowllm.core.schema import ToolCall +from flowllm.extensions.file_tool import GrepOp as FlowGrepOp + + +@C.register_op() +class GrepOp(FlowGrepOp): + """Grep text search operation. + + This operation searches for text patterns in files using regular expressions. + Supports glob pattern filtering and result limiting. + """ + + file_path = __file__ + + def build_tool_call(self) -> ToolCall: + """Build and return the tool call schema for this operator.""" + tool_params = { + "name": "Grep", + "description": self.get_prompt("tool_desc"), + "input_schema": { + "pattern": { + "type": "string", + "description": self.get_prompt("pattern"), + "required": True, + }, + "path": { + "type": "string", + "description": self.get_prompt("path"), + "required": False, + }, + "glob": { + "type": "string", + "description": self.get_prompt("glob"), + "required": False, + }, + "limit": { + "type": "number", + "description": self.get_prompt("limit"), + "required": False, + }, + }, + } + + return ToolCall(**tool_params) diff --git a/reme_ai/context/file_system_tool/grep_prompt.yaml b/reme_ai/context/file_tool/grep_prompt.yaml similarity index 100% rename from reme_ai/context/file_system_tool/grep_prompt.yaml rename to reme_ai/context/file_tool/grep_prompt.yaml diff --git a/reme_ai/context/file_tool/read_file_op.py b/reme_ai/context/file_tool/read_file_op.py new file mode 100644 index 00000000..254fce61 --- /dev/null +++ b/reme_ai/context/file_tool/read_file_op.py @@ -0,0 +1,46 @@ +"""Read file operation module. + +This module provides a tool operation for reading file contents. +It supports reading entire files or specific line ranges for large files. +""" + +from flowllm.core.context import C +from flowllm.core.schema import ToolCall +from flowllm.extensions.file_tool import ReadFileOp as FlowReadFileOp + + +@C.register_op() +class ReadFileOp(FlowReadFileOp): + """Read file operation. + + This operation reads and returns the content of a specified file. + For text files, it can read specific line ranges using offset and limit. + """ + + file_path = __file__ + + def build_tool_call(self) -> ToolCall: + """Build and return the tool call schema for this operator.""" + tool_params = { + "name": "ReadFile", + "description": self.get_prompt("tool_desc"), + "input_schema": { + "absolute_path": { + "type": "string", + "description": self.get_prompt("absolute_path"), + "required": True, + }, + "offset": { + "type": "number", + "description": self.get_prompt("offset"), + "required": False, + }, + "limit": { + "type": "number", + "description": self.get_prompt("limit"), + "required": False, + }, + }, + } + + return ToolCall(**tool_params) diff --git a/reme_ai/context/file_system_tool/read_file_prompt.yaml b/reme_ai/context/file_tool/read_file_prompt.yaml similarity index 100% rename from reme_ai/context/file_system_tool/read_file_prompt.yaml rename to reme_ai/context/file_tool/read_file_prompt.yaml diff --git a/reme_ai/context/offload/context_compact.py b/reme_ai/context/offload/context_compact.py index 4529ca2a..3f9a0dcb 100644 --- a/reme_ai/context/offload/context_compact.py +++ b/reme_ai/context/offload/context_compact.py @@ -1,44 +1,145 @@ +""" +Context compaction module for reducing token usage in conversation contexts. + +This module provides functionality to compress large tool messages by storing +their full content in external files and keeping only previews in the context. +This helps manage context window limits while preserving important information. +""" + +import json +from pathlib import Path +from typing import List +from uuid import uuid4 + from flowllm.core.context import C +from flowllm.core.enumeration import Role from flowllm.core.op import BaseAsyncOp -from flowllm.core.schema import ToolCall +from flowllm.core.schema import Message +from loguru import logger @C.register_op() class ContextCompactOp(BaseAsyncOp): + """ + Context compaction operation that reduces token usage by compressing tool messages. - def __init__(self, ratio: 0.3, **kwargs): + When the total token count exceeds the threshold, this operation compresses large tool + messages by truncating their content and storing the full content in external files. + This helps manage context window limits while preserving recent tool messages. + """ + + def __init__( + self, + all_token_threshold: int = 20000, + tool_token_threshold: int = 2000, + tool_left_char_len: int = 100, + keep_recent: int = 1, + storage_path: str = "./", + exclude_tools: List[str] = None, + **kwargs, + ): + """ + Initialize the context compaction operation. + + Args: + all_token_threshold: Maximum total token count before compaction is triggered. + tool_token_threshold: Maximum token count for a single tool message before it's compressed. + tool_left_char_len: Number of characters to keep in the compressed tool message preview. + keep_recent: Number of recent tool messages to keep uncompressed. + storage_path: Directory path where compressed tool message contents will be stored. + exclude_tools: List of tool names to exclude from compaction (not currently used). + **kwargs: Additional arguments passed to the base class. + """ super().__init__(**kwargs) - self.ratio: float = ratio + self.all_token_threshold: int = all_token_threshold + self.tool_token_threshold: int = tool_token_threshold + self.tool_left_char_len: int = tool_left_char_len + self.keep_recent: int = keep_recent + self.storage_path: Path = Path(storage_path) + self.exclude_tools: List[str] = exclude_tools - def async_execute(self): - messages = self.context.messages + async def async_execute(self): + """ + Execute the context compaction operation. - self.llm.achat(messages=messages, tools=None) - self.token_count() + The operation: + 1. Calculates the total token count of all messages + 2. If below threshold, returns messages unchanged + 3. Otherwise, compresses large tool messages by: + - Keeping only a preview of the content + - Storing full content in external files + - Preserving recent tool messages + """ + # Convert context messages to Message objects + messages = [Message(**x) for x in self.context.messages] - self.context["messages"] = messages - self.context["offloaded_data"] = {"f": "ddd"} + # Calculate total token count + token_cnt: int = self.token_count(messages) + logger.info(f"Context compaction check: total token count={token_cnt}, threshold={self.all_token_threshold}") + # If token count is within threshold, no compaction needed + if token_cnt <= self.all_token_threshold: + self.context.response.answer = self.context.messages + logger.info( + f"Token count ({token_cnt}) is within threshold ({self.all_token_threshold}), no compaction needed", + ) + return -""" -1. token counter 计数，最长上下文X20%=20K - 1. openai tiktoken / hagggingface / modelscope / rule-based - self.token_counter.count() -2. tool: - 1. dump_tool(write_file) - 2. grep / rip_grep / read_file -3. compact: - if > threashold(20K): - tool_call -> write_file - 写一个引用：/xx/xxx/xxx.txt。是否保留前面的token（） -4. compress: - 1. prompt - 2. context -> write_file + # Filter tool messages for processing + tool_messages = [x for x in messages if x.role is Role.TOOL] + # If there are too few tool messages, no compaction needed + if len(tool_messages) <= self.keep_recent: + self.context.response.answer = self.context.messages + logger.info( + f"Tool message count ({len(tool_messages)}) is less than or " + f"equal to keep_recent ({self.keep_recent}), no compaction needed", + ) + return -1. case study: -2. messages - 1. agentscope: reme - 2. http远程 reme + # Exclude recent tool messages from compaction (keep them intact) + tool_messages = tool_messages[: -self.keep_recent] + logger.info( + f"Processing {len(tool_messages)} tool messages for " + f"compaction (keeping {self.keep_recent} recent messages)", + ) -""" \ No newline at end of file + # Dictionary to store file paths and their compressed content (for potential batch writing) + write_file_dict = {} + + # Process each tool message + for tool_message in tool_messages: + # Calculate token count for this specific tool message + tool_token_cnt = self.token_count([tool_message]) + + # Skip if token count is within threshold + if tool_token_cnt <= self.tool_token_threshold: + logger.info( + f"Skipping tool message (tool_call_id={tool_message.tool_call_id}): " + f"token count ({tool_token_cnt}) is within threshold ({self.tool_token_threshold})", + ) + continue + + # Create compressed preview of the tool message content + compact_result = tool_message.content[: self.tool_left_char_len] + "..." + + # Generate file name from tool_call_id or create a unique identifier + file_name = tool_message.tool_call_id or uuid4().hex + path = self.storage_path / f"{file_name}.txt" + + # Store the mapping for potential batch writing + write_file_dict[str(path)] = compact_result + + # Log the compaction action + logger.info( + f"Compacting tool message (tool_call_id={tool_message.tool_call_id}): " + f"token count={tool_token_cnt}, saving full content to {path}", + ) + + # Update tool message content with preview and file reference + compact_result += f" (detailed result is stored in {path})" + tool_message.content = compact_result + + # Return the compacted messages as JSON + self.context.response.answer = json.dumps([x.model_dump() for x in messages], ensure_ascii=False, indent=2) + logger.info(f"Context compaction completed: {len(write_file_dict)} tool messages were compacted") From 722a80ac4b775dc21212778486055ebf329057bc Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Tue, 18 Nov 2025 15:48:05 +0800 Subject: [PATCH 04/39] rename --- .../context/offload/{context_compact.py => context_compact_op.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename reme_ai/context/offload/{context_compact.py => context_compact_op.py} (100%) diff --git a/reme_ai/context/offload/context_compact.py b/reme_ai/context/offload/context_compact_op.py similarity index 100% rename from reme_ai/context/offload/context_compact.py rename to reme_ai/context/offload/context_compact_op.py From 079afae9e888b585eab7e58a5f199e35643b4990 Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Tue, 18 Nov 2025 18:30:06 +0800 Subject: [PATCH 05/39] feat(context): implement context compaction with simple dump --- reme_ai/context/offload/context_compact_op.py | 2 +- test_op/test_context_compact_op.py | 79 +++++++++++++++++++ 2 files changed, 80 insertions(+), 1 deletion(-) create mode 100644 test_op/test_context_compact_op.py diff --git a/reme_ai/context/offload/context_compact_op.py b/reme_ai/context/offload/context_compact_op.py index 3f9a0dcb..9f5a81ef 100644 --- a/reme_ai/context/offload/context_compact_op.py +++ b/reme_ai/context/offload/context_compact_op.py @@ -141,5 +141,5 @@ class ContextCompactOp(BaseAsyncOp): tool_message.content = compact_result # Return the compacted messages as JSON - self.context.response.answer = json.dumps([x.model_dump() for x in messages], ensure_ascii=False, indent=2) + self.context.response.answer = json.dumps([x.simple_dump() for x in messages], ensure_ascii=False, indent=2) logger.info(f"Context compaction completed: {len(write_file_dict)} tool messages were compacted") diff --git a/test_op/test_context_compact_op.py b/test_op/test_context_compact_op.py new file mode 100644 index 00000000..5cdf8b96 --- /dev/null +++ b/test_op/test_context_compact_op.py @@ -0,0 +1,79 @@ +"""Test script for ContextCompactOp. + +This script provides test cases for ContextCompactOp class. +It can be run directly with: python test_context_compact_op.py +""" + +import asyncio + +from flowllm.core.enumeration import Role +from flowllm.core.schema import Message + +from reme_ai.context.offload.context_compact_op import ContextCompactOp +from reme_ai.main import ReMeApp + + +async def async_main(): + """Test function for ContextCompactOp.""" + async with ReMeApp(): + # Create test messages with system, user, assistant, tool sequence + messages = [ + Message(role=Role.SYSTEM, content="You are a helpful assistant."), + Message(role=Role.USER, content="What is the weather today?"), + Message( + role=Role.ASSISTANT, + content="I'll check the weather for you.", + ), + Message( + role=Role.TOOL, + content="A" * 5000, # Large tool message that should be compacted + tool_call_id="call_001", + ), + Message( + role=Role.ASSISTANT, + content="Let me also check the forecast.", + ), + Message( + role=Role.TOOL, + content="B" * 5000, # Another large tool message + tool_call_id="call_002", + ), + Message( + role=Role.USER, + content="What about tomorrow?", + ), + Message( + role=Role.ASSISTANT, + content="I'll check tomorrow's weather.", + ), + Message( + role=Role.TOOL, + content="C" * 5000, # Third large tool message + tool_call_id="call_003", + ), + Message( + role=Role.TOOL, + content="Recent result", # Recent tool message (should be kept) + tool_call_id="call_004", + ), + ] + + # Create op with lower thresholds for testing + op = ContextCompactOp( + all_token_threshold=1000, # Low threshold to trigger compaction + tool_token_threshold=100, # Low threshold to compact tool messages + tool_left_char_len=50, # Keep 50 chars in preview + keep_recent=1, # Keep 1 recent tool message + storage_path="./test_compact_storage", + ) + + # Execute the compaction + await op.async_call(messages=[m.model_dump() for m in messages]) + + # Print results + result = op.context.response.answer + print(f"Context compaction result: {result}") + + +if __name__ == "__main__": + asyncio.run(async_main()) From 43b33a6d63ef0373839408bc904d8829b18ad4c2 Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Tue, 18 Nov 2025 19:21:09 +0800 Subject: [PATCH 06/39] feat(config): add token_count configuration for Qwen models --- reme_ai/config/default.yaml | 45 +++++++++++++++++++++++++++++++++++++ 1 file changed, 45 insertions(+) diff --git a/reme_ai/config/default.yaml b/reme_ai/config/default.yaml index 7d1dcee0..8e8c233f 100644 --- a/reme_ai/config/default.yaml +++ b/reme_ai/config/default.yaml @@ -162,38 +162,83 @@ llm: model_name: qwen3-30b-a3b-instruct-2507 params: temperature: 0.6 + token_count: # Optional + model_name: Qwen/Qwen3-30B-A3B-Instruct-2507 + backend: hf + params: + use_mirror: true qwen3_30b_instruct: backend: openai_compatible model_name: qwen3-30b-a3b-instruct-2507 + token_count: # Optional + model_name: Qwen/Qwen3-30B-A3B-Instruct-2507 + backend: hf + params: + use_mirror: true qwen3_30b_thinking: backend: openai_compatible model_name: qwen3-30b-a3b-thinking-2507 + token_count: # Optional + model_name: Qwen/Qwen3-30B-A3B-Thinking-2507 + backend: hf + params: + use_mirror: true qwen3_235b_instruct: backend: openai_compatible model_name: qwen3-235b-a22b-instruct-2507 + token_count: # Optional + model_name: Qwen/Qwen3-235B-A22B-Instruct-2507 + backend: hf + params: + use_mirror: true qwen3_235b_thinking: backend: openai_compatible model_name: qwen3-235b-a22b-thinking-2507 + token_count: # Optional + model_name: Qwen/Qwen3-235B-A22B-Thinking-2507 + backend: hf + params: + use_mirror: true qwen3_80b_instruct: backend: openai_compatible model_name: qwen3-next-80b-a3b-instruct + token_count: # Optional + model_name: Qwen/Qwen3-Next-80B-A3B-Instruct + backend: hf + params: + use_mirror: true qwen3_80b_thinking: backend: openai_compatible model_name: qwen3-next-80b-a3b-thinking + token_count: # Optional + model_name: Qwen/Qwen3-Next-80B-A3B-Thinking + backend: hf + params: + use_mirror: true qwen3_max_instruct: backend: openai_compatible model_name: qwen3-max + token_count: # Optional + model_name: Qwen/Qwen3-Next-80B-A3B-Instruct # NOTE: We use another model as a substitute. + backend: hf + params: + use_mirror: true qwen25_max_instruct: backend: openai_compatible model_name: qwen-max-2025-01-25 + token_count: # Optional + model_name: Qwen/Qwen2.5-72B-Instruct # NOTE: We use another model as a substitute. + backend: hf + params: + use_mirror: true embedding_model: default: From 9ae1ea193832c04ed0f6fb38fa34d525f37f7c9f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=96=B9=E5=BA=94?= Date: Wed, 19 Nov 2025 14:21:19 +0800 Subject: [PATCH 07/39] feat: initialize dev_1119_zwk with current working copy --- reme_ai/context/offload/LOGIC_COMPARISON.md | 138 + reme_ai/context/offload/README.md | 164 + ...ntext_01617e1e7b1841c0832c842a2dbeec8f.txt | 32 + ...ntext_049e933167ee4e7c9373187adb88c2a4.txt | 32 + ...ntext_0660c1b9698e4feebb84e5a6e0e9ee43.txt | 32 + ...ntext_06b540621f1c4be8a06a6d77c54f56e3.txt | 8 + ...ntext_0c66739297bc4b6595ff7170ec378837.txt | 8 + ...ntext_1601144041b346068d2ab706a86afe82.txt | 92 + ...ntext_189445ca3dbe446dbf418e60e8e6d2d9.txt | 92 + ...ntext_190c9b0a0dcc45ca9333175e9a27d7df.txt | 14 + ...ntext_1fbccb0fca6445c8bbc989c95c790937.txt | 92 + .../context_20251118_195636_168668.json | 32 + .../context_20251118_195858_361977.json | 32 + 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+3. ❌ 问题：包含了系统消息、历史消息、最近消息的所有 token +4. 判断是否超过阈值 +5. 如果超过，才分离系统消息和待压缩消息 +6. 执行压缩 +``` + +**问题**： +- 系统消息通常很大（例如：详细的 system prompt），会占用大量 token +- 即使历史消息不多，也可能因为系统消息触发压缩 +- 最近消息也参与了阈值计算，但实际上不会被压缩 + +### 新逻辑流程（先分离，只计算待压缩部分）✅ + +``` +1. 接收所有消息 +2. 分离消息： + - 系统消息（默认不压缩） + - 待压缩消息（旧的历史消息） + - 最近消息（不压缩） +3. ✅ 只计算待压缩消息的 token 数 +4. 判断是否超过阈值 +5. 如果超过，才执行压缩 +6. 执行压缩 +``` + +**优势**： +- ✅ 精确控制：只针对真正需要压缩的内容进行判断 +- ✅ 避免误触发：系统消息不影响压缩决策 +- ✅ 更合理：最近消息本来就要保留，不应参与阈值计算 + +## 实际案例对比 + +### 案例 1：大型系统消息场景 + +**场景**： +- 系统消息：5000 tokens（包含详细的角色设定、规则、示例等） +- 历史消息（7条）：4000 tokens +- 最近消息（3条）：2000 tokens +- **阈值设置**：10000 tokens + +#### 旧逻辑： +``` +总 token = 5000 + 4000 + 2000 = 11000 tokens +11000 > 10000 ✓ 触发压缩 +压缩 7 条历史消息（4000 tokens） +``` +**结果**：虽然历史消息只有 4000 tokens，但因为系统消息很大，还是触发了压缩。 + +#### 新逻辑： +``` +系统消息：5000 tokens - 保留（不参与计算） +待压缩消息：4000 tokens - 检查阈值 +4000 < 10000 ✗ 不触发压缩 +最近消息：2000 tokens - 保留 +``` +**结果**：历史消息只有 4000 tokens，不需要压缩，节省了 LLM API 调用成本！ + +--- + +### 案例 2：大量历史消息场景 + +**场景**： +- 系统消息：1000 tokens +- 历史消息（50条）：25000 tokens +- 最近消息（3条）：2000 tokens +- **阈值设置**：10000 tokens + +#### 旧逻辑： +``` +总 token = 1000 + 25000 + 2000 = 28000 tokens +28000 > 10000 ✓ 触发压缩 +压缩 50 条历史消息（25000 tokens） +``` + +#### 新逻辑： +``` +系统消息：1000 tokens - 保留 +待压缩消息：25000 tokens - 检查阈值 +25000 > 10000 ✓ 触发压缩 +最近消息：2000 tokens - 保留 +压缩 50 条历史消息（25000 tokens） +``` +**结果**：两种逻辑都正确触发压缩，新逻辑计算更精确。 + +--- + +### 案例 3：边界情况 + +**场景**： +- 系统消息：8000 tokens +- 历史消息（5条）：3000 tokens +- 最近消息（3条）：1000 tokens +- **阈值设置**：10000 tokens + +#### 旧逻辑： +``` +总 token = 8000 + 3000 + 1000 = 12000 tokens +12000 > 10000 ✓ 触发压缩 +压缩 5 条历史消息（3000 tokens） +``` +**问题**：只有 3000 tokens 的历史消息被压缩，压缩效果有限，但要调用 LLM。 + +#### 新逻辑： +``` +系统消息：8000 tokens - 保留 +待压缩消息：3000 tokens - 检查阈值 +3000 < 10000 ✗ 不触发压缩 +最近消息：1000 tokens - 保留 +``` +**结果**：避免了不必要的压缩，节省成本！ + +## 总结 + +| 指标 | 旧逻辑 | 新逻辑 | +|------|--------|--------| +| **计算对象** | 所有消息 | 仅待压缩消息 | +| **系统消息影响** | ❌ 会影响 | ✅ 不影响 | +| **最近消息影响** | ❌ 会影响 | ✅ 不影响 | +| **压缩精确性** | ⚠️ 可能误触发 | ✅ 精确控制 | +| **成本控制** | ⚠️ 可能浪费 API 调用 | ✅ 更节省成本 | +| **适用场景** | 简单场景 | 所有场景 | + +## 关键改进点 + +1. **🎯 精确的阈值控制**：只针对真正需要压缩的内容 +2. **💰 成本优化**：避免不必要的 LLM API 调用 +3. **🔧 逻辑合理性**：系统消息和最近消息本就不压缩，不应影响决策 +4. **📊 更好的可预测性**：用户可以更准确地预测何时触发压缩 + diff --git a/reme_ai/context/offload/README.md b/reme_ai/context/offload/README.md new file mode 100644 index 00000000..5c41124f --- /dev/null +++ b/reme_ai/context/offload/README.md @@ -0,0 +1,164 @@ +# Context Offload Operations + +This module provides operations to reduce token usage in conversation contexts. + +## Operations + +### 1. ContextCompactOp (简单截断) +- **功能**: 通过截断和文件存储来压缩大型工具消息 +- **方式**: 保留消息的前 N 个字符，将完整内容保存到文件 +- **适用场景**: 工具调用结果非常大但不需要完整内容的情况 + +### 2. ContextCompressOp (LLM 智能压缩) +- **功能**: 使用语言模型智能压缩对话历史 +- **方式**: LLM 生成对话摘要，保留关键信息 +- **适用场景**: 需要保持对话连贯性和上下文理解的情况 +- **可溯源**: 原始消息保存到文件，可以随时查看完整内容 + +## ContextCompressOp 使用说明 + +### 基本用法 + +```python +from reme_ai.context.offload.context_compress_op import ContextCompressOp + +# 创建压缩操作 +compress_op = ContextCompressOp( + all_token_threshold=10000, # Token 阈值 + keep_recent=5, # 保留最近 5 条消息 + compress_system_message=False, # 不压缩系统消息 + storage_path="./compressed_contexts" # 原始消息存储路径 +) + +# 执行压缩 +await compress_op.async_call(messages=your_messages) + +# 获取结果 +compressed_messages = compress_op.context.response.answer +original_path = compress_op.context.response.metadata["original_messages_path"] +``` + +### 可溯源特性 + +当消息被压缩后，原始消息会被保存到 JSON 文件中，你可以： + +1. **在压缩消息中看到文件路径**: +``` +[Compressed conversation history] +[LLM 生成的摘要内容...] + +(Original 6 messages are stored in: ./compressed_contexts/context_20241118_143520_123456.json) +``` + +2. **从 metadata 中获取路径**: +```python +original_path = compress_op.context.response.metadata["original_messages_path"] +compressed_count = compress_op.context.response.metadata["compressed_message_count"] +``` + +3. **读取原始消息**: +```python +import json + +with open(original_path, 'r', encoding='utf-8') as f: + original_messages = json.load(f) + +# original_messages 是一个列表，包含所有原始消息 +# 每条消息包含: role, content, name, tool_call_id 等字段 +``` + +### 文件格式 + +保存的 JSON 文件格式示例： + +```json +[ + { + "role": "user", + "content": "I need help building a REST API...", + "name": null, + "tool_call_id": null + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is...", + "name": null, + "tool_call_id": null + } +] +``` + +### 参数说明 + +| 参数 | 类型 | 默认值 | 说明 | +|------|------|--------|------| +| `all_token_threshold` | int | 20000 | 触发压缩的 token 阈值 | +| `keep_recent` | int | 5 | 保留最近多少条消息不压缩 | +| `compress_system_message` | bool | False | 是否压缩系统消息 | +| `storage_path` | str | "./compressed_contexts" | 原始消息存储目录 | + +### 压缩流程 + +1. **分离消息**: + - 系统消息（可选择是否压缩，默认不压缩） + - 待压缩消息（较旧的消息，排除最近的 N 条） + - 最近消息（保持原样） +2. **检查 token 数**: 只计算待压缩消息的 token 数，如果低于阈值，不压缩 +3. **保存原始消息**: 将待压缩的消息保存到 JSON 文件 +4. **LLM 压缩**: 调用语言模型生成简洁摘要 +5. **构建新上下文**: 系统消息 + 压缩摘要 + 最近消息 +6. **添加溯源信息**: 在压缩消息中添加原始文件路径 + +> **重要**: Token 阈值只针对待压缩的消息部分（不包括系统消息和最近消息），这样可以更精确地控制压缩时机。 + +### 测试示例 + +运行测试脚本查看完整示例： + +```bash +python reme_ai/context/offload/test_context_compress.py +``` + +测试包括： +- Test 1: Token 数低于阈值（不压缩） +- Test 2: Token 数超过阈值（执行压缩） +- Test 3: 包含系统消息的压缩 +- Test 4: 演示可溯源性（读取原始文件） + +### 优势 + +✅ **智能压缩**: 使用 LLM 理解内容，生成高质量摘要 +✅ **保留上下文**: 保持对话连贯性和关键信息 +✅ **完全可溯源**: 所有原始内容都保存在文件中 +✅ **灵活配置**: 可以控制压缩阈值、保留消息数等 +✅ **透明可见**: 压缩后的消息明确标注原始文件位置 +✅ **精确控制**: Token 阈值只针对待压缩内容，避免因系统消息触发不必要的压缩 + +### 逻辑优势示例 + +假设有以下对话： +- 1 条系统消息 (2000 tokens) +- 10 条历史消息 (8000 tokens) +- 3 条最近消息 (3000 tokens) + +**旧逻辑**（先计算总 token）: +``` +总 token = 13000 > 阈值 10000 → 触发压缩 +``` + +**新逻辑**（先分离，只计算待压缩部分）: +``` +系统消息 (2000 tokens) - 保留 +待压缩消息 (8000 tokens) - 检查：8000 < 阈值 10000 → 不压缩 +最近消息 (3000 tokens) - 保留 +``` + +这样可以避免因为系统消息占用大量 token 而触发不必要的压缩！ + +### 注意事项 + +⚠️ **存储空间**: 原始消息会保存到磁盘，注意定期清理旧文件 +⚠️ **LLM 成本**: 每次压缩都会调用 LLM，会产生 API 成本 +⚠️ **压缩质量**: 压缩质量取决于使用的 LLM 模型能力 +⚠️ **文件管理**: 需要自行管理存储目录，避免文件过多 + diff --git a/reme_ai/context/offload/compressed_contexts/context_01617e1e7b1841c0832c842a2dbeec8f.txt b/reme_ai/context/offload/compressed_contexts/context_01617e1e7b1841c0832c842a2dbeec8f.txt new file mode 100644 index 00000000..06384283 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_01617e1e7b1841c0832c842a2dbeec8f.txt @@ -0,0 +1,32 @@ +[ + { + "role": "assistant", + "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "How do I reverse a string in Python?", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "What's the difference between a list and a tuple?", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Lists are mutable (you can change their content after creation) and use square brackets `[]`. Tuples are immutable and use parentheses `()`. Lists are better for collections that need to change, while tuples are faster and can be used as dictionary keys.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_049e933167ee4e7c9373187adb88c2a4.txt b/reme_ai/context/offload/compressed_contexts/context_049e933167ee4e7c9373187adb88c2a4.txt new file mode 100644 index 00000000..06384283 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_049e933167ee4e7c9373187adb88c2a4.txt @@ -0,0 +1,32 @@ +[ + { + "role": "assistant", + "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "How do I reverse a string in Python?", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "What's the difference between a list and a tuple?", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Lists are mutable (you can change their content after creation) and use square brackets `[]`. Tuples are immutable and use parentheses `()`. Lists are better for collections that need to change, while tuples are faster and can be used as dictionary keys.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_0660c1b9698e4feebb84e5a6e0e9ee43.txt b/reme_ai/context/offload/compressed_contexts/context_0660c1b9698e4feebb84e5a6e0e9ee43.txt new file mode 100644 index 00000000..da64fc5f --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_0660c1b9698e4feebb84e5a6e0e9ee43.txt @@ -0,0 +1,32 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_06b540621f1c4be8a06a6d77c54f56e3.txt b/reme_ai/context/offload/compressed_contexts/context_06b540621f1c4be8a06a6d77c54f56e3.txt new file mode 100644 index 00000000..a36c5da1 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_06b540621f1c4be8a06a6d77c54f56e3.txt @@ -0,0 +1,8 @@ +[ + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_0c66739297bc4b6595ff7170ec378837.txt b/reme_ai/context/offload/compressed_contexts/context_0c66739297bc4b6595ff7170ec378837.txt new file mode 100644 index 00000000..2b9493a6 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_0c66739297bc4b6595ff7170ec378837.txt @@ -0,0 +1,8 @@ +[ + { + "role": "user", + "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_1601144041b346068d2ab706a86afe82.txt b/reme_ai/context/offload/compressed_contexts/context_1601144041b346068d2ab706a86afe82.txt new file mode 100644 index 00000000..5441820a --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_1601144041b346068d2ab706a86afe82.txt @@ -0,0 +1,92 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Before we proceed, I also need rate limiting and input validation.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_189445ca3dbe446dbf418e60e8e6d2d9.txt b/reme_ai/context/offload/compressed_contexts/context_189445ca3dbe446dbf418e60e8e6d2d9.txt new file mode 100644 index 00000000..5441820a --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_189445ca3dbe446dbf418e60e8e6d2d9.txt @@ -0,0 +1,92 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Before we proceed, I also need rate limiting and input validation.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_190c9b0a0dcc45ca9333175e9a27d7df.txt b/reme_ai/context/offload/compressed_contexts/context_190c9b0a0dcc45ca9333175e9a27d7df.txt new file mode 100644 index 00000000..f80e3aa1 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_190c9b0a0dcc45ca9333175e9a27d7df.txt @@ -0,0 +1,14 @@ +[ + { + "role": "user", + "content": "Before we proceed, I also need rate limiting and input validation.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_1fbccb0fca6445c8bbc989c95c790937.txt b/reme_ai/context/offload/compressed_contexts/context_1fbccb0fca6445c8bbc989c95c790937.txt new file mode 100644 index 00000000..5441820a --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_1fbccb0fca6445c8bbc989c95c790937.txt @@ -0,0 +1,92 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Before we proceed, I also need rate limiting and input validation.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_20251118_195636_168668.json b/reme_ai/context/offload/compressed_contexts/context_20251118_195636_168668.json new file mode 100644 index 00000000..da64fc5f --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_20251118_195636_168668.json @@ -0,0 +1,32 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_20251118_195858_361977.json b/reme_ai/context/offload/compressed_contexts/context_20251118_195858_361977.json new file mode 100644 index 00000000..da64fc5f --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_20251118_195858_361977.json @@ -0,0 +1,32 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_20251118_200350_820013.json b/reme_ai/context/offload/compressed_contexts/context_20251118_200350_820013.json new file mode 100644 index 00000000..da64fc5f --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_20251118_200350_820013.json @@ -0,0 +1,32 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_20251118_200352_509367.json b/reme_ai/context/offload/compressed_contexts/context_20251118_200352_509367.json new file mode 100644 index 00000000..4400b6a5 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_20251118_200352_509367.json @@ -0,0 +1,44 @@ +[ + { + "role": "system", + "content": "You are a helpful AI assistant specialized in software development.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_29d57307293d4424a26d4e314d42ee0b.txt b/reme_ai/context/offload/compressed_contexts/context_29d57307293d4424a26d4e314d42ee0b.txt new file mode 100644 index 00000000..06384283 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_29d57307293d4424a26d4e314d42ee0b.txt @@ -0,0 +1,32 @@ +[ + { + "role": "assistant", + "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "How do I reverse a string in Python?", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "What's the difference between a list and a tuple?", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Lists are mutable (you can change their content after creation) and use square brackets `[]`. Tuples are immutable and use parentheses `()`. Lists are better for collections that need to change, while tuples are faster and can be used as dictionary keys.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_2c08da662cdb461682841ea56c976f90.txt b/reme_ai/context/offload/compressed_contexts/context_2c08da662cdb461682841ea56c976f90.txt new file mode 100644 index 00000000..5441820a --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_2c08da662cdb461682841ea56c976f90.txt @@ -0,0 +1,92 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Before we proceed, I also need rate limiting and input validation.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_2e6f197f77a04faf9feb8247c9eeccbb.txt b/reme_ai/context/offload/compressed_contexts/context_2e6f197f77a04faf9feb8247c9eeccbb.txt new file mode 100644 index 00000000..8552744a --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_2e6f197f77a04faf9feb8247c9eeccbb.txt @@ -0,0 +1,8 @@ +[ + { + "role": "assistant", + "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_331e8f73fd774547965d45ad3fb674ea.txt b/reme_ai/context/offload/compressed_contexts/context_331e8f73fd774547965d45ad3fb674ea.txt new file mode 100644 index 00000000..06384283 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_331e8f73fd774547965d45ad3fb674ea.txt @@ -0,0 +1,32 @@ +[ + { + "role": "assistant", + "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "How do I reverse a string in Python?", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "What's the difference between a list and a tuple?", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Lists are mutable (you can change their content after creation) and use square brackets `[]`. Tuples are immutable and use parentheses `()`. Lists are better for collections that need to change, while tuples are faster and can be used as dictionary keys.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_35bc9b9b78e84f098156dc3d570d3085.txt b/reme_ai/context/offload/compressed_contexts/context_35bc9b9b78e84f098156dc3d570d3085.txt new file mode 100644 index 00000000..06384283 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_35bc9b9b78e84f098156dc3d570d3085.txt @@ -0,0 +1,32 @@ +[ + { + "role": "assistant", + "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "How do I reverse a string in Python?", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "What's the difference between a list and a tuple?", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Lists are mutable (you can change their content after creation) and use square brackets `[]`. Tuples are immutable and use parentheses `()`. Lists are better for collections that need to change, while tuples are faster and can be used as dictionary keys.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_361563e5c33443d3b54db4f885d8f24a.txt b/reme_ai/context/offload/compressed_contexts/context_361563e5c33443d3b54db4f885d8f24a.txt new file mode 100644 index 00000000..d6932d3f --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_361563e5c33443d3b54db4f885d8f24a.txt @@ -0,0 +1,38 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_3845fdd115db4a2590861a067eb2eead.txt b/reme_ai/context/offload/compressed_contexts/context_3845fdd115db4a2590861a067eb2eead.txt new file mode 100644 index 00000000..6dbb6d0a --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_3845fdd115db4a2590861a067eb2eead.txt @@ -0,0 +1,14 @@ +[ + { + "role": "user", + "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_3875a31c4c9e43079b237c83843c34bb.txt b/reme_ai/context/offload/compressed_contexts/context_3875a31c4c9e43079b237c83843c34bb.txt new file mode 100644 index 00000000..05f3b082 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_3875a31c4c9e43079b237c83843c34bb.txt @@ -0,0 +1,80 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Before we proceed, I also need rate limiting and input validation.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_389f9797b9334e6188c1c7d8d1f32063.txt b/reme_ai/context/offload/compressed_contexts/context_389f9797b9334e6188c1c7d8d1f32063.txt new file mode 100644 index 00000000..5441820a --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_389f9797b9334e6188c1c7d8d1f32063.txt @@ -0,0 +1,92 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Before we proceed, I also need rate limiting and input validation.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_3d39a47a03014f3aaf62020c6c19de9d.txt b/reme_ai/context/offload/compressed_contexts/context_3d39a47a03014f3aaf62020c6c19de9d.txt new file mode 100644 index 00000000..6b88db0b --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_3d39a47a03014f3aaf62020c6c19de9d.txt @@ -0,0 +1,56 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Before we proceed, I also need rate limiting and input validation.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_4b43f50c51884702b848219e25de3550.txt b/reme_ai/context/offload/compressed_contexts/context_4b43f50c51884702b848219e25de3550.txt new file mode 100644 index 00000000..97042328 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_4b43f50c51884702b848219e25de3550.txt @@ -0,0 +1,14 @@ +[ + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_4b65a54f2e444021b11081a37093c9f9.txt b/reme_ai/context/offload/compressed_contexts/context_4b65a54f2e444021b11081a37093c9f9.txt new file mode 100644 index 00000000..06384283 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_4b65a54f2e444021b11081a37093c9f9.txt @@ -0,0 +1,32 @@ +[ + { + "role": "assistant", + "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "How do I reverse a string in Python?", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "What's the difference between a list and a tuple?", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Lists are mutable (you can change their content after creation) and use square brackets `[]`. Tuples are immutable and use parentheses `()`. Lists are better for collections that need to change, while tuples are faster and can be used as dictionary keys.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_534db77a5d374cb08672e37500989684.txt b/reme_ai/context/offload/compressed_contexts/context_534db77a5d374cb08672e37500989684.txt new file mode 100644 index 00000000..d6932d3f --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_534db77a5d374cb08672e37500989684.txt @@ -0,0 +1,38 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_55f687abf6ae44c3be0ef6dba07dbd61.txt b/reme_ai/context/offload/compressed_contexts/context_55f687abf6ae44c3be0ef6dba07dbd61.txt new file mode 100644 index 00000000..06384283 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_55f687abf6ae44c3be0ef6dba07dbd61.txt @@ -0,0 +1,32 @@ +[ + { + "role": "assistant", + "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "How do I reverse a string in Python?", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "What's the difference between a list and a tuple?", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Lists are mutable (you can change their content after creation) and use square brackets `[]`. Tuples are immutable and use parentheses `()`. Lists are better for collections that need to change, while tuples are faster and can be used as dictionary keys.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_57e94ce53d814810aa1c6056eab9ad9e.txt b/reme_ai/context/offload/compressed_contexts/context_57e94ce53d814810aa1c6056eab9ad9e.txt new file mode 100644 index 00000000..d6932d3f --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_57e94ce53d814810aa1c6056eab9ad9e.txt @@ -0,0 +1,38 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_59d29d3cbc6149d2aa29c3e1227701a5.txt b/reme_ai/context/offload/compressed_contexts/context_59d29d3cbc6149d2aa29c3e1227701a5.txt new file mode 100644 index 00000000..5441820a --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_59d29d3cbc6149d2aa29c3e1227701a5.txt @@ -0,0 +1,92 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Before we proceed, I also need rate limiting and input validation.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_5a94fc8c142a4d7caeb45ff004b0c1ce.txt b/reme_ai/context/offload/compressed_contexts/context_5a94fc8c142a4d7caeb45ff004b0c1ce.txt new file mode 100644 index 00000000..a36c5da1 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_5a94fc8c142a4d7caeb45ff004b0c1ce.txt @@ -0,0 +1,8 @@ +[ + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_5c5067c1f3c04dca85ddf0af971ecc17.txt b/reme_ai/context/offload/compressed_contexts/context_5c5067c1f3c04dca85ddf0af971ecc17.txt new file mode 100644 index 00000000..5441820a --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_5c5067c1f3c04dca85ddf0af971ecc17.txt @@ -0,0 +1,92 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Before we proceed, I also need rate limiting and input validation.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_5e09eb840ade4d7bab4924573b232cab.txt b/reme_ai/context/offload/compressed_contexts/context_5e09eb840ade4d7bab4924573b232cab.txt new file mode 100644 index 00000000..97042328 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_5e09eb840ade4d7bab4924573b232cab.txt @@ -0,0 +1,14 @@ +[ + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_6574b020b19847ac9674216751578112.txt b/reme_ai/context/offload/compressed_contexts/context_6574b020b19847ac9674216751578112.txt new file mode 100644 index 00000000..5441820a --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_6574b020b19847ac9674216751578112.txt @@ -0,0 +1,92 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Before we proceed, I also need rate limiting and input validation.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_69bdb21ebf244c3a8ca04f2e751202e6.txt b/reme_ai/context/offload/compressed_contexts/context_69bdb21ebf244c3a8ca04f2e751202e6.txt new file mode 100644 index 00000000..5441820a --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_69bdb21ebf244c3a8ca04f2e751202e6.txt @@ -0,0 +1,92 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Before we proceed, I also need rate limiting and input validation.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_7e0a6a841c4f4b20b30a4a2d1d91305e.txt b/reme_ai/context/offload/compressed_contexts/context_7e0a6a841c4f4b20b30a4a2d1d91305e.txt new file mode 100644 index 00000000..4ca81fee --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_7e0a6a841c4f4b20b30a4a2d1d91305e.txt @@ -0,0 +1,20 @@ +[ + { + "role": "assistant", + "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_8f37e06516b9431a9bc395130d99ab9e.txt b/reme_ai/context/offload/compressed_contexts/context_8f37e06516b9431a9bc395130d99ab9e.txt new file mode 100644 index 00000000..12a9a7e3 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_8f37e06516b9431a9bc395130d99ab9e.txt @@ -0,0 +1,20 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_90a767b9f9824091898032b028f2d394.txt b/reme_ai/context/offload/compressed_contexts/context_90a767b9f9824091898032b028f2d394.txt new file mode 100644 index 00000000..a36c5da1 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_90a767b9f9824091898032b028f2d394.txt @@ -0,0 +1,8 @@ +[ + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_a0d1cda8e9ce4ae8ab9fc00b831e2ae1.txt b/reme_ai/context/offload/compressed_contexts/context_a0d1cda8e9ce4ae8ab9fc00b831e2ae1.txt new file mode 100644 index 00000000..12a9a7e3 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_a0d1cda8e9ce4ae8ab9fc00b831e2ae1.txt @@ -0,0 +1,20 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_a76d937829a54014a6cbdcd1782a951e.txt b/reme_ai/context/offload/compressed_contexts/context_a76d937829a54014a6cbdcd1782a951e.txt new file mode 100644 index 00000000..c550a82a --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_a76d937829a54014a6cbdcd1782a951e.txt @@ -0,0 +1,116 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Before we proceed, I also need rate limiting and input validation.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "How do I reverse a string in Python?", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "What's the difference between a list and a tuple?", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_ad69013b93bb4c2c85354393f5093035.txt b/reme_ai/context/offload/compressed_contexts/context_ad69013b93bb4c2c85354393f5093035.txt new file mode 100644 index 00000000..5441820a --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_ad69013b93bb4c2c85354393f5093035.txt @@ -0,0 +1,92 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Before we proceed, I also need rate limiting and input validation.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_ae95a70b6b994431b0ba3dc598b2a7f2.txt b/reme_ai/context/offload/compressed_contexts/context_ae95a70b6b994431b0ba3dc598b2a7f2.txt new file mode 100644 index 00000000..c550a82a --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_ae95a70b6b994431b0ba3dc598b2a7f2.txt @@ -0,0 +1,116 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Before we proceed, I also need rate limiting and input validation.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "How do I reverse a string in Python?", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "What's the difference between a list and a tuple?", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_b5fcec5c2ec14641bc74dbfacfd808f2.txt b/reme_ai/context/offload/compressed_contexts/context_b5fcec5c2ec14641bc74dbfacfd808f2.txt new file mode 100644 index 00000000..a36c5da1 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_b5fcec5c2ec14641bc74dbfacfd808f2.txt @@ -0,0 +1,8 @@ +[ + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_b7825c52a9464d4a819399e4edfa1d2d.txt b/reme_ai/context/offload/compressed_contexts/context_b7825c52a9464d4a819399e4edfa1d2d.txt new file mode 100644 index 00000000..dbbe5728 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_b7825c52a9464d4a819399e4edfa1d2d.txt @@ -0,0 +1,26 @@ +[ + { + "role": "user", + "content": "How do I reverse a string in Python?", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "What's the difference between a list and a tuple?", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Lists are mutable (you can change their content after creation) and use square brackets `[]`. Tuples are immutable and use parentheses `()`. Lists are better for collections that need to change, while tuples are faster and can be used as dictionary keys.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_b7f96d05a44a4a3397597a248bfd83be.txt b/reme_ai/context/offload/compressed_contexts/context_b7f96d05a44a4a3397597a248bfd83be.txt new file mode 100644 index 00000000..97042328 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_b7f96d05a44a4a3397597a248bfd83be.txt @@ -0,0 +1,14 @@ +[ + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_bc1af8ba309b4a6eb2cfa49a9666072e.txt b/reme_ai/context/offload/compressed_contexts/context_bc1af8ba309b4a6eb2cfa49a9666072e.txt new file mode 100644 index 00000000..da64fc5f --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_bc1af8ba309b4a6eb2cfa49a9666072e.txt @@ -0,0 +1,32 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_be22de248c4e42c1b71277bc3b14a0b8.txt b/reme_ai/context/offload/compressed_contexts/context_be22de248c4e42c1b71277bc3b14a0b8.txt new file mode 100644 index 00000000..d6932d3f --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_be22de248c4e42c1b71277bc3b14a0b8.txt @@ -0,0 +1,38 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_c061b74cce3f4e448081641d982df38b.txt b/reme_ai/context/offload/compressed_contexts/context_c061b74cce3f4e448081641d982df38b.txt new file mode 100644 index 00000000..d6932d3f --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_c061b74cce3f4e448081641d982df38b.txt @@ -0,0 +1,38 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_c3f88115df6b49b88b08b1ee1bba049e.txt b/reme_ai/context/offload/compressed_contexts/context_c3f88115df6b49b88b08b1ee1bba049e.txt new file mode 100644 index 00000000..a36c5da1 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_c3f88115df6b49b88b08b1ee1bba049e.txt @@ -0,0 +1,8 @@ +[ + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_c58aa222099c4f278171467d72c1e684.txt b/reme_ai/context/offload/compressed_contexts/context_c58aa222099c4f278171467d72c1e684.txt new file mode 100644 index 00000000..2a49e3b6 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_c58aa222099c4f278171467d72c1e684.txt @@ -0,0 +1,14 @@ +[ + { + "role": "assistant", + "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_c74cfa2e7b064a6e979d07befb861e1f.txt b/reme_ai/context/offload/compressed_contexts/context_c74cfa2e7b064a6e979d07befb861e1f.txt new file mode 100644 index 00000000..12a9a7e3 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_c74cfa2e7b064a6e979d07befb861e1f.txt @@ -0,0 +1,20 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_ca8665986b6b40158955790e8b791f33.txt b/reme_ai/context/offload/compressed_contexts/context_ca8665986b6b40158955790e8b791f33.txt new file mode 100644 index 00000000..5441820a --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_ca8665986b6b40158955790e8b791f33.txt @@ -0,0 +1,92 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Before we proceed, I also need rate limiting and input validation.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_cabc805de26845bf85dcb119aca1687e.txt b/reme_ai/context/offload/compressed_contexts/context_cabc805de26845bf85dcb119aca1687e.txt new file mode 100644 index 00000000..d6932d3f --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_cabc805de26845bf85dcb119aca1687e.txt @@ -0,0 +1,38 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_cc8ea8b616de4cadb1bac5770a1adc2f.txt b/reme_ai/context/offload/compressed_contexts/context_cc8ea8b616de4cadb1bac5770a1adc2f.txt new file mode 100644 index 00000000..a36c5da1 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_cc8ea8b616de4cadb1bac5770a1adc2f.txt @@ -0,0 +1,8 @@ +[ + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_cf80cb2db5fa482e868d121a92eea393.txt b/reme_ai/context/offload/compressed_contexts/context_cf80cb2db5fa482e868d121a92eea393.txt new file mode 100644 index 00000000..12a9a7e3 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_cf80cb2db5fa482e868d121a92eea393.txt @@ -0,0 +1,20 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_d163c619765e46b991c78c0619ee9b8f.txt b/reme_ai/context/offload/compressed_contexts/context_d163c619765e46b991c78c0619ee9b8f.txt new file mode 100644 index 00000000..da64fc5f --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_d163c619765e46b991c78c0619ee9b8f.txt @@ -0,0 +1,32 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_d298845d7b314a09bc836160b4c4531e.txt b/reme_ai/context/offload/compressed_contexts/context_d298845d7b314a09bc836160b4c4531e.txt new file mode 100644 index 00000000..c550a82a --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_d298845d7b314a09bc836160b4c4531e.txt @@ -0,0 +1,116 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Before we proceed, I also need rate limiting and input validation.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "How do I reverse a string in Python?", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "What's the difference between a list and a tuple?", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_d5ee189be0a84f709119710e2ab2bb8d.txt b/reme_ai/context/offload/compressed_contexts/context_d5ee189be0a84f709119710e2ab2bb8d.txt new file mode 100644 index 00000000..c550a82a --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_d5ee189be0a84f709119710e2ab2bb8d.txt @@ -0,0 +1,116 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Before we proceed, I also need rate limiting and input validation.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "How do I reverse a string in Python?", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "What's the difference between a list and a tuple?", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_db326957109f4cdfb01cb29777762344.txt b/reme_ai/context/offload/compressed_contexts/context_db326957109f4cdfb01cb29777762344.txt new file mode 100644 index 00000000..5441820a --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_db326957109f4cdfb01cb29777762344.txt @@ -0,0 +1,92 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Before we proceed, I also need rate limiting and input validation.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_dce5cbcfb71641a0a9423a995c7ac219.txt b/reme_ai/context/offload/compressed_contexts/context_dce5cbcfb71641a0a9423a995c7ac219.txt new file mode 100644 index 00000000..5441820a --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_dce5cbcfb71641a0a9423a995c7ac219.txt @@ -0,0 +1,92 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Before we proceed, I also need rate limiting and input validation.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_e1a7bb467f0f4d30b9e4443d173e01a9.txt b/reme_ai/context/offload/compressed_contexts/context_e1a7bb467f0f4d30b9e4443d173e01a9.txt new file mode 100644 index 00000000..97042328 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_e1a7bb467f0f4d30b9e4443d173e01a9.txt @@ -0,0 +1,14 @@ +[ + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_e5547c1ec3864dc698a3fbf579496279.txt b/reme_ai/context/offload/compressed_contexts/context_e5547c1ec3864dc698a3fbf579496279.txt new file mode 100644 index 00000000..12a9a7e3 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_e5547c1ec3864dc698a3fbf579496279.txt @@ -0,0 +1,20 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_e612e4fb5f19435db116cf62c7ed7245.txt b/reme_ai/context/offload/compressed_contexts/context_e612e4fb5f19435db116cf62c7ed7245.txt new file mode 100644 index 00000000..12a9a7e3 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_e612e4fb5f19435db116cf62c7ed7245.txt @@ -0,0 +1,20 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_f68dc93a2c6c4dcc8654c02102eb93bc.txt b/reme_ai/context/offload/compressed_contexts/context_f68dc93a2c6c4dcc8654c02102eb93bc.txt new file mode 100644 index 00000000..d6932d3f --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_f68dc93a2c6c4dcc8654c02102eb93bc.txt @@ -0,0 +1,38 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_f8fea52f9b764642aeae9ee1adcd6f72.txt b/reme_ai/context/offload/compressed_contexts/context_f8fea52f9b764642aeae9ee1adcd6f72.txt new file mode 100644 index 00000000..06384283 --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_f8fea52f9b764642aeae9ee1adcd6f72.txt @@ -0,0 +1,32 @@ +[ + { + "role": "assistant", + "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "How do I reverse a string in Python?", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "What's the difference between a list and a tuple?", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Lists are mutable (you can change their content after creation) and use square brackets `[]`. Tuples are immutable and use parentheses `()`. Lists are better for collections that need to change, while tuples are faster and can be used as dictionary keys.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_fa1dd140c152466996c526cd7b38b3e4.txt b/reme_ai/context/offload/compressed_contexts/context_fa1dd140c152466996c526cd7b38b3e4.txt new file mode 100644 index 00000000..5441820a --- /dev/null +++ b/reme_ai/context/offload/compressed_contexts/context_fa1dd140c152466996c526cd7b38b3e4.txt @@ -0,0 +1,92 @@ +[ + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "Before we proceed, I also need rate limiting and input validation.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", + "name": null, + "tool_call_id": "" + }, + { + "role": "assistant", + "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", + "name": null, + "tool_call_id": "" + }, + { + "role": "user", + "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", + "name": null, + "tool_call_id": "" + } +] \ No newline at end of file diff --git a/reme_ai/context/offload/context_compress_op.py b/reme_ai/context/offload/context_compress_op.py new file mode 100644 index 00000000..d4f107ad --- /dev/null +++ b/reme_ai/context/offload/context_compress_op.py @@ -0,0 +1,444 @@ +""" +Context compression module for reducing token usage in conversation contexts using LLM. + +This module provides functionality to compress conversation history by using a language +model to generate concise summaries of older messages while preserving recent messages. +This helps manage context window limits while maintaining conversation coherence. +""" + +import json +import re +import xml.etree.ElementTree as ET +from pathlib import Path +from typing import List +from uuid import uuid4 + +from flowllm.core.context import C +from flowllm.core.enumeration import Role +from flowllm.core.op import BaseAsyncOp +from flowllm.core.schema import Message +from flowllm.core.utils import extract_content +from loguru import logger + + +@C.register_op() +class ContextCompressOp(BaseAsyncOp): + """ + Context compression operation that uses LLM to reduce token usage. + + When the total token count exceeds the threshold, this operation uses a language + model to compress older messages into a concise summary while keeping recent + messages intact. This preserves conversation context while reducing token usage. + """ + + file_path: str = __file__ + + def __init__( + self, + all_token_threshold: int = 20000, + keep_recent: int = 5, + storage_path: str = "./compressed_contexts", + micro_summary_token_threshold: int = None, + **kwargs, + ): + """ + Initialize the context compression operation. + + Args: + all_token_threshold: Maximum total token count before compression is triggered. + keep_recent: Number of recent messages to keep uncompressed. + storage_path: Directory path where original messages will be stored for traceability. + micro_summary_token_threshold: Token threshold for each compression group. + If set, messages will be split into groups of this size and compressed separately. + If None, all messages will be compressed together. + **kwargs: Additional arguments passed to the base class. + + Note: + System messages are NEVER compressed to preserve important system instructions. + """ + super().__init__(**kwargs) + self.all_token_threshold: int = all_token_threshold + self.keep_recent: int = keep_recent + self.storage_path: Path = Path(storage_path) + self.micro_summary_token_threshold: int = micro_summary_token_threshold + + assert micro_summary_token_threshold is None or micro_summary_token_threshold > 0, "Micro summary token threshold must be greater than 0" + + # Create storage directory if it doesn't exist + self.storage_path.mkdir(parents=True, exist_ok=True) + + def _save_original_messages(self, messages: List[Message]) -> str: + """Save original messages to file for traceability. + + Args: + messages: List of messages to save + + Returns: + Path to the saved file + """ + # Generate unique filename with timestamp + file_name = f"context_{uuid4().hex}.txt" + file_path = self.storage_path / file_name + + # Convert messages to serializable format + messages_data = [ + { + "role": msg.role.value if hasattr(msg.role, "value") else str(msg.role), + "content": msg.content, + "name": getattr(msg, "name", None), + "tool_call_id": getattr(msg, "tool_call_id", None), + } + for msg in messages + ] + + # Save to file + with open(file_path, "w", encoding="utf-8") as f: + json.dump(messages_data, f, ensure_ascii=False, indent=2) + + logger.info(f"Saved {len(messages)} original messages to {file_path}") + return str(file_path) + + def _split_messages_by_token_threshold( + self, messages: List[Message], token_threshold: int + ) -> List[List[Message]]: + """Split messages into groups based on token threshold. + + Args: + messages: List of messages to split + token_threshold: Maximum token count for each group + + Returns: + List of message groups, each within the token threshold + """ + if not messages: + return [] + + groups = [] + current_group = [] + current_token_count = 0 + + for msg in messages: + msg_tokens = self.token_count([msg]) + + # If single message exceeds threshold, put it in its own group + if msg_tokens > token_threshold: + if current_group: + groups.append(current_group) + current_group = [] + current_token_count = 0 + groups.append([msg]) + continue + + # If adding this message would exceed threshold, start new group + if current_token_count + msg_tokens > token_threshold and current_group: + groups.append(current_group) + current_group = [msg] + current_token_count = msg_tokens + else: + current_group.append(msg) + current_token_count += msg_tokens + + # Add the last group if it has messages + if current_group: + groups.append(current_group) + + logger.info( + f"Split {len(messages)} messages into {len(groups)} groups " + f"with token threshold {token_threshold}" + ) + return groups + + @staticmethod + def _extract_xml_fragments(text: str) -> str: + """ + Extract XML fragments from text, removing scratchpad elements. + + Scans text to extract complete and parseable top-level XML fragments, + excluding elements. If state_snapshot XML is found, returns it; + otherwise returns the original text. + + Args: + text: Input text potentially containing XML fragments + + Returns: + Extracted XML content or original text + """ + try: + # Remove scratchpad elements + new_text = re.sub(r'.*?', '', text, flags=re.S | re.I) + # Extract balanced XML tags + extract_xml = [m[0] for m in re.findall(r'(<(\w+)[^>]*>(?:[^<]|<(?!/\2))*)', new_text)] + + # Validate XML parsing + valid_xml = [] + for xml_str in extract_xml: + try: + ET.fromstring(xml_str) + valid_xml.append(xml_str) + except ET.ParseError: + continue + + # Return state_snapshot if found, otherwise original text + if valid_xml and any("" in xml for xml in valid_xml): + return next(xml for xml in valid_xml if "" in xml) + elif valid_xml: + return valid_xml[0] + else: + return text + except Exception as e: + logger.warning(f"Failed to extract XML fragments: {e}. Returning original text.") + return text + + + @staticmethod + def _format_messages_for_compression(messages: List[Message]) -> str: + """Format messages into a readable text for compression. + + Args: + messages: List of messages to format + + Returns: + Formatted string representation of messages + """ + lines = [] + for i, msg in enumerate(messages, 1): + role_name = msg.role.value if hasattr(msg.role, "value") else str(msg.role) + lines.append(f"[Message {i} - {role_name}]") + lines.append(msg.content) + lines.append("") # Empty line between messages + + return "\n".join(lines) + + async def _compress_messages_with_llm(self, messages_to_compress: List[Message]) -> str: + """Use LLM to compress messages into a concise summary. + + Args: + messages_to_compress: List of messages to compress + + Returns: + Compressed summary text + """ + # Format messages for the prompt + formatted_messages = self._format_messages_for_compression(messages_to_compress) + + # Create prompt for compression + prompt = self.prompt_format( + prompt_name="compress_context_prompt", + messages_content=formatted_messages, + ) + + def parse_compressed_result(message: Message) -> str: + """Parse LLM response to extract compressed content. + + Args: + message: LLM response message + + Returns: + Compressed content string + """ + content = message.content.strip() + # Try to extract content from txt code block + compressed = extract_content(content, "txt") + + # If no code block found, use the raw content + if not compressed: + compressed = content + + logger.info( + f"Compressed {len(messages_to_compress)} messages into " + f"{len(compressed)} characters (reduction: " + f"{len(formatted_messages)} -> {len(compressed)})", + ) + return compressed + + # Call LLM to generate compressed summary + result = await self.llm.achat( + messages=[Message(role=Role.USER, content=prompt)], + callback_fn=parse_compressed_result, + ) + + return result + + async def async_execute(self): + """ + Execute the context compression operation. + + The operation: + 1. Splits messages into system messages, messages to compress, and recent messages + 2. Calculates token count of messages to compress + 3. If below threshold, returns messages unchanged + 4. Otherwise, uses LLM to compress older messages by: + - Saving original messages to file + - Generating a concise summary of older messages + - Replacing older messages with a single summary message + """ + # Convert context messages to Message objects + messages = [Message(**x) for x in self.context.messages] + + # Check if we have enough messages to compress + if len(messages) <= self.keep_recent: + self.context.response.answer = self.context.messages + logger.info( + f"Message count ({len(messages)}) is less than or " + f"equal to keep_recent ({self.keep_recent}), no compression needed", + ) + return + + # Split messages into those to compress and those to keep + messages_to_compress = messages[: -self.keep_recent] + recent_messages = messages[-self.keep_recent :] + + # Always filter out system messages (system messages are never compressed) + system_messages = [m for m in messages_to_compress if m.role is Role.SYSTEM] + messages_to_compress = [m for m in messages_to_compress if m.role is not Role.SYSTEM] + logger.info( + f"Excluding {len(system_messages)} system messages from compression, " + f"{len(messages_to_compress)} messages remaining for compression check", + ) + + # If nothing to compress after filtering, return original messages + if not messages_to_compress: + self.context.response.answer = self.context.messages + logger.info("No messages to compress after filtering, returning original messages") + return + + # Calculate token count of messages to compress (only the content that will be compressed) + compress_token_cnt: int = self.token_count(messages_to_compress) + logger.info( + f"Context compression check: messages_to_compress token count={compress_token_cnt}, " + f"threshold={self.all_token_threshold}" + ) + + # If token count is within threshold, no compression needed + if compress_token_cnt <= self.all_token_threshold: + self.context.response.answer = self.context.messages + logger.info( + f"Messages to compress token count ({compress_token_cnt}) is within threshold " + f"({self.all_token_threshold}), no compression needed", + ) + return + + # Determine whether to use micro-compression (split into groups) + if self.micro_summary_token_threshold is not None and self.micro_summary_token_threshold > 0: + # Split messages into groups based on micro threshold + message_groups = self._split_messages_by_token_threshold( + messages_to_compress, self.micro_summary_token_threshold + ) + + # Compress each group separately + compressed_messages = [] + total_original_tokens = 0 + total_compressed_tokens = 0 + + for group_idx, group in enumerate(message_groups, 1): + # Calculate original token count for this group + group_original_tokens = self.token_count(group) + total_original_tokens += group_original_tokens + + # Save original messages for this group + group_file_path = self._save_original_messages(group) + + # Compress this group + logger.info( + f"Compressing group {group_idx}/{len(message_groups)} " + f"({len(group)} messages, {group_original_tokens} tokens)" + ) + group_summary = await self._compress_messages_with_llm(group) + group_summary = self._extract_xml_fragments(group_summary) + + # Create compressed message for this group + compressed_message = Message( + role=Role.SYSTEM, + content=( + f"[Compressed conversation history - Part {group_idx}/{len(message_groups)}]\n" + f"{group_summary}\n\n" + f"(Original {len(group)} messages are stored in: {group_file_path})" + ), + ) + + # Check if compression actually reduced tokens for this group + compressed_tokens = self.token_count([compressed_message]) + + if compressed_tokens >= group_original_tokens: + logger.warning( + f"Group {group_idx} compression did not reduce tokens: " + f"{group_original_tokens} -> {compressed_tokens}. Using original messages." + ) + self.context.response.answer = self.context.messages + return + + + else: + logger.info( + f"Group {group_idx} compression successful: " + f"{group_original_tokens} -> {compressed_tokens} tokens " + f"(reduction: {group_original_tokens - compressed_tokens} tokens, " + f"{100 * (1 - compressed_tokens / group_original_tokens):.1f}%)" + ) + compressed_messages.append(compressed_message) + total_compressed_tokens += compressed_tokens + + # Construct new message list: system messages + all compressed messages + recent messages + new_messages = system_messages + compressed_messages + recent_messages + + logger.info( + f"Context compression completed using micro-compression: " + f"{len(messages)} messages -> {len(new_messages)} messages " + f"({len(message_groups)} compressed groups), " + f"total tokens: {total_original_tokens} -> {total_compressed_tokens}" + ) + else: + # Original single compression logic + # Calculate original token count + original_tokens = self.token_count(messages_to_compress) + + # Save original messages to file for traceability + original_file_path = self._save_original_messages(messages_to_compress) + + # Use LLM to compress messages + logger.info( + f"Starting LLM compression of {len(messages_to_compress)} messages " + f"({original_tokens} tokens), keeping {len(recent_messages)} recent messages", + ) + compressed_summary = await self._compress_messages_with_llm(messages_to_compress) + compressed_summary = self._extract_xml_fragments(compressed_summary) + + # Create a new system message with the compressed content and file reference + compressed_message = Message( + role=Role.SYSTEM, + content=( + f"[Compressed conversation history]\n" + f"{compressed_summary}\n\n" + f"(Original {len(messages_to_compress)} messages are stored in: {original_file_path})" + ), + ) + + # Check if compression actually reduced tokens + compressed_tokens = self.token_count([compressed_message]) + + if compressed_tokens >= original_tokens: + logger.warning( + f"Compression did not reduce tokens: {original_tokens} -> {compressed_tokens}. " + f"Returning original messages." + ) + self.context.response.answer = self.context.messages + return + + logger.info( + f"Compression successful: {original_tokens} -> {compressed_tokens} tokens " + f"(reduction: {original_tokens - compressed_tokens} tokens, " + f"{100 * (1 - compressed_tokens / original_tokens):.1f}%)" + ) + + # Construct new message list: system messages + compressed message + recent messages + new_messages = system_messages + [compressed_message] + recent_messages + + logger.info( + f"Context compression completed: " + f"{len(str(messages))} messages -> {len(new_messages)} messages" + ) + + # Return the compressed messages as JSON + self.context.response.answer = json.dumps([x.model_dump() for x in new_messages], ensure_ascii=False, indent=2) + + diff --git a/reme_ai/context/offload/context_compress_prompt.yaml b/reme_ai/context/offload/context_compress_prompt.yaml new file mode 100644 index 00000000..24c0d4cb --- /dev/null +++ b/reme_ai/context/offload/context_compress_prompt.yaml @@ -0,0 +1,87 @@ +compress_context_prompt_zh: | + 你是将扮演一个功能为“将内部聊天记录总结为给定结构”的组件。 + + 当会话历史记录变得太大时，将调用您将整个历史记录提取为简洁的结构化XML快照。这个快照非常重要，因为它会成为Agent对过去内容**唯一**的记忆。后续的对话将Agent仅基于此snapshot恢复其工作。所有重要的细节、计划、错误和用户指令都必须保留。 + + 首先，你在个人的思考整个历史内容。检查用户的总体目标、代理的操作、工具输出、文件修改以及任何未解决的问题。找出对未来行动至关重要的每一条信息。 + + 在你推理完成后，生成最终的 XML对象。信息要非常密集。不要省略任何不重要的对话填充。 + + 结构必须如下： + + + + + + + + + + + + + + + + + + + + 下面是需要压缩的对话历史记录： + + ’‘’ + {messages_content} + ’‘’ + + 首先，您将在一个私有的中考虑整个历史。审查用户的总体目标。请你一定要使用中文来回答和压缩。 + + +compress_context_prompt: | + You are the component that summarizes internal chat history into a given structure. + + When the conversation history grows too large, you will be invoked to distill the entire history into a concise, structured XML snapshot. This snapshot is CRITICAL, as it will become the agent's *only* memory of the past. The agent will resume its work based solely on this snapshot. All crucial details, plans, errors, and user directives MUST be preserved. + + First, you will think through the entire history in a private . Review the user's overall goal, the agent's actions, tool outputs, file modifications, and any unresolved questions. Identify every piece of information that is essential for future actions. + + After your reasoning is complete, generate the final XML object. Be incredibly dense with information. Omit any irrelevant conversational filler. + + The structure MUST be as follows: + + + + + + + + + + + + + + + + + + + Here is the conversation history that needs to be compressed: + ’’’ + {messages_content} + ’’’ + First, you will think through the entire history in a private . Review the user's overall goal, the agent' \ No newline at end of file diff --git a/reme_ai/context/offload/test_context_compress.py b/reme_ai/context/offload/test_context_compress.py new file mode 100644 index 00000000..f833aa39 --- /dev/null +++ b/reme_ai/context/offload/test_context_compress.py @@ -0,0 +1,206 @@ +""" +Test script for ContextCompressOp. + +This script demonstrates how to use the context compression operation to reduce +token usage in conversation histories using language models. +""" + +import asyncio +from flowllm.core.enumeration import Role +from flowllm.core.schema import Message +from loguru import logger + +from reme_ai.context.offload.context_compress_op import ContextCompressOp +from reme_ai.main import ReMeApp + + +async def main(): + """Main function to test ContextCompressOp.""" + + async with ReMeApp(): + logger.info("=" * 80) + logger.info("Testing ContextCompressOp - LLM-based Context Compression") + logger.info("=" * 80) + + # Create a mock conversation with multiple messages + messages = [ + { + "role":"system", + "content":"You are a helpful AI assistant specialized in software development.", + }, + { + "role":"user", + "content":"I need help building a REST API in Python. I want to use FastAPI.", + }, + { + "role":"assistant", + "content":"Great choice! FastAPI is an excellent framework for building REST APIs. " + "It's fast, modern, and has automatic API documentation. To get started, you'll need " + "to install FastAPI and uvicorn. Would you like me to guide you through setting up " + "your first endpoint?", + }, + { + "role":"user", + "content":"Yes please. I want to create a user management API with CRUD operations.", + }, + { + "role":"assistant", + "content":"Perfect! For a user management API, I recommend this structure:\n" + "1. Define a User model using Pydantic\n" + "2. Create POST /users endpoint for creating users\n" + "3. Create GET /users and GET /users/{id} for reading\n" + "4. Create PUT /users/{id} for updates\n" + "5. Create DELETE /users/{id} for deletion\n" + "We'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + }, + { + "role":"user", + "content":"Let's use PostgreSQL. Also, I need JWT authentication.", + }, + { + "role":"assistant", + "content":"Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use " + "python-jose library. Here's what we'll implement:\n" + "1. User registration endpoint\n" + "2. Login endpoint that returns JWT token\n" + "3. Protected endpoints that require valid JWT\n" + "4. Password hashing using bcrypt\n" + "Let me show you the code for the User model first.", + }, + { + "role":"user", + "content":"Before we proceed, I also need rate limiting and input validation.", + }, + { + "role":"assistant", + "content":"Good thinking! For rate limiting, we can use slowapi library which integrates " + "well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, " + "but we can add custom validators. I'll also add request validation middleware. " + "Let's start implementing all of this step by step.", + }, + { + "role": "user", + "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant." + }, + { + "role": "assistant", + "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?" + }, + { + "role": "user", + "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns." + }, + { + "role": "assistant", + "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?" + }, + { + "role": "user", + "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics." + }, + { + "role": "assistant", + "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?" + }, + { + "role": "user", + "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features." + }, + { + "role": "assistant", + "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer." + }, + { + "role": "user", + "content": "How do I reverse a string in Python?" + }, + { + "role": "assistant", + "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1." + }, + { + "role": "user", + "content": "What's the difference between a list and a tuple?" + }, + { + "role": "assistant", + "content": "Lists are mutable (you can change their content after creation) and use square brackets `[]`. Tuples are immutable and use parentheses `()`. Lists are better for collections that need to change, while tuples are faster and can be used as dictionary keys." + }, + { + "role": "user", + "content": "How do I handle exceptions in Python?" + }, + { + "role": "assistant", + "content": "Use try-except blocks: `try:` followed by your risky code, then `except SpecificError:` to catch particular exceptions. Always catch specific exceptions rather than the generic `Exception` when possible. You can also use `finally:` for cleanup code that runs regardless." + } + + ] + # + # # Test 1: Messages below threshold (should not compress) + # logger.info("\n" + "=" * 60) + # logger.info("Test 1: Messages below threshold (should skip compression)") + # logger.info("=" * 60) + # + # compress_op1 = ContextCompressOp( + # all_token_threshold=50000, # High threshold, won't trigger + # keep_recent=3, + # ) + # + # await compress_op1.async_call(messages=messages) + # + # result_messages1 = compress_op1.context.response.answer + # logger.info(f"✓ Result: {len(result_messages1)} messages (unchanged)") + # + # # Test 2: Messages above threshold (should compress) + # logger.info("\n" + "=" * 60) + # logger.info("Test 2: Messages above threshold (should compress)") + # logger.info("=" * 60) + # + # compress_op2 = ContextCompressOp( + # all_token_threshold=2000, # Low threshold, will trigger + # keep_recent=3, # Keep last 3 messages + # compress_system_message=False, # Don't compress system messages + # ) + # + # await compress_op2.async_call(messages=messages) + # + # result_messages2 = compress_op2.context.response.answer + # logger.info(f"✓ Result: {len(result_messages2)} messages (compressed)") + # + # # Display compression results + # logger.info("\n" + "=" * 60) + # logger.info("Compression Result Details:") + # logger.info("=" * 60) + # logger.info(f"Original messages: {len(messages)}") + # logger.info(f"Compressed messages: {len(result_messages2)}") + # + # # Test 3: Messages above threshold (should compress) + # logger.info("\n" + "=!" * 30) + # logger.info("Test 3: Messages above micro threshold (should compress)") + # logger.info("=!" * 30) + + compress_op2 = ContextCompressOp( + all_token_threshold=2000, # Low threshold, will trigger + keep_recent=2, # Keep last 3 messages + compress_system_message=False, # Don't compress system messages + micro_summary_token_threshold=1500, + language="zh", + ) + + await compress_op2.async_call(messages=messages) + + 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test/compressed_contexts/context_69685266619e4452bb3acbbd0122fcfd.txt (100%) rename reme_ai/context/offload/compressed_contexts/context_049e933167ee4e7c9373187adb88c2a4.txt => test/compressed_contexts/context_9f6b9d982c584471880c56858303ea14.txt (100%) rename reme_ai/context/offload/compressed_contexts/context_189445ca3dbe446dbf418e60e8e6d2d9.txt => test/compressed_contexts/context_bf0e0009fc4541edb2309339dbc97829.txt (100%) rename reme_ai/context/offload/compressed_contexts/context_a76d937829a54014a6cbdcd1782a951e.txt => test/compressed_contexts/context_c6cfb71eead94de7823d7af51c3b9c9a.txt (100%) create mode 100644 test/test_context_compress.py diff --git a/reme_ai/context/offload/LOGIC_COMPARISON.md b/reme_ai/context/offload/LOGIC_COMPARISON.md deleted file mode 100644 index 4a696ef2..00000000 --- a/reme_ai/context/offload/LOGIC_COMPARISON.md +++ /dev/null @@ -1,138 +0,0 @@ -# Context Compression Logic Comparison - -## 旧逻辑 vs 新逻辑对比 - -### 旧逻辑流程（先计算总 token） - -``` -1. 接收所有消息 -2. 计算所有消息的 token 总数 -3. ❌ 问题：包含了系统消息、历史消息、最近消息的所有 token -4. 判断是否超过阈值 -5. 如果超过，才分离系统消息和待压缩消息 -6. 执行压缩 -``` - -**问题**： -- 系统消息通常很大（例如：详细的 system prompt），会占用大量 token -- 即使历史消息不多，也可能因为系统消息触发压缩 -- 最近消息也参与了阈值计算，但实际上不会被压缩 - -### 新逻辑流程（先分离，只计算待压缩部分）✅ - -``` -1. 接收所有消息 -2. 分离消息： - - 系统消息（默认不压缩） - - 待压缩消息（旧的历史消息） - - 最近消息（不压缩） -3. ✅ 只计算待压缩消息的 token 数 -4. 判断是否超过阈值 -5. 如果超过，才执行压缩 -6. 执行压缩 -``` - -**优势**： -- ✅ 精确控制：只针对真正需要压缩的内容进行判断 -- ✅ 避免误触发：系统消息不影响压缩决策 -- ✅ 更合理：最近消息本来就要保留，不应参与阈值计算 - -## 实际案例对比 - -### 案例 1：大型系统消息场景 - -**场景**： -- 系统消息：5000 tokens（包含详细的角色设定、规则、示例等） -- 历史消息（7条）：4000 tokens -- 最近消息（3条）：2000 tokens -- **阈值设置**：10000 tokens - -#### 旧逻辑： -``` -总 token = 5000 + 4000 + 2000 = 11000 tokens -11000 > 10000 ✓ 触发压缩 -压缩 7 条历史消息（4000 tokens） -``` -**结果**：虽然历史消息只有 4000 tokens，但因为系统消息很大，还是触发了压缩。 - -#### 新逻辑： -``` -系统消息：5000 tokens - 保留（不参与计算） -待压缩消息：4000 tokens - 检查阈值 -4000 < 10000 ✗ 不触发压缩 -最近消息：2000 tokens - 保留 -``` -**结果**：历史消息只有 4000 tokens，不需要压缩，节省了 LLM API 调用成本！ - ---- - -### 案例 2：大量历史消息场景 - -**场景**： -- 系统消息：1000 tokens -- 历史消息（50条）：25000 tokens -- 最近消息（3条）：2000 tokens -- **阈值设置**：10000 tokens - -#### 旧逻辑： -``` -总 token = 1000 + 25000 + 2000 = 28000 tokens -28000 > 10000 ✓ 触发压缩 -压缩 50 条历史消息（25000 tokens） -``` - -#### 新逻辑： -``` -系统消息：1000 tokens - 保留 -待压缩消息：25000 tokens - 检查阈值 -25000 > 10000 ✓ 触发压缩 -最近消息：2000 tokens - 保留 -压缩 50 条历史消息（25000 tokens） -``` -**结果**：两种逻辑都正确触发压缩，新逻辑计算更精确。 - ---- - -### 案例 3：边界情况 - -**场景**： -- 系统消息：8000 tokens -- 历史消息（5条）：3000 tokens -- 最近消息（3条）：1000 tokens -- **阈值设置**：10000 tokens - -#### 旧逻辑： -``` -总 token = 8000 + 3000 + 1000 = 12000 tokens -12000 > 10000 ✓ 触发压缩 -压缩 5 条历史消息（3000 tokens） -``` -**问题**：只有 3000 tokens 的历史消息被压缩，压缩效果有限，但要调用 LLM。 - -#### 新逻辑： -``` -系统消息：8000 tokens - 保留 -待压缩消息：3000 tokens - 检查阈值 -3000 < 10000 ✗ 不触发压缩 -最近消息：1000 tokens - 保留 -``` -**结果**：避免了不必要的压缩，节省成本！ - -## 总结 - -| 指标 | 旧逻辑 | 新逻辑 | -|------|--------|--------| -| **计算对象** | 所有消息 | 仅待压缩消息 | -| **系统消息影响** | ❌ 会影响 | ✅ 不影响 | -| **最近消息影响** | ❌ 会影响 | ✅ 不影响 | -| **压缩精确性** | ⚠️ 可能误触发 | ✅ 精确控制 | -| **成本控制** | ⚠️ 可能浪费 API 调用 | ✅ 更节省成本 | -| **适用场景** | 简单场景 | 所有场景 | - -## 关键改进点 - -1. **🎯 精确的阈值控制**：只针对真正需要压缩的内容 -2. **💰 成本优化**：避免不必要的 LLM API 调用 -3. **🔧 逻辑合理性**：系统消息和最近消息本就不压缩，不应影响决策 -4. **📊 更好的可预测性**：用户可以更准确地预测何时触发压缩 - diff --git a/reme_ai/context/offload/README.md b/reme_ai/context/offload/README.md deleted file mode 100644 index 5c41124f..00000000 --- a/reme_ai/context/offload/README.md +++ /dev/null @@ -1,164 +0,0 @@ -# Context Offload Operations - -This module provides operations to reduce token usage in conversation contexts. - -## Operations - -### 1. ContextCompactOp (简单截断) -- **功能**: 通过截断和文件存储来压缩大型工具消息 -- **方式**: 保留消息的前 N 个字符，将完整内容保存到文件 -- **适用场景**: 工具调用结果非常大但不需要完整内容的情况 - -### 2. ContextCompressOp (LLM 智能压缩) -- **功能**: 使用语言模型智能压缩对话历史 -- **方式**: LLM 生成对话摘要，保留关键信息 -- **适用场景**: 需要保持对话连贯性和上下文理解的情况 -- **可溯源**: 原始消息保存到文件，可以随时查看完整内容 - -## ContextCompressOp 使用说明 - -### 基本用法 - -```python -from reme_ai.context.offload.context_compress_op import ContextCompressOp - -# 创建压缩操作 -compress_op = ContextCompressOp( - all_token_threshold=10000, # Token 阈值 - keep_recent=5, # 保留最近 5 条消息 - compress_system_message=False, # 不压缩系统消息 - storage_path="./compressed_contexts" # 原始消息存储路径 -) - -# 执行压缩 -await compress_op.async_call(messages=your_messages) - -# 获取结果 -compressed_messages = compress_op.context.response.answer -original_path = compress_op.context.response.metadata["original_messages_path"] -``` - -### 可溯源特性 - -当消息被压缩后，原始消息会被保存到 JSON 文件中，你可以： - -1. **在压缩消息中看到文件路径**: -``` -[Compressed conversation history] -[LLM 生成的摘要内容...] - -(Original 6 messages are stored in: ./compressed_contexts/context_20241118_143520_123456.json) -``` - -2. **从 metadata 中获取路径**: -```python -original_path = compress_op.context.response.metadata["original_messages_path"] -compressed_count = compress_op.context.response.metadata["compressed_message_count"] -``` - -3. **读取原始消息**: -```python -import json - -with open(original_path, 'r', encoding='utf-8') as f: - original_messages = json.load(f) - -# original_messages 是一个列表，包含所有原始消息 -# 每条消息包含: role, content, name, tool_call_id 等字段 -``` - -### 文件格式 - -保存的 JSON 文件格式示例： - -```json -[ - { - "role": "user", - "content": "I need help building a REST API...", - "name": null, - "tool_call_id": null - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is...", - "name": null, - "tool_call_id": null - } -] -``` - -### 参数说明 - -| 参数 | 类型 | 默认值 | 说明 | -|------|------|--------|------| -| `all_token_threshold` | int | 20000 | 触发压缩的 token 阈值 | -| `keep_recent` | int | 5 | 保留最近多少条消息不压缩 | -| `compress_system_message` | bool | False | 是否压缩系统消息 | -| `storage_path` | str | "./compressed_contexts" | 原始消息存储目录 | - -### 压缩流程 - -1. **分离消息**: - - 系统消息（可选择是否压缩，默认不压缩） - - 待压缩消息（较旧的消息，排除最近的 N 条） - - 最近消息（保持原样） -2. **检查 token 数**: 只计算待压缩消息的 token 数，如果低于阈值，不压缩 -3. **保存原始消息**: 将待压缩的消息保存到 JSON 文件 -4. **LLM 压缩**: 调用语言模型生成简洁摘要 -5. **构建新上下文**: 系统消息 + 压缩摘要 + 最近消息 -6. **添加溯源信息**: 在压缩消息中添加原始文件路径 - -> **重要**: Token 阈值只针对待压缩的消息部分（不包括系统消息和最近消息），这样可以更精确地控制压缩时机。 - -### 测试示例 - -运行测试脚本查看完整示例： - -```bash -python reme_ai/context/offload/test_context_compress.py -``` - -测试包括： -- Test 1: Token 数低于阈值（不压缩） -- Test 2: Token 数超过阈值（执行压缩） -- Test 3: 包含系统消息的压缩 -- Test 4: 演示可溯源性（读取原始文件） - -### 优势 - -✅ **智能压缩**: 使用 LLM 理解内容，生成高质量摘要 -✅ **保留上下文**: 保持对话连贯性和关键信息 -✅ **完全可溯源**: 所有原始内容都保存在文件中 -✅ **灵活配置**: 可以控制压缩阈值、保留消息数等 -✅ **透明可见**: 压缩后的消息明确标注原始文件位置 -✅ **精确控制**: Token 阈值只针对待压缩内容，避免因系统消息触发不必要的压缩 - -### 逻辑优势示例 - -假设有以下对话： -- 1 条系统消息 (2000 tokens) -- 10 条历史消息 (8000 tokens) -- 3 条最近消息 (3000 tokens) - -**旧逻辑**（先计算总 token）: -``` -总 token = 13000 > 阈值 10000 → 触发压缩 -``` - -**新逻辑**（先分离，只计算待压缩部分）: -``` -系统消息 (2000 tokens) - 保留 -待压缩消息 (8000 tokens) - 检查：8000 < 阈值 10000 → 不压缩 -最近消息 (3000 tokens) - 保留 -``` - -这样可以避免因为系统消息占用大量 token 而触发不必要的压缩！ - -### 注意事项 - -⚠️ **存储空间**: 原始消息会保存到磁盘，注意定期清理旧文件 -⚠️ **LLM 成本**: 每次压缩都会调用 LLM，会产生 API 成本 -⚠️ **压缩质量**: 压缩质量取决于使用的 LLM 模型能力 -⚠️ **文件管理**: 需要自行管理存储目录，避免文件过多 - diff --git a/reme_ai/context/offload/compressed_contexts/context_0660c1b9698e4feebb84e5a6e0e9ee43.txt b/reme_ai/context/offload/compressed_contexts/context_0660c1b9698e4feebb84e5a6e0e9ee43.txt deleted file mode 100644 index da64fc5f..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_0660c1b9698e4feebb84e5a6e0e9ee43.txt +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_06b540621f1c4be8a06a6d77c54f56e3.txt b/reme_ai/context/offload/compressed_contexts/context_06b540621f1c4be8a06a6d77c54f56e3.txt deleted file mode 100644 index a36c5da1..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_06b540621f1c4be8a06a6d77c54f56e3.txt +++ /dev/null @@ -1,8 +0,0 @@ -[ - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_0c66739297bc4b6595ff7170ec378837.txt b/reme_ai/context/offload/compressed_contexts/context_0c66739297bc4b6595ff7170ec378837.txt deleted file mode 100644 index 2b9493a6..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_0c66739297bc4b6595ff7170ec378837.txt +++ /dev/null @@ -1,8 +0,0 @@ -[ - { - "role": "user", - "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_190c9b0a0dcc45ca9333175e9a27d7df.txt b/reme_ai/context/offload/compressed_contexts/context_190c9b0a0dcc45ca9333175e9a27d7df.txt deleted file mode 100644 index f80e3aa1..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_190c9b0a0dcc45ca9333175e9a27d7df.txt +++ /dev/null @@ -1,14 +0,0 @@ -[ - { - "role": "user", - "content": "Before we proceed, I also need rate limiting and input validation.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_1fbccb0fca6445c8bbc989c95c790937.txt b/reme_ai/context/offload/compressed_contexts/context_1fbccb0fca6445c8bbc989c95c790937.txt deleted file mode 100644 index 5441820a..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_1fbccb0fca6445c8bbc989c95c790937.txt +++ /dev/null @@ -1,92 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Before we proceed, I also need rate limiting and input validation.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_20251118_195636_168668.json b/reme_ai/context/offload/compressed_contexts/context_20251118_195636_168668.json deleted file mode 100644 index da64fc5f..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_20251118_195636_168668.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_20251118_195858_361977.json b/reme_ai/context/offload/compressed_contexts/context_20251118_195858_361977.json deleted file mode 100644 index da64fc5f..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_20251118_195858_361977.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_20251118_200350_820013.json b/reme_ai/context/offload/compressed_contexts/context_20251118_200350_820013.json deleted file mode 100644 index da64fc5f..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_20251118_200350_820013.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_20251118_200352_509367.json b/reme_ai/context/offload/compressed_contexts/context_20251118_200352_509367.json deleted file mode 100644 index 4400b6a5..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_20251118_200352_509367.json +++ /dev/null @@ -1,44 +0,0 @@ -[ - { - "role": "system", - "content": "You are a helpful AI assistant specialized in software development.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_29d57307293d4424a26d4e314d42ee0b.txt b/reme_ai/context/offload/compressed_contexts/context_29d57307293d4424a26d4e314d42ee0b.txt deleted file mode 100644 index 06384283..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_29d57307293d4424a26d4e314d42ee0b.txt +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "role": "assistant", - "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "How do I reverse a string in Python?", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "What's the difference between a list and a tuple?", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Lists are mutable (you can change their content after creation) and use square brackets `[]`. Tuples are immutable and use parentheses `()`. Lists are better for collections that need to change, while tuples are faster and can be used as dictionary keys.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_2c08da662cdb461682841ea56c976f90.txt b/reme_ai/context/offload/compressed_contexts/context_2c08da662cdb461682841ea56c976f90.txt deleted file mode 100644 index 5441820a..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_2c08da662cdb461682841ea56c976f90.txt +++ /dev/null @@ -1,92 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Before we proceed, I also need rate limiting and input validation.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_2e6f197f77a04faf9feb8247c9eeccbb.txt b/reme_ai/context/offload/compressed_contexts/context_2e6f197f77a04faf9feb8247c9eeccbb.txt deleted file mode 100644 index 8552744a..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_2e6f197f77a04faf9feb8247c9eeccbb.txt +++ /dev/null @@ -1,8 +0,0 @@ -[ - { - "role": "assistant", - "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_331e8f73fd774547965d45ad3fb674ea.txt b/reme_ai/context/offload/compressed_contexts/context_331e8f73fd774547965d45ad3fb674ea.txt deleted file mode 100644 index 06384283..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_331e8f73fd774547965d45ad3fb674ea.txt +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "role": "assistant", - "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "How do I reverse a string in Python?", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "What's the difference between a list and a tuple?", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Lists are mutable (you can change their content after creation) and use square brackets `[]`. Tuples are immutable and use parentheses `()`. Lists are better for collections that need to change, while tuples are faster and can be used as dictionary keys.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_35bc9b9b78e84f098156dc3d570d3085.txt b/reme_ai/context/offload/compressed_contexts/context_35bc9b9b78e84f098156dc3d570d3085.txt deleted file mode 100644 index 06384283..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_35bc9b9b78e84f098156dc3d570d3085.txt +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "role": "assistant", - "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "How do I reverse a string in Python?", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "What's the difference between a list and a tuple?", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Lists are mutable (you can change their content after creation) and use square brackets `[]`. Tuples are immutable and use parentheses `()`. Lists are better for collections that need to change, while tuples are faster and can be used as dictionary keys.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_361563e5c33443d3b54db4f885d8f24a.txt b/reme_ai/context/offload/compressed_contexts/context_361563e5c33443d3b54db4f885d8f24a.txt deleted file mode 100644 index d6932d3f..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_361563e5c33443d3b54db4f885d8f24a.txt +++ /dev/null @@ -1,38 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_3845fdd115db4a2590861a067eb2eead.txt b/reme_ai/context/offload/compressed_contexts/context_3845fdd115db4a2590861a067eb2eead.txt deleted file mode 100644 index 6dbb6d0a..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_3845fdd115db4a2590861a067eb2eead.txt +++ /dev/null @@ -1,14 +0,0 @@ -[ - { - "role": "user", - "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_3875a31c4c9e43079b237c83843c34bb.txt b/reme_ai/context/offload/compressed_contexts/context_3875a31c4c9e43079b237c83843c34bb.txt deleted file mode 100644 index 05f3b082..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_3875a31c4c9e43079b237c83843c34bb.txt +++ /dev/null @@ -1,80 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Before we proceed, I also need rate limiting and input validation.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_389f9797b9334e6188c1c7d8d1f32063.txt b/reme_ai/context/offload/compressed_contexts/context_389f9797b9334e6188c1c7d8d1f32063.txt deleted file mode 100644 index 5441820a..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_389f9797b9334e6188c1c7d8d1f32063.txt +++ /dev/null @@ -1,92 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Before we proceed, I also need rate limiting and input validation.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_3d39a47a03014f3aaf62020c6c19de9d.txt b/reme_ai/context/offload/compressed_contexts/context_3d39a47a03014f3aaf62020c6c19de9d.txt deleted file mode 100644 index 6b88db0b..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_3d39a47a03014f3aaf62020c6c19de9d.txt +++ /dev/null @@ -1,56 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Before we proceed, I also need rate limiting and input validation.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_4b43f50c51884702b848219e25de3550.txt b/reme_ai/context/offload/compressed_contexts/context_4b43f50c51884702b848219e25de3550.txt deleted file mode 100644 index 97042328..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_4b43f50c51884702b848219e25de3550.txt +++ /dev/null @@ -1,14 +0,0 @@ -[ - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_4b65a54f2e444021b11081a37093c9f9.txt b/reme_ai/context/offload/compressed_contexts/context_4b65a54f2e444021b11081a37093c9f9.txt deleted file mode 100644 index 06384283..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_4b65a54f2e444021b11081a37093c9f9.txt +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "role": "assistant", - "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "How do I reverse a string in Python?", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "What's the difference between a list and a tuple?", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Lists are mutable (you can change their content after creation) and use square brackets `[]`. Tuples are immutable and use parentheses `()`. Lists are better for collections that need to change, while tuples are faster and can be used as dictionary keys.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_534db77a5d374cb08672e37500989684.txt b/reme_ai/context/offload/compressed_contexts/context_534db77a5d374cb08672e37500989684.txt deleted file mode 100644 index d6932d3f..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_534db77a5d374cb08672e37500989684.txt +++ /dev/null @@ -1,38 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_55f687abf6ae44c3be0ef6dba07dbd61.txt b/reme_ai/context/offload/compressed_contexts/context_55f687abf6ae44c3be0ef6dba07dbd61.txt deleted file mode 100644 index 06384283..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_55f687abf6ae44c3be0ef6dba07dbd61.txt +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "role": "assistant", - "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "How do I reverse a string in Python?", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "What's the difference between a list and a tuple?", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Lists are mutable (you can change their content after creation) and use square brackets `[]`. Tuples are immutable and use parentheses `()`. Lists are better for collections that need to change, while tuples are faster and can be used as dictionary keys.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_57e94ce53d814810aa1c6056eab9ad9e.txt b/reme_ai/context/offload/compressed_contexts/context_57e94ce53d814810aa1c6056eab9ad9e.txt deleted file mode 100644 index d6932d3f..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_57e94ce53d814810aa1c6056eab9ad9e.txt +++ /dev/null @@ -1,38 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_59d29d3cbc6149d2aa29c3e1227701a5.txt b/reme_ai/context/offload/compressed_contexts/context_59d29d3cbc6149d2aa29c3e1227701a5.txt deleted file mode 100644 index 5441820a..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_59d29d3cbc6149d2aa29c3e1227701a5.txt +++ /dev/null @@ -1,92 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Before we proceed, I also need rate limiting and input validation.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_5a94fc8c142a4d7caeb45ff004b0c1ce.txt b/reme_ai/context/offload/compressed_contexts/context_5a94fc8c142a4d7caeb45ff004b0c1ce.txt deleted file mode 100644 index a36c5da1..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_5a94fc8c142a4d7caeb45ff004b0c1ce.txt +++ /dev/null @@ -1,8 +0,0 @@ -[ - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_5c5067c1f3c04dca85ddf0af971ecc17.txt b/reme_ai/context/offload/compressed_contexts/context_5c5067c1f3c04dca85ddf0af971ecc17.txt deleted file mode 100644 index 5441820a..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_5c5067c1f3c04dca85ddf0af971ecc17.txt +++ /dev/null @@ -1,92 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Before we proceed, I also need rate limiting and input validation.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_5e09eb840ade4d7bab4924573b232cab.txt b/reme_ai/context/offload/compressed_contexts/context_5e09eb840ade4d7bab4924573b232cab.txt deleted file mode 100644 index 97042328..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_5e09eb840ade4d7bab4924573b232cab.txt +++ /dev/null @@ -1,14 +0,0 @@ -[ - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_6574b020b19847ac9674216751578112.txt b/reme_ai/context/offload/compressed_contexts/context_6574b020b19847ac9674216751578112.txt deleted file mode 100644 index 5441820a..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_6574b020b19847ac9674216751578112.txt +++ /dev/null @@ -1,92 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Before we proceed, I also need rate limiting and input validation.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_69bdb21ebf244c3a8ca04f2e751202e6.txt b/reme_ai/context/offload/compressed_contexts/context_69bdb21ebf244c3a8ca04f2e751202e6.txt deleted file mode 100644 index 5441820a..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_69bdb21ebf244c3a8ca04f2e751202e6.txt +++ /dev/null @@ -1,92 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Before we proceed, I also need rate limiting and input validation.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_7e0a6a841c4f4b20b30a4a2d1d91305e.txt b/reme_ai/context/offload/compressed_contexts/context_7e0a6a841c4f4b20b30a4a2d1d91305e.txt deleted file mode 100644 index 4ca81fee..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_7e0a6a841c4f4b20b30a4a2d1d91305e.txt +++ /dev/null @@ -1,20 +0,0 @@ -[ - { - "role": "assistant", - "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_8f37e06516b9431a9bc395130d99ab9e.txt b/reme_ai/context/offload/compressed_contexts/context_8f37e06516b9431a9bc395130d99ab9e.txt deleted file mode 100644 index 12a9a7e3..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_8f37e06516b9431a9bc395130d99ab9e.txt +++ /dev/null @@ -1,20 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_90a767b9f9824091898032b028f2d394.txt b/reme_ai/context/offload/compressed_contexts/context_90a767b9f9824091898032b028f2d394.txt deleted file mode 100644 index a36c5da1..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_90a767b9f9824091898032b028f2d394.txt +++ /dev/null @@ -1,8 +0,0 @@ -[ - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_a0d1cda8e9ce4ae8ab9fc00b831e2ae1.txt b/reme_ai/context/offload/compressed_contexts/context_a0d1cda8e9ce4ae8ab9fc00b831e2ae1.txt deleted file mode 100644 index 12a9a7e3..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_a0d1cda8e9ce4ae8ab9fc00b831e2ae1.txt +++ /dev/null @@ -1,20 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_ad69013b93bb4c2c85354393f5093035.txt b/reme_ai/context/offload/compressed_contexts/context_ad69013b93bb4c2c85354393f5093035.txt deleted file mode 100644 index 5441820a..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_ad69013b93bb4c2c85354393f5093035.txt +++ /dev/null @@ -1,92 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Before we proceed, I also need rate limiting and input validation.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_ae95a70b6b994431b0ba3dc598b2a7f2.txt b/reme_ai/context/offload/compressed_contexts/context_ae95a70b6b994431b0ba3dc598b2a7f2.txt deleted file mode 100644 index c550a82a..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_ae95a70b6b994431b0ba3dc598b2a7f2.txt +++ /dev/null @@ -1,116 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Before we proceed, I also need rate limiting and input validation.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "How do I reverse a string in Python?", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "What's the difference between a list and a tuple?", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_b5fcec5c2ec14641bc74dbfacfd808f2.txt b/reme_ai/context/offload/compressed_contexts/context_b5fcec5c2ec14641bc74dbfacfd808f2.txt deleted file mode 100644 index a36c5da1..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_b5fcec5c2ec14641bc74dbfacfd808f2.txt +++ /dev/null @@ -1,8 +0,0 @@ -[ - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_b7825c52a9464d4a819399e4edfa1d2d.txt b/reme_ai/context/offload/compressed_contexts/context_b7825c52a9464d4a819399e4edfa1d2d.txt deleted file mode 100644 index dbbe5728..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_b7825c52a9464d4a819399e4edfa1d2d.txt +++ /dev/null @@ -1,26 +0,0 @@ -[ - { - "role": "user", - "content": "How do I reverse a string in Python?", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "What's the difference between a list and a tuple?", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Lists are mutable (you can change their content after creation) and use square brackets `[]`. Tuples are immutable and use parentheses `()`. Lists are better for collections that need to change, while tuples are faster and can be used as dictionary keys.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_b7f96d05a44a4a3397597a248bfd83be.txt b/reme_ai/context/offload/compressed_contexts/context_b7f96d05a44a4a3397597a248bfd83be.txt deleted file mode 100644 index 97042328..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_b7f96d05a44a4a3397597a248bfd83be.txt +++ /dev/null @@ -1,14 +0,0 @@ -[ - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_bc1af8ba309b4a6eb2cfa49a9666072e.txt b/reme_ai/context/offload/compressed_contexts/context_bc1af8ba309b4a6eb2cfa49a9666072e.txt deleted file mode 100644 index da64fc5f..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_bc1af8ba309b4a6eb2cfa49a9666072e.txt +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_be22de248c4e42c1b71277bc3b14a0b8.txt b/reme_ai/context/offload/compressed_contexts/context_be22de248c4e42c1b71277bc3b14a0b8.txt deleted file mode 100644 index d6932d3f..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_be22de248c4e42c1b71277bc3b14a0b8.txt +++ /dev/null @@ -1,38 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_c061b74cce3f4e448081641d982df38b.txt b/reme_ai/context/offload/compressed_contexts/context_c061b74cce3f4e448081641d982df38b.txt deleted file mode 100644 index d6932d3f..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_c061b74cce3f4e448081641d982df38b.txt +++ /dev/null @@ -1,38 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_c3f88115df6b49b88b08b1ee1bba049e.txt b/reme_ai/context/offload/compressed_contexts/context_c3f88115df6b49b88b08b1ee1bba049e.txt deleted file mode 100644 index a36c5da1..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_c3f88115df6b49b88b08b1ee1bba049e.txt +++ /dev/null @@ -1,8 +0,0 @@ -[ - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_c58aa222099c4f278171467d72c1e684.txt b/reme_ai/context/offload/compressed_contexts/context_c58aa222099c4f278171467d72c1e684.txt deleted file mode 100644 index 2a49e3b6..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_c58aa222099c4f278171467d72c1e684.txt +++ /dev/null @@ -1,14 +0,0 @@ -[ - { - "role": "assistant", - "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_c74cfa2e7b064a6e979d07befb861e1f.txt b/reme_ai/context/offload/compressed_contexts/context_c74cfa2e7b064a6e979d07befb861e1f.txt deleted file mode 100644 index 12a9a7e3..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_c74cfa2e7b064a6e979d07befb861e1f.txt +++ /dev/null @@ -1,20 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_ca8665986b6b40158955790e8b791f33.txt b/reme_ai/context/offload/compressed_contexts/context_ca8665986b6b40158955790e8b791f33.txt deleted file mode 100644 index 5441820a..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_ca8665986b6b40158955790e8b791f33.txt +++ /dev/null @@ -1,92 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Before we proceed, I also need rate limiting and input validation.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_cabc805de26845bf85dcb119aca1687e.txt b/reme_ai/context/offload/compressed_contexts/context_cabc805de26845bf85dcb119aca1687e.txt deleted file mode 100644 index d6932d3f..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_cabc805de26845bf85dcb119aca1687e.txt +++ /dev/null @@ -1,38 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_cc8ea8b616de4cadb1bac5770a1adc2f.txt b/reme_ai/context/offload/compressed_contexts/context_cc8ea8b616de4cadb1bac5770a1adc2f.txt deleted file mode 100644 index a36c5da1..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_cc8ea8b616de4cadb1bac5770a1adc2f.txt +++ /dev/null @@ -1,8 +0,0 @@ -[ - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_cf80cb2db5fa482e868d121a92eea393.txt b/reme_ai/context/offload/compressed_contexts/context_cf80cb2db5fa482e868d121a92eea393.txt deleted file mode 100644 index 12a9a7e3..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_cf80cb2db5fa482e868d121a92eea393.txt +++ /dev/null @@ -1,20 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_d163c619765e46b991c78c0619ee9b8f.txt b/reme_ai/context/offload/compressed_contexts/context_d163c619765e46b991c78c0619ee9b8f.txt deleted file mode 100644 index da64fc5f..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_d163c619765e46b991c78c0619ee9b8f.txt +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_d298845d7b314a09bc836160b4c4531e.txt b/reme_ai/context/offload/compressed_contexts/context_d298845d7b314a09bc836160b4c4531e.txt deleted file mode 100644 index c550a82a..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_d298845d7b314a09bc836160b4c4531e.txt +++ /dev/null @@ -1,116 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Before we proceed, I also need rate limiting and input validation.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "How do I reverse a string in Python?", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "What's the difference between a list and a tuple?", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_d5ee189be0a84f709119710e2ab2bb8d.txt b/reme_ai/context/offload/compressed_contexts/context_d5ee189be0a84f709119710e2ab2bb8d.txt deleted file mode 100644 index c550a82a..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_d5ee189be0a84f709119710e2ab2bb8d.txt +++ /dev/null @@ -1,116 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Before we proceed, I also need rate limiting and input validation.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "How do I reverse a string in Python?", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "What's the difference between a list and a tuple?", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_db326957109f4cdfb01cb29777762344.txt b/reme_ai/context/offload/compressed_contexts/context_db326957109f4cdfb01cb29777762344.txt deleted file mode 100644 index 5441820a..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_db326957109f4cdfb01cb29777762344.txt +++ /dev/null @@ -1,92 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Before we proceed, I also need rate limiting and input validation.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_dce5cbcfb71641a0a9423a995c7ac219.txt b/reme_ai/context/offload/compressed_contexts/context_dce5cbcfb71641a0a9423a995c7ac219.txt deleted file mode 100644 index 5441820a..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_dce5cbcfb71641a0a9423a995c7ac219.txt +++ /dev/null @@ -1,92 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Before we proceed, I also need rate limiting and input validation.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_e1a7bb467f0f4d30b9e4443d173e01a9.txt b/reme_ai/context/offload/compressed_contexts/context_e1a7bb467f0f4d30b9e4443d173e01a9.txt deleted file mode 100644 index 97042328..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_e1a7bb467f0f4d30b9e4443d173e01a9.txt +++ /dev/null @@ -1,14 +0,0 @@ -[ - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_e5547c1ec3864dc698a3fbf579496279.txt b/reme_ai/context/offload/compressed_contexts/context_e5547c1ec3864dc698a3fbf579496279.txt deleted file mode 100644 index 12a9a7e3..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_e5547c1ec3864dc698a3fbf579496279.txt +++ /dev/null @@ -1,20 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_e612e4fb5f19435db116cf62c7ed7245.txt b/reme_ai/context/offload/compressed_contexts/context_e612e4fb5f19435db116cf62c7ed7245.txt deleted file mode 100644 index 12a9a7e3..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_e612e4fb5f19435db116cf62c7ed7245.txt +++ /dev/null @@ -1,20 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_f68dc93a2c6c4dcc8654c02102eb93bc.txt b/reme_ai/context/offload/compressed_contexts/context_f68dc93a2c6c4dcc8654c02102eb93bc.txt deleted file mode 100644 index d6932d3f..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_f68dc93a2c6c4dcc8654c02102eb93bc.txt +++ /dev/null @@ -1,38 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_f8fea52f9b764642aeae9ee1adcd6f72.txt b/reme_ai/context/offload/compressed_contexts/context_f8fea52f9b764642aeae9ee1adcd6f72.txt deleted file mode 100644 index 06384283..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_f8fea52f9b764642aeae9ee1adcd6f72.txt +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "role": "assistant", - "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "How do I reverse a string in Python?", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "What's the difference between a list and a tuple?", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Lists are mutable (you can change their content after creation) and use square brackets `[]`. Tuples are immutable and use parentheses `()`. Lists are better for collections that need to change, while tuples are faster and can be used as dictionary keys.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/compressed_contexts/context_fa1dd140c152466996c526cd7b38b3e4.txt b/reme_ai/context/offload/compressed_contexts/context_fa1dd140c152466996c526cd7b38b3e4.txt deleted file mode 100644 index 5441820a..00000000 --- a/reme_ai/context/offload/compressed_contexts/context_fa1dd140c152466996c526cd7b38b3e4.txt +++ /dev/null @@ -1,92 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Before we proceed, I also need rate limiting and input validation.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/reme_ai/context/offload/context_compress_op.py b/reme_ai/context/offload/context_compress_op.py index d4f107ad..7cc2c141 100644 --- a/reme_ai/context/offload/context_compress_op.py +++ b/reme_ai/context/offload/context_compress_op.py @@ -48,7 +48,7 @@ class ContextCompressOp(BaseAsyncOp): all_token_threshold: Maximum total token count before compression is triggered. keep_recent: Number of recent messages to keep uncompressed. storage_path: Directory path where original messages will be stored for traceability. - micro_summary_token_threshold: Token threshold for each compression group. + micro_summary_token_threshold: Token threshold for each compression group. If set, messages will be split into groups of this size and compressed separately. If None, all messages will be compressed together. **kwargs: Additional arguments passed to the base class. @@ -62,8 +62,10 @@ class ContextCompressOp(BaseAsyncOp): self.storage_path: Path = Path(storage_path) self.micro_summary_token_threshold: int = micro_summary_token_threshold - assert micro_summary_token_threshold is None or micro_summary_token_threshold > 0, "Micro summary token threshold must be greater than 0" - + assert ( + micro_summary_token_threshold is None or micro_summary_token_threshold > 0 + ), "Micro summary token threshold must be greater than 0" + # Create storage directory if it doesn't exist self.storage_path.mkdir(parents=True, exist_ok=True) @@ -99,7 +101,9 @@ class ContextCompressOp(BaseAsyncOp): return str(file_path) def _split_messages_by_token_threshold( - self, messages: List[Message], token_threshold: int + self, + messages: List[Message], + token_threshold: int, ) -> List[List[Message]]: """Split messages into groups based on token threshold. @@ -119,7 +123,7 @@ class ContextCompressOp(BaseAsyncOp): for msg in messages: msg_tokens = self.token_count([msg]) - + # If single message exceeds threshold, put it in its own group if msg_tokens > token_threshold: if current_group: @@ -128,7 +132,7 @@ class ContextCompressOp(BaseAsyncOp): current_token_count = 0 groups.append([msg]) continue - + # If adding this message would exceed threshold, start new group if current_token_count + msg_tokens > token_threshold and current_group: groups.append(current_group) @@ -143,8 +147,7 @@ class ContextCompressOp(BaseAsyncOp): groups.append(current_group) logger.info( - f"Split {len(messages)} messages into {len(groups)} groups " - f"with token threshold {token_threshold}" + f"Split {len(messages)} messages into {len(groups)} groups " f"with token threshold {token_threshold}", ) return groups @@ -152,23 +155,23 @@ class ContextCompressOp(BaseAsyncOp): def _extract_xml_fragments(text: str) -> str: """ Extract XML fragments from text, removing scratchpad elements. - + Scans text to extract complete and parseable top-level XML fragments, excluding elements. If state_snapshot XML is found, returns it; otherwise returns the original text. - + Args: text: Input text potentially containing XML fragments - + Returns: Extracted XML content or original text """ try: # Remove scratchpad elements - new_text = re.sub(r'.*?', '', text, flags=re.S | re.I) + new_text = re.sub(r".*?", "", text, flags=re.S | re.I) # Extract balanced XML tags - extract_xml = [m[0] for m in re.findall(r'(<(\w+)[^>]*>(?:[^<]|<(?!/\2))*)', new_text)] - + extract_xml = [m[0] for m in re.findall(r"(<(\w+)[^>]*>(?:[^<]|<(?!/\2))*)", new_text)] + # Validate XML parsing valid_xml = [] for xml_str in extract_xml: @@ -177,7 +180,7 @@ class ContextCompressOp(BaseAsyncOp): valid_xml.append(xml_str) except ET.ParseError: continue - + # Return state_snapshot if found, otherwise original text if valid_xml and any("" in xml for xml in valid_xml): return next(xml for xml in valid_xml if "" in xml) @@ -189,7 +192,6 @@ class ContextCompressOp(BaseAsyncOp): logger.warning(f"Failed to extract XML fragments: {e}. Returning original text.") return text - @staticmethod def _format_messages_for_compression(messages: List[Message]) -> str: """Format messages into a readable text for compression. @@ -259,6 +261,157 @@ class ContextCompressOp(BaseAsyncOp): return result + async def _compress_with_micro_groups( + self, + messages_to_compress: List[Message], + system_messages: List[Message], + recent_messages: List[Message], + ) -> List[Message]: + """Compress messages by splitting into groups and compressing each separately. + + Args: + messages_to_compress: Messages to be compressed + system_messages: System messages to preserve + recent_messages: Recent messages to keep uncompressed + + Returns: + List of new messages after compression + """ + # Split messages into groups based on micro threshold + message_groups = self._split_messages_by_token_threshold( + messages_to_compress, + self.micro_summary_token_threshold, + ) + + # Compress each group separately + compressed_messages = [] + total_original_tokens = 0 + total_compressed_tokens = 0 + + for group_idx, group in enumerate(message_groups, 1): + # Calculate original token count for this group + group_original_tokens = self.token_count(group) + total_original_tokens += group_original_tokens + + # Save original messages for this group + group_file_path = self._save_original_messages(group) + + # Compress this group + logger.info( + f"Compressing group {group_idx}/{len(message_groups)} " + f"({len(group)} messages, {group_original_tokens} tokens)", + ) + group_summary = await self._compress_messages_with_llm(group) + group_summary = self._extract_xml_fragments(group_summary) + + # Create compressed message for this group + compressed_message = Message( + role=Role.SYSTEM, + content=( + f"[Compressed conversation history - Part {group_idx}/{len(message_groups)}]\n" + f"{group_summary}\n\n" + f"(Original {len(group)} messages are stored in: {group_file_path})" + ), + ) + + # Check if compression actually reduced tokens for this group + compressed_tokens = self.token_count([compressed_message]) + + if compressed_tokens >= group_original_tokens: + logger.warning( + f"Group {group_idx} compression did not reduce tokens: " + f"{group_original_tokens} -> {compressed_tokens}. Using original messages.", + ) + return None + + logger.info( + f"Group {group_idx} compression successful: " + f"{group_original_tokens} -> {compressed_tokens} tokens " + f"(reduction: {group_original_tokens - compressed_tokens} tokens, " + f"{100 * (1 - compressed_tokens / group_original_tokens):.1f}%)", + ) + compressed_messages.append(compressed_message) + total_compressed_tokens += compressed_tokens + + # Construct new message list: system messages + all compressed messages + recent messages + new_messages = system_messages + compressed_messages + recent_messages + + logger.info( + f"Context compression completed using micro-compression: " + f"{len(messages_to_compress) + len(system_messages) + len(recent_messages)} messages -> " + f"{len(new_messages)} messages ({len(message_groups)} compressed groups), " + f"total tokens: {total_original_tokens} -> {total_compressed_tokens}", + ) + + return new_messages + + async def _compress_all_together( + self, + messages_to_compress: List[Message], + system_messages: List[Message], + recent_messages: List[Message], + ) -> List[Message]: + """Compress all messages together into a single summary. + + Args: + messages_to_compress: Messages to be compressed + system_messages: System messages to preserve + recent_messages: Recent messages to keep uncompressed + + Returns: + List of new messages after compression + """ + # Calculate original token count + original_tokens = self.token_count(messages_to_compress) + + # Save original messages to file for traceability + original_file_path = self._save_original_messages(messages_to_compress) + + # Use LLM to compress messages + logger.info( + f"Starting LLM compression of {len(messages_to_compress)} messages " + f"({original_tokens} tokens), keeping {len(recent_messages)} recent messages", + ) + compressed_summary = await self._compress_messages_with_llm(messages_to_compress) + compressed_summary = self._extract_xml_fragments(compressed_summary) + + # Create a new system message with the compressed content and file reference + compressed_message = Message( + role=Role.SYSTEM, + content=( + f"[Compressed conversation history]\n" + f"{compressed_summary}\n\n" + f"(Original {len(messages_to_compress)} messages are stored in: {original_file_path})" + ), + ) + + # Check if compression actually reduced tokens + compressed_tokens = self.token_count([compressed_message]) + + if compressed_tokens >= original_tokens: + logger.warning( + f"Compression did not reduce tokens: {original_tokens} -> {compressed_tokens}. " + f"Returning original messages.", + ) + return None + + logger.info( + f"Compression successful: {original_tokens} -> {compressed_tokens} tokens " + f"(reduction: {original_tokens - compressed_tokens} tokens, " + f"{100 * (1 - compressed_tokens / original_tokens):.1f}%)", + ) + + # Construct new message list: system messages + compressed message + recent messages + new_messages = system_messages + [compressed_message] + recent_messages + + logger.info( + f"Context compression completed: " + f"{len(messages_to_compress) + len(system_messages) + len(recent_messages)} messages -> " + f"{len(new_messages)} messages", + ) + + return new_messages + async def async_execute(self): """ Execute the context compression operation. @@ -306,7 +459,7 @@ class ContextCompressOp(BaseAsyncOp): compress_token_cnt: int = self.token_count(messages_to_compress) logger.info( f"Context compression check: messages_to_compress token count={compress_token_cnt}, " - f"threshold={self.all_token_threshold}" + f"threshold={self.all_token_threshold}", ) # If token count is within threshold, no compression needed @@ -318,127 +471,24 @@ class ContextCompressOp(BaseAsyncOp): ) return - # Determine whether to use micro-compression (split into groups) + # Determine whether to use micro-compression (split into groups) or compress all together if self.micro_summary_token_threshold is not None and self.micro_summary_token_threshold > 0: - # Split messages into groups based on micro threshold - message_groups = self._split_messages_by_token_threshold( - messages_to_compress, self.micro_summary_token_threshold - ) - - # Compress each group separately - compressed_messages = [] - total_original_tokens = 0 - total_compressed_tokens = 0 - - for group_idx, group in enumerate(message_groups, 1): - # Calculate original token count for this group - group_original_tokens = self.token_count(group) - total_original_tokens += group_original_tokens - - # Save original messages for this group - group_file_path = self._save_original_messages(group) - - # Compress this group - logger.info( - f"Compressing group {group_idx}/{len(message_groups)} " - f"({len(group)} messages, {group_original_tokens} tokens)" - ) - group_summary = await self._compress_messages_with_llm(group) - group_summary = self._extract_xml_fragments(group_summary) - - # Create compressed message for this group - compressed_message = Message( - role=Role.SYSTEM, - content=( - f"[Compressed conversation history - Part {group_idx}/{len(message_groups)}]\n" - f"{group_summary}\n\n" - f"(Original {len(group)} messages are stored in: {group_file_path})" - ), - ) - - # Check if compression actually reduced tokens for this group - compressed_tokens = self.token_count([compressed_message]) - - if compressed_tokens >= group_original_tokens: - logger.warning( - f"Group {group_idx} compression did not reduce tokens: " - f"{group_original_tokens} -> {compressed_tokens}. Using original messages." - ) - self.context.response.answer = self.context.messages - return - - - else: - logger.info( - f"Group {group_idx} compression successful: " - f"{group_original_tokens} -> {compressed_tokens} tokens " - f"(reduction: {group_original_tokens - compressed_tokens} tokens, " - f"{100 * (1 - compressed_tokens / group_original_tokens):.1f}%)" - ) - compressed_messages.append(compressed_message) - total_compressed_tokens += compressed_tokens - - # Construct new message list: system messages + all compressed messages + recent messages - new_messages = system_messages + compressed_messages + recent_messages - - logger.info( - f"Context compression completed using micro-compression: " - f"{len(messages)} messages -> {len(new_messages)} messages " - f"({len(message_groups)} compressed groups), " - f"total tokens: {total_original_tokens} -> {total_compressed_tokens}" + new_messages = await self._compress_with_micro_groups( + messages_to_compress, + system_messages, + recent_messages, ) else: - # Original single compression logic - # Calculate original token count - original_tokens = self.token_count(messages_to_compress) - - # Save original messages to file for traceability - original_file_path = self._save_original_messages(messages_to_compress) - - # Use LLM to compress messages - logger.info( - f"Starting LLM compression of {len(messages_to_compress)} messages " - f"({original_tokens} tokens), keeping {len(recent_messages)} recent messages", - ) - compressed_summary = await self._compress_messages_with_llm(messages_to_compress) - compressed_summary = self._extract_xml_fragments(compressed_summary) - - # Create a new system message with the compressed content and file reference - compressed_message = Message( - role=Role.SYSTEM, - content=( - f"[Compressed conversation history]\n" - f"{compressed_summary}\n\n" - f"(Original {len(messages_to_compress)} messages are stored in: {original_file_path})" - ), + new_messages = await self._compress_all_together( + messages_to_compress, + system_messages, + recent_messages, ) - # Check if compression actually reduced tokens - compressed_tokens = self.token_count([compressed_message]) - - if compressed_tokens >= original_tokens: - logger.warning( - f"Compression did not reduce tokens: {original_tokens} -> {compressed_tokens}. " - f"Returning original messages." - ) - self.context.response.answer = self.context.messages - return - - logger.info( - f"Compression successful: {original_tokens} -> {compressed_tokens} tokens " - f"(reduction: {original_tokens - compressed_tokens} tokens, " - f"{100 * (1 - compressed_tokens / original_tokens):.1f}%)" - ) - - # Construct new message list: system messages + compressed message + recent messages - new_messages = system_messages + [compressed_message] + recent_messages - - logger.info( - f"Context compression completed: " - f"{len(str(messages))} messages -> {len(new_messages)} messages" - ) + # If compression failed (returned None), use original messages + if new_messages is None: + self.context.response.answer = self.context.messages + return # Return the compressed messages as JSON self.context.response.answer = json.dumps([x.model_dump() for x in new_messages], ensure_ascii=False, indent=2) - - diff --git a/reme_ai/context/offload/context_compress_prompt.yaml b/reme_ai/context/offload/context_compress_prompt.yaml index 24c0d4cb..109ae94f 100644 --- a/reme_ai/context/offload/context_compress_prompt.yaml +++ b/reme_ai/context/offload/context_compress_prompt.yaml @@ -1,20 +1,20 @@ compress_context_prompt_zh: | 你是将扮演一个功能为“将内部聊天记录总结为给定结构”的组件。 - + 当会话历史记录变得太大时，将调用您将整个历史记录提取为简洁的结构化XML快照。这个快照非常重要，因为它会成为Agent对过去内容**唯一**的记忆。后续的对话将Agent仅基于此snapshot恢复其工作。所有重要的细节、计划、错误和用户指令都必须保留。 - + 首先，你在个人的思考整个历史内容。检查用户的总体目标、代理的操作、工具输出、文件修改以及任何未解决的问题。找出对未来行动至关重要的每一条信息。 - + 在你推理完成后，生成最终的 XML对象。信息要非常密集。不要省略任何不重要的对话填充。 - + 结构必须如下： - + - + - + - + 下面是需要压缩的对话历史记录： - + ’‘’ {messages_content} ’‘’ @@ -60,7 +60,7 @@ compress_context_prompt: | - + - + - + Here is the conversation history that needs to be compressed: ’’’ {messages_content} diff --git a/reme_ai/context/offload/test_context_compress.py b/reme_ai/context/offload/test_context_compress.py deleted file mode 100644 index f833aa39..00000000 --- a/reme_ai/context/offload/test_context_compress.py +++ /dev/null @@ -1,206 +0,0 @@ -""" -Test script for ContextCompressOp. - -This script demonstrates how to use the context compression operation to reduce -token usage in conversation histories using language models. -""" - -import asyncio -from flowllm.core.enumeration import Role -from flowllm.core.schema import Message -from loguru import logger - -from reme_ai.context.offload.context_compress_op import ContextCompressOp -from reme_ai.main import ReMeApp - - -async def main(): - """Main function to test ContextCompressOp.""" - - async with ReMeApp(): - logger.info("=" * 80) - logger.info("Testing ContextCompressOp - LLM-based Context Compression") - logger.info("=" * 80) - - # Create a mock conversation with multiple messages - messages = [ - { - "role":"system", - "content":"You are a helpful AI assistant specialized in software development.", - }, - { - "role":"user", - "content":"I need help building a REST API in Python. I want to use FastAPI.", - }, - { - "role":"assistant", - "content":"Great choice! FastAPI is an excellent framework for building REST APIs. " - "It's fast, modern, and has automatic API documentation. To get started, you'll need " - "to install FastAPI and uvicorn. Would you like me to guide you through setting up " - "your first endpoint?", - }, - { - "role":"user", - "content":"Yes please. I want to create a user management API with CRUD operations.", - }, - { - "role":"assistant", - "content":"Perfect! For a user management API, I recommend this structure:\n" - "1. Define a User model using Pydantic\n" - "2. Create POST /users endpoint for creating users\n" - "3. Create GET /users and GET /users/{id} for reading\n" - "4. Create PUT /users/{id} for updates\n" - "5. Create DELETE /users/{id} for deletion\n" - "We'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - }, - { - "role":"user", - "content":"Let's use PostgreSQL. Also, I need JWT authentication.", - }, - { - "role":"assistant", - "content":"Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use " - "python-jose library. Here's what we'll implement:\n" - "1. User registration endpoint\n" - "2. Login endpoint that returns JWT token\n" - "3. Protected endpoints that require valid JWT\n" - "4. Password hashing using bcrypt\n" - "Let me show you the code for the User model first.", - }, - { - "role":"user", - "content":"Before we proceed, I also need rate limiting and input validation.", - }, - { - "role":"assistant", - "content":"Good thinking! For rate limiting, we can use slowapi library which integrates " - "well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, " - "but we can add custom validators. I'll also add request validation middleware. " - "Let's start implementing all of this step by step.", - }, - { - "role": "user", - "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant." - }, - { - "role": "assistant", - "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?" - }, - { - "role": "user", - "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns." - }, - { - "role": "assistant", - "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?" - }, - { - "role": "user", - "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics." - }, - { - "role": "assistant", - "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?" - }, - { - "role": "user", - "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features." - }, - { - "role": "assistant", - "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer." - }, - { - "role": "user", - "content": "How do I reverse a string in Python?" - }, - { - "role": "assistant", - "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1." - }, - { - "role": "user", - "content": "What's the difference between a list and a tuple?" - }, - { - "role": "assistant", - "content": "Lists are mutable (you can change their content after creation) and use square brackets `[]`. Tuples are immutable and use parentheses `()`. Lists are better for collections that need to change, while tuples are faster and can be used as dictionary keys." - }, - { - "role": "user", - "content": "How do I handle exceptions in Python?" - }, - { - "role": "assistant", - "content": "Use try-except blocks: `try:` followed by your risky code, then `except SpecificError:` to catch particular exceptions. Always catch specific exceptions rather than the generic `Exception` when possible. You can also use `finally:` for cleanup code that runs regardless." - } - - ] - # - # # Test 1: Messages below threshold (should not compress) - # logger.info("\n" + "=" * 60) - # logger.info("Test 1: Messages below threshold (should skip compression)") - # logger.info("=" * 60) - # - # compress_op1 = ContextCompressOp( - # all_token_threshold=50000, # High threshold, won't trigger - # keep_recent=3, - # ) - # - # await compress_op1.async_call(messages=messages) - # - # result_messages1 = compress_op1.context.response.answer - # logger.info(f"✓ Result: {len(result_messages1)} messages (unchanged)") - # - # # Test 2: Messages above threshold (should compress) - # logger.info("\n" + "=" * 60) - # logger.info("Test 2: Messages above threshold (should compress)") - # logger.info("=" * 60) - # - # compress_op2 = ContextCompressOp( - # all_token_threshold=2000, # Low threshold, will trigger - # keep_recent=3, # Keep last 3 messages - # compress_system_message=False, # Don't compress system messages - # ) - # - # await compress_op2.async_call(messages=messages) - # - # result_messages2 = compress_op2.context.response.answer - # logger.info(f"✓ Result: {len(result_messages2)} messages (compressed)") - # - # # Display compression results - # logger.info("\n" + "=" * 60) - # logger.info("Compression Result Details:") - # logger.info("=" * 60) - # logger.info(f"Original messages: {len(messages)}") - # logger.info(f"Compressed messages: {len(result_messages2)}") - # - # # Test 3: Messages above threshold (should compress) - # logger.info("\n" + "=!" * 30) - # logger.info("Test 3: Messages above micro threshold (should compress)") - # logger.info("=!" * 30) - - compress_op2 = ContextCompressOp( - all_token_threshold=2000, # Low threshold, will trigger - keep_recent=2, # Keep last 3 messages - compress_system_message=False, # Don't compress system messages - micro_summary_token_threshold=1500, - language="zh", - ) - - await compress_op2.async_call(messages=messages) - - result_messages2 = compress_op2.context.response.answer - logger.info(f"✓ Result: {len(result_messages2)} messages (compressed)") - - # Display compression results - logger.info("\n" + "=" * 60) - logger.info("Compression Result Details:") - logger.info("=" * 60) - logger.info(f"Original messages: {len(messages)}") - logger.info(f"Compressed messages: {len(result_messages2)}") - - -if __name__ == "__main__": - asyncio.run(main()) - diff --git a/reme_ai/context/offload/compressed_contexts/context_1601144041b346068d2ab706a86afe82.txt b/test/compressed_contexts/context_37946620cb0f4dea8e4920b553b90a40.txt similarity index 100% rename from reme_ai/context/offload/compressed_contexts/context_1601144041b346068d2ab706a86afe82.txt rename to test/compressed_contexts/context_37946620cb0f4dea8e4920b553b90a40.txt diff --git a/reme_ai/context/offload/compressed_contexts/context_01617e1e7b1841c0832c842a2dbeec8f.txt b/test/compressed_contexts/context_69685266619e4452bb3acbbd0122fcfd.txt similarity index 100% rename from reme_ai/context/offload/compressed_contexts/context_01617e1e7b1841c0832c842a2dbeec8f.txt rename to test/compressed_contexts/context_69685266619e4452bb3acbbd0122fcfd.txt diff --git a/reme_ai/context/offload/compressed_contexts/context_049e933167ee4e7c9373187adb88c2a4.txt b/test/compressed_contexts/context_9f6b9d982c584471880c56858303ea14.txt similarity index 100% rename from reme_ai/context/offload/compressed_contexts/context_049e933167ee4e7c9373187adb88c2a4.txt rename to test/compressed_contexts/context_9f6b9d982c584471880c56858303ea14.txt diff --git a/reme_ai/context/offload/compressed_contexts/context_189445ca3dbe446dbf418e60e8e6d2d9.txt b/test/compressed_contexts/context_bf0e0009fc4541edb2309339dbc97829.txt similarity index 100% rename from reme_ai/context/offload/compressed_contexts/context_189445ca3dbe446dbf418e60e8e6d2d9.txt rename to test/compressed_contexts/context_bf0e0009fc4541edb2309339dbc97829.txt diff --git a/reme_ai/context/offload/compressed_contexts/context_a76d937829a54014a6cbdcd1782a951e.txt b/test/compressed_contexts/context_c6cfb71eead94de7823d7af51c3b9c9a.txt similarity index 100% rename from reme_ai/context/offload/compressed_contexts/context_a76d937829a54014a6cbdcd1782a951e.txt rename to test/compressed_contexts/context_c6cfb71eead94de7823d7af51c3b9c9a.txt diff --git a/test/test_context_compress.py b/test/test_context_compress.py new file mode 100644 index 00000000..27203f71 --- /dev/null +++ b/test/test_context_compress.py @@ -0,0 +1,285 @@ +""" +Test script for ContextCompressOp. + +This script demonstrates how to use the context compression operation to reduce +token usage in conversation histories using language models. +""" + +import asyncio +from flowllm.core.enumeration import Role +from flowllm.core.schema import Message +from loguru import logger + +from reme_ai.context.offload.context_compress_op import ContextCompressOp +from reme_ai.main import ReMeApp + + +async def main(): + """Main function to test ContextCompressOp.""" + + async with ReMeApp(): + logger.info("=" * 80) + logger.info("Testing ContextCompressOp - LLM-based Context Compression") + logger.info("=" * 80) + + # Create a mock conversation with multiple messages + messages = [ + { + "role": "system", + "content": "You are a helpful AI assistant specialized in software development.", + }, + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. " + "It's fast, modern, and has automatic API documentation. To get started, you'll need " + "to install FastAPI and uvicorn. Would you like me to guide you through setting up " + "your first endpoint?", + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n" + "1. Define a User model using Pydantic\n" + "2. Create POST /users endpoint for creating users\n" + "3. Create GET /users and GET /users/{id} for reading\n" + "4. Create PUT /users/{id} for updates\n" + "5. Create DELETE /users/{id} for deletion\n" + "We'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use " + "python-jose library. Here's what we'll implement:\n" + "1. User registration endpoint\n" + "2. Login endpoint that returns JWT token\n" + "3. Protected endpoints that require valid JWT\n" + "4. Password hashing using bcrypt\n" + "Let me show you the code for the User model first.", + }, + { + "role": "user", + "content": "Before we proceed, I also need rate limiting and input validation.", + }, + { + "role": "assistant", + "content": "Good thinking! For rate limiting, we can use slowapi library which integrates " + "well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, " + "but we can add custom validators. I'll also add request validation middleware. " + "Let's start implementing all of this step by step.", + }, + { + "role": "user", + "content": "I need to build a distributed task queue system in Python that can handle millions of tasks" + " per day. It needs to be horizontally scalable and fault-tolerant.", + }, + { + "role": "assistant", + "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, " + "I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task " + "distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3." + " **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**:" + " Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and " + "status queries\n\nFor the core library, we can build on top of Celery or create a custo" + "m solution. Would you like me to design the system architecture first, or do you have pr" + "eferences for specific technologies?", + }, + { + "role": "user", + "content": "I want to build this from scratch without Celery. I need custom features like task" + " priorities, retry policies with exponential backoff, and task dependencies. Also," + " I need it to support both synchronous and asynchronous task execution patterns.", + }, + { + "role": "assistant", + "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive " + "system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based" + " on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3." + " **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**:" + " Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task " + "states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time " + "performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n-" + " Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock" + " algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for " + "failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing " + "services\n\nWould you like me to start with the core task model and priority queue " + "implementation?", + }, + { + "role": "user", + "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a " + "web dashboard for monitoring, and the ability to dynamically scale workers based on queue" + " depth. Plus, I need task deduplication and exactly-once processing semantics.", + }, + { + "role": "assistant", + "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time " + "Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub " + "for multi-instance communication\n2. **Event Stream**: Task state changes published to Re" + "dis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptio" + "ns\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, " + "processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or cu" + "stom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on hi" + "storical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has " + "UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task " + "IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing" + "\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- " + "Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency g" + "raphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violati" + "ons\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys w" + "ith TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with na" + "mespace support\n\nThis is getting quite comprehensive! Should I start with the core tas" + "k model and database schema, or would you prefer to begin with the WebSocket real-time up" + "date system?", + }, + { + "role": "user", + "content": "This is getting complex. I also need multi-region support with eventual consistency, the " + "ability to pause/resume task processing by task type, and A/B testing capabilities for " + "different worker implementations. Also, I need comprehensive audit logging and GDPR comp" + "liance features.", + }, + { + "role": "assistant", + "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the compl" + "ete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with" + " health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup " + "using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-win" + "s for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when p" + "ossible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Adv" + "anced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis fe" + "ature flags with immediate propagation\n - Rate limits per task type with burst capaci" + "ty\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n" + " - Task routing based on consistent hashing of task ID\n - Variant assignment with s" + "tickiness\n - Statistical significance tracking for performance metrics\n - Automati" + "c winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **A" + "udit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n -" + " Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n " + " - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n " + " - Right to be forgotten: Cascade delete with verification\n - Data portability: JSO" + "N export of all user tasks\n - Consent management: Task processing consent tracking" + "\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:*" + "*\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance " + "Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource" + " consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n" + "**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **" + "Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC is" + "olation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n" + "\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2." + " **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Rand" + "om worker termination testing\n4. **Capacity Planning**: Automated infrastructure provi" + "sioning\n\nThis is becoming a mission-critical system! Would you like me to start with " + "the database schema and core task lifecycle management, or should we begin with the mul" + "ti-region replication strategy? I can also create a detailed implementation roadmap wi" + "th phases if you prefer.", + }, + { + "role": "user", + "content": "How do I reverse a string in Python?", + }, + { + "role": "assistant", + "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::" + "-1]`. The `[::-1]` slice notation means start at the end of the string and end at posit" + "ion 0, moving with the step -1.", + }, + { + "role": "user", + "content": "What's the difference between a list and a tuple?", + }, + { + "role": "assistant", + "content": "Lists are mutable (you can change their content after creation) and use square bracket" + "s `[]`. Tuples are immutable and use parentheses `()`. Lists are better for collectio" + "ns that need to change, while tuples are faster and can be used as dictionary keys.", + }, + { + "role": "user", + "content": "How do I handle exceptions in Python?", + }, + { + "role": "assistant", + "content": "Use try-except blocks: `try:` followed by your risky code, then `except SpecificError" + ":` to catch particular exceptions. Always catch specific exceptions rather than the g" + "eneric `Exception` when possible. You can also use `finally:` for cleanup code that ru" + "ns regardless.", + }, + + ] + + # Test 1: Messages below threshold (should not compress) + logger.info("\n" + "=" * 60) + logger.info("Test 1: Messages below threshold (should skip compression)") + logger.info("=" * 60) + + compress_op1 = ContextCompressOp( + all_token_threshold=50000, # High threshold, won't trigger + keep_recent=3, + ) + + await compress_op1.async_call(messages=messages) + + result_messages1 = compress_op1.context.response.answer + logger.info(f"✓ Result: {len(result_messages1)} messages (unchanged)") + + # Test 2: Messages above threshold (should compress) + logger.info("\n" + "=" * 60) + logger.info("Test 2: Messages above threshold (should compress)") + logger.info("=" * 60) + + compress_op2 = ContextCompressOp( + all_token_threshold=2000, # Low threshold, will trigger + keep_recent=3, # Keep last 3 messages + compress_system_message=False, # Don't compress system messages + ) + + await compress_op2.async_call(messages=messages) + + result_messages2 = compress_op2.context.response.answer + logger.info(f"✓ Result: {len(result_messages2)} messages (compressed)") + + # Display compression results + logger.info("\n" + "=" * 60) + logger.info("Compression Result Details:") + logger.info("=" * 60) + logger.info(f"Original messages: {len(messages)}") + logger.info(f"Compressed messages: {len(result_messages2)}") + + # Test 3: Messages above threshold (should compress) + logger.info("\n" + "=!" * 30) + logger.info("Test 3: Messages above micro threshold (should compress)") + logger.info("=!" * 30) + + compress_op2 = ContextCompressOp( + all_token_threshold=2000, # Low threshold, will trigger + keep_recent=2, # Keep last 3 messages + compress_system_message=False, # Don't compress system messages + micro_summary_token_threshold=1500, + language="zh", + ) + + await compress_op2.async_call(messages=messages) + + result_messages2 = compress_op2.context.response.answer + logger.info(f"✓ Result: {len(result_messages2)} messages (compressed)") + + # Display compression results + logger.info("\n" + "=" * 60) + logger.info("Compression Result Details:") + logger.info("=" * 60) + logger.info(f"Original messages: {len(messages)}") + logger.info(f"Compressed messages: {len(result_messages2)}") + + +if __name__ == "__main__": + asyncio.run(main()) From 44e662762ea78c8f290924c15d17e5d6bbfd9583 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=96=B9=E5=BA=94?= Date: Wed, 19 Nov 2025 15:14:32 +0800 Subject: [PATCH 09/39] delete log --- ...ntext_37946620cb0f4dea8e4920b553b90a40.txt | 92 -------------- ...ntext_69685266619e4452bb3acbbd0122fcfd.txt | 32 ----- ...ntext_9f6b9d982c584471880c56858303ea14.txt | 32 ----- ...ntext_bf0e0009fc4541edb2309339dbc97829.txt | 92 -------------- ...ntext_c6cfb71eead94de7823d7af51c3b9c9a.txt | 116 ------------------ 5 files changed, 364 deletions(-) delete mode 100644 test/compressed_contexts/context_37946620cb0f4dea8e4920b553b90a40.txt delete mode 100644 test/compressed_contexts/context_69685266619e4452bb3acbbd0122fcfd.txt delete mode 100644 test/compressed_contexts/context_9f6b9d982c584471880c56858303ea14.txt delete mode 100644 test/compressed_contexts/context_bf0e0009fc4541edb2309339dbc97829.txt delete mode 100644 test/compressed_contexts/context_c6cfb71eead94de7823d7af51c3b9c9a.txt diff --git a/test/compressed_contexts/context_37946620cb0f4dea8e4920b553b90a40.txt b/test/compressed_contexts/context_37946620cb0f4dea8e4920b553b90a40.txt deleted file mode 100644 index 5441820a..00000000 --- a/test/compressed_contexts/context_37946620cb0f4dea8e4920b553b90a40.txt +++ /dev/null @@ -1,92 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Before we proceed, I also need rate limiting and input validation.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/test/compressed_contexts/context_69685266619e4452bb3acbbd0122fcfd.txt b/test/compressed_contexts/context_69685266619e4452bb3acbbd0122fcfd.txt deleted file mode 100644 index 06384283..00000000 --- a/test/compressed_contexts/context_69685266619e4452bb3acbbd0122fcfd.txt +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "role": "assistant", - "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "How do I reverse a string in Python?", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "What's the difference between a list and a tuple?", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Lists are mutable (you can change their content after creation) and use square brackets `[]`. Tuples are immutable and use parentheses `()`. Lists are better for collections that need to change, while tuples are faster and can be used as dictionary keys.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/test/compressed_contexts/context_9f6b9d982c584471880c56858303ea14.txt b/test/compressed_contexts/context_9f6b9d982c584471880c56858303ea14.txt deleted file mode 100644 index 06384283..00000000 --- a/test/compressed_contexts/context_9f6b9d982c584471880c56858303ea14.txt +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "role": "assistant", - "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "How do I reverse a string in Python?", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "What's the difference between a list and a tuple?", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Lists are mutable (you can change their content after creation) and use square brackets `[]`. Tuples are immutable and use parentheses `()`. Lists are better for collections that need to change, while tuples are faster and can be used as dictionary keys.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/test/compressed_contexts/context_bf0e0009fc4541edb2309339dbc97829.txt b/test/compressed_contexts/context_bf0e0009fc4541edb2309339dbc97829.txt deleted file mode 100644 index 5441820a..00000000 --- a/test/compressed_contexts/context_bf0e0009fc4541edb2309339dbc97829.txt +++ /dev/null @@ -1,92 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Before we proceed, I also need rate limiting and input validation.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file diff --git a/test/compressed_contexts/context_c6cfb71eead94de7823d7af51c3b9c9a.txt b/test/compressed_contexts/context_c6cfb71eead94de7823d7af51c3b9c9a.txt deleted file mode 100644 index c550a82a..00000000 --- a/test/compressed_contexts/context_c6cfb71eead94de7823d7af51c3b9c9a.txt +++ /dev/null @@ -1,116 +0,0 @@ -[ - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. It's fast, modern, and has automatic API documentation. To get started, you'll need to install FastAPI and uvicorn. Would you like me to guide you through setting up your first endpoint?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n1. Define a User model using Pydantic\n2. Create POST /users endpoint for creating users\n3. Create GET /users and GET /users/{id} for reading\n4. Create PUT /users/{id} for updates\n5. Create DELETE /users/{id} for deletion\nWe'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use python-jose library. Here's what we'll implement:\n1. User registration endpoint\n2. Login endpoint that returns JWT token\n3. Protected endpoints that require valid JWT\n4. Password hashing using bcrypt\nLet me show you the code for the User model first.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "Before we proceed, I also need rate limiting and input validation.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Good thinking! For rate limiting, we can use slowapi library which integrates well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, but we can add custom validators. I'll also add request validation middleware. Let's start implementing all of this step by step.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I need to build a distributed task queue system in Python that can handle millions of tasks per day. It needs to be horizontally scalable and fault-tolerant.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3. **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**: Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and status queries\n\nFor the core library, we can build on top of Celery or create a custom solution. Would you like me to design the system architecture first, or do you have preferences for specific technologies?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "I want to build this from scratch without Celery. I need custom features like task priorities, retry policies with exponential backoff, and task dependencies. Also, I need it to support both synchronous and asynchronous task execution patterns.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3. **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**: Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n- Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing services\n\nWould you like me to start with the core task model and priority queue implementation?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a web dashboard for monitoring, and the ability to dynamically scale workers based on queue depth. Plus, I need task deduplication and exactly-once processing semantics.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub for multi-instance communication\n2. **Event Stream**: Task state changes published to Redis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptions\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or custom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on historical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency graphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violations\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys with TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with namespace support\n\nThis is getting quite comprehensive! Should I start with the core task model and database schema, or would you prefer to begin with the WebSocket real-time update system?", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "This is getting complex. I also need multi-region support with eventual consistency, the ability to pause/resume task processing by task type, and A/B testing capabilities for different worker implementations. Also, I need comprehensive audit logging and GDPR compliance features.", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the complete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-wins for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when possible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Advanced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis feature flags with immediate propagation\n - Rate limits per task type with burst capacity\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n - Task routing based on consistent hashing of task ID\n - Variant assignment with stickiness\n - Statistical significance tracking for performance metrics\n - Automatic winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **Audit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n - Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n - Right to be forgotten: Cascade delete with verification\n - Data portability: JSON export of all user tasks\n - Consent management: Task processing consent tracking\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:**\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC isolation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2. **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Random worker termination testing\n4. **Capacity Planning**: Automated infrastructure provisioning\n\nThis is becoming a mission-critical system! Would you like me to start with the database schema and core task lifecycle management, or should we begin with the multi-region replication strategy? I can also create a detailed implementation roadmap with phases if you prefer.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "How do I reverse a string in Python?", - "name": null, - "tool_call_id": "" - }, - { - "role": "assistant", - "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::-1]`. The `[::-1]` slice notation means start at the end of the string and end at position 0, moving with the step -1.", - "name": null, - "tool_call_id": "" - }, - { - "role": "user", - "content": "What's the difference between a list and a tuple?", - "name": null, - "tool_call_id": "" - } -] \ No newline at end of file From 441babcea724dcb62a75b250d3755d764b2123df Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=96=B9=E5=BA=94?= Date: Wed, 19 Nov 2025 15:16:10 +0800 Subject: [PATCH 10/39] update test context compress file name --- test/{test_context_compress.py => test_context_compress_op.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename test/{test_context_compress.py => test_context_compress_op.py} (100%) diff --git a/test/test_context_compress.py b/test/test_context_compress_op.py similarity index 100% rename from test/test_context_compress.py rename to test/test_context_compress_op.py From 1199c7442a0d449a82c62f5887097ac78ba17622 Mon Sep 17 00:00:00 2001 From: Zhouwk <57825291+nitwtog@users.noreply.github.com> Date: Wed, 19 Nov 2025 16:13:28 +0800 Subject: [PATCH 11/39] Delete uv.lock --- uv.lock | 5731 ------------------------------------------------------- 1 file changed, 5731 deletions(-) delete mode 100644 uv.lock diff --git a/uv.lock b/uv.lock deleted file mode 100644 index 264b3e6f..00000000 --- a/uv.lock +++ /dev/null @@ -1,5731 +0,0 @@ -version = 1 -revision = 3 -requires-python = ">=3.10" -resolution-markers = [ - 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It includes ready-to-use operations for: + +- ContextCompactOp: Compact tool messages by storing full content in external files +- ContextCompressOp: Compress conversation history using LLM to generate concise summaries +- ContextOffloadOp: Orchestrate compaction and compression to reduce token usage +""" + +from .context_compact_op import ContextCompactOp +from .context_compress_op import ContextCompressOp +from .context_offload_op import ContextOffloadOp + +__all__ = [ + "ContextCompactOp", + "ContextCompressOp", + "ContextOffloadOp", +] diff --git a/reme_ai/context/offload/context_compact_op.py b/reme_ai/context/offload/context_compact_op.py index 9f5a81ef..96a5b1c1 100644 --- a/reme_ai/context/offload/context_compact_op.py +++ b/reme_ai/context/offload/context_compact_op.py @@ -6,9 +6,7 @@ their full content in external files and keeping only previews in the context. This helps manage context window limits while preserving important information. """ -import json from pathlib import Path -from typing import List from uuid import uuid4 from flowllm.core.context import C @@ -28,36 +26,6 @@ class ContextCompactOp(BaseAsyncOp): This helps manage context window limits while preserving recent tool messages. """ - def __init__( - self, - all_token_threshold: int = 20000, - tool_token_threshold: int = 2000, - tool_left_char_len: int = 100, - keep_recent: int = 1, - storage_path: str = "./", - exclude_tools: List[str] = None, - **kwargs, - ): - """ - Initialize the context compaction operation. - - Args: - all_token_threshold: Maximum total token count before compaction is triggered. - tool_token_threshold: Maximum token count for a single tool message before it's compressed. - tool_left_char_len: Number of characters to keep in the compressed tool message preview. - keep_recent: Number of recent tool messages to keep uncompressed. - storage_path: Directory path where compressed tool message contents will be stored. - exclude_tools: List of tool names to exclude from compaction (not currently used). - **kwargs: Additional arguments passed to the base class. - """ - super().__init__(**kwargs) - self.all_token_threshold: int = all_token_threshold - self.tool_token_threshold: int = tool_token_threshold - self.tool_left_char_len: int = tool_left_char_len - self.keep_recent: int = keep_recent - self.storage_path: Path = Path(storage_path) - self.exclude_tools: List[str] = exclude_tools - async def async_execute(self): """ Execute the context compaction operation. @@ -70,41 +38,41 @@ class ContextCompactOp(BaseAsyncOp): - Storing full content in external files - Preserving recent tool messages """ + # Get configuration from context + max_total_tokens: int = self.context.get("max_total_tokens", 20000) + max_tool_message_tokens: int = self.context.get("max_tool_message_tokens", 2000) + preview_char_length: int = self.context.get("preview_char_length", 100) + keep_recent_count: int = self.context.get("keep_recent_count", 1) + storage_path: Path = Path(self.context.get("storage_path", "")) + assert keep_recent_count > 0, "keep_recent_count must be greater than 0" + # Convert context messages to Message objects messages = [Message(**x) for x in self.context.messages] + messages_to_compress = [x for x in messages if x.role is not Role.SYSTEM][:-keep_recent_count] + + # If nothing to compress after filtering, return original messages + if not messages_to_compress: + self.context.response.answer = self.context.messages + self.context.response.success = True + logger.info("No messages to compress after filtering, returning original messages") + return + + logger.info(f"{len(messages_to_compress)} messages remaining for compression check") # Calculate total token count - token_cnt: int = self.token_count(messages) - logger.info(f"Context compaction check: total token count={token_cnt}, threshold={self.all_token_threshold}") + compact_token_cnt: int = self.token_count(messages_to_compress) + logger.info(f"Context compaction check: total token count={compact_token_cnt}, threshold={max_total_tokens}") # If token count is within threshold, no compaction needed - if token_cnt <= self.all_token_threshold: + if compact_token_cnt <= max_total_tokens: self.context.response.answer = self.context.messages - logger.info( - f"Token count ({token_cnt}) is within threshold ({self.all_token_threshold}), no compaction needed", - ) + logger.info(f"Token count ({compact_token_cnt}) is within ({max_total_tokens}), no compaction needed") return # Filter tool messages for processing - tool_messages = [x for x in messages if x.role is Role.TOOL] + tool_messages = [x for x in messages_to_compress if x.role is Role.TOOL] - # If there are too few tool messages, no compaction needed - if len(tool_messages) <= self.keep_recent: - self.context.response.answer = self.context.messages - logger.info( - f"Tool message count ({len(tool_messages)}) is less than or " - f"equal to keep_recent ({self.keep_recent}), no compaction needed", - ) - return - - # Exclude recent tool messages from compaction (keep them intact) - tool_messages = tool_messages[: -self.keep_recent] - logger.info( - f"Processing {len(tool_messages)} tool messages for " - f"compaction (keeping {self.keep_recent} recent messages)", - ) - - # Dictionary to store file paths and their compressed content (for potential batch writing) + # Dictionary to store file paths and their full content (for potential batch writing) write_file_dict = {} # Process each tool message @@ -113,22 +81,25 @@ class ContextCompactOp(BaseAsyncOp): tool_token_cnt = self.token_count([tool_message]) # Skip if token count is within threshold - if tool_token_cnt <= self.tool_token_threshold: + if tool_token_cnt <= max_tool_message_tokens: logger.info( f"Skipping tool message (tool_call_id={tool_message.tool_call_id}): " - f"token count ({tool_token_cnt}) is within threshold ({self.tool_token_threshold})", + f"token count ({tool_token_cnt}) is within threshold ({max_tool_message_tokens})", ) continue - # Create compressed preview of the tool message content - compact_result = tool_message.content[: self.tool_left_char_len] + "..." + # Save original full content before modifying + original_content = tool_message.content # Generate file name from tool_call_id or create a unique identifier file_name = tool_message.tool_call_id or uuid4().hex - path = self.storage_path / f"{file_name}.txt" + path = storage_path / f"{file_name}.txt" - # Store the mapping for potential batch writing - write_file_dict[str(path)] = compact_result + # Store the full content for batch writing + write_file_dict[path.as_posix()] = original_content + + # Create compressed preview of the tool message content + compact_result = original_content[:preview_char_length] + "..." # Log the compaction action logger.info( @@ -140,6 +111,24 @@ class ContextCompactOp(BaseAsyncOp): compact_result += f" (detailed result is stored in {path})" tool_message.content = compact_result + # Store write_file_dict in context for potential batch writing + if write_file_dict: + self.context.write_file_dict = write_file_dict + # Return the compacted messages as JSON - self.context.response.answer = json.dumps([x.simple_dump() for x in messages], ensure_ascii=False, indent=2) + self.context.response.answer = [x.simple_dump() for x in messages] logger.info(f"Context compaction completed: {len(write_file_dict)} tool messages were compacted") + + async def async_default_execute(self, e: Exception = None, **_kwargs): + """Handle execution errors by returning original messages. + + This method is called when an exception occurs during async_execute. It preserves + the original messages and marks the operation as unsuccessful. + + Args: + e: The exception that occurred during execution, if any. + **_kwargs: Additional keyword arguments (unused but required by interface). + """ + self.context.response.answer = self.context.messages + self.context.response.success = False + self.context.response.metadata["error"] = str(e) diff --git a/reme_ai/context/offload/context_compress_op.py b/reme_ai/context/offload/context_compress_op.py index 7cc2c141..cf71a9b3 100644 --- a/reme_ai/context/offload/context_compress_op.py +++ b/reme_ai/context/offload/context_compress_op.py @@ -4,22 +4,29 @@ Context compression module for reducing token usage in conversation contexts usi This module provides functionality to compress conversation history by using a language model to generate concise summaries of older messages while preserving recent messages. This helps manage context window limits while maintaining conversation coherence. + +The compression process: +1. Identifies messages that exceed token thresholds +2. Splits messages into groups if needed +3. Uses LLM to generate compressed summaries of older message groups +4. Stores original messages to files for potential retrieval +5. Appends compressed summaries to the system message while preserving recent messages """ import json -import re -import xml.etree.ElementTree as ET from pathlib import Path -from typing import List +from typing import List, Tuple from uuid import uuid4 from flowllm.core.context import C from flowllm.core.enumeration import Role from flowllm.core.op import BaseAsyncOp from flowllm.core.schema import Message -from flowllm.core.utils import extract_content from loguru import logger +from reme_ai.utils import merge_messages_content +from reme_ai.utils.op_utils import extract_xml_tag_content + @C.register_op() class ContextCompressOp(BaseAsyncOp): @@ -29,90 +36,46 @@ class ContextCompressOp(BaseAsyncOp): When the total token count exceeds the threshold, this operation uses a language model to compress older messages into a concise summary while keeping recent messages intact. This preserves conversation context while reducing token usage. + + Attributes: + file_path: Path to the operation file, used for configuration. + + Context Parameters: + max_total_tokens (int): Maximum token count threshold for compression. + Defaults to 20000. Does not include keep_recent_count messages or system messages. + group_token_threshold (int, optional): Maximum token count per compression group. + If None or 0, all messages are compressed in a single group. + keep_recent_count (int): Number of recent messages to preserve without compression. + Defaults to 2. Must be non-negative. + chat_id (str): Unique identifier for the chat session, used for file naming. + Defaults to a generated UUID if not provided. """ file_path: str = __file__ - def __init__( - self, - all_token_threshold: int = 20000, - keep_recent: int = 5, - storage_path: str = "./compressed_contexts", - micro_summary_token_threshold: int = None, - **kwargs, - ): - """ - Initialize the context compression operation. + def get_store_path(self, name: str) -> Path: + """Get the storage path for a given file name. Args: - all_token_threshold: Maximum total token count before compression is triggered. - keep_recent: Number of recent messages to keep uncompressed. - storage_path: Directory path where original messages will be stored for traceability. - micro_summary_token_threshold: Token threshold for each compression group. - If set, messages will be split into groups of this size and compressed separately. - If None, all messages will be compressed together. - **kwargs: Additional arguments passed to the base class. - - Note: - System messages are NEVER compressed to preserve important system instructions. - """ - super().__init__(**kwargs) - self.all_token_threshold: int = all_token_threshold - self.keep_recent: int = keep_recent - self.storage_path: Path = Path(storage_path) - self.micro_summary_token_threshold: int = micro_summary_token_threshold - - assert ( - micro_summary_token_threshold is None or micro_summary_token_threshold > 0 - ), "Micro summary token threshold must be greater than 0" - - # Create storage directory if it doesn't exist - self.storage_path.mkdir(parents=True, exist_ok=True) - - def _save_original_messages(self, messages: List[Message]) -> str: - """Save original messages to file for traceability. - - Args: - messages: List of messages to save + name: Name of the file to store. Returns: - Path to the saved file + Path object representing the full path to the storage location. """ - # Generate unique filename with timestamp - file_name = f"context_{uuid4().hex}.txt" - file_path = self.storage_path / file_name + return Path(self.context.store_dir) / name - # Convert messages to serializable format - messages_data = [ - { - "role": msg.role.value if hasattr(msg.role, "value") else str(msg.role), - "content": msg.content, - "name": getattr(msg, "name", None), - "tool_call_id": getattr(msg, "tool_call_id", None), - } - for msg in messages - ] - - # Save to file - with open(file_path, "w", encoding="utf-8") as f: - json.dump(messages_data, f, ensure_ascii=False, indent=2) - - logger.info(f"Saved {len(messages)} original messages to {file_path}") - return str(file_path) - - def _split_messages_by_token_threshold( - self, - messages: List[Message], - token_threshold: int, - ) -> List[List[Message]]: + def _split_messages_by_token_threshold(self, messages: List[Message], token_threshold: int) -> List[List[Message]]: """Split messages into groups based on token threshold. + Messages are grouped such that each group's token count does not exceed the threshold, + except when a single message exceeds the threshold, in which case it forms its own group. + Args: messages: List of messages to split - token_threshold: Maximum token count for each group + token_threshold: Maximum token count for each group (may be exceeded by single messages) Returns: - List of message groups, each within the token threshold + List of message groups, where each group attempts to stay within the token threshold """ if not messages: return [] @@ -146,112 +109,42 @@ class ContextCompressOp(BaseAsyncOp): if current_group: groups.append(current_group) - logger.info( - f"Split {len(messages)} messages into {len(groups)} groups " f"with token threshold {token_threshold}", - ) + logger.info(f"Split {len(messages)} messages into {len(groups)} groups with token threshold {token_threshold}") return groups - @staticmethod - def _extract_xml_fragments(text: str) -> str: - """ - Extract XML fragments from text, removing scratchpad elements. - - Scans text to extract complete and parseable top-level XML fragments, - excluding elements. If state_snapshot XML is found, returns it; - otherwise returns the original text. - - Args: - text: Input text potentially containing XML fragments - - Returns: - Extracted XML content or original text - """ - try: - # Remove scratchpad elements - new_text = re.sub(r".*?", "", text, flags=re.S | re.I) - # Extract balanced XML tags - extract_xml = [m[0] for m in re.findall(r"(<(\w+)[^>]*>(?:[^<]|<(?!/\2))*)", new_text)] - - # Validate XML parsing - valid_xml = [] - for xml_str in extract_xml: - try: - ET.fromstring(xml_str) - valid_xml.append(xml_str) - except ET.ParseError: - continue - - # Return state_snapshot if found, otherwise original text - if valid_xml and any("" in xml for xml in valid_xml): - return next(xml for xml in valid_xml if "" in xml) - elif valid_xml: - return valid_xml[0] - else: - return text - except Exception as e: - logger.warning(f"Failed to extract XML fragments: {e}. Returning original text.") - return text - - @staticmethod - def _format_messages_for_compression(messages: List[Message]) -> str: - """Format messages into a readable text for compression. - - Args: - messages: List of messages to format - - Returns: - Formatted string representation of messages - """ - lines = [] - for i, msg in enumerate(messages, 1): - role_name = msg.role.value if hasattr(msg.role, "value") else str(msg.role) - lines.append(f"[Message {i} - {role_name}]") - lines.append(msg.content) - lines.append("") # Empty line between messages - - return "\n".join(lines) - async def _compress_messages_with_llm(self, messages_to_compress: List[Message]) -> str: - """Use LLM to compress messages into a concise summary. + """Compress a list of messages using LLM to generate a summary. + + This method formats the messages into a prompt, sends it to the LLM, and extracts + the compressed state snapshot from the response. The LLM response is expected to + contain XML tags for scratchpad and state_snapshot. Args: - messages_to_compress: List of messages to compress + messages_to_compress: List of Message objects to compress into a summary. Returns: - Compressed summary text - """ - # Format messages for the prompt - formatted_messages = self._format_messages_for_compression(messages_to_compress) + Compressed summary string extracted from the LLM response. Returns empty string + if LLM returns None or if state_snapshot cannot be extracted. - # Create prompt for compression + Note: + If state_snapshot extraction fails, the full content is used as fallback. + """ prompt = self.prompt_format( - prompt_name="compress_context_prompt", - messages_content=formatted_messages, + "compress_context_prompt", + messages_content=merge_messages_content(messages_to_compress), ) def parse_compressed_result(message: Message) -> str: - """Parse LLM response to extract compressed content. - - Args: - message: LLM response message - - Returns: - Compressed content string - """ content = message.content.strip() - # Try to extract content from txt code block - compressed = extract_content(content, "txt") - # If no code block found, use the raw content - if not compressed: - compressed = content + scratchpad = extract_xml_tag_content(content, "scratchpad") + state_snapshot = extract_xml_tag_content(content, "state_snapshot") + logger.info(f"Parsed scratchpad: \n{scratchpad} \nstate_snapshot: \n{state_snapshot}") - logger.info( - f"Compressed {len(messages_to_compress)} messages into " - f"{len(compressed)} characters (reduction: " - f"{len(formatted_messages)} -> {len(compressed)})", - ) - return compressed + if state_snapshot is None: + logger.warning("Failed to extract state_snapshot from LLM response, using full content as fallback") + return content + return state_snapshot # Call LLM to generate compressed summary result = await self.llm.achat( @@ -259,158 +152,81 @@ class ContextCompressOp(BaseAsyncOp): callback_fn=parse_compressed_result, ) + if result is None: + logger.error("LLM returned None, using empty string as fallback") + return "" + return result - async def _compress_with_micro_groups( + async def _compress_with_groups( self, - messages_to_compress: List[Message], - system_messages: List[Message], - recent_messages: List[Message], - ) -> List[Message]: - """Compress messages by splitting into groups and compressing each separately. + system_message: Message, + message_groups: List[List[Message]], + ) -> Tuple[dict, list]: + """Compress multiple message groups and prepare them for storage. + + This method processes each message group, compresses it using LLM, and determines + whether compression is beneficial. If compression reduces token count, the original + messages are saved to files and compressed summaries are appended to the system + message. Otherwise, original messages are preserved in the return list. Args: - messages_to_compress: Messages to be compressed - system_messages: System messages to preserve - recent_messages: Recent messages to keep uncompressed + system_message: The system message to append compressed summaries to. + message_groups: List of message groups, where each group is a list of Message + objects to be compressed together. Returns: - List of new messages after compression + A tuple containing: + - write_file_dict: Dictionary mapping file paths to JSON-serialized message + strings for messages that were successfully compressed. + - return_messages: List of Message objects including the modified system + message with compressed summaries and any messages that couldn't be + compressed or didn't benefit from compression. """ - # Split messages into groups based on micro threshold - message_groups = self._split_messages_by_token_threshold( - messages_to_compress, - self.micro_summary_token_threshold, - ) + write_file_dict = {} + return_messages = [] + chat_id: str = self.context.get("chat_id", uuid4().hex) - # Compress each group separately - compressed_messages = [] - total_original_tokens = 0 - total_compressed_tokens = 0 + # Create a copy of system_message to avoid modifying the original + system_message_copy = Message(role=system_message.role, content=system_message.content) - for group_idx, group in enumerate(message_groups, 1): - # Calculate original token count for this group - group_original_tokens = self.token_count(group) - total_original_tokens += group_original_tokens + for g_idx, messages in enumerate(message_groups): + group_original_tokens = self.token_count(messages) + messages_str = json.dumps([x.simple_dump() for x in messages], ensure_ascii=False, indent=2) + store_path = Path(self.context.store_dir) / f"{chat_id}_{g_idx}" - # Save original messages for this group - group_file_path = self._save_original_messages(group) + logger.info(f"Compress {g_idx}/{len(message_groups)} ({len(messages)}, {group_original_tokens} tokens)") + group_summary = await self._compress_messages_with_llm(messages) - # Compress this group - logger.info( - f"Compressing group {group_idx}/{len(message_groups)} " - f"({len(group)} messages, {group_original_tokens} tokens)", + if not group_summary: + logger.warning(f"Group {g_idx} compression returned empty summary, using original messages.") + return_messages.extend(messages) + continue + + compress_content = ( + f"[Compressed conversation history - Part {g_idx}/{len(message_groups)}]\n{group_summary}\n\n" + f"(Original {len(messages)} messages are stored in: {store_path.as_posix()})\n" ) - group_summary = await self._compress_messages_with_llm(group) - group_summary = self._extract_xml_fragments(group_summary) - - # Create compressed message for this group - compressed_message = Message( - role=Role.SYSTEM, - content=( - f"[Compressed conversation history - Part {group_idx}/{len(message_groups)}]\n" - f"{group_summary}\n\n" - f"(Original {len(group)} messages are stored in: {group_file_path})" - ), - ) - - # Check if compression actually reduced tokens for this group - compressed_tokens = self.token_count([compressed_message]) + compressed_tokens = self.token_count([Message(content=compress_content)]) if compressed_tokens >= group_original_tokens: logger.warning( - f"Group {group_idx} compression did not reduce tokens: " + f"Group {g_idx} compression did not reduce tokens: " f"{group_original_tokens} -> {compressed_tokens}. Using original messages.", ) - return None + return_messages.extend(messages) + else: + system_message_copy.content += compress_content + write_file_dict[store_path.as_posix()] = messages_str + logger.info( + f"Group {g_idx} compression successful: " + f"{group_original_tokens} -> {compressed_tokens} tokens " + f"(reduction: {group_original_tokens - compressed_tokens} tokens, " + f"{100 * (1 - compressed_tokens / group_original_tokens):.1f}%)", + ) - logger.info( - f"Group {group_idx} compression successful: " - f"{group_original_tokens} -> {compressed_tokens} tokens " - f"(reduction: {group_original_tokens - compressed_tokens} tokens, " - f"{100 * (1 - compressed_tokens / group_original_tokens):.1f}%)", - ) - compressed_messages.append(compressed_message) - total_compressed_tokens += compressed_tokens - - # Construct new message list: system messages + all compressed messages + recent messages - new_messages = system_messages + compressed_messages + recent_messages - - logger.info( - f"Context compression completed using micro-compression: " - f"{len(messages_to_compress) + len(system_messages) + len(recent_messages)} messages -> " - f"{len(new_messages)} messages ({len(message_groups)} compressed groups), " - f"total tokens: {total_original_tokens} -> {total_compressed_tokens}", - ) - - return new_messages - - async def _compress_all_together( - self, - messages_to_compress: List[Message], - system_messages: List[Message], - recent_messages: List[Message], - ) -> List[Message]: - """Compress all messages together into a single summary. - - Args: - messages_to_compress: Messages to be compressed - system_messages: System messages to preserve - recent_messages: Recent messages to keep uncompressed - - Returns: - List of new messages after compression - """ - # Calculate original token count - original_tokens = self.token_count(messages_to_compress) - - # Save original messages to file for traceability - original_file_path = self._save_original_messages(messages_to_compress) - - # Use LLM to compress messages - logger.info( - f"Starting LLM compression of {len(messages_to_compress)} messages " - f"({original_tokens} tokens), keeping {len(recent_messages)} recent messages", - ) - compressed_summary = await self._compress_messages_with_llm(messages_to_compress) - compressed_summary = self._extract_xml_fragments(compressed_summary) - - # Create a new system message with the compressed content and file reference - compressed_message = Message( - role=Role.SYSTEM, - content=( - f"[Compressed conversation history]\n" - f"{compressed_summary}\n\n" - f"(Original {len(messages_to_compress)} messages are stored in: {original_file_path})" - ), - ) - - # Check if compression actually reduced tokens - compressed_tokens = self.token_count([compressed_message]) - - if compressed_tokens >= original_tokens: - logger.warning( - f"Compression did not reduce tokens: {original_tokens} -> {compressed_tokens}. " - f"Returning original messages.", - ) - return None - - logger.info( - f"Compression successful: {original_tokens} -> {compressed_tokens} tokens " - f"(reduction: {original_tokens - compressed_tokens} tokens, " - f"{100 * (1 - compressed_tokens / original_tokens):.1f}%)", - ) - - # Construct new message list: system messages + compressed message + recent messages - new_messages = system_messages + [compressed_message] + recent_messages - - logger.info( - f"Context compression completed: " - f"{len(messages_to_compress) + len(system_messages) + len(recent_messages)} messages -> " - f"{len(new_messages)} messages", - ) - - return new_messages + return_messages = [system_message_copy] + return_messages + return write_file_dict, return_messages async def async_execute(self): """ @@ -423,31 +239,38 @@ class ContextCompressOp(BaseAsyncOp): 4. Otherwise, uses LLM to compress older messages by: - Saving original messages to file - Generating a concise summary of older messages - - Replacing older messages with a single summary message + - Appending compressed summaries to the system message + - Preserving messages that couldn't be compressed or didn't benefit from compression """ + # Get configuration from context + # Note: max_total_tokens does not include keep_recent_count messages or system messages + max_total_tokens: int = self.context.get("max_total_tokens", 20000) + group_token_threshold: int = self.context.get("group_token_threshold", None) + keep_recent_count: int = self.context.get("keep_recent_count", 2) + + # Validate keep_recent_count + if keep_recent_count < 0: + logger.warning(f"keep_recent_count ({keep_recent_count}) is negative, setting to 0") + keep_recent_count = 0 + # Convert context messages to Message objects messages = [Message(**x) for x in self.context.messages] - # Check if we have enough messages to compress - if len(messages) <= self.keep_recent: - self.context.response.answer = self.context.messages - logger.info( - f"Message count ({len(messages)}) is less than or " - f"equal to keep_recent ({self.keep_recent}), no compression needed", - ) - return + # Extract system message (should be exactly one) + system_message = [x for x in messages if x.role is Role.SYSTEM] + if len(system_message) > 1: + raise ValueError("Expected exactly one system message") - # Split messages into those to compress and those to keep - messages_to_compress = messages[: -self.keep_recent] - recent_messages = messages[-self.keep_recent :] + if len(system_message) == 0: + system_message = Message(role=Role.SYSTEM, content="") + else: + system_message = system_message[0] - # Always filter out system messages (system messages are never compressed) - system_messages = [m for m in messages_to_compress if m.role is Role.SYSTEM] - messages_to_compress = [m for m in messages_to_compress if m.role is not Role.SYSTEM] - logger.info( - f"Excluding {len(system_messages)} system messages from compression, " - f"{len(messages_to_compress)} messages remaining for compression check", + messages_without_system = [x for x in messages if x.role is not Role.SYSTEM] + messages_to_compress = ( + messages_without_system[:-keep_recent_count] if keep_recent_count > 0 else messages_without_system ) + recent_messages = messages_without_system[-keep_recent_count:] if keep_recent_count > 0 else [] # If nothing to compress after filtering, return original messages if not messages_to_compress: @@ -455,40 +278,38 @@ class ContextCompressOp(BaseAsyncOp): logger.info("No messages to compress after filtering, returning original messages") return + logger.info(f"{len(messages_to_compress)} messages remaining for compression check") + # Calculate token count of messages to compress (only the content that will be compressed) compress_token_cnt: int = self.token_count(messages_to_compress) - logger.info( - f"Context compression check: messages_to_compress token count={compress_token_cnt}, " - f"threshold={self.all_token_threshold}", - ) + logger.info(f"Context compression check: token count={compress_token_cnt} threshold={max_total_tokens}") # If token count is within threshold, no compression needed - if compress_token_cnt <= self.all_token_threshold: + if compress_token_cnt <= max_total_tokens: self.context.response.answer = self.context.messages - logger.info( - f"Messages to compress token count ({compress_token_cnt}) is within threshold " - f"({self.all_token_threshold}), no compression needed", - ) + logger.info(f"messages_to_compress ({compress_token_cnt}) is within threshold ({max_total_tokens})") return - # Determine whether to use micro-compression (split into groups) or compress all together - if self.micro_summary_token_threshold is not None and self.micro_summary_token_threshold > 0: - new_messages = await self._compress_with_micro_groups( - messages_to_compress, - system_messages, - recent_messages, - ) + if group_token_threshold is not None and group_token_threshold > 0: + message_groups = self._split_messages_by_token_threshold(messages_to_compress, group_token_threshold) else: - new_messages = await self._compress_all_together( - messages_to_compress, - system_messages, - recent_messages, - ) + message_groups = [messages_to_compress] - # If compression failed (returned None), use original messages - if new_messages is None: - self.context.response.answer = self.context.messages - return + write_file_dict, return_messages = await self._compress_with_groups(system_message, message_groups) - # Return the compressed messages as JSON - self.context.response.answer = json.dumps([x.model_dump() for x in new_messages], ensure_ascii=False, indent=2) + self.context.write_file_dict = write_file_dict + self.context.response.answer = [x.simple_dump() for x in (return_messages + recent_messages)] + + async def async_default_execute(self, e: Exception = None, **_kwargs): + """Handle execution errors by returning original messages. + + This method is called when an exception occurs during async_execute. It preserves + the original messages and marks the operation as unsuccessful. + + Args: + e: The exception that occurred during execution, if any. + **_kwargs: Additional keyword arguments (unused but required by interface). + """ + self.context.response.answer = self.context.messages + self.context.response.success = False + self.context.response.metadata["error"] = str(e) diff --git a/reme_ai/context/offload/context_compress_prompt.yaml b/reme_ai/context/offload/context_compress_prompt.yaml index 109ae94f..435d7bb1 100644 --- a/reme_ai/context/offload/context_compress_prompt.yaml +++ b/reme_ai/context/offload/context_compress_prompt.yaml @@ -1,58 +1,13 @@ -compress_context_prompt_zh: | - 你是将扮演一个功能为“将内部聊天记录总结为给定结构”的组件。 - - 当会话历史记录变得太大时，将调用您将整个历史记录提取为简洁的结构化XML快照。这个快照非常重要，因为它会成为Agent对过去内容**唯一**的记忆。后续的对话将Agent仅基于此snapshot恢复其工作。所有重要的细节、计划、错误和用户指令都必须保留。 - - 首先，你在个人的思考整个历史内容。检查用户的总体目标、代理的操作、工具输出、文件修改以及任何未解决的问题。找出对未来行动至关重要的每一条信息。 - - 在你推理完成后，生成最终的 XML对象。信息要非常密集。不要省略任何不重要的对话填充。 - - 结构必须如下： - - - - - - - - - - - - - - - - - - - - 下面是需要压缩的对话历史记录： - - ’‘’ - {messages_content} - ’‘’ - - 首先，您将在一个私有的中考虑整个历史。审查用户的总体目标。请你一定要使用中文来回答和压缩。 - - compress_context_prompt: | + Here is the conversation history that needs to be compressed: + ’’’ + {messages_content} + ’’’ + You are the component that summarizes internal chat history into a given structure. - When the conversation history grows too large, you will be invoked to distill the entire history into a concise, structured XML snapshot. This snapshot is CRITICAL, as it will become the agent's *only* memory of the past. The agent will resume its work based solely on this snapshot. All crucial details, plans, errors, and user directives MUST be preserved. - First, you will think through the entire history in a private . Review the user's overall goal, the agent's actions, tool outputs, file modifications, and any unresolved questions. Identify every piece of information that is essential for future actions. - After your reasoning is complete, generate the final XML object. Be incredibly dense with information. Omit any irrelevant conversational filler. - The structure MUST be as follows: @@ -80,8 +35,43 @@ compress_context_prompt: | - Here is the conversation history that needs to be compressed: - ’’’ + First, you will think through the entire history in a private . Then, generate the . + +compress_context_prompt_zh: | + 以下是需要压缩的对话历史： + ''' {messages_content} - ’’’ - First, you will think through the entire history in a private . Review the user's overall goal, the agent' \ No newline at end of file + ''' + + 你是负责将内部聊天历史总结为给定结构的组件。 + 当对话历史变得过长时，你将被调用来将整个历史提炼成一个简洁、结构化的 XML 快照。这个快照至关重要，因为它将成为智能体对过去的*唯一*记忆。智能体将仅基于这个快照继续工作。所有关键细节、计划、错误和用户指令都必须被保留。 + 首先，你将在私有的 中思考整个历史。回顾用户的总体目标、智能体的行动、工具输出、文件修改以及任何未解决的问题。识别对未来行动至关重要的每一条信息。 + 完成推理后，生成最终的 XML 对象。信息要极其密集。省略任何无关的对话填充内容。 + 结构必须如下： + + + + + + + + + + + + + + + + + + + 首先，你将在私有的 中思考整个历史。然后，生成 。 \ No newline at end of file diff --git a/reme_ai/context/offload/context_offload_op.py b/reme_ai/context/offload/context_offload_op.py new file mode 100644 index 00000000..62086ffc --- /dev/null +++ b/reme_ai/context/offload/context_offload_op.py @@ -0,0 +1,83 @@ +""" +Context offload module for managing context window limits through compaction and compression. + +This module provides a high-level operation that orchestrates context compaction and compression +to reduce token usage. It first attempts to compact tool messages, and if the compaction ratio +is not sufficient, it applies LLM-based compression to further reduce token count. + +The offload process: +1. Compacts tool messages by storing full content in external files +2. Evaluates the compaction effectiveness by comparing token counts +3. If compaction ratio exceeds threshold, applies LLM-based compression +""" + +from flowllm.core.context import C +from flowllm.core.op import BaseAsyncOp +from flowllm.core.schema import Message +from loguru import logger + + +@C.register_op() +class ContextOffloadOp(BaseAsyncOp): + """ + Context offload operation that orchestrates compaction and compression to reduce token usage. + + This operation combines context compaction and compression strategies to manage context + window limits. It first applies compaction to tool messages, then evaluates the effectiveness. + If the compaction ratio (compressed tokens / original tokens) exceeds a threshold, it + applies additional LLM-based compression to further reduce token count. + + Context Parameters: + compact_ratio_threshold (float): Threshold for compaction ratio above which compression + is applied. Defaults to 0.75. If the ratio of compressed tokens to original tokens + exceeds this value, compression will be triggered. + """ + + async def async_execute(self): + """ + Execute the context offload operation. + + The operation performs the following steps: + 1. Applies context compaction to reduce token usage in tool messages + 2. Calculates the compaction ratio (compressed tokens / original tokens) + 3. If the ratio exceeds the threshold, applies LLM-based compression to further + reduce token count + + The compaction operation stores full tool message content in external files and + keeps only previews in the context. If this doesn't sufficiently reduce tokens, + the compression operation uses LLM to generate concise summaries of older messages. + """ + from .context_compact_op import ContextCompactOp + from .context_compress_op import ContextCompressOp + + context_compact_op = ContextCompactOp() + context_compress_op = ContextCompressOp() + + await context_compact_op.async_call(context=self.context) + + origin_messages = [Message(**x) for x in self.context.messages] + origin_token_cnt = self.token_count(origin_messages) + + result_messages = [Message(**x) for x in self.context.response.answer] + answer_token_cnt = self.token_count(result_messages) + + compact_ratio = answer_token_cnt / origin_token_cnt + + compact_ratio_threshold: float = self.context.get("compact_ratio_threshold", 0.75) + if compact_ratio > compact_ratio_threshold: + logger.info(f"Compact ratio {compact_ratio:.2f} > {compact_ratio_threshold:.2f}, compress answer") + await context_compress_op.async_call(context=self.context) + + async def async_default_execute(self, e: Exception = None, **_kwargs): + """Handle execution errors by returning original messages. + + This method is called when an exception occurs during async_execute. It preserves + the original messages and marks the operation as unsuccessful. + + Args: + e: The exception that occurred during execution, if any. + **_kwargs: Additional keyword arguments (unused but required by interface). + """ + self.context.response.answer = self.context.messages + self.context.response.success = False + self.context.response.metadata["error"] = str(e) diff --git a/reme_ai/utils/op_utils.py b/reme_ai/utils/op_utils.py index 8345d008..a4d86645 100644 --- a/reme_ai/utils/op_utils.py +++ b/reme_ai/utils/op_utils.py @@ -130,3 +130,24 @@ def parse_update_insight_response(response_text: str, language: str = "en") -> s logger.warning("No insight content found in response") return "" + + +def extract_xml_tag_content(text: str, tag_name: str) -> str | None: + """Extract content from XML tag in text. + + Args: + text: The text containing XML tags. + tag_name: The name of the XML tag to extract (e.g., 'state_snapshot'). + + Returns: + str: The content inside the XML tag, or None if not found. + """ + # Use re.DOTALL to make . match newline characters + pattern = rf"<{tag_name}>(.*?)" + match = re.search(pattern, text, re.DOTALL) + + if match: + content = match.group(1).strip() + return content + + return None diff --git a/test/test_context_compress_op.py b/test/test_context_compress_op.py index 27203f71..520ed033 100644 --- a/test/test_context_compress_op.py +++ b/test/test_context_compress_op.py @@ -222,12 +222,13 @@ async def main(): logger.info("Test 1: Messages below threshold (should skip compression)") logger.info("=" * 60) - compress_op1 = ContextCompressOp( - all_token_threshold=50000, # High threshold, won't trigger - keep_recent=3, - ) + compress_op1 = ContextCompressOp() - await compress_op1.async_call(messages=messages) + await compress_op1.async_call( + messages=messages, + max_total_tokens=50000, # High threshold, won't trigger + keep_recent_count=3, + ) result_messages1 = compress_op1.context.response.answer logger.info(f"✓ Result: {len(result_messages1)} messages (unchanged)") @@ -237,14 +238,15 @@ async def main(): logger.info("Test 2: Messages above threshold (should compress)") logger.info("=" * 60) - compress_op2 = ContextCompressOp( - all_token_threshold=2000, # Low threshold, will trigger - keep_recent=3, # Keep last 3 messages + compress_op2 = ContextCompressOp() + + await compress_op2.async_call( + messages=messages, + max_total_tokens=2000, # Low threshold, will trigger + keep_recent_count=3, # Keep last 3 messages compress_system_message=False, # Don't compress system messages ) - await compress_op2.async_call(messages=messages) - result_messages2 = compress_op2.context.response.answer logger.info(f"✓ Result: {len(result_messages2)} messages (compressed)") @@ -260,16 +262,17 @@ async def main(): logger.info("Test 3: Messages above micro threshold (should compress)") logger.info("=!" * 30) - compress_op2 = ContextCompressOp( - all_token_threshold=2000, # Low threshold, will trigger - keep_recent=2, # Keep last 3 messages + compress_op2 = ContextCompressOp() + + await compress_op2.async_call( + messages=messages, + max_total_tokens=2000, # Low threshold, will trigger + keep_recent_count=2, # Keep last 2 messages compress_system_message=False, # Don't compress system messages - micro_summary_token_threshold=1500, + group_token_threshold=1500, language="zh", ) - await compress_op2.async_call(messages=messages) - result_messages2 = compress_op2.context.response.answer logger.info(f"✓ Result: {len(result_messages2)} messages (compressed)") diff --git a/test_op/test_context_compact_op.py b/test_op/test_context_compact_op.py index 5cdf8b96..61a69e11 100644 --- a/test_op/test_context_compact_op.py +++ b/test_op/test_context_compact_op.py @@ -59,16 +59,17 @@ async def async_main(): ] # Create op with lower thresholds for testing - op = ContextCompactOp( - all_token_threshold=1000, # Low threshold to trigger compaction - tool_token_threshold=100, # Low threshold to compact tool messages - tool_left_char_len=50, # Keep 50 chars in preview - keep_recent=1, # Keep 1 recent tool message - storage_path="./test_compact_storage", - ) + op = ContextCompactOp() # Execute the compaction - await op.async_call(messages=[m.model_dump() for m in messages]) + await op.async_call( + messages=[m.model_dump() for m in messages], + max_total_tokens=1000, # Low threshold to trigger compaction + max_tool_message_tokens=100, # Low threshold to compact tool messages + preview_char_length=50, # Keep 50 chars in preview + keep_recent_count=1, # Keep 1 recent tool message + storage_path="./test_compact_storage", + ) # Print results result = op.context.response.answer From 3156c3e0d34705373bbf81c6674f8833e897fd4f Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Wed, 19 Nov 2025 21:56:01 +0800 Subject: [PATCH 13/39] feat(context): implement context management modes for offload operations --- reme_ai/context/offload/context_offload_op.py | 68 +++++++++++++------ reme_ai/enumeration/__init__.py | 2 + reme_ai/enumeration/context_manage_enum.py | 21 ++++++ 3 files changed, 69 insertions(+), 22 deletions(-) create mode 100644 reme_ai/enumeration/context_manage_enum.py diff --git a/reme_ai/context/offload/context_offload_op.py b/reme_ai/context/offload/context_offload_op.py index 62086ffc..1c086249 100644 --- a/reme_ai/context/offload/context_offload_op.py +++ b/reme_ai/context/offload/context_offload_op.py @@ -16,6 +16,8 @@ from flowllm.core.op import BaseAsyncOp from flowllm.core.schema import Message from loguru import logger +from reme_ai.enumeration import ContextManageEnum + @C.register_op() class ContextOffloadOp(BaseAsyncOp): @@ -28,45 +30,67 @@ class ContextOffloadOp(BaseAsyncOp): applies additional LLM-based compression to further reduce token count. Context Parameters: + context_manage_mode (ContextManageEnum): The context management mode to use. + - COMPACT: Only applies context compaction to tool messages. + - COMPRESS: Only applies LLM-based compression to messages. + - AUTO: Applies compaction first, then compression if compaction ratio exceeds threshold. + Defaults to AUTO. compact_ratio_threshold (float): Threshold for compaction ratio above which compression - is applied. Defaults to 0.75. If the ratio of compressed tokens to original tokens - exceeds this value, compression will be triggered. + is applied. Only used in AUTO mode. Defaults to 0.75. If the ratio of compressed + tokens to original tokens exceeds this value, compression will be triggered. """ async def async_execute(self): """ Execute the context offload operation. - The operation performs the following steps: - 1. Applies context compaction to reduce token usage in tool messages - 2. Calculates the compaction ratio (compressed tokens / original tokens) - 3. If the ratio exceeds the threshold, applies LLM-based compression to further - reduce token count + The operation behavior depends on the context_manage_mode: + - COMPACT: Only applies context compaction to reduce token usage in tool messages. + - COMPRESS: Only applies LLM-based compression to generate concise summaries. + - AUTO: Applies compaction first, then compression if compaction ratio exceeds threshold. The compaction operation stores full tool message content in external files and - keeps only previews in the context. If this doesn't sufficiently reduce tokens, - the compression operation uses LLM to generate concise summaries of older messages. + keeps only previews in the context. The compression operation uses LLM to generate + concise summaries of older messages. """ from .context_compact_op import ContextCompactOp from .context_compress_op import ContextCompressOp + # Get the context management mode from context, default to AUTO + context_manage_mode = self.context.get("context_manage_mode", ContextManageEnum.AUTO) + if isinstance(context_manage_mode, str): + context_manage_mode = ContextManageEnum(context_manage_mode) + context_compact_op = ContextCompactOp() context_compress_op = ContextCompressOp() - await context_compact_op.async_call(context=self.context) - - origin_messages = [Message(**x) for x in self.context.messages] - origin_token_cnt = self.token_count(origin_messages) - - result_messages = [Message(**x) for x in self.context.response.answer] - answer_token_cnt = self.token_count(result_messages) - - compact_ratio = answer_token_cnt / origin_token_cnt - - compact_ratio_threshold: float = self.context.get("compact_ratio_threshold", 0.75) - if compact_ratio > compact_ratio_threshold: - logger.info(f"Compact ratio {compact_ratio:.2f} > {compact_ratio_threshold:.2f}, compress answer") + if context_manage_mode == ContextManageEnum.COMPACT: + # Only apply compaction + logger.info("Context management mode: COMPACT") + await context_compact_op.async_call(context=self.context) + elif context_manage_mode == ContextManageEnum.COMPRESS: + # Only apply compression + logger.info("Context management mode: COMPRESS") await context_compress_op.async_call(context=self.context) + elif context_manage_mode == ContextManageEnum.AUTO: + # Apply compaction first, then compression if needed + logger.info("Context management mode: AUTO") + await context_compact_op.async_call(context=self.context) + + origin_messages = [Message(**x) for x in self.context.messages] + origin_token_cnt = self.token_count(origin_messages) + + result_messages = [Message(**x) for x in self.context.response.answer] + answer_token_cnt = self.token_count(result_messages) + + compact_ratio = answer_token_cnt / origin_token_cnt + + compact_ratio_threshold: float = self.context.get("compact_ratio_threshold", 0.75) + if compact_ratio > compact_ratio_threshold: + logger.info(f"Compact ratio {compact_ratio:.2f} > {compact_ratio_threshold:.2f}, compress answer") + await context_compress_op.async_call(context=self.context) + else: + raise ValueError(f"Unknown context management mode: {context_manage_mode}") async def async_default_execute(self, e: Exception = None, **_kwargs): """Handle execution errors by returning original messages. diff --git a/reme_ai/enumeration/__init__.py b/reme_ai/enumeration/__init__.py index a6466cf3..482d29e1 100644 --- a/reme_ai/enumeration/__init__.py +++ b/reme_ai/enumeration/__init__.py @@ -4,8 +4,10 @@ This module provides enumerations used throughout the ReMe system, including language enumerations and other type definitions. """ +from reme_ai.enumeration.context_manage_enum import ContextManageEnum from reme_ai.enumeration.language_enum import LanguageEnum __all__ = [ + "ContextManageEnum", "LanguageEnum", ] diff --git a/reme_ai/enumeration/context_manage_enum.py b/reme_ai/enumeration/context_manage_enum.py new file mode 100644 index 00000000..3c290369 --- /dev/null +++ b/reme_ai/enumeration/context_manage_enum.py @@ -0,0 +1,21 @@ +"""Context management enumeration module. + +This module provides enumerations for context management strategies in the ReMe system. +""" + +from enum import Enum + + +class ContextManageEnum(str, Enum): + """ + An enumeration representing context management strategies. + + Members: + - COMPACT: Represents the compact context management strategy. + - COMPRESS: Represents the compress context management strategy. + - AUTO: Represents the automatic context management strategy. + """ + + COMPACT = "compact" + COMPRESS = "compress" + AUTO = "auto" From 1886da9020fe66cb7521c7f7d5bfd9e23d14fb24 Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Thu, 20 Nov 2025 11:13:26 +0800 Subject: [PATCH 14/39] feat(context): implement context offload with compaction and compression --- reme_ai/config/default.yaml | 81 ++++++++++++++++++- reme_ai/context/offload/context_compact_op.py | 17 ++-- .../context/offload/context_compress_op.py | 26 +++--- 3 files changed, 102 insertions(+), 22 deletions(-) diff --git a/reme_ai/config/default.yaml b/reme_ai/config/default.yaml index 8e8c233f..4c8fdc81 100644 --- a/reme_ai/config/default.yaml +++ b/reme_ai/config/default.yaml @@ -156,6 +156,83 @@ flow: description: "user query" required: true + context_offload: + flow_content: ContextOffloadOp() >> BatchWriteFileOp() + description: "Manages context window limits by compacting tool messages and compressing conversation history. First compacts large tool messages by storing full content in external files, then applies LLM-based compression if compaction ratio exceeds threshold. This helps reduce token usage while preserving important information." + input_schema: + messages: + type: array + description: "List of conversation messages to process for context offloading" + required: true + context_manage_mode: + type: string + description: "Context management mode: 'compact' only applies compaction to tool messages, 'compress' only applies LLM-based compression, 'auto' applies compaction first then compression if compaction ratio exceeds threshold. Defaults to 'auto'." + required: false + enum: ["compact", "compress", "auto"] + max_total_tokens: + type: integer + description: "Maximum token count threshold for triggering compression/compaction. For compaction, this is the total token count threshold. For compression, this excludes keep_recent_count messages and system messages. Defaults to 20000." + required: false + max_tool_message_tokens: + type: integer + description: "Maximum token count per tool message before compaction is applied. Tool messages exceeding this threshold will have their full content stored in external files with only a preview kept in context. Defaults to 2000." + required: false + group_token_threshold: + type: integer + description: "Maximum token count per compression group when using LLM-based compression. If None or 0, all messages are compressed in a single group. Messages exceeding this threshold individually will form their own group. Only used in 'compress' or 'auto' mode." + required: false + keep_recent_count: + type: integer + description: "Number of recent messages to preserve without compression or compaction. These messages remain unchanged to maintain conversation context. Defaults to 1 for compaction and 2 for compression." + required: false + store_dir: + type: string + description: "Directory path for storing offloaded message content. Full tool message content and compressed message groups are saved as files in this directory. Required for compaction and compression operations." + required: false + chat_id: + type: string + description: "Unique identifier for the chat session, used for file naming when storing compressed message groups. If not provided, a UUID will be generated automatically." + required: false + + context_offload_for_agentscope: + flow_content: ContextOffloadOp() + description: "Context offload operation for AgentScope integration. Manages context window limits by compacting tool messages and compressing conversation history without batch file writing. Same functionality as context_offload but without the BatchWriteFileOp step." + input_schema: + messages: + type: array + description: "List of conversation messages to process for context offloading" + required: true + context_manage_mode: + type: string + description: "Context management mode: 'compact' only applies compaction to tool messages, 'compress' only applies LLM-based compression, 'auto' applies compaction first then compression if compaction ratio exceeds threshold. Defaults to 'auto'." + required: false + enum: ["compact", "compress", "auto"] + max_total_tokens: + type: integer + description: "Maximum token count threshold for triggering compression/compaction. For compaction, this is the total token count threshold. For compression, this excludes keep_recent_count messages and system messages. Defaults to 20000." + required: false + max_tool_message_tokens: + type: integer + description: "Maximum token count per tool message before compaction is applied. Tool messages exceeding this threshold will have their full content stored in external files with only a preview kept in context. Defaults to 2000." + required: false + group_token_threshold: + type: integer + description: "Maximum token count per compression group when using LLM-based compression. If None or 0, all messages are compressed in a single group. Messages exceeding this threshold individually will form their own group. Only used in 'compress' or 'auto' mode." + required: false + keep_recent_count: + type: integer + description: "Number of recent messages to preserve without compression or compaction. These messages remain unchanged to maintain conversation context. Defaults to 1 for compaction and 2 for compression." + required: false + store_dir: + type: string + description: "Directory path for storing offloaded message content. Full tool message content and compressed message groups are saved as files in this directory. Required for compaction and compression operations." + required: false + chat_id: + type: string + description: "Unique identifier for the chat session, used for file naming when storing compressed message groups. If not provided, a UUID will be generated automatically." + required: false + + llm: default: backend: openai_compatible @@ -164,9 +241,7 @@ llm: temperature: 0.6 token_count: # Optional model_name: Qwen/Qwen3-30B-A3B-Instruct-2507 - backend: hf - params: - use_mirror: true + backend: base qwen3_30b_instruct: backend: openai_compatible diff --git a/reme_ai/context/offload/context_compact_op.py b/reme_ai/context/offload/context_compact_op.py index 96a5b1c1..7bd878a1 100644 --- a/reme_ai/context/offload/context_compact_op.py +++ b/reme_ai/context/offload/context_compact_op.py @@ -43,7 +43,11 @@ class ContextCompactOp(BaseAsyncOp): max_tool_message_tokens: int = self.context.get("max_tool_message_tokens", 2000) preview_char_length: int = self.context.get("preview_char_length", 100) keep_recent_count: int = self.context.get("keep_recent_count", 1) - storage_path: Path = Path(self.context.get("storage_path", "")) + store_dir: Path = Path(self.context.get("store_dir", "")) + + assert max_total_tokens > 0, "max_total_tokens must be greater than 0" + assert max_tool_message_tokens > 0, "max_tool_message_tokens must be greater than 0" + assert preview_char_length >= 0, "preview_char_length must be greater than 0" assert keep_recent_count > 0, "keep_recent_count must be greater than 0" # Convert context messages to Message objects @@ -53,7 +57,6 @@ class ContextCompactOp(BaseAsyncOp): # If nothing to compress after filtering, return original messages if not messages_to_compress: self.context.response.answer = self.context.messages - self.context.response.success = True logger.info("No messages to compress after filtering, returning original messages") return @@ -93,10 +96,10 @@ class ContextCompactOp(BaseAsyncOp): # Generate file name from tool_call_id or create a unique identifier file_name = tool_message.tool_call_id or uuid4().hex - path = storage_path / f"{file_name}.txt" + store_path = store_dir / f"{file_name}.txt" # Store the full content for batch writing - write_file_dict[path.as_posix()] = original_content + write_file_dict[store_path.as_posix()] = original_content # Create compressed preview of the tool message content compact_result = original_content[:preview_char_length] + "..." @@ -104,11 +107,11 @@ class ContextCompactOp(BaseAsyncOp): # Log the compaction action logger.info( f"Compacting tool message (tool_call_id={tool_message.tool_call_id}): " - f"token count={tool_token_cnt}, saving full content to {path}", + f"token count={tool_token_cnt}, saving full content to {store_path}", ) # Update tool message content with preview and file reference - compact_result += f" (detailed result is stored in {path})" + compact_result += f" (detailed result is stored in {store_path})" tool_message.content = compact_result # Store write_file_dict in context for potential batch writing @@ -117,6 +120,8 @@ class ContextCompactOp(BaseAsyncOp): # Return the compacted messages as JSON self.context.response.answer = [x.simple_dump() for x in messages] + self.context.response.metadata["write_file_dict"] = write_file_dict + logger.info(f"Context compaction completed: {len(write_file_dict)} tool messages were compacted") async def async_default_execute(self, e: Exception = None, **_kwargs): diff --git a/reme_ai/context/offload/context_compress_op.py b/reme_ai/context/offload/context_compress_op.py index cf71a9b3..218f6e21 100644 --- a/reme_ai/context/offload/context_compress_op.py +++ b/reme_ai/context/offload/context_compress_op.py @@ -144,6 +144,7 @@ class ContextCompressOp(BaseAsyncOp): if state_snapshot is None: logger.warning("Failed to extract state_snapshot from LLM response, using full content as fallback") return content + return state_snapshot # Call LLM to generate compressed summary @@ -193,7 +194,7 @@ class ContextCompressOp(BaseAsyncOp): for g_idx, messages in enumerate(message_groups): group_original_tokens = self.token_count(messages) messages_str = json.dumps([x.simple_dump() for x in messages], ensure_ascii=False, indent=2) - store_path = Path(self.context.store_dir) / f"{chat_id}_{g_idx}" + store_path = Path(self.context.get("store_dir", "")) / f"{chat_id}_{g_idx}.json" logger.info(f"Compress {g_idx}/{len(message_groups)} ({len(messages)}, {group_original_tokens} tokens)") group_summary = await self._compress_messages_with_llm(messages) @@ -216,7 +217,7 @@ class ContextCompressOp(BaseAsyncOp): ) return_messages.extend(messages) else: - system_message_copy.content += compress_content + system_message_copy.content += compress_content + "\n\n" write_file_dict[store_path.as_posix()] = messages_str logger.info( f"Group {g_idx} compression successful: " @@ -248,18 +249,15 @@ class ContextCompressOp(BaseAsyncOp): group_token_threshold: int = self.context.get("group_token_threshold", None) keep_recent_count: int = self.context.get("keep_recent_count", 2) - # Validate keep_recent_count - if keep_recent_count < 0: - logger.warning(f"keep_recent_count ({keep_recent_count}) is negative, setting to 0") - keep_recent_count = 0 + assert max_total_tokens > 0, "max_total_tokens must be positive" + assert keep_recent_count >= 0, "keep_recent_count must be non-negative" # Convert context messages to Message objects messages = [Message(**x) for x in self.context.messages] # Extract system message (should be exactly one) system_message = [x for x in messages if x.role is Role.SYSTEM] - if len(system_message) > 1: - raise ValueError("Expected exactly one system message") + assert len(system_message) <= 1, f"Expected at most one system message, got {len(system_message)}" if len(system_message) == 0: system_message = Message(role=Role.SYSTEM, content="") @@ -267,10 +265,8 @@ class ContextCompressOp(BaseAsyncOp): system_message = system_message[0] messages_without_system = [x for x in messages if x.role is not Role.SYSTEM] - messages_to_compress = ( - messages_without_system[:-keep_recent_count] if keep_recent_count > 0 else messages_without_system - ) - recent_messages = messages_without_system[-keep_recent_count:] if keep_recent_count > 0 else [] + messages_to_compress = messages_without_system[:-keep_recent_count] + recent_messages = messages_without_system[-keep_recent_count:] # If nothing to compress after filtering, return original messages if not messages_to_compress: @@ -297,8 +293,12 @@ class ContextCompressOp(BaseAsyncOp): write_file_dict, return_messages = await self._compress_with_groups(system_message, message_groups) - self.context.write_file_dict = write_file_dict + # Store write_file_dict in context for potential batch writing + if write_file_dict: + self.context.write_file_dict = write_file_dict + self.context.response.answer = [x.simple_dump() for x in (return_messages + recent_messages)] + self.context.response.metadata["write_file_dict"] = write_file_dict async def async_default_execute(self, e: Exception = None, **_kwargs): """Handle execution errors by returning original messages. From f670929c69b2e2ca7b97998170288e0a1dade969 Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Thu, 20 Nov 2025 11:51:47 +0800 Subject: [PATCH 15/39] test(context): update context compaction and compression tests --- test/test_context_compress_op.py | 288 ---------------------------- test_op/test_context_compact_op.py | 7 +- test_op/test_context_compress_op.py | 285 +++++++++++++++++++++++++++ 3 files changed, 289 insertions(+), 291 deletions(-) delete mode 100644 test/test_context_compress_op.py create mode 100644 test_op/test_context_compress_op.py diff --git a/test/test_context_compress_op.py b/test/test_context_compress_op.py deleted file mode 100644 index 520ed033..00000000 --- a/test/test_context_compress_op.py +++ /dev/null @@ -1,288 +0,0 @@ -""" -Test script for ContextCompressOp. - -This script demonstrates how to use the context compression operation to reduce -token usage in conversation histories using language models. -""" - -import asyncio -from flowllm.core.enumeration import Role -from flowllm.core.schema import Message -from loguru import logger - -from reme_ai.context.offload.context_compress_op import ContextCompressOp -from reme_ai.main import ReMeApp - - -async def main(): - """Main function to test ContextCompressOp.""" - - async with ReMeApp(): - logger.info("=" * 80) - logger.info("Testing ContextCompressOp - LLM-based Context Compression") - logger.info("=" * 80) - - # Create a mock conversation with multiple messages - messages = [ - { - "role": "system", - "content": "You are a helpful AI assistant specialized in software development.", - }, - { - "role": "user", - "content": "I need help building a REST API in Python. I want to use FastAPI.", - }, - { - "role": "assistant", - "content": "Great choice! FastAPI is an excellent framework for building REST APIs. " - "It's fast, modern, and has automatic API documentation. To get started, you'll need " - "to install FastAPI and uvicorn. Would you like me to guide you through setting up " - "your first endpoint?", - }, - { - "role": "user", - "content": "Yes please. I want to create a user management API with CRUD operations.", - }, - { - "role": "assistant", - "content": "Perfect! For a user management API, I recommend this structure:\n" - "1. Define a User model using Pydantic\n" - "2. Create POST /users endpoint for creating users\n" - "3. Create GET /users and GET /users/{id} for reading\n" - "4. Create PUT /users/{id} for updates\n" - "5. Create DELETE /users/{id} for deletion\n" - "We'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", - }, - { - "role": "user", - "content": "Let's use PostgreSQL. Also, I need JWT authentication.", - }, - { - "role": "assistant", - "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use " - "python-jose library. Here's what we'll implement:\n" - "1. User registration endpoint\n" - "2. Login endpoint that returns JWT token\n" - "3. Protected endpoints that require valid JWT\n" - "4. Password hashing using bcrypt\n" - "Let me show you the code for the User model first.", - }, - { - "role": "user", - "content": "Before we proceed, I also need rate limiting and input validation.", - }, - { - "role": "assistant", - "content": "Good thinking! For rate limiting, we can use slowapi library which integrates " - "well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, " - "but we can add custom validators. I'll also add request validation middleware. " - "Let's start implementing all of this step by step.", - }, - { - "role": "user", - "content": "I need to build a distributed task queue system in Python that can handle millions of tasks" - " per day. It needs to be horizontally scalable and fault-tolerant.", - }, - { - "role": "assistant", - "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, " - "I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task " - "distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3." - " **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**:" - " Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and " - "status queries\n\nFor the core library, we can build on top of Celery or create a custo" - "m solution. Would you like me to design the system architecture first, or do you have pr" - "eferences for specific technologies?", - }, - { - "role": "user", - "content": "I want to build this from scratch without Celery. I need custom features like task" - " priorities, retry policies with exponential backoff, and task dependencies. Also," - " I need it to support both synchronous and asynchronous task execution patterns.", - }, - { - "role": "assistant", - "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive " - "system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based" - " on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3." - " **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**:" - " Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task " - "states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time " - "performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n-" - " Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock" - " algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for " - "failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing " - "services\n\nWould you like me to start with the core task model and priority queue " - "implementation?", - }, - { - "role": "user", - "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a " - "web dashboard for monitoring, and the ability to dynamically scale workers based on queue" - " depth. Plus, I need task deduplication and exactly-once processing semantics.", - }, - { - "role": "assistant", - "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time " - "Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub " - "for multi-instance communication\n2. **Event Stream**: Task state changes published to Re" - "dis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptio" - "ns\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, " - "processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or cu" - "stom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on hi" - "storical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has " - "UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task " - "IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing" - "\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- " - "Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency g" - "raphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violati" - "ons\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys w" - "ith TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with na" - "mespace support\n\nThis is getting quite comprehensive! Should I start with the core tas" - "k model and database schema, or would you prefer to begin with the WebSocket real-time up" - "date system?", - }, - { - "role": "user", - "content": "This is getting complex. I also need multi-region support with eventual consistency, the " - "ability to pause/resume task processing by task type, and A/B testing capabilities for " - "different worker implementations. Also, I need comprehensive audit logging and GDPR comp" - "liance features.", - }, - { - "role": "assistant", - "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the compl" - "ete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with" - " health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup " - "using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-win" - "s for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when p" - "ossible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Adv" - "anced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis fe" - "ature flags with immediate propagation\n - Rate limits per task type with burst capaci" - "ty\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n" - " - Task routing based on consistent hashing of task ID\n - Variant assignment with s" - "tickiness\n - Statistical significance tracking for performance metrics\n - Automati" - "c winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **A" - "udit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n -" - " Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n " - " - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n " - " - Right to be forgotten: Cascade delete with verification\n - Data portability: JSO" - "N export of all user tasks\n - Consent management: Task processing consent tracking" - "\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:*" - "*\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance " - "Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource" - " consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n" - "**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **" - "Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC is" - "olation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n" - "\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2." - " **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Rand" - "om worker termination testing\n4. **Capacity Planning**: Automated infrastructure provi" - "sioning\n\nThis is becoming a mission-critical system! Would you like me to start with " - "the database schema and core task lifecycle management, or should we begin with the mul" - "ti-region replication strategy? I can also create a detailed implementation roadmap wi" - "th phases if you prefer.", - }, - { - "role": "user", - "content": "How do I reverse a string in Python?", - }, - { - "role": "assistant", - "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::" - "-1]`. The `[::-1]` slice notation means start at the end of the string and end at posit" - "ion 0, moving with the step -1.", - }, - { - "role": "user", - "content": "What's the difference between a list and a tuple?", - }, - { - "role": "assistant", - "content": "Lists are mutable (you can change their content after creation) and use square bracket" - "s `[]`. Tuples are immutable and use parentheses `()`. Lists are better for collectio" - "ns that need to change, while tuples are faster and can be used as dictionary keys.", - }, - { - "role": "user", - "content": "How do I handle exceptions in Python?", - }, - { - "role": "assistant", - "content": "Use try-except blocks: `try:` followed by your risky code, then `except SpecificError" - ":` to catch particular exceptions. Always catch specific exceptions rather than the g" - "eneric `Exception` when possible. You can also use `finally:` for cleanup code that ru" - "ns regardless.", - }, - - ] - - # Test 1: Messages below threshold (should not compress) - logger.info("\n" + "=" * 60) - logger.info("Test 1: Messages below threshold (should skip compression)") - logger.info("=" * 60) - - compress_op1 = ContextCompressOp() - - await compress_op1.async_call( - messages=messages, - max_total_tokens=50000, # High threshold, won't trigger - keep_recent_count=3, - ) - - result_messages1 = compress_op1.context.response.answer - logger.info(f"✓ Result: {len(result_messages1)} messages (unchanged)") - - # Test 2: Messages above threshold (should compress) - logger.info("\n" + "=" * 60) - logger.info("Test 2: Messages above threshold (should compress)") - logger.info("=" * 60) - - compress_op2 = ContextCompressOp() - - await compress_op2.async_call( - messages=messages, - max_total_tokens=2000, # Low threshold, will trigger - keep_recent_count=3, # Keep last 3 messages - compress_system_message=False, # Don't compress system messages - ) - - result_messages2 = compress_op2.context.response.answer - logger.info(f"✓ Result: {len(result_messages2)} messages (compressed)") - - # Display compression results - logger.info("\n" + "=" * 60) - logger.info("Compression Result Details:") - logger.info("=" * 60) - logger.info(f"Original messages: {len(messages)}") - logger.info(f"Compressed messages: {len(result_messages2)}") - - # Test 3: Messages above threshold (should compress) - logger.info("\n" + "=!" * 30) - logger.info("Test 3: Messages above micro threshold (should compress)") - logger.info("=!" * 30) - - compress_op2 = ContextCompressOp() - - await compress_op2.async_call( - messages=messages, - max_total_tokens=2000, # Low threshold, will trigger - keep_recent_count=2, # Keep last 2 messages - compress_system_message=False, # Don't compress system messages - group_token_threshold=1500, - language="zh", - ) - - result_messages2 = compress_op2.context.response.answer - logger.info(f"✓ Result: {len(result_messages2)} messages (compressed)") - - # Display compression results - logger.info("\n" + "=" * 60) - logger.info("Compression Result Details:") - logger.info("=" * 60) - logger.info(f"Original messages: {len(messages)}") - logger.info(f"Compressed messages: {len(result_messages2)}") - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/test_op/test_context_compact_op.py b/test_op/test_context_compact_op.py index 61a69e11..9cc5727b 100644 --- a/test_op/test_context_compact_op.py +++ b/test_op/test_context_compact_op.py @@ -9,7 +9,8 @@ import asyncio from flowllm.core.enumeration import Role from flowllm.core.schema import Message -from reme_ai.context.offload.context_compact_op import ContextCompactOp +from reme_ai.context.file_tool import BatchWriteFileOp +from reme_ai.context.offload import ContextCompactOp from reme_ai.main import ReMeApp @@ -59,7 +60,7 @@ async def async_main(): ] # Create op with lower thresholds for testing - op = ContextCompactOp() + op = ContextCompactOp() >> BatchWriteFileOp() # Execute the compaction await op.async_call( @@ -68,7 +69,7 @@ async def async_main(): max_tool_message_tokens=100, # Low threshold to compact tool messages preview_char_length=50, # Keep 50 chars in preview keep_recent_count=1, # Keep 1 recent tool message - storage_path="./test_compact_storage", + store_dir="./test_compact_storage", ) # Print results diff --git a/test_op/test_context_compress_op.py b/test_op/test_context_compress_op.py new file mode 100644 index 00000000..e35ed71c --- /dev/null +++ b/test_op/test_context_compress_op.py @@ -0,0 +1,285 @@ +""" +Test script for ContextCompressOp. + +This script demonstrates how to use the context compression operation to reduce +token usage in conversation histories using language models. +""" + +import asyncio + +from loguru import logger + +from reme_ai.context.offload.context_compress_op import ContextCompressOp +from reme_ai.main import ReMeApp + + +async def main(): + """Main function to test ContextCompressOp.""" + + async with ReMeApp(): + logger.info("=" * 80) + logger.info("Testing ContextCompressOp - LLM-based Context Compression") + logger.info("=" * 80) + + # Create a mock conversation with multiple messages + messages = [ + { + "role": "system", + "content": "You are a helpful AI assistant specialized in software development.", + }, + { + "role": "user", + "content": "I need help building a REST API in Python. I want to use FastAPI.", + }, + { + "role": "assistant", + "content": "Great choice! FastAPI is an excellent framework for building REST APIs. " + "It's fast, modern, and has automatic API documentation. To get started, you'll need " + "to install FastAPI and uvicorn. Would you like me to guide you through setting up " + "your first endpoint?", + }, + { + "role": "user", + "content": "Yes please. I want to create a user management API with CRUD operations.", + }, + { + "role": "assistant", + "content": "Perfect! For a user management API, I recommend this structure:\n" + "1. Define a User model using Pydantic\n" + "2. Create POST /users endpoint for creating users\n" + "3. Create GET /users and GET /users/{id} for reading\n" + "4. Create PUT /users/{id} for updates\n" + "5. Create DELETE /users/{id} for deletion\n" + "We'll also need a database. Would you prefer SQLite, PostgreSQL, or MongoDB?", + }, + { + "role": "user", + "content": "Let's use PostgreSQL. Also, I need JWT authentication.", + }, + { + "role": "assistant", + "content": "Excellent. PostgreSQL is a robust choice. For JWT authentication, we'll use " + "python-jose library. Here's what we'll implement:\n" + "1. User registration endpoint\n" + "2. Login endpoint that returns JWT token\n" + "3. Protected endpoints that require valid JWT\n" + "4. Password hashing using bcrypt\n" + "Let me show you the code for the User model first.", + }, + { + "role": "user", + "content": "Before we proceed, I also need rate limiting and input validation.", + }, + { + "role": "assistant", + "content": "Good thinking! For rate limiting, we can use slowapi library which integrates " + "well with FastAPI. For input validation, Pydantic (which FastAPI uses) handles most of it, " + "but we can add custom validators. I'll also add request validation middleware. " + "Let's start implementing all of this step by step.", + }, + { + "role": "user", + "content": "I need to build a distributed task queue system in Python that can handle millions of tasks" + " per day. It needs to be horizontally scalable and fault-tolerant.", + }, + { + "role": "assistant", + "content": "That's a challenging but exciting project! For a high-throughput distributed task queue, " + "I recommend a architecture with:\n\n1. **Message Broker**: Redis or RabbitMQ for task " + "distribution\n2. **Task Workers**: Multiple worker processes across multiple machines\n3." + " **Result Backend**: Redis or PostgreSQL for storing task results\n4. **Monitoring**:" + " Prometheus + Grafana for metrics\n5. **API Layer**: FastAPI for task submission and " + "status queries\n\nFor the core library, we can build on top of Celery or create a custo" + "m solution. Would you like me to design the system architecture first, or do you have pr" + "eferences for specific technologies?", + }, + { + "role": "user", + "content": "I want to build this from scratch without Celery. I need custom features like task" + " priorities, retry policies with exponential backoff, and task dependencies. Also," + " I need it to support both synchronous and asynchronous task execution patterns.", + }, + { + "role": "assistant", + "content": "Excellent! Building from scratch gives us full control. Let me design a comprehensive " + "system architecture:\n\n**Core Components:**\n1. **Task Router**: Distributes tasks based" + " on priority queues\n2. **Worker Manager**: Handles worker lifecycle and load balancing\n3." + " **Retry Engine**: Implements exponential backoff with jitter\n4. **Dependency Graph**:" + " Manages task dependencies using topological sorting\n5. **State Manager**: Tracks task " + "states (pending, running, completed, failed)\n6. **Metrics Collector**: Real-time " + "performance metrics\n\n**Data Structures:**\n- Priority queues using Redis Sorted Sets\n-" + " Task metadata in PostgreSQL with JSONB columns\n- Distributed locks with Redis Redlock" + " algorithm\n\n**Features:**\n- At-least-once delivery guarantee\n- Dead letter queue for " + "failed tasks\n- Rate limiting per task type\n- Circuit breaker pattern for failing " + "services\n\nWould you like me to start with the core task model and priority queue " + "implementation?", + }, + { + "role": "user", + "content": "That sounds good. But I also need WebSocket support for real-time task status updates, a " + "web dashboard for monitoring, and the ability to dynamically scale workers based on queue" + " depth. Plus, I need task deduplication and exactly-once processing semantics.", + }, + { + "role": "assistant", + "content": "Perfect! Let me enhance the architecture with these advanced requirements:\n\n**Real-time " + "Features:**\n1. **WebSocket Manager**: Using Python's websocket-server with Redis pub/sub " + "for multi-instance communication\n2. **Event Stream**: Task state changes published to Re" + "dis streams\n3. **Client SDK**: JavaScript/TypeScript library for real-time subscriptio" + "ns\n\n**Auto-scaling Architecture:**\n1. **Metrics Aggregator**: Collects queue depth, " + "processing latency, error rates\n2. **Scaler Service**: Kubernetes HPA integration or cu" + "stom Docker Swarm scaling\n3. **Predictive Scaling**: ML model to predict load based on hi" + "storical patterns\n\n**Exactly-once Processing:**\n1. **Idempotency Keys**: Each task has " + "UUID-based idempotency key\n2. **Deduplication Store**: Redis with TTL for processed task " + "IDs\n3. **Transactional Outbox**: PostgreSQL outbox pattern for reliable event publishing" + "\n4. **Two-phase Commit**: For distributed task processing\n\n**Dashboard Features:**\n- " + "Real-time queue metrics with D3.js visualizations\n- Task timeline view with dependency g" + "raphs\n- Worker health monitoring with automatic restart\n- Alert system for SLA violati" + "ons\n\n**Additional Components:**\n- Task scheduler for delayed tasks using Redis keys w" + "ith TTL\n- Batch processing support for bulk operations\n- Multi-tenant isolation with na" + "mespace support\n\nThis is getting quite comprehensive! Should I start with the core tas" + "k model and database schema, or would you prefer to begin with the WebSocket real-time up" + "date system?", + }, + { + "role": "user", + "content": "This is getting complex. I also need multi-region support with eventual consistency, the " + "ability to pause/resume task processing by task type, and A/B testing capabilities for " + "different worker implementations. Also, I need comprehensive audit logging and GDPR comp" + "liance features.", + }, + { + "role": "assistant", + "content": "Absolutely! This is evolving into an enterprise-grade system. Let me architect the compl" + "ete solution:\n\n**Multi-region Architecture:**\n1. **Global Load Balancer**: GeoDNS with" + " health checks\n2. **Cross-region Replication**: Redis Cluster with active-active setup " + "using CRDTs\n3. **Conflict Resolution**: Vector clocks for task ordering, last-writer-win" + "s for metadata\n4. **Region-aware Routing**: Route tasks to workers in same region when p" + "ossible\n5. **Failover Mechanism**: Automatic region failover with 30-second RTO\n\n**Adv" + "anced Control Features:**\n1. **Task Type Governance**: \n - Pause/resume via Redis fe" + "ature flags with immediate propagation\n - Rate limits per task type with burst capaci" + "ty\n - Resource quotas (CPU/memory) per task category\n2. **A/B Testing Framework**:\n" + " - Task routing based on consistent hashing of task ID\n - Variant assignment with s" + "tickiness\n - Statistical significance tracking for performance metrics\n - Automati" + "c winner selection based on success rate and latency\n\n**Compliance & Audit:**\n1. **A" + "udit Trail**:\n - Immutable task history in PostgreSQL with row-level security\n -" + " Change data capture (CDC) using Debezium\n - Cryptographic signing of audit logs\n " + " - 7-year retention policy with automated archival to S3\n2. **GDPR Compliance**:\n " + " - Right to be forgotten: Cascade delete with verification\n - Data portability: JSO" + "N export of all user tasks\n - Consent management: Task processing consent tracking" + "\n - Data anonymization: PII encryption with rotating keys\n\n**Enhanced Monitoring:*" + "*\n1. **Distributed Tracing**: OpenTelemetry integration with Jaeger\n2. **Performance " + "Profiling**: Py-spy continuous profiling\n3. **Cost Analytics**: Per-task-type resource" + " consumption tracking\n4. **SLA Monitoring**: 99.9% task processing latency targets\n\n" + "**Security Features:**\n1. **Zero-trust Architecture**: mTLS between all services\n2. **" + "Secrets Management**: HashiCorp Vault integration\n3. **Network Segmentation**: VPC is" + "olation with private subnets\n4. **Encryption**: AES-256 at rest, TLS 1.3 in transit\n" + "\n**Operational Excellence:**\n1. **Blue-green Deployments**: Zero-downtime updates\n2." + " **Canary Releases**: Gradual rollout of worker changes\n3. **Chaos Engineering**: Rand" + "om worker termination testing\n4. **Capacity Planning**: Automated infrastructure provi" + "sioning\n\nThis is becoming a mission-critical system! Would you like me to start with " + "the database schema and core task lifecycle management, or should we begin with the mul" + "ti-region replication strategy? I can also create a detailed implementation roadmap wi" + "th phases if you prefer.", + }, + { + "role": "user", + "content": "How do I reverse a string in Python?", + }, + { + "role": "assistant", + "content": "You can reverse a string in Python using slicing: `reversed_string = original_string[::" + "-1]`. The `[::-1]` slice notation means start at the end of the string and end at posit" + "ion 0, moving with the step -1.", + }, + { + "role": "user", + "content": "What's the difference between a list and a tuple?", + }, + { + "role": "assistant", + "content": "Lists are mutable (you can change their content after creation) and use square bracket" + "s `[]`. Tuples are immutable and use parentheses `()`. Lists are better for collectio" + "ns that need to change, while tuples are faster and can be used as dictionary keys.", + }, + { + "role": "user", + "content": "How do I handle exceptions in Python?", + }, + { + "role": "assistant", + "content": "Use try-except blocks: `try:` followed by your risky code, then `except SpecificError" + ":` to catch particular exceptions. Always catch specific exceptions rather than the g" + "eneric `Exception` when possible. You can also use `finally:` for cleanup code that ru" + "ns regardless.", + }, + ] + + # Test 1: Messages below threshold (should not compress) + logger.info("\n" + "=" * 60) + logger.info("Test 1: Messages below threshold (should skip compression)") + logger.info("=" * 60) + + compress_op1 = ContextCompressOp() + + await compress_op1.async_call( + messages=messages, + max_total_tokens=50000, # High threshold, won't trigger + keep_recent_count=2, + ) + + result_messages1 = compress_op1.context.response.answer + logger.info(f"✓ Result: {len(result_messages1)} messages (unchanged)") + + # Test 2: Messages above threshold (should compress) + logger.info("\n" + "=" * 60) + logger.info("Test 2: Messages above threshold (should compress)") + logger.info("=" * 60) + + compress_op2 = ContextCompressOp() + + await compress_op2.async_call( + messages=messages, + max_total_tokens=2000, # Low threshold, will trigger + keep_recent_count=2, + compress_system_message=False, + ) + + result_messages2 = compress_op2.context.response.answer + logger.info(f"✓ Result: {len(result_messages2)} messages (compressed)") + + # Display compression results + logger.info("\n" + "=" * 60) + logger.info("Compression Result Details:") + logger.info("=" * 60) + logger.info(f"Original messages: {len(messages)}") + logger.info(f"Compressed messages: {len(result_messages2)}") + + # Test 3: Messages above threshold (should compress) + logger.info("\n" + "=!" * 30) + logger.info("Test 3: Messages above micro threshold (should compress)") + logger.info("=!" * 30) + + compress_op2 = ContextCompressOp() + + await compress_op2.async_call( + messages=messages, + max_total_tokens=2000, # Low threshold, will trigger + keep_recent_count=2, + compress_system_message=False, # Don't compress system messages + group_token_threshold=1500, + ) + + result_messages2 = compress_op2.context.response.answer + logger.info(f"✓ Result: {len(result_messages2)} messages (compressed)") + + # Display compression results + logger.info("\n" + "=" * 60) + logger.info("Compression Result Details:") + logger.info("=" * 60) + logger.info(f"Original messages: {len(messages)}") + logger.info(f"Compressed messages: {len(result_messages2)}") + + +if __name__ == "__main__": + asyncio.run(main()) From 1893ed3c37c24fd1b3926b03d874883c9329a8fd Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Thu, 20 Nov 2025 12:17:34 +0800 Subject: [PATCH 16/39] feat(context): add test cases for ContextOffloadOp --- .gitignore | 3 +- test_op/test_context_offload_op.py | 247 +++++++++++++++++++++++++++++ 2 files changed, 249 insertions(+), 1 deletion(-) create mode 100644 test_op/test_context_offload_op.py diff --git a/.gitignore b/.gitignore index a707d866..4f720a02 100644 --- a/.gitignore +++ b/.gitignore @@ -29,4 +29,5 @@ file_vector_store/* cookbook/appworld/file_vector_store/* /.venv/ site/* -docs/_build/* \ No newline at end of file +docs/_build/* +test_compact_storage/* \ No newline at end of file diff --git a/test_op/test_context_offload_op.py b/test_op/test_context_offload_op.py new file mode 100644 index 00000000..b5b87117 --- /dev/null +++ b/test_op/test_context_offload_op.py @@ -0,0 +1,247 @@ +"""Test script for ContextOffloadOp. + +This script provides test cases for ContextOffloadOp class. +It can be run directly with: python test_context_offload_op.py +""" + +import asyncio + +from flowllm.core.enumeration import Role +from flowllm.core.schema import Message +from loguru import logger + +from reme_ai.context.file_tool import BatchWriteFileOp +from reme_ai.context.offload.context_offload_op import ContextOffloadOp +from reme_ai.enumeration import ContextManageEnum +from reme_ai.main import ReMeApp + + +async def test_compact_mode(): + """Test COMPACT mode - Only apply compaction.""" + logger.info("\n" + "=" * 60) + logger.info("Test: COMPACT mode - Only apply compaction") + logger.info("=" * 60) + + # Create test messages with system, user, assistant, tool sequence + messages = [ + Message(role=Role.SYSTEM, content="You are a helpful assistant."), + Message(role=Role.USER, content="What is the weather today?"), + Message( + role=Role.ASSISTANT, + content="I'll check the weather for you.", + ), + Message( + role=Role.TOOL, + content="A" * 5000, # Large tool message that should be compacted + tool_call_id="call_001", + ), + Message( + role=Role.ASSISTANT, + content="Let me also check the forecast.", + ), + Message( + role=Role.TOOL, + content="B" * 5000, # Another large tool message + tool_call_id="call_002", + ), + Message( + role=Role.USER, + content="What about tomorrow?", + ), + Message( + role=Role.ASSISTANT, + content="I'll check tomorrow's weather.", + ), + Message( + role=Role.TOOL, + content="C" * 5000, # Third large tool message + tool_call_id="call_003", + ), + Message( + role=Role.TOOL, + content="Recent result", # Recent tool message (should be kept) + tool_call_id="call_004", + ), + ] + + op = ContextOffloadOp() >> BatchWriteFileOp() + + await op.async_call( + messages=[m.model_dump() for m in messages], + context_manage_mode=ContextManageEnum.COMPACT, + max_total_tokens=1000, # Low threshold to trigger compaction + max_tool_message_tokens=100, # Low threshold to compact tool messages + preview_char_length=50, # Keep 50 chars in preview + keep_recent_count=1, # Keep 1 recent tool message + store_dir="./test_compact_storage", + ) + + result = op.context.response.answer + logger.info(f"✓ COMPACT mode result: {len(result)} messages") + logger.info(f" Success: {op.context.response.success}") + + +async def test_compress_mode(): + """Test COMPRESS mode - Only apply compression.""" + logger.info("\n" + "=" * 60) + logger.info("Test: COMPRESS mode - Only apply compression") + logger.info("=" * 60) + + # Create test messages with system, user, assistant, tool sequence + messages = [ + Message(role=Role.SYSTEM, content="You are a helpful assistant."), + Message(role=Role.USER, content="What is the weather today?"), + Message( + role=Role.ASSISTANT, + content="I'll check the weather for you.", + ), + Message( + role=Role.TOOL, + content="A" * 5000, # Large tool message that should be compacted + tool_call_id="call_001", + ), + Message( + role=Role.ASSISTANT, + content="Let me also check the forecast.", + ), + Message( + role=Role.TOOL, + content="B" * 5000, # Another large tool message + tool_call_id="call_002", + ), + Message( + role=Role.USER, + content="What about tomorrow?", + ), + Message( + role=Role.ASSISTANT, + content="I'll check tomorrow's weather.", + ), + Message( + role=Role.TOOL, + content="C" * 5000, # Third large tool message + tool_call_id="call_003", + ), + Message( + role=Role.TOOL, + content="Recent result", # Recent tool message (should be kept) + tool_call_id="call_004", + ), + ] + + op = ContextOffloadOp() >> BatchWriteFileOp() + + await op.async_call( + messages=[m.model_dump() for m in messages], + context_manage_mode=ContextManageEnum.COMPRESS, + max_total_tokens=2000, # Low threshold to trigger compression + keep_recent_count=2, + store_dir="./test_compact_storage", + ) + + result = op.context.response.answer + logger.info(f"✓ COMPRESS mode result: {len(result)} messages") + logger.info(f" Success: {op.context.response.success}") + + +async def test_auto_mode(): + """Test AUTO mode - Apply compaction first, then compression if needed.""" + logger.info("\n" + "=" * 60) + logger.info("Test: AUTO mode - Apply compaction first, then compression if needed") + logger.info("=" * 60) + + # Create messages with extensive user content to ensure compact ratio exceeds threshold + auto_messages = [ + Message(role=Role.SYSTEM, content="You are a helpful assistant."), + Message(role=Role.USER, content="What is the weather today?"), + Message( + role=Role.ASSISTANT, + content="I'll check the weather for you.", + ), + Message( + role=Role.TOOL, + content="A" * 5000, # Large tool message that should be compacted + tool_call_id="call_001", + ), + Message( + role=Role.USER, + content="I need detailed information about the weather forecast for the next week. " + "Please provide temperature, humidity, wind speed, and precipitation chances for each day. " + "Also, I want to know about any weather warnings or advisories. " + "This is very important for my travel planning." * 50, # Long user message + ), + Message( + role=Role.ASSISTANT, + content="I'll gather comprehensive weather information for you. Let me check multiple sources." * 3, + ), + Message( + role=Role.TOOL, + content="B" * 5000, # Another large tool message + tool_call_id="call_002", + ), + Message( + role=Role.USER, + content="Can you also provide information about air quality, UV index, and sunrise/sunset times? " + "I'm planning outdoor activities and need to know the best times to be outside. " + "Also, please include historical weather data for comparison." * 4, # More long user content + ), + Message( + role=Role.ASSISTANT, + content="Absolutely! I'll get all that information for you including air quality metrics and UV data." * 2, + ), + Message( + role=Role.TOOL, + content="C" * 5000, # Third large tool message + tool_call_id="call_003", + ), + Message( + role=Role.USER, + content="What about tomorrow?", + ), + Message( + role=Role.ASSISTANT, + content="I'll check tomorrow's weather.", + ), + Message( + role=Role.TOOL, + content="Recent result", # Recent tool message (should be kept) + tool_call_id="call_004", + ), + ] + + op = ContextOffloadOp() >> BatchWriteFileOp() + + await op.async_call( + messages=[m.model_dump() for m in auto_messages], + context_manage_mode=ContextManageEnum.AUTO, + compact_ratio_threshold=0.2, # Low threshold, should trigger compression after compact + max_total_tokens=1000, + max_tool_message_tokens=100, + preview_char_length=50, + keep_recent_count=1, + store_dir="./test_compact_storage", + ) + + result = op.context.response.answer + logger.info(f"✓ AUTO mode result: {len(result)} messages") + logger.info(f" Success: {op.context.response.success}") + + +async def async_main(): + """Test function for ContextOffloadOp.""" + async with ReMeApp(): + logger.info("=" * 80) + logger.info("Testing ContextOffloadOp - Context Management Orchestration") + logger.info("=" * 80) + + # await test_compact_mode() + # await test_compress_mode() + await test_auto_mode() + + logger.info("\n" + "=" * 80) + logger.info("All tests completed!") + logger.info("=" * 80) + + +if __name__ == "__main__": + asyncio.run(async_main()) From 2ff75fd580c07718c780addfeb67ceb9b7eebae5 Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Thu, 20 Nov 2025 17:01:29 +0800 Subject: [PATCH 17/39] feat(agent): add agentic retrieve operator with RAG-friendly tools --- cookbook/context_demo.py | 7 ++ reme_ai/agent/react/__init__.py | 2 + reme_ai/agent/react/agentic_retrieve_op.py | 113 ++++++++++++++++++ .../agent/react/agentic_retrieve_prompt.yaml | 5 + reme_ai/agent/react/simple_react_op.py | 2 +- reme_ai/config/default.yaml | 5 +- reme_ai/context/file_tool/grep_prompt.yaml | 10 ++ .../context/file_tool/read_file_prompt.yaml | 8 ++ reme_ai/context/offload/context_compact_op.py | 6 +- .../context/offload/context_compress_op.py | 8 +- test/http_client_test.py | 23 +++- 11 files changed, 182 insertions(+), 7 deletions(-) create mode 100644 cookbook/context_demo.py create mode 100644 reme_ai/agent/react/agentic_retrieve_op.py create mode 100644 reme_ai/agent/react/agentic_retrieve_prompt.yaml diff --git a/cookbook/context_demo.py b/cookbook/context_demo.py new file mode 100644 index 00000000..c57eb933 --- /dev/null +++ b/cookbook/context_demo.py @@ -0,0 +1,7 @@ +def main(): + ... + + +if __name__ == "__main__": + def main(): + ... diff --git a/reme_ai/agent/react/__init__.py b/reme_ai/agent/react/__init__.py index afb89a82..11adaed4 100644 --- a/reme_ai/agent/react/__init__.py +++ b/reme_ai/agent/react/__init__.py @@ -4,8 +4,10 @@ This module provides ReAct (Reasoning and Acting) agent implementations for answering user queries through iterative reasoning and search actions. """ +from .agentic_retrieve_op import AgenticRetrieveOp from .simple_react_op import SimpleReactOp __all__ = [ + "AgenticRetrieveOp", "SimpleReactOp", ] diff --git a/reme_ai/agent/react/agentic_retrieve_op.py b/reme_ai/agent/react/agentic_retrieve_op.py new file mode 100644 index 00000000..0e792617 --- /dev/null +++ b/reme_ai/agent/react/agentic_retrieve_op.py @@ -0,0 +1,113 @@ +"""Async React agent operator tailored for retrieval workflows.""" + +from typing import Dict, List + +from flowllm.core.context import C +from flowllm.core.op import BaseAsyncToolOp +from flowllm.core.schema import ToolCall, Message +from flowllm.gallery.agent import ReactAgentOp + + +@C.register_op() +class AgenticRetrieveOp(ReactAgentOp): + """React agent that exposes RAG-friendly tools and context policies.""" + + file_path: str = __file__ + + def __init__( + self, + llm: str = "qwen3_30b_instruct", + max_steps: int = 5, + add_think_tool: bool = False, + **kwargs, + ): + super().__init__(llm=llm, max_steps=max_steps, add_think_tool=add_think_tool, **kwargs) + + def build_tool_call(self) -> ToolCall: + """Expose metadata describing how to invoke the agent.""" + return ToolCall( + **{ + "description": "A React agent that answers user queries.", + "input_schema": { + "messages": { + "type": "array", + "description": "messages", + "required": True, + }, + "context_manage_mode": { + "type": "string", + "description": "Context management mode: 'compact' (only compacts tool messages), 'compress' " + "(only LLM-based compression), 'auto' (compaction first then compression if " + "needed). Defaults to 'auto'.", + "required": True, + "enum": ["compact", "compress", "auto"], + }, + "max_total_tokens": { + "type": "integer", + "description": "Maximum token threshold for triggering compression/compaction. For compaction " + "this is total tokens; for compression this excludes keep_recent_count and " + "system messages. Defaults to 20000.", + "required": False, + }, + "max_tool_message_tokens": { + "type": "integer", + "description": "Maximum token count per tool message before compaction applies. Exceeding " + "messages store full content externally with a preview in context. Defaults " + "to 2000.", + "required": False, + }, + "group_token_threshold": { + "type": "integer", + "description": "Maximum tokens per compression group for LLM-based compression. None/0 " + "compresses all messages together. Oversized messages form their own group. " + "Used in 'compress' or 'auto' mode.", + "required": False, + }, + "keep_recent_count": { + "type": "integer", + "description": "Number of recent messages preserved without compression/compaction. Defaults " + "to 1 for compaction and 2 for compression.", + "required": False, + }, + "store_dir": { + "type": "string", + "description": "Directory for storing offloaded contents. Required for compaction/compression " + "to save full tool messages and compressed groups.", + "required": False, + }, + "chat_id": { + "type": "string", + "description": "Chat session identifier for naming stored files. Defaults to auto-generated " + "UUID if omitted.", + "required": False, + }, + }, + }, + ) + + def build_tool_op_dict(self) -> dict: + """Collect available tool operators from the execution context.""" + from reme_ai.context.file_tool import GrepOp, ReadFileOp + + grep_op = GrepOp(language=self.language) + read_file_op = ReadFileOp(language=self.language) + tool_op_dict: Dict[str, BaseAsyncToolOp] = { + grep_op.tool_call.name: grep_op, + read_file_op.tool_call.name: read_file_op, + } + + return tool_op_dict + + def build_messages(self) -> List[Message]: + """Build the initial message history for the LLM.""" + return self.context.messages + + async def before_chat(self, messages: List[Message]): + """Run context offload to trim prior messages before invoking the agent.""" + from reme_ai.context.offload import ContextOffloadOp + + op = ContextOffloadOp() + await op.async_call(**self.input_dict) + messages = op.context.response.answer + messages = [Message(**x) for x in messages] + return messages diff --git a/reme_ai/agent/react/agentic_retrieve_prompt.yaml b/reme_ai/agent/react/agentic_retrieve_prompt.yaml new file mode 100644 index 00000000..84e65a6c --- /dev/null +++ b/reme_ai/agent/react/agentic_retrieve_prompt.yaml @@ -0,0 +1,5 @@ +system_prompt: | + You are a helpful assistant. The current time is {time}. + +system_prompt_zh: | + 你是一个有用的助手。当前时间是 {time}。 diff --git a/reme_ai/agent/react/simple_react_op.py b/reme_ai/agent/react/simple_react_op.py index ae1157ce..8c04e87d 100644 --- a/reme_ai/agent/react/simple_react_op.py +++ b/reme_ai/agent/react/simple_react_op.py @@ -8,7 +8,7 @@ reasoning and search actions. import asyncio from flowllm.core.context import C, FlowContext -from flowllm.gallery import ReactSearchOp +from flowllm.gallery.agent import ReactSearchOp @C.register_op() diff --git a/reme_ai/config/default.yaml b/reme_ai/config/default.yaml index 4c8fdc81..52330320 100644 --- a/reme_ai/config/default.yaml +++ b/reme_ai/config/default.yaml @@ -156,6 +156,9 @@ flow: description: "user query" required: true + agentic_retrieve: + flow_content: AgenticRetrieveOp() + context_offload: flow_content: ContextOffloadOp() >> BatchWriteFileOp() description: "Manages context window limits by compacting tool messages and compressing conversation history. First compacts large tool messages by storing full content in external files, then applies LLM-based compression if compaction ratio exceeds threshold. This helps reduce token usage while preserving important information." @@ -205,7 +208,7 @@ flow: context_manage_mode: type: string description: "Context management mode: 'compact' only applies compaction to tool messages, 'compress' only applies LLM-based compression, 'auto' applies compaction first then compression if compaction ratio exceeds threshold. Defaults to 'auto'." - required: false + required: true enum: ["compact", "compress", "auto"] max_total_tokens: type: integer diff --git a/reme_ai/context/file_tool/grep_prompt.yaml b/reme_ai/context/file_tool/grep_prompt.yaml index 0c9a256b..0f49feac 100644 --- a/reme_ai/context/file_tool/grep_prompt.yaml +++ b/reme_ai/context/file_tool/grep_prompt.yaml @@ -1,15 +1,25 @@ tool_desc: | + 一个支持完整正则语法（如 "log.*Error"、"function\\s+\\w+"）的高效搜索工具，可用 glob 过滤文件并限制返回结果，适合跨文件快速定位代码或文本内容。 +tool_desc_zh: | A powerful search tool for finding patterns in files using regular expressions. Supports full regex syntax (e.g., "log.*Error", "function\\s+\\w+"), glob pattern filtering, and result limiting. Ideal for searching code or text content across multiple files. pattern: | + 需要在文件内容中匹配的正则表达式模式。 +pattern_zh: | The regular expression pattern to search for in file contents. path: | + 可选：要执行搜索的目录，默认为当前工作目录。 +path_zh: | Optional: The directory to search in. Defaults to current working directory. glob: | + 可选：用于过滤目标文件的 glob 模式（如 "*.js"、"*.{ts,tsx}"）。 +glob_zh: | Optional: Glob pattern to filter files (e.g., "*.js", "*.{ts,tsx}"). limit: | + 可选：设置最多返回的匹配行数；未指定时会返回所有匹配。 +limit_zh: | Optional: Maximum number of matching lines to return. Shows all matches if not specified. diff --git a/reme_ai/context/file_tool/read_file_prompt.yaml b/reme_ai/context/file_tool/read_file_prompt.yaml index 52f6a466..eb083182 100644 --- a/reme_ai/context/file_tool/read_file_prompt.yaml +++ b/reme_ai/context/file_tool/read_file_prompt.yaml @@ -1,12 +1,20 @@ tool_desc: | + 读取指定文件内容的工具；对文本文件可通过 offset 与 limit 获取特定行区间，便于分页浏览大文件。 +tool_desc_zh: | Reads and returns the content of a specified file. For text files, it can read specific line ranges using the 'offset' and 'limit' parameters. Use offset and limit to paginate through large files. absolute_path: | + 必填：待读取文件的绝对路径（如 "/home/user/project/file.txt"），不支持相对路径。 +absolute_path_zh: | The absolute path to the file to read (e.g., '/home/user/project/file.txt'). Relative paths are not supported. You must provide an absolute path. offset: | + 可选：文本文件起始读取的 0 基行号；需与 limit 同时使用，适合分页查看大文件。 +offset_zh: | Optional: For text files, the 0-based line number to start reading from. Requires 'limit' to be set. Use for paginating through large files. limit: | + 可选：文本文件最多读取的行数；与 offset 配合实现分页，若仅设 offset 则会读到文件末尾。 +limit_zh: | Optional: For text files, maximum number of lines to read. Use with 'offset' to paginate through large files. If omitted when offset is provided, reads from offset to the end of the file. diff --git a/reme_ai/context/offload/context_compact_op.py b/reme_ai/context/offload/context_compact_op.py index 7bd878a1..6e4031b8 100644 --- a/reme_ai/context/offload/context_compact_op.py +++ b/reme_ai/context/offload/context_compact_op.py @@ -48,11 +48,13 @@ class ContextCompactOp(BaseAsyncOp): assert max_total_tokens > 0, "max_total_tokens must be greater than 0" assert max_tool_message_tokens > 0, "max_tool_message_tokens must be greater than 0" assert preview_char_length >= 0, "preview_char_length must be greater than 0" - assert keep_recent_count > 0, "keep_recent_count must be greater than 0" + assert keep_recent_count >= 0, "keep_recent_count must be greater than 0" # Convert context messages to Message objects messages = [Message(**x) for x in self.context.messages] - messages_to_compress = [x for x in messages if x.role is not Role.SYSTEM][:-keep_recent_count] + messages_to_compress = [x for x in messages if x.role is not Role.SYSTEM] + if keep_recent_count > 0: + messages_to_compress = messages_to_compress[:-keep_recent_count] # If nothing to compress after filtering, return original messages if not messages_to_compress: diff --git a/reme_ai/context/offload/context_compress_op.py b/reme_ai/context/offload/context_compress_op.py index 218f6e21..7713ee18 100644 --- a/reme_ai/context/offload/context_compress_op.py +++ b/reme_ai/context/offload/context_compress_op.py @@ -265,8 +265,12 @@ class ContextCompressOp(BaseAsyncOp): system_message = system_message[0] messages_without_system = [x for x in messages if x.role is not Role.SYSTEM] - messages_to_compress = messages_without_system[:-keep_recent_count] - recent_messages = messages_without_system[-keep_recent_count:] + if keep_recent_count > 0: + messages_to_compress = messages_without_system[:-keep_recent_count] + recent_messages = messages_without_system[-keep_recent_count:] + else: + messages_to_compress = messages_without_system + recent_messages = [] # If nothing to compress after filtering, return original messages if not messages_to_compress: diff --git a/test/http_client_test.py b/test/http_client_test.py index 326f2cf9..acd1254c 100644 --- a/test/http_client_test.py +++ b/test/http_client_test.py @@ -129,6 +129,26 @@ async def run3(session): print(json.dumps(result, ensure_ascii=False)) +async def run4(session): + workspace_id = "default4" + + async with session.post( + f"{base_url}/agentic_retrieve", + json={ + "messages": [ + {"role": "user", "content": "hello" * 10000}, + ], + "workspace_id": workspace_id, + "context_manage_mode": "auto", + "keep_recent_count": 0, + "max_total_tokens": 10000, + }, + headers={"Content-Type": "application/json"}, + ) as response: + result = await response.json() + print(json.dumps(result, ensure_ascii=False)) + + async def main(): async with aiohttp.ClientSession() as session: @@ -137,7 +157,8 @@ async def main(): # await run1(session) # await run2(session) - await run3(session) + # await run3(session) + await run4(session) if __name__ == "__main__": From 396f9c7e6bdfa83ca9ac2fdc113ff071e7fe5479 Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Thu, 20 Nov 2025 17:40:28 +0800 Subject: [PATCH 18/39] refactor(agent): convert build methods to async --- reme_ai/agent/react/agentic_retrieve_op.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/reme_ai/agent/react/agentic_retrieve_op.py b/reme_ai/agent/react/agentic_retrieve_op.py index 0e792617..92883642 100644 --- a/reme_ai/agent/react/agentic_retrieve_op.py +++ b/reme_ai/agent/react/agentic_retrieve_op.py @@ -85,7 +85,7 @@ class AgenticRetrieveOp(ReactAgentOp): }, ) - def build_tool_op_dict(self) -> dict: + async def build_tool_op_dict(self) -> dict: """Collect available tool operators from the execution context.""" from reme_ai.context.file_tool import GrepOp, ReadFileOp @@ -98,7 +98,7 @@ class AgenticRetrieveOp(ReactAgentOp): return tool_op_dict - def build_messages(self) -> List[Message]: + async def build_messages(self) -> List[Message]: """Build the initial message history for the LLM.""" return self.context.messages From 660eed93bb882db66d5d07dd12e948fc4e3cdc28 Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Fri, 21 Nov 2025 10:37:59 +0800 Subject: [PATCH 19/39] feat(file_tool): add save_answer option to BatchWriteFileOp --- reme_ai/config/default.yaml | 1 - reme_ai/context/file_tool/batch_write_file_op.py | 6 +++++- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/reme_ai/config/default.yaml b/reme_ai/config/default.yaml index 52330320..f17449ac 100644 --- a/reme_ai/config/default.yaml +++ b/reme_ai/config/default.yaml @@ -243,7 +243,6 @@ llm: params: temperature: 0.6 token_count: # Optional - model_name: Qwen/Qwen3-30B-A3B-Instruct-2507 backend: base qwen3_30b_instruct: diff --git a/reme_ai/context/file_tool/batch_write_file_op.py b/reme_ai/context/file_tool/batch_write_file_op.py index ae2b2a5c..149a111e 100644 --- a/reme_ai/context/file_tool/batch_write_file_op.py +++ b/reme_ai/context/file_tool/batch_write_file_op.py @@ -20,6 +20,10 @@ class BatchWriteFileOp(BaseAsyncOp): WriteFileOp. Returns a combined result of all write operations. """ + def __init__(self, save_answer: bool = False, **kwargs): + super().__init__(**kwargs) + self.save_answer: bool = save_answer + async def async_execute(self): """Execute the batch write file operation. @@ -37,7 +41,7 @@ class BatchWriteFileOp(BaseAsyncOp): # Process each file in the dictionary result = [] for file_path, content in write_file_dict.items(): - write_op = WriteFileOp() + write_op = WriteFileOp(save_answer=self.save_answer) await write_op.async_call(file_path=file_path, content=content) result.append(write_op.output) From b5b3e61ced4b0357fd70ad522f58ea38443ebbec Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Fri, 21 Nov 2025 10:49:33 +0800 Subject: [PATCH 20/39] fix(file_tool): remove redundant result combination logic --- reme_ai/context/file_tool/batch_write_file_op.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/reme_ai/context/file_tool/batch_write_file_op.py b/reme_ai/context/file_tool/batch_write_file_op.py index 149a111e..535b05cb 100644 --- a/reme_ai/context/file_tool/batch_write_file_op.py +++ b/reme_ai/context/file_tool/batch_write_file_op.py @@ -44,6 +44,3 @@ class BatchWriteFileOp(BaseAsyncOp): write_op = WriteFileOp(save_answer=self.save_answer) await write_op.async_call(file_path=file_path, content=content) result.append(write_op.output) - - # Combine all results into a single response - self.context.response.answer = "\n".join(result) From dc1ebec1745c9cd30114bab71ae0c7dcc6d1362b Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Fri, 21 Nov 2025 14:59:12 +0800 Subject: [PATCH 21/39] feat(agent): integrate batch file writing with context offload --- reme_ai/agent/react/agentic_retrieve_op.py | 3 ++- reme_ai/main.py | 1 - 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/reme_ai/agent/react/agentic_retrieve_op.py b/reme_ai/agent/react/agentic_retrieve_op.py index 92883642..ee29bd1a 100644 --- a/reme_ai/agent/react/agentic_retrieve_op.py +++ b/reme_ai/agent/react/agentic_retrieve_op.py @@ -105,8 +105,9 @@ class AgenticRetrieveOp(ReactAgentOp): async def before_chat(self, messages: List[Message]): """Run context offload to trim prior messages before invoking the agent.""" from reme_ai.context.offload import ContextOffloadOp + from reme_ai.context.file_tool import BatchWriteFileOp - op = ContextOffloadOp() + op = ContextOffloadOp() >> BatchWriteFileOp() await op.async_call(**self.input_dict) messages = op.context.response.answer messages = [Message(**x) for x in messages] diff --git a/reme_ai/main.py b/reme_ai/main.py index 8de69055..fea9e6c0 100644 --- a/reme_ai/main.py +++ b/reme_ai/main.py @@ -6,7 +6,6 @@ which extends FlowLLM with specialized memory management capabilities including: - Task Memory: Store and retrieve task execution histories - Tool Memory: Track tool usage patterns and experiences - Personal Memory: Manage user preferences and personal information -- SOP Memory: Standard Operating Procedures for recurring workflows """ import asyncio From 4e5ba45eaf67f93fdfbee4acd50fc4f3c8de3f0c Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Fri, 21 Nov 2025 15:09:56 +0800 Subject: [PATCH 22/39] feat(agent): implement asynchronous tool execution for React agent --- reme_ai/agent/react/agentic_retrieve_op.py | 53 +++++++++++++++++++++- 1 file changed, 51 insertions(+), 2 deletions(-) diff --git a/reme_ai/agent/react/agentic_retrieve_op.py b/reme_ai/agent/react/agentic_retrieve_op.py index ee29bd1a..5d8d0686 100644 --- a/reme_ai/agent/react/agentic_retrieve_op.py +++ b/reme_ai/agent/react/agentic_retrieve_op.py @@ -3,9 +3,11 @@ from typing import Dict, List from flowllm.core.context import C +from flowllm.core.enumeration import Role from flowllm.core.op import BaseAsyncToolOp from flowllm.core.schema import ToolCall, Message from flowllm.gallery.agent import ReactAgentOp +from loguru import logger @C.register_op() @@ -18,10 +20,11 @@ class AgenticRetrieveOp(ReactAgentOp): self, llm: str = "qwen3_30b_instruct", max_steps: int = 5, - add_think_tool: bool = False, **kwargs, ): - super().__init__(llm=llm, max_steps=max_steps, add_think_tool=add_think_tool, **kwargs) + """Initialize the agent runtime configuration.""" + super().__init__(llm=llm, **kwargs) + self.max_steps: int = max_steps def build_tool_call(self) -> ToolCall: """Expose metadata describing how to invoke the agent.""" @@ -112,3 +115,49 @@ class AgenticRetrieveOp(ReactAgentOp): messages = op.context.response.answer messages = [Message(**x) for x in messages] return messages + + async def execute_tool(self, op: BaseAsyncToolOp, tool_call: ToolCall): + """Execute a tool operation asynchronously using the provided tool call arguments.""" + self.submit_async_task(op.async_call, **tool_call.argument_dict) + + async def async_execute(self): + """Main execution loop that alternates LLM calls and tool invocations.""" + tool_op_dict = await self.build_tool_op_dict() + messages = await self.build_messages() + + for i in range(self.max_steps): + messages = await self.before_chat(messages) + + assistant_message: Message = await self.llm.achat( + messages=messages, + tools=[op.tool_call for op in tool_op_dict.values()], + ) + messages.append(assistant_message) + logger.info(f"round{i + 1}.assistant={assistant_message.model_dump_json()}") + + if not assistant_message.tool_calls: + break + + op_list: List[BaseAsyncToolOp] = [] + for j, tool_call in enumerate(assistant_message.tool_calls): + if tool_call.name not in tool_op_dict: + logger.exception(f"unknown tool_call.name={tool_call.name}") + continue + + logger.info(f"round{i + 1}.{j} submit tool_calls={tool_call.name} argument={tool_call.argument_dict}") + + op_copy: BaseAsyncToolOp = tool_op_dict[tool_call.name].copy() + op_copy.tool_call.id = tool_call.id + op_list.append(op_copy) + await self.execute_tool(op_copy, tool_call) + + await self.join_async_task() + + for j, op in enumerate(op_list): + tool_result = str(op.output) + tool_message = Message(role=Role.TOOL, content=tool_result, tool_call_id=op.tool_call.id) + messages.append(tool_message) + logger.info(f"round{i + 1}.{j} join tool_result={tool_result[:200]}...\n\n") + + self.set_output(messages[-1].content) + self.context.response.messages = messages From de4c57497df2f93c89336e6006d306163843fc5f Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Fri, 21 Nov 2025 15:10:40 +0800 Subject: [PATCH 23/39] refactor(agent): replace ReactAgentOp with BaseAsyncToolOp --- reme_ai/agent/react/agentic_retrieve_op.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/reme_ai/agent/react/agentic_retrieve_op.py b/reme_ai/agent/react/agentic_retrieve_op.py index 5d8d0686..9b961f48 100644 --- a/reme_ai/agent/react/agentic_retrieve_op.py +++ b/reme_ai/agent/react/agentic_retrieve_op.py @@ -6,12 +6,11 @@ from flowllm.core.context import C from flowllm.core.enumeration import Role from flowllm.core.op import BaseAsyncToolOp from flowllm.core.schema import ToolCall, Message -from flowllm.gallery.agent import ReactAgentOp from loguru import logger @C.register_op() -class AgenticRetrieveOp(ReactAgentOp): +class AgenticRetrieveOp(BaseAsyncToolOp): """React agent that exposes RAG-friendly tools and context policies.""" file_path: str = __file__ From f10b5228d6c28fac51072af9de8a01d9b73ca298 Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Fri, 21 Nov 2025 15:33:23 +0800 Subject: [PATCH 24/39] refactor(agent): restructure agentic retrieve operation execution flow --- reme_ai/agent/react/agentic_retrieve_op.py | 40 ++++++---------------- 1 file changed, 11 insertions(+), 29 deletions(-) diff --git a/reme_ai/agent/react/agentic_retrieve_op.py b/reme_ai/agent/react/agentic_retrieve_op.py index 9b961f48..2e80031c 100644 --- a/reme_ai/agent/react/agentic_retrieve_op.py +++ b/reme_ai/agent/react/agentic_retrieve_op.py @@ -87,9 +87,11 @@ class AgenticRetrieveOp(BaseAsyncToolOp): }, ) - async def build_tool_op_dict(self) -> dict: - """Collect available tool operators from the execution context.""" + async def async_execute(self): + """Main execution loop that alternates LLM calls and tool invocations.""" from reme_ai.context.file_tool import GrepOp, ReadFileOp + from reme_ai.context.offload import ContextOffloadOp + from reme_ai.context.file_tool import BatchWriteFileOp grep_op = GrepOp(language=self.language) read_file_op = ReadFileOp(language=self.language) @@ -98,34 +100,14 @@ class AgenticRetrieveOp(BaseAsyncToolOp): read_file_op.tool_call.name: read_file_op, } - return tool_op_dict - - async def build_messages(self) -> List[Message]: - """Build the initial message history for the LLM.""" - return self.context.messages - - async def before_chat(self, messages: List[Message]): - """Run context offload to trim prior messages before invoking the agent.""" - from reme_ai.context.offload import ContextOffloadOp - from reme_ai.context.file_tool import BatchWriteFileOp - - op = ContextOffloadOp() >> BatchWriteFileOp() - await op.async_call(**self.input_dict) - messages = op.context.response.answer - messages = [Message(**x) for x in messages] - return messages - - async def execute_tool(self, op: BaseAsyncToolOp, tool_call: ToolCall): - """Execute a tool operation asynchronously using the provided tool call arguments.""" - self.submit_async_task(op.async_call, **tool_call.argument_dict) - - async def async_execute(self): - """Main execution loop that alternates LLM calls and tool invocations.""" - tool_op_dict = await self.build_tool_op_dict() - messages = await self.build_messages() + messages = [Message(**x) for x in self.context.messages] + context_kwargs = self.input_dict.copy() + context_kwargs.pop("messages", None) for i in range(self.max_steps): - messages = await self.before_chat(messages) + op = ContextOffloadOp() >> BatchWriteFileOp() + await op.async_call(messages=[x.simple_dump() for x in messages], **context_kwargs) + messages = [Message(**x) for x in op.context.response.answer] assistant_message: Message = await self.llm.achat( messages=messages, @@ -148,7 +130,7 @@ class AgenticRetrieveOp(BaseAsyncToolOp): op_copy: BaseAsyncToolOp = tool_op_dict[tool_call.name].copy() op_copy.tool_call.id = tool_call.id op_list.append(op_copy) - await self.execute_tool(op_copy, tool_call) + self.submit_async_task(op.async_call, **tool_call.argument_dict) await self.join_async_task() From c586f8633f519cae3d6c150970ca61a5e303f8c9 Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Fri, 21 Nov 2025 15:43:56 +0800 Subject: [PATCH 25/39] feat(agent): implement ReAct agent with context management for RAG workflows --- reme_ai/agent/react/agentic_retrieve_op.py | 142 +++++++++++++++++++-- 1 file changed, 133 insertions(+), 9 deletions(-) diff --git a/reme_ai/agent/react/agentic_retrieve_op.py b/reme_ai/agent/react/agentic_retrieve_op.py index 2e80031c..f356163f 100644 --- a/reme_ai/agent/react/agentic_retrieve_op.py +++ b/reme_ai/agent/react/agentic_retrieve_op.py @@ -1,4 +1,17 @@ -"""Async React agent operator tailored for retrieval workflows.""" +""" +Async React agent operator tailored for retrieval workflows. + +This module implements a ReAct (Reasoning + Acting) agent pattern that combines +language model reasoning with tool execution. The agent iteratively: +1. Processes and manages conversation context (compaction/compression) +2. Generates reasoning and tool calls via LLM +3. Executes tools (e.g., file search, reading) +4. Incorporates tool results back into the conversation +5. Repeats until a final answer is reached or max_steps is exceeded + +The agent is specifically designed for RAG (Retrieval-Augmented Generation) workflows, +providing context management capabilities to handle long conversations efficiently. +""" from typing import Dict, List @@ -11,7 +24,25 @@ from loguru import logger @C.register_op() class AgenticRetrieveOp(BaseAsyncToolOp): - """React agent that exposes RAG-friendly tools and context policies.""" + """ + ReAct agent that exposes RAG-friendly tools and context policies. + + This agent implements the ReAct pattern where the LLM alternates between: + - Reasoning: Analyzing the problem and deciding what to do + - Acting: Executing tools to gather information + - Observing: Processing tool results and updating understanding + + The agent supports sophisticated context management to handle long conversations + through compaction (storing large tool messages externally) and compression + (LLM-based summarization of message history). + + Available tools: + - GrepOp: Search for patterns in files + - ReadFileOp: Read file contents + + The agent automatically manages context size by offloading large messages and + compressing conversation history when token limits are approached. + """ file_path: str = __file__ @@ -21,12 +52,32 @@ class AgenticRetrieveOp(BaseAsyncToolOp): max_steps: int = 5, **kwargs, ): - """Initialize the agent runtime configuration.""" + """ + Initialize the agent runtime configuration. + + Args: + llm: Identifier for the language model to use. Defaults to "qwen3_30b_instruct". + max_steps: Maximum number of reasoning-action cycles before stopping. + Each cycle includes: context management -> LLM reasoning -> tool execution. + Defaults to 5 steps. + **kwargs: Additional arguments passed to the base BaseAsyncToolOp class. + """ super().__init__(llm=llm, **kwargs) + # Maximum number of ReAct iterations (reasoning + tool execution cycles) self.max_steps: int = max_steps def build_tool_call(self) -> ToolCall: - """Expose metadata describing how to invoke the agent.""" + """ + Expose metadata describing how to invoke the agent. + + This method defines the tool schema that other components use to invoke + this agent. It specifies all input parameters including conversation messages + and context management configuration. + + Returns: + ToolCall: A schema object describing the agent's interface, including + all parameters for message handling and context management. + """ return ToolCall( **{ "description": "A React agent that answers user queries.", @@ -88,57 +139,130 @@ class AgenticRetrieveOp(BaseAsyncToolOp): ) async def async_execute(self): - """Main execution loop that alternates LLM calls and tool invocations.""" + """ + Main execution loop implementing the ReAct (Reasoning + Acting) pattern. + + This method orchestrates the iterative agent workflow: + 1. Context Management: Compact/compress conversation history if needed + 2. Reasoning: LLM analyzes context and decides on actions (may include tool calls) + 3. Acting: Execute requested tools in parallel + 4. Observing: Incorporate tool results back into conversation + 5. Repeat until final answer (no tool calls) or max_steps reached + + The loop continues until: + - The LLM produces a response without tool calls (final answer) + - Maximum number of steps (max_steps) is reached + + Each iteration is called a "round" and represents one reasoning-action cycle. + """ + # Import tool operators that the agent can use from reme_ai.context.file_tool import GrepOp, ReadFileOp from reme_ai.context.offload import ContextOffloadOp from reme_ai.context.file_tool import BatchWriteFileOp + # Initialize available tools for the agent + # GrepOp: Search for patterns/text in files (useful for code search) grep_op = GrepOp(language=self.language) + # ReadFileOp: Read and return file contents read_file_op = ReadFileOp(language=self.language) + + # Create a dictionary mapping tool names to their operator instances + # This allows quick lookup when the LLM requests a specific tool tool_op_dict: Dict[str, BaseAsyncToolOp] = { grep_op.tool_call.name: grep_op, read_file_op.tool_call.name: read_file_op, } + # Convert input messages to Message objects for processing messages = [Message(**x) for x in self.context.messages] + + # Extract context management parameters from input, excluding messages + # These will be passed to the context management pipeline context_kwargs = self.input_dict.copy() context_kwargs.pop("messages", None) + # Main ReAct loop: iterate up to max_steps times for i in range(self.max_steps): + # Step 1: Context Management Phase + # Create a pipeline: ContextOffloadOp (compacts/compresses) -> BatchWriteFileOp (saves offloaded content) + # The >> operator chains these operations together op = ContextOffloadOp() >> BatchWriteFileOp() + + # Apply context management to current message history + # This may compact large tool messages or compress old messages based on context_manage_mode await op.async_call(messages=[x.simple_dump() for x in messages], **context_kwargs) + + # Update messages with the processed/optimized version from context management + # Large messages may now reference external files instead of containing full content messages = [Message(**x) for x in op.context.response.answer] + # Step 2: Reasoning Phase + # LLM analyzes the conversation context and decides what to do + # It may generate a final answer or request tool calls to gather more information assistant_message: Message = await self.llm.achat( - messages=messages, - tools=[op.tool_call for op in tool_op_dict.values()], + messages=messages, # Current conversation history (possibly optimized) + tools=[op.tool_call for op in tool_op_dict.values()], # Available tools the LLM can use ) + + # Add the LLM's response to the conversation history messages.append(assistant_message) logger.info(f"round{i + 1}.assistant={assistant_message.model_dump_json()}") + # Step 3: Check if we have a final answer + # If the LLM didn't request any tools, it has provided a final answer + # Exit the loop as the agent's work is complete if not assistant_message.tool_calls: break + # Step 4: Acting Phase - Execute requested tools + # The LLM requested one or more tools to gather information + # We'll execute them in parallel for efficiency op_list: List[BaseAsyncToolOp] = [] + + # Process each tool call requested by the LLM for j, tool_call in enumerate(assistant_message.tool_calls): + # Validate that the requested tool exists in our available tools if tool_call.name not in tool_op_dict: logger.exception(f"unknown tool_call.name={tool_call.name}") continue logger.info(f"round{i + 1}.{j} submit tool_calls={tool_call.name} argument={tool_call.argument_dict}") + # Create a copy of the tool operator for this specific tool call + # Each tool call needs its own operator instance with the correct call ID op_copy: BaseAsyncToolOp = tool_op_dict[tool_call.name].copy() - op_copy.tool_call.id = tool_call.id + op_copy.tool_call.id = tool_call.id # Match the ID from LLM's tool call request op_list.append(op_copy) - self.submit_async_task(op.async_call, **tool_call.argument_dict) + # Submit the tool execution as an async task (runs in parallel with other tools) + # The op_copy instance is used to execute the tool with the specific call ID and arguments + self.submit_async_task(op_copy.async_call, **tool_call.argument_dict) + + # Wait for all submitted tool executions to complete + # This ensures we have all results before proceeding await self.join_async_task() + # Step 5: Observing Phase - Incorporate tool results into conversation + # Process each completed tool execution and add results to message history for j, op in enumerate(op_list): + # Extract the tool execution result as a string tool_result = str(op.output) + + # Create a tool message with the result, linked to the original tool call via ID + # This allows the LLM to associate results with the specific tool calls it made tool_message = Message(role=Role.TOOL, content=tool_result, tool_call_id=op.tool_call.id) messages.append(tool_message) + + # Log the tool result (truncated to first 200 chars for readability) logger.info(f"round{i + 1}.{j} join tool_result={tool_result[:200]}...\n\n") + # Loop continues: next iteration will process these tool results, + # manage context again if needed, and let LLM reason about the new information + + # After loop completes, set the final output + # The last message should be the LLM's final answer (without tool calls) self.set_output(messages[-1].content) + + # Store the complete conversation history in the context response + # This includes all reasoning steps, tool calls, and tool results self.context.response.messages = messages From f478e33b2c032a6a4e9a91fce088b6a23e67455b Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Fri, 21 Nov 2025 15:47:29 +0800 Subject: [PATCH 26/39] fix(agent): update context response storage to use answer field --- reme_ai/agent/react/agentic_retrieve_op.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/reme_ai/agent/react/agentic_retrieve_op.py b/reme_ai/agent/react/agentic_retrieve_op.py index f356163f..9e59d3ad 100644 --- a/reme_ai/agent/react/agentic_retrieve_op.py +++ b/reme_ai/agent/react/agentic_retrieve_op.py @@ -265,4 +265,4 @@ class AgenticRetrieveOp(BaseAsyncToolOp): # Store the complete conversation history in the context response # This includes all reasoning steps, tool calls, and tool results - self.context.response.messages = messages + self.context.response.answer = messages From e5b1ee152a772d997fd5e65eaa139e03dadafe45 Mon Sep 17 00:00:00 2001 From: dongxiaolong Date: Mon, 24 Nov 2025 11:55:54 +0800 Subject: [PATCH 27/39] fix: use attribute access for VectorNode in DeleteMemoryOp --- reme_ai/vector_store/delete_memory_op.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/reme_ai/vector_store/delete_memory_op.py b/reme_ai/vector_store/delete_memory_op.py index 090760be..68c26795 100644 --- a/reme_ai/vector_store/delete_memory_op.py +++ b/reme_ai/vector_store/delete_memory_op.py @@ -48,10 +48,11 @@ class DeleteMemoryOp(BaseAsyncOp): deleted_memory_ids = [] for node in nodes: - freq = node["metadata"]["metadata"]["freq"] - utility = node["metadata"]["metadata"]["utility"] + # VectorNode 对象需要使用属性访问，不是字典访问 + freq = node.metadata.get("freq", 0) + utility = node.metadata.get("utility", 0) if freq >= freq_threshold: if utility * 1.0 / freq < utility_threshold: - deleted_memory_ids.append(node["unique_id"]) + deleted_memory_ids.append(node.unique_id) self.context.deleted_memory_ids = deleted_memory_ids From b421515c73cb12838c5d14b7b2553c615d5b15c5 Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Mon, 24 Nov 2025 21:17:02 +0800 Subject: [PATCH 28/39] refactor(core): restructure context management and working memory modules --- .gitignore | 4 +- docs/vector_store_api_guide.md | 469 ++++++++---------- reme_ai/__init__.py | 2 - reme_ai/agent/react/agentic_retrieve_op.py | 50 +- reme_ai/config/default.yaml | 59 ++- reme_ai/context/__init__.py | 14 - reme_ai/context/file_tool/grep_op.py | 52 -- reme_ai/context/file_tool/read_file_op.py | 46 -- .../context/file_tool/read_file_prompt.yaml | 20 - reme_ai/context/offload/__init__.py | 19 - reme_ai/context/offload/context_offload_op.py | 107 ---- reme_ai/enumeration/__init__.py | 4 +- reme_ai/enumeration/context_manage_enum.py | 21 - reme_ai/enumeration/working_summary_mode.py | 22 + reme_ai/main.py | 6 - .../working}/__init__.py | 6 +- .../working}/batch_write_file_op.py | 6 +- reme_ai/retrieve/working/grep_op.py | 114 +++++ .../working}/grep_prompt.yaml | 15 +- reme_ai/retrieve/working/read_file_op.py | 120 +++++ .../retrieve/working/read_file_prompt.yaml | 19 + reme_ai/retrieve/working/write_file_op.py | 89 ++++ .../retrieve/working/write_file_prompt.yaml | 9 + reme_ai/summary/working/__init__.py | 23 + .../working/message_compact_op.py} | 45 +- .../working/message_compress_op.py} | 31 +- .../working/message_compress_prompt.yaml} | 0 reme_ai/summary/working/message_offload_op.py | 112 +++++ test_op/test_agentic_retrieve_op.py | 112 +++++ ...mpact_op.py => test_message_compact_op.py} | 12 +- ...ress_op.py => test_message_compress_op.py} | 16 +- ...fload_op.py => test_message_offload_op.py} | 36 +- 32 files changed, 998 insertions(+), 662 deletions(-) delete mode 100644 reme_ai/context/__init__.py delete mode 100644 reme_ai/context/file_tool/grep_op.py delete mode 100644 reme_ai/context/file_tool/read_file_op.py delete mode 100644 reme_ai/context/file_tool/read_file_prompt.yaml delete mode 100644 reme_ai/context/offload/__init__.py delete mode 100644 reme_ai/context/offload/context_offload_op.py delete mode 100644 reme_ai/enumeration/context_manage_enum.py create mode 100644 reme_ai/enumeration/working_summary_mode.py rename reme_ai/{context/file_tool => retrieve/working}/__init__.py (63%) rename reme_ai/{context/file_tool => retrieve/working}/batch_write_file_op.py (89%) create mode 100644 reme_ai/retrieve/working/grep_op.py rename reme_ai/{context/file_tool => retrieve/working}/grep_prompt.yaml (70%) create mode 100644 reme_ai/retrieve/working/read_file_op.py create mode 100644 reme_ai/retrieve/working/read_file_prompt.yaml create mode 100644 reme_ai/retrieve/working/write_file_op.py create mode 100644 reme_ai/retrieve/working/write_file_prompt.yaml create mode 100644 reme_ai/summary/working/__init__.py rename reme_ai/{context/offload/context_compact_op.py => summary/working/message_compact_op.py} (78%) rename reme_ai/{context/offload/context_compress_op.py => summary/working/message_compress_op.py} (93%) rename reme_ai/{context/offload/context_compress_prompt.yaml => summary/working/message_compress_prompt.yaml} (100%) create mode 100644 reme_ai/summary/working/message_offload_op.py create mode 100644 test_op/test_agentic_retrieve_op.py rename test_op/{test_context_compact_op.py => test_message_compact_op.py} (88%) rename test_op/{test_context_compress_op.py => test_message_compress_op.py} (97%) rename test_op/{test_context_offload_op.py => test_message_offload_op.py} (89%) diff --git a/.gitignore b/.gitignore index 4f720a02..b3537023 100644 --- a/.gitignore +++ b/.gitignore @@ -30,4 +30,6 @@ cookbook/appworld/file_vector_store/* /.venv/ site/* docs/_build/* -test_compact_storage/* \ No newline at end of file +test_compact_storage/* +test_working_memory/* +*.code-workspace \ No newline at end of file diff --git a/docs/vector_store_api_guide.md b/docs/vector_store_api_guide.md index e522aaf6..a431c443 100644 --- a/docs/vector_store_api_guide.md +++ b/docs/vector_store_api_guide.md @@ -12,68 +12,128 @@ kernelspec: name: python3 --- -# Vector Store Configuration Guide +### Vector Store User Guide -This guide covers how to configure vector store backends in ReMe using the `default.yaml` configuration file. +Vector Store is a component designed for storing, managing, and retrieving vector embeddings. It supports features such as workspace management, similarity search, and metadata filtering. -## 📋 Overview +## Core Concepts -ReMe provides multiple vector store backends for different use cases: +**Workspace**: Each workspace is an independent vector storage unit used to organize and manage related vector nodes. -- **LocalVectorStore** (`backend=local`) - 📁 Simple file-based storage for development and small datasets -- **ChromaVectorStore** (`backend=chroma`) - 🔮 Embedded vector database for moderate scale -- **EsVectorStore** (`backend=elasticsearch`) - 🔍 Elasticsearch-based storage for production and large scale -- **QdrantVectorStore** (`backend=qdrant`) - 🎯 High-performance vector database with advanced filtering -- **MemoryVectorStore** (`backend=memory`) - ⚡ In-memory storage for ultra-fast access and testing +**VectorNode**: A data unit containing text content, vector embedding, and metadata. It serves as the fundamental unit for storage and retrieval. -All vector stores implement the `BaseVectorStore` interface, providing a consistent API across implementations. +**Embedding Model**: Used to convert text into vector embeddings. It supports automatic generation of both node vectors and query vectors. -## 📊 Comparison Table +## Available Implementations -| Feature | LocalVectorStore | ChromaVectorStore | EsVectorStore | QdrantVectorStore | MemoryVectorStore | -|----------------------|------------------|-------------------|---------------|-------------------|-------------------| -| **Storage** | File (JSONL) | Embedded DB | Elasticsearch | Qdrant Server | In-Memory | -| **Performance** | Medium | Good | Excellent | Excellent | Ultra-Fast | -| **Scalability** | < 10K vectors | < 1M vectors | > 1M vectors | > 10M vectors | < 1M vectors | -| **Persistence** | ✅ Auto | ✅ Auto | ✅ Auto | ✅ Auto | ⚠️ Manual | -| **Setup Complexity** | 🟢 Simple | 🟡 Medium | 🔴 Complex | 🟡 Medium | 🟢 Simple | -| **Dependencies** | None | ChromaDB | Elasticsearch | Qdrant | None | -| **Filtering** | ❌ Basic | ✅ Metadata | ✅ Advanced | ✅ Advanced | ❌ Basic | -| **Concurrency** | ❌ Limited | ✅ Good | ✅ Excellent | ✅ Excellent | ❌ Single Process | -| **Async Support** | ❌ No | ❌ No | ❌ No | ✅ Native | ❌ No | -| **Best For** | Development | Local Apps | Production | Production/Cloud | Testing | +FlowLLM provides multiple Vector Store implementations tailored to different use cases: -## ⚙️ Configuration in default.yaml +- **LocalVectorStore** ([source code](https://github.com/flowllm-ai/flowllm/blob/main/flowllm/core/vector_store/local_vector_store.py)): A file-based local implementation that persists data in JSONL format. Suitable for single-machine deployments and small-scale datasets. +- **MemoryVectorStore** ([source code](https://github.com/flowllm-ai/flowllm/blob/main/flowllm/core/vector_store/memory_vector_store.py)): An in-memory implementation offering fast access speeds. Ideal for temporary data or testing scenarios. +- **QdrantVectorStore** ([source code](https://github.com/flowllm-ai/flowllm/blob/main/flowllm/core/vector_store/qdrant_vector_store.py)): Built on the Qdrant vector database, supporting high-performance vector search. Recommended for large-scale production environments. +- **ChromaVectorStore** ([source code](https://github.com/flowllm-ai/flowllm/blob/main/flowllm/core/vector_store/chroma_vector_store.py)): Based on ChromaDB, providing persistent storage and metadata filtering capabilities. +- **EsVectorStore** ([source code](https://github.com/flowllm-ai/flowllm/blob/main/flowllm/core/vector_store/es_vector_store.py)): Built on Elasticsearch, enabling powerful combined full-text and vector search functionalities. -All vector stores are configured in the `vector_store` section of `reme_ai/config/default.yaml`. The configuration structure is: +All Vector Store implementations inherit from **BaseVectorStore** ([source code](https://github.com/flowllm-ai/flowllm/blob/main/flowllm/core/vector_store/base_vector_store.py)), ensuring a consistent interface specification. + +## Core Features + +### Workspace Management + +- **Create Workspace**: Create a new workspace for storing vector nodes. +- **Delete Workspace**: Remove a workspace along with all its data. +- **Check Workspace Existence**: Verify whether a specified workspace exists. +- **List Workspaces**: Retrieve a list of all existing workspaces. +- **Copy Workspace**: Duplicate data from one workspace to another. + +### Node Operations + +- **Insert Nodes**: Insert vector nodes into a workspace, supporting single or batch insertion with automatic vector embedding generation. +- **Delete Nodes**: Remove specific nodes by their IDs. +- **Iterate Nodes**: Traverse all nodes within a workspace. + +### Vector Search + +- **Similarity Search**: Perform vector similarity searches based on text queries, returning the top-K most similar results. +- **Metadata Filtering**: Apply filtering conditions based on metadata, including exact matches and range queries. +- **Similarity Scores**: Search results include similarity scores to evaluate match quality. + +### Data Import/Export + +- **Export Workspace**: Export workspace data to a file or specified path. +- **Import Workspace**: Import data into a workspace from a file or a list of nodes. +- **Callback Functions**: Support callback functions during import/export for data transformation. + +## Synchronous and Asynchronous Interfaces + +All Vector Store implementations provide both synchronous and asynchronous interfaces: + +- **Synchronous Interface**: Direct method calls suitable for synchronous code environments. +- **Asynchronous Interface**: Prefixed with `async_`, designed for asynchronous environments and offering better concurrency performance. + +The asynchronous interface is particularly useful in the following scenarios: +- Using asynchronous embedding models for vector generation. +- Performing batch operations in high-concurrency environments. +- Integrating with other asynchronous components. + +## Configuration Options + +### General Configuration + +- **embedding_model**: Instance of the embedding model used to generate vector embeddings. +- **batch_size**: Batch size for bulk operations (default: 1024). + +### LocalVectorStore Configuration + +- **store_dir**: Storage directory path (default: `./local_vector_store`). + +### MemoryVectorStore Configuration + +- **store_dir**: Persistence directory (default: `./memory_vector_store`). + +### QdrantVectorStore Configuration + +- **url**: Qdrant service URL (optional; used for Qdrant Cloud or custom deployments). +- **host**: Qdrant server host (default: `localhost`). +- **port**: Qdrant server port (default: `6333`). +- **api_key**: API key for Qdrant Cloud authentication. +- **distance**: Distance metric—supports COSINE, EUCLIDEAN, DOT (default: COSINE). + +### ChromaVectorStore Configuration + +- **store_dir**: ChromaDB data storage directory (default: `./chroma_vector_store`). + +### EsVectorStore Configuration + +- **hosts**: Elasticsearch host address(es), either a string or a list (default: `http://localhost:9200`). +- **basic_auth**: Basic authentication credentials (username and password). + +## Configuration File Examples + +Configure Vector Store in `flowllm/config/default.yaml` under the `vector_store` section. The basic structure is as follows: ```yaml vector_store: default: - backend: # Required: local, chroma, elasticsearch, qdrant, or memory - embedding_model: default # Required: Name of the embedding model configuration - params: # Optional: Backend-specific parameters - # Backend-specific parameters go here + backend: # Required: vector store backend type + embedding_model: default # Required: name of embedding model config + params: # Optional: backend-specific parameters + # Backend-specific parameters ``` -### Configuration Fields +### Configuration Field Descriptions -- **`backend`** (required): The vector store backend to use. Valid values: `local`, `chroma`, `elasticsearch`, `qdrant`, `memory` -- **`embedding_model`** (required): The name of the embedding model configuration from the `embedding_model` section -- **`params`** (optional): A dictionary of backend-specific parameters that will be passed to the vector store constructor +- **`backend`** (required): Vector store backend type. Options: `local`, `memory`, `chroma`, `qdrant`, `elasticsearch`. +- **`embedding_model`** (required): Name of the embedding model configuration, referencing the `embedding_model` section. +- **`params`** (optional): Dictionary of backend-specific parameters passed to the vector store constructor. -## 📁 Vector Store Backend Configurations +### Configuration Examples by Type -### 1. LocalVectorStore (`backend=local`) +#### 1. LocalVectorStore Configuration -A simple file-based vector store that saves data to local JSONL files. +Simplest local file-based storage, ideal for development and testing. -#### 💡 When to Use -- **Development and testing** - No external dependencies required 🛠️ -- **Small datasets** - Suitable for datasets with < 10,000 vectors 📊 -- **Single-user applications** - Limited concurrent access support 👤 - -#### ⚙️ Configuration +**Implementation**: [`flowllm/core/vector_store/local_vector_store.py`](https://github.com/flowllm-ai/flowllm/blob/main/flowllm/core/vector_store/local_vector_store.py) ```yaml vector_store: @@ -81,25 +141,29 @@ vector_store: backend: local embedding_model: default params: - store_dir: "./local_vector_store" # Directory to store JSONL files (default: "./local_vector_store") - batch_size: 1024 # Batch size for operations (default: 1024) + store_dir: "./local_vector_store" # Storage directory (optional; default: "./local_vector_store") ``` -#### Configuration Parameters +#### 2. MemoryVectorStore Configuration -- **`store_dir`** (optional): Directory path where workspace files are stored. Default: `"./local_vector_store"` -- **`batch_size`** (optional): Batch size for bulk operations. Default: `1024` +In-memory storage with fast access, suitable for temporary data or testing. -### 2. ChromaVectorStore (`backend=chroma`) +**Implementation**: [`flowllm/core/vector_store/memory_vector_store.py`](https://github.com/flowllm-ai/flowllm/blob/main/flowllm/core/vector_store/memory_vector_store.py) -An embedded vector database that provides persistent storage with advanced features. +```yaml +vector_store: + default: + backend: memory + embedding_model: default + params: + store_dir: "./memory_vector_store" # Persistence directory (optional; default: "./memory_vector_store") +``` -#### 💡 When to Use -- **Local development** with persistence requirements 🏠 -- **Medium-scale applications** (10K - 1M vectors) 📈 -- **Applications requiring metadata filtering** 🔍 +#### 3. ChromaVectorStore Configuration -#### ⚙️ Configuration +Persistent storage based on ChromaDB with metadata filtering support. + +**Implementation**: [`flowllm/core/vector_store/chroma_vector_store.py`](https://github.com/flowllm-ai/flowllm/blob/main/flowllm/core/vector_store/chroma_vector_store.py) ```yaml vector_store: @@ -107,48 +171,44 @@ vector_store: backend: chroma embedding_model: default params: - store_dir: "./chroma_vector_store" # Directory for Chroma database (default: "./chroma_vector_store") - batch_size: 1024 # Batch size for operations (default: 1024) + store_dir: "./chroma_vector_store" # ChromaDB data directory (optional; default: "./chroma_vector_store") ``` -#### Configuration Parameters +#### 4. QdrantVectorStore Configuration -- **`store_dir`** (optional): Directory path where ChromaDB data is persisted. Default: `"./chroma_vector_store"` -- **`batch_size`** (optional): Batch size for bulk operations. Default: `1024` +**Implementation**: [`flowllm/core/vector_store/qdrant_vector_store.py`](https://github.com/flowllm-ai/flowllm/blob/main/flowllm/core/vector_store/qdrant_vector_store.py) -### 3. EsVectorStore (`backend=elasticsearch`) +**Local Qdrant Instance**: -Production-grade vector search using Elasticsearch with advanced filtering and scaling capabilities. - -#### 💡 When to Use -- **Production environments** requiring high availability 🏭 -- **Large-scale applications** (1M+ vectors) 🚀 -- **Complex filtering requirements** on metadata 🎯 - -#### 🛠️ Setup Elasticsearch - -Before using EsVectorStore, set up Elasticsearch: - -##### Option 1: Docker Run -```bash -# Pull the latest Elasticsearch image -docker pull docker.elastic.co/elasticsearch/elasticsearch-wolfi:9.0.0 - -# Run Elasticsearch container -docker run -p 9200:9200 \ - -e "discovery.type=single-node" \ - -e "xpack.security.enabled=false" \ - -e "xpack.license.self_generated.type=trial" \ - -e "http.host=0.0.0.0" \ - docker.elastic.co/elasticsearch/elasticsearch-wolfi:9.0.0 +```yaml +vector_store: + default: + backend: qdrant + embedding_model: default + params: + host: "localhost" # Qdrant server host (optional; default: localhost) + port: 6333 # Qdrant server port (optional; default: 6333) + distance: "COSINE" # Distance metric (optional; default: COSINE; options: COSINE, EUCLIDEAN, DOT) ``` -##### Environment Configuration -```bash -export FLOW_ES_HOSTS=http://localhost:9200 +**Qdrant Cloud Configuration**: + +```yaml +vector_store: + default: + backend: qdrant + embedding_model: default + params: + url: "https://your-cluster.qdrant.io:6333" # Qdrant Cloud URL + api_key: "your-api-key-here" # API key + distance: "COSINE" ``` -#### ⚙️ Configuration +#### 5. EsVectorStore Configuration + +**Implementation**: [`flowllm/core/vector_store/es_vector_store.py`](https://github.com/flowllm-ai/flowllm/blob/main/flowllm/core/vector_store/es_vector_store.py) + +**Basic Configuration (Local Elasticsearch)**: ```yaml vector_store: @@ -156,167 +216,11 @@ vector_store: backend: elasticsearch embedding_model: default params: - hosts: "http://localhost:9200" # Elasticsearch host(s) - can be string or list (default: from FLOW_ES_HOSTS env var or "http://localhost:9200") - basic_auth: null # Optional: ("username", "password") tuple for authentication - batch_size: 1024 # Batch size for bulk operations (default: 1024) + hosts: "http://localhost:9200" # Elasticsearch host(s) (optional; default: http://localhost:9200) ``` -#### Configuration Parameters +**Configuration with Authentication**: -- **`hosts`** (optional): Elasticsearch host(s) as a string or list of strings. Defaults to the `FLOW_ES_HOSTS` environment variable or `"http://localhost:9200"` if not set -- **`basic_auth`** (optional): Tuple of `("username", "password")` for basic authentication. Default: `null` (no authentication) -- **`batch_size`** (optional): Batch size for bulk operations. Default: `1024` - -### 4. QdrantVectorStore (`backend=qdrant`) - -A high-performance vector database designed for production workloads with native async support and advanced filtering. - -#### 💡 When to Use -- **Production environments** requiring high performance and reliability 🏭 -- **Large-scale applications** (10M+ vectors) with excellent horizontal scaling 🚀 -- **Applications requiring native async operations** for better concurrency ⚡ -- **Complex filtering and metadata queries** on large datasets 🎯 -- **Cloud-native deployments** with Qdrant Cloud support ☁️ - -#### 🛠️ Setup Qdrant - -Before using QdrantVectorStore, set up Qdrant: - -##### Option 1: Docker Run (Recommended for Development) -```bash -# Pull the latest Qdrant image -docker pull qdrant/qdrant - -# Run Qdrant container -docker run -p 6333:6333 -p 6334:6334 \ - -v $(pwd)/qdrant_storage:/qdrant/storage:z \ - qdrant/qdrant -``` - -##### Option 2: Qdrant Cloud -For production, you can use [Qdrant Cloud](https://cloud.qdrant.io/) for managed hosting. - -##### Environment Configuration -```bash -# For local setup -export FLOW_QDRANT_HOST=localhost -export FLOW_QDRANT_PORT=6333 - -# For cloud setup (optional) -export FLOW_QDRANT_API_KEY=your-api-key -``` - -#### ⚙️ Configuration - -##### Local Qdrant Instance -```yaml -vector_store: - default: - backend: qdrant - embedding_model: default - params: - host: "localhost" # Qdrant host (default: from FLOW_QDRANT_HOST env var or "localhost") - port: 6333 # Qdrant port (default: from FLOW_QDRANT_PORT env var or 6333) - batch_size: 1024 # Batch size for operations (default: 1024) - distance: "COSINE" # Distance metric: "COSINE", "EUCLIDEAN", or "DOT" (default: "COSINE") -``` - -##### Qdrant Cloud or Remote Server -```yaml -vector_store: - default: - backend: qdrant - embedding_model: default - params: - url: "https://your-cluster.qdrant.io:6333" # Qdrant server URL (if provided, host and port are ignored) - api_key: "your-api-key" # API key for Qdrant Cloud authentication - batch_size: 1024 # Batch size for operations (default: 1024) - distance: "COSINE" # Distance metric (default: "COSINE") -``` - -#### Configuration Parameters - -- **`url`** (optional): Complete URL for connecting to Qdrant. If provided, `host` and `port` are ignored. Useful for Qdrant Cloud or custom deployments -- **`host`** (optional): Host address of the Qdrant server. Defaults to the `FLOW_QDRANT_HOST` environment variable or `"localhost"` if not set -- **`port`** (optional): Port number of the Qdrant server. Defaults to the `FLOW_QDRANT_PORT` environment variable or `6333` if not set -- **`api_key`** (optional): API key for authentication (required for Qdrant Cloud). Can also be set via `FLOW_QDRANT_API_KEY` environment variable -- **`distance`** (optional): Distance metric for vector similarity. Valid values: `"COSINE"`, `"EUCLIDEAN"`, `"DOT"`. Default: `"COSINE"` -- **`batch_size`** (optional): Batch size for bulk operations. Default: `1024` - -#### 🌟 Key Features - -- **Native Async Support** - All operations have async equivalents for better concurrency -- **Upsert Operations** - Insert automatically updates existing nodes with the same ID -- **Advanced Filtering** - Support for term and range filters on metadata -- **High Performance** - Optimized for large-scale vector similarity search -- **Horizontal Scaling** - Supports clustering for distributed deployments -- **Multiple Distance Metrics** - Cosine, Euclidean, and Dot Product similarity -- **Persistent Storage** - Data is automatically persisted to disk -- **Efficient Iteration** - Scroll through large collections with pagination - -### 5. MemoryVectorStore (`backend=memory`) - -An ultra-fast in-memory vector store that keeps all data in RAM for maximum performance. - -#### 💡 When to Use -- **Testing and development** - Fastest possible operations for unit tests 🧪 -- **Small to medium datasets** that fit in memory (< 1M vectors) 💾 -- **Applications requiring ultra-low latency** search operations ⚡ -- **Temporary workspaces** that don't need persistence 🚀 - -#### ⚙️ Configuration - -```yaml -vector_store: - default: - backend: memory - embedding_model: default - params: - store_dir: "./memory_vector_store" # Directory for backup/restore operations (default: "./memory_vector_store") - batch_size: 1024 # Batch size for operations (default: 1024) -``` - -#### Configuration Parameters - -- **`store_dir`** (optional): Directory path for backup/restore operations. Default: `"./memory_vector_store"` -- **`batch_size`** (optional): Batch size for bulk operations. Default: `1024` - -#### ⚡ Performance Benefits - -- **Zero I/O latency** - All operations happen in RAM -- **Instant search results** - No disk or network overhead -- **Perfect for testing** - Fast setup and teardown -- **Memory efficient** - Only stores what you need - -#### 🚨 Important Notes - -- **Data is volatile** - Lost when process ends unless explicitly saved -- **Memory usage** - Entire dataset must fit in available RAM -- **No persistence** - Use `dump_workspace()` to save to disk -- **Single process** - Not suitable for distributed applications - -## 📝 Example Configurations - -### Minimal Configuration (Memory Store) -```yaml -vector_store: - default: - backend: memory - embedding_model: default -``` - -### Local File Storage -```yaml -vector_store: - default: - backend: local - embedding_model: default - params: - store_dir: "./my_vector_store" - batch_size: 2048 -``` - -### Elasticsearch Production Setup ```yaml vector_store: default: @@ -324,40 +228,29 @@ vector_store: embedding_model: default params: hosts: "http://elasticsearch.example.com:9200" - basic_auth: ["username", "password"] - batch_size: 2048 + basic_auth: ["username", "password"] # Basic auth credentials ``` -### Qdrant Cloud Setup +**Multi-Host Configuration**: + ```yaml vector_store: default: - backend: qdrant + backend: elasticsearch embedding_model: default params: - url: "https://your-cluster.qdrant.io:6333" - api_key: "your-api-key-here" - distance: "COSINE" - batch_size: 1024 + hosts: + - "http://es-node1:9200" + - "http://es-node2:9200" + - "http://es-node3:9200" ``` -## 🔄 Environment Variables +### Complete Configuration Example -Some vector store backends support environment variables for configuration: - -- **Elasticsearch**: `FLOW_ES_HOSTS` - Elasticsearch host(s) -- **Qdrant**: - - `FLOW_QDRANT_HOST` - Qdrant host (default: "localhost") - - `FLOW_QDRANT_PORT` - Qdrant port (default: 6333) - - `FLOW_QDRANT_API_KEY` - Qdrant API key for authentication - -Environment variables are used as fallbacks when parameters are not explicitly set in the YAML configuration. - -## 🧩 Integration with Embedding Models - -All vector stores require an embedding model configuration. The `embedding_model` field in the vector store configuration references a model defined in the `embedding_model` section of `default.yaml`: +Below is a complete `default.yaml` example including both embedding model and vector store configurations: ```yaml +# Embedding model configuration embedding_model: default: backend: openai_compatible @@ -365,10 +258,46 @@ embedding_model: params: dimensions: 1024 +# Vector store configuration vector_store: default: - backend: memory - embedding_model: default # References the embedding_model.default configuration + backend: elasticsearch + embedding_model: default + params: + hosts: "http://localhost:9200" ``` -The embedding model configuration provides the model name, backend, and parameters needed for generating vector embeddings. +### Environment Variable Support + +Certain Vector Stores support environment variables as a supplement to YAML configuration: + +- **Elasticsearch**: `FLOW_ES_HOSTS` – Elasticsearch host address. +- **Qdrant**: + - `FLOW_QDRANT_HOST` – Qdrant host (default: `localhost`) + - `FLOW_QDRANT_PORT` – Qdrant port (default: `6333`) + - `FLOW_QDRANT_API_KEY` – Qdrant API key + +When parameters are not explicitly specified in the YAML configuration, the system falls back to environment variables. + +## Metadata Filtering + +Two types of metadata filtering are supported: + +- **Exact Match**: Specify field values for exact matching. +- **Range Queries**: Use operators `gte`, `lte`, `gt`, `lt` for numeric range queries. +- **Nested Fields**: Access nested metadata fields using dot notation. + +## Usage Recommendations + +- **Development & Testing**: Use MemoryVectorStore or LocalVectorStore—no additional services required. +- **Small-Scale Applications**: Use LocalVectorStore or ChromaVectorStore for simplicity and ease of use. +- **Production Environments**: Use QdrantVectorStore or EsVectorStore for high performance and scalability. +- **Hybrid Search**: Use EsVectorStore to combine vector search with full-text search capabilities. + +## Important Notes + +- Ensure the embedding model’s output dimension matches the Vector Store configuration. +- For large-scale data, use professional vector databases (e.g., Qdrant, Elasticsearch). +- Asynchronous interfaces deliver better performance in asynchronous environments. +- Regularly back up critical data, especially when using in-memory storage. +- Choose an appropriate batch size based on your data scale to optimize performance. diff --git a/reme_ai/__init__.py b/reme_ai/__init__.py index ffbb4ec8..62450870 100644 --- a/reme_ai/__init__.py +++ b/reme_ai/__init__.py @@ -8,7 +8,6 @@ os.environ["FLOW_APP_NAME"] = "ReMe" from . import agent # noqa: E402 from . import config # noqa: E402 from . import constants # noqa: E402 -from . import context # noqa: E402 from . import enumeration # noqa: E402 from . import retrieve # noqa: E402 from . import schema # noqa: E402 @@ -22,7 +21,6 @@ __all__ = [ "agent", "config", "constants", - "context", "enumeration", "retrieve", "schema", diff --git a/reme_ai/agent/react/agentic_retrieve_op.py b/reme_ai/agent/react/agentic_retrieve_op.py index 9e59d3ad..aab28e1a 100644 --- a/reme_ai/agent/react/agentic_retrieve_op.py +++ b/reme_ai/agent/react/agentic_retrieve_op.py @@ -11,6 +11,19 @@ language model reasoning with tool execution. The agent iteratively: The agent is specifically designed for RAG (Retrieval-Augmented Generation) workflows, providing context management capabilities to handle long conversations efficiently. + +Context management is controlled via ``working_summary_mode`` and +``compact_ratio_threshold`` parameters, which are forwarded to +``MessageOffloadOp``. ``working_summary_mode`` selects between: +- ``compact`` – only compact verbose tool messages by storing full content externally + and keeping short previews in the context. +- ``compress`` – only apply LLM-based compression to generate a compact state snapshot. +- ``auto`` – first run compaction, then optionally run compression if the + compaction ratio is not sufficient (default). + +``compact_ratio_threshold`` is only used in ``auto`` mode and defines the compaction +ratio (tokens after compaction divided by original tokens) above which an additional +LLM-based compression pass is applied. It defaults to ``0.75``. """ from typing import Dict, List @@ -36,6 +49,12 @@ class AgenticRetrieveOp(BaseAsyncToolOp): through compaction (storing large tool messages externally) and compression (LLM-based summarization of message history). + Context management behavior is configured via ``working_summary_mode`` and + ``compact_ratio_threshold`` (see module docstring for details). These options are + passed to ``MessageOffloadOp`` to control whether the agent only compacts tool + messages, only compresses history, or applies an automatic compaction-then- + compression pipeline. + Available tools: - GrepOp: Search for patterns in files - ReadFileOp: Read file contents @@ -49,7 +68,7 @@ class AgenticRetrieveOp(BaseAsyncToolOp): def __init__( self, llm: str = "qwen3_30b_instruct", - max_steps: int = 5, + max_steps: int = 20, **kwargs, ): """ @@ -87,14 +106,21 @@ class AgenticRetrieveOp(BaseAsyncToolOp): "description": "messages", "required": True, }, - "context_manage_mode": { + "working_summary_mode": { "type": "string", - "description": "Context management mode: 'compact' (only compacts tool messages), 'compress' " - "(only LLM-based compression), 'auto' (compaction first then compression if " - "needed). Defaults to 'auto'.", - "required": True, + "description": "summary strategy: 'compact' only compacts large tool messages, 'compress' " + "only applies LLM-based compression, 'auto' first compacts then optionally compresses when " + "reduction is insufficient. Defaults to 'auto'.", + "required": False, "enum": ["compact", "compress", "auto"], }, + "compact_ratio_threshold": { + "type": "number", + "description": "Only used in 'auto' mode. Threshold for compaction (tokens after compaction " + "divided by original tokens). When the ratio is greater than this value, an additional " + "LLM-based compression pass is triggered. Defaults to 0.75.", + "required": False, + }, "max_total_tokens": { "type": "integer", "description": "Maximum token threshold for triggering compression/compaction. For compaction " @@ -156,9 +182,8 @@ class AgenticRetrieveOp(BaseAsyncToolOp): Each iteration is called a "round" and represents one reasoning-action cycle. """ # Import tool operators that the agent can use - from reme_ai.context.file_tool import GrepOp, ReadFileOp - from reme_ai.context.offload import ContextOffloadOp - from reme_ai.context.file_tool import BatchWriteFileOp + from reme_ai.retrieve.working import GrepOp, ReadFileOp, BatchWriteFileOp + from reme_ai.summary.working import MessageOffloadOp # Initialize available tools for the agent # GrepOp: Search for patterns/text in files (useful for code search) @@ -184,9 +209,9 @@ class AgenticRetrieveOp(BaseAsyncToolOp): # Main ReAct loop: iterate up to max_steps times for i in range(self.max_steps): # Step 1: Context Management Phase - # Create a pipeline: ContextOffloadOp (compacts/compresses) -> BatchWriteFileOp (saves offloaded content) + # Create a pipeline: MessageOffloadOp (compacts/compresses) -> BatchWriteFileOp (saves offloaded content) # The >> operator chains these operations together - op = ContextOffloadOp() >> BatchWriteFileOp() + op = MessageOffloadOp() >> BatchWriteFileOp() # Apply context management to current message history # This may compact large tool messages or compress old messages based on context_manage_mode @@ -194,6 +219,7 @@ class AgenticRetrieveOp(BaseAsyncToolOp): # Update messages with the processed/optimized version from context management # Large messages may now reference external files instead of containing full content + logger.info(f"round{i + 1}.offload={op.context.response.answer}") messages = [Message(**x) for x in op.context.response.answer] # Step 2: Reasoning Phase @@ -265,4 +291,4 @@ class AgenticRetrieveOp(BaseAsyncToolOp): # Store the complete conversation history in the context response # This includes all reasoning steps, tool calls, and tool results - self.context.response.answer = messages + self.context.response.metadata["messages"] = [x.simple_dump(add_reasoning=True) for x in messages] diff --git a/reme_ai/config/default.yaml b/reme_ai/config/default.yaml index f17449ac..ef73afc1 100644 --- a/reme_ai/config/default.yaml +++ b/reme_ai/config/default.yaml @@ -159,19 +159,23 @@ flow: agentic_retrieve: flow_content: AgenticRetrieveOp() - context_offload: - flow_content: ContextOffloadOp() >> BatchWriteFileOp() + summary_working_memory: + flow_content: MessageOffloadOp() >> BatchWriteFileOp() description: "Manages context window limits by compacting tool messages and compressing conversation history. First compacts large tool messages by storing full content in external files, then applies LLM-based compression if compaction ratio exceeds threshold. This helps reduce token usage while preserving important information." input_schema: messages: type: array description: "List of conversation messages to process for context offloading" required: true - context_manage_mode: + working_summary_mode: type: string - description: "Context management mode: 'compact' only applies compaction to tool messages, 'compress' only applies LLM-based compression, 'auto' applies compaction first then compression if compaction ratio exceeds threshold. Defaults to 'auto'." + description: "Working summary strategy: 'compact' only compacts large tool messages, 'compress' only applies LLM-based compression, 'auto' first compacts then optionally compresses when reduction is insufficient. Defaults to 'auto'." required: false enum: ["compact", "compress", "auto"] + compact_ratio_threshold: + type: number + description: "Only used in 'auto' mode. Threshold for compaction ratio (tokens after compaction divided by original tokens). When the ratio is greater than this value, an additional LLM-based compression pass is triggered. Defaults to 0.75." + required: false max_total_tokens: type: integer description: "Maximum token count threshold for triggering compression/compaction. For compaction, this is the total token count threshold. For compression, this excludes keep_recent_count messages and system messages. Defaults to 20000." @@ -197,19 +201,29 @@ flow: description: "Unique identifier for the chat session, used for file naming when storing compressed message groups. If not provided, a UUID will be generated automatically." required: false - context_offload_for_agentscope: - flow_content: ContextOffloadOp() + grep_working_memory: + flow_content: GrepOp() + + read_working_memory: + flow_content: ReadFileOp() + + summary_working_memory_for_agentscope: + flow_content: MessageOffloadOp() description: "Context offload operation for AgentScope integration. Manages context window limits by compacting tool messages and compressing conversation history without batch file writing. Same functionality as context_offload but without the BatchWriteFileOp step." input_schema: messages: type: array description: "List of conversation messages to process for context offloading" required: true - context_manage_mode: + working_summary_mode: type: string - description: "Context management mode: 'compact' only applies compaction to tool messages, 'compress' only applies LLM-based compression, 'auto' applies compaction first then compression if compaction ratio exceeds threshold. Defaults to 'auto'." - required: true + description: "Working summary strategy: 'compact' only compacts large tool messages, 'compress' only applies LLM-based compression, 'auto' first compacts then optionally compresses when reduction is insufficient. Defaults to 'auto'." + required: false enum: ["compact", "compress", "auto"] + compact_ratio_threshold: + type: number + description: "Only used in 'auto' mode. Threshold for compaction ratio (tokens after compaction divided by original tokens). When the ratio is greater than this value, an additional LLM-based compression pass is triggered. Defaults to 0.75." + required: false max_total_tokens: type: integer description: "Maximum token count threshold for triggering compression/compaction. For compaction, this is the total token count threshold. For compression, this excludes keep_recent_count messages and system messages. Defaults to 20000." @@ -245,6 +259,33 @@ llm: token_count: # Optional backend: base + qwen3_coder_plus: + backend: openai_compatible + model_name: qwen3-coder-plus + token_count: # Optional + model_name: Qwen/Qwen3-Coder-480B-A35B-Instruct + backend: hf + params: + use_mirror: true + + qwen3_coder_480b_instruct: + backend: openai_compatible + model_name: qwen3-coder-480b-a35b-instruct + token_count: # Optional + model_name: Qwen/Qwen3-Coder-480B-A35B-Instruct + backend: hf + params: + use_mirror: true + + qwen3_coder_30b_instruct: + backend: openai_compatible + model_name: qwen3-coder-30b-a3b-instruct + token_count: # Optional + model_name: Qwen/Qwen3-Coder-30B-A3B-Instruct + backend: hf + params: + use_mirror: true + qwen3_30b_instruct: backend: openai_compatible model_name: qwen3-30b-a3b-instruct-2507 diff --git a/reme_ai/context/__init__.py b/reme_ai/context/__init__.py deleted file mode 100644 index aa96c17a..00000000 --- a/reme_ai/context/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -"""Context management module for ReMe framework. - -This module provides submodules for different types of context management operations: -- file_tool: File-related operations for reading, writing, and searching files -- offload: Context offload operations for reducing token usage and managing context windows -""" - -from . import file_tool -from . import offload - -__all__ = [ - "file_tool", - "offload", -] diff --git a/reme_ai/context/file_tool/grep_op.py b/reme_ai/context/file_tool/grep_op.py deleted file mode 100644 index df25bf86..00000000 --- a/reme_ai/context/file_tool/grep_op.py +++ /dev/null @@ -1,52 +0,0 @@ -"""Grep text search operation module. - -This module provides a tool operation for searching text patterns in files. -It enables efficient content-based search using regular expressions, with support -for glob pattern filtering and result limiting. -""" - -from flowllm.core.context import C -from flowllm.core.schema import ToolCall -from flowllm.extensions.file_tool import GrepOp as FlowGrepOp - - -@C.register_op() -class GrepOp(FlowGrepOp): - """Grep text search operation. - - This operation searches for text patterns in files using regular expressions. - Supports glob pattern filtering and result limiting. - """ - - file_path = __file__ - - def build_tool_call(self) -> ToolCall: - """Build and return the tool call schema for this operator.""" - tool_params = { - "name": "Grep", - "description": self.get_prompt("tool_desc"), - "input_schema": { - "pattern": { - "type": "string", - "description": self.get_prompt("pattern"), - "required": True, - }, - "path": { - "type": "string", - "description": self.get_prompt("path"), - "required": False, - }, - "glob": { - "type": "string", - "description": self.get_prompt("glob"), - "required": False, - }, - "limit": { - "type": "number", - "description": self.get_prompt("limit"), - "required": False, - }, - }, - } - - return ToolCall(**tool_params) diff --git a/reme_ai/context/file_tool/read_file_op.py b/reme_ai/context/file_tool/read_file_op.py deleted file mode 100644 index 254fce61..00000000 --- a/reme_ai/context/file_tool/read_file_op.py +++ /dev/null @@ -1,46 +0,0 @@ -"""Read file operation module. - -This module provides a tool operation for reading file contents. -It supports reading entire files or specific line ranges for large files. -""" - -from flowllm.core.context import C -from flowllm.core.schema import ToolCall -from flowllm.extensions.file_tool import ReadFileOp as FlowReadFileOp - - -@C.register_op() -class ReadFileOp(FlowReadFileOp): - """Read file operation. - - This operation reads and returns the content of a specified file. - For text files, it can read specific line ranges using offset and limit. - """ - - file_path = __file__ - - def build_tool_call(self) -> ToolCall: - """Build and return the tool call schema for this operator.""" - tool_params = { - "name": "ReadFile", - "description": self.get_prompt("tool_desc"), - "input_schema": { - "absolute_path": { - "type": "string", - "description": self.get_prompt("absolute_path"), - "required": True, - }, - "offset": { - "type": "number", - "description": self.get_prompt("offset"), - "required": False, - }, - "limit": { - "type": "number", - "description": self.get_prompt("limit"), - "required": False, - }, - }, - } - - return ToolCall(**tool_params) diff --git a/reme_ai/context/file_tool/read_file_prompt.yaml b/reme_ai/context/file_tool/read_file_prompt.yaml deleted file mode 100644 index eb083182..00000000 --- a/reme_ai/context/file_tool/read_file_prompt.yaml +++ /dev/null @@ -1,20 +0,0 @@ -tool_desc: | - 读取指定文件内容的工具；对文本文件可通过 offset 与 limit 获取特定行区间，便于分页浏览大文件。 -tool_desc_zh: | - Reads and returns the content of a specified file. For text files, it can read specific line ranges using the 'offset' and 'limit' parameters. Use offset and limit to paginate through large files. - -absolute_path: | - 必填：待读取文件的绝对路径（如 "/home/user/project/file.txt"），不支持相对路径。 -absolute_path_zh: | - The absolute path to the file to read (e.g., '/home/user/project/file.txt'). Relative paths are not supported. You must provide an absolute path. - -offset: | - 可选：文本文件起始读取的 0 基行号；需与 limit 同时使用，适合分页查看大文件。 -offset_zh: | - Optional: For text files, the 0-based line number to start reading from. Requires 'limit' to be set. Use for paginating through large files. - -limit: | - 可选：文本文件最多读取的行数；与 offset 配合实现分页，若仅设 offset 则会读到文件末尾。 -limit_zh: | - Optional: For text files, maximum number of lines to read. Use with 'offset' to paginate through large files. If omitted when offset is provided, reads from offset to the end of the file. - diff --git a/reme_ai/context/offload/__init__.py b/reme_ai/context/offload/__init__.py deleted file mode 100644 index 626fe0fd..00000000 --- a/reme_ai/context/offload/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -"""Context offload package for ReMe framework. - -This package provides context management operations that can be used in LLM-powered flows -to reduce token usage and manage context window limits. It includes ready-to-use operations for: - -- ContextCompactOp: Compact tool messages by storing full content in external files -- ContextCompressOp: Compress conversation history using LLM to generate concise summaries -- ContextOffloadOp: Orchestrate compaction and compression to reduce token usage -""" - -from .context_compact_op import ContextCompactOp -from .context_compress_op import ContextCompressOp -from .context_offload_op import ContextOffloadOp - -__all__ = [ - "ContextCompactOp", - "ContextCompressOp", - "ContextOffloadOp", -] diff --git a/reme_ai/context/offload/context_offload_op.py b/reme_ai/context/offload/context_offload_op.py deleted file mode 100644 index 1c086249..00000000 --- a/reme_ai/context/offload/context_offload_op.py +++ /dev/null @@ -1,107 +0,0 @@ -""" -Context offload module for managing context window limits through compaction and compression. - -This module provides a high-level operation that orchestrates context compaction and compression -to reduce token usage. It first attempts to compact tool messages, and if the compaction ratio -is not sufficient, it applies LLM-based compression to further reduce token count. - -The offload process: -1. Compacts tool messages by storing full content in external files -2. Evaluates the compaction effectiveness by comparing token counts -3. If compaction ratio exceeds threshold, applies LLM-based compression -""" - -from flowllm.core.context import C -from flowllm.core.op import BaseAsyncOp -from flowllm.core.schema import Message -from loguru import logger - -from reme_ai.enumeration import ContextManageEnum - - -@C.register_op() -class ContextOffloadOp(BaseAsyncOp): - """ - Context offload operation that orchestrates compaction and compression to reduce token usage. - - This operation combines context compaction and compression strategies to manage context - window limits. It first applies compaction to tool messages, then evaluates the effectiveness. - If the compaction ratio (compressed tokens / original tokens) exceeds a threshold, it - applies additional LLM-based compression to further reduce token count. - - Context Parameters: - context_manage_mode (ContextManageEnum): The context management mode to use. - - COMPACT: Only applies context compaction to tool messages. - - COMPRESS: Only applies LLM-based compression to messages. - - AUTO: Applies compaction first, then compression if compaction ratio exceeds threshold. - Defaults to AUTO. - compact_ratio_threshold (float): Threshold for compaction ratio above which compression - is applied. Only used in AUTO mode. Defaults to 0.75. If the ratio of compressed - tokens to original tokens exceeds this value, compression will be triggered. - """ - - async def async_execute(self): - """ - Execute the context offload operation. - - The operation behavior depends on the context_manage_mode: - - COMPACT: Only applies context compaction to reduce token usage in tool messages. - - COMPRESS: Only applies LLM-based compression to generate concise summaries. - - AUTO: Applies compaction first, then compression if compaction ratio exceeds threshold. - - The compaction operation stores full tool message content in external files and - keeps only previews in the context. The compression operation uses LLM to generate - concise summaries of older messages. - """ - from .context_compact_op import ContextCompactOp - from .context_compress_op import ContextCompressOp - - # Get the context management mode from context, default to AUTO - context_manage_mode = self.context.get("context_manage_mode", ContextManageEnum.AUTO) - if isinstance(context_manage_mode, str): - context_manage_mode = ContextManageEnum(context_manage_mode) - - context_compact_op = ContextCompactOp() - context_compress_op = ContextCompressOp() - - if context_manage_mode == ContextManageEnum.COMPACT: - # Only apply compaction - logger.info("Context management mode: COMPACT") - await context_compact_op.async_call(context=self.context) - elif context_manage_mode == ContextManageEnum.COMPRESS: - # Only apply compression - logger.info("Context management mode: COMPRESS") - await context_compress_op.async_call(context=self.context) - elif context_manage_mode == ContextManageEnum.AUTO: - # Apply compaction first, then compression if needed - logger.info("Context management mode: AUTO") - await context_compact_op.async_call(context=self.context) - - origin_messages = [Message(**x) for x in self.context.messages] - origin_token_cnt = self.token_count(origin_messages) - - result_messages = [Message(**x) for x in self.context.response.answer] - answer_token_cnt = self.token_count(result_messages) - - compact_ratio = answer_token_cnt / origin_token_cnt - - compact_ratio_threshold: float = self.context.get("compact_ratio_threshold", 0.75) - if compact_ratio > compact_ratio_threshold: - logger.info(f"Compact ratio {compact_ratio:.2f} > {compact_ratio_threshold:.2f}, compress answer") - await context_compress_op.async_call(context=self.context) - else: - raise ValueError(f"Unknown context management mode: {context_manage_mode}") - - async def async_default_execute(self, e: Exception = None, **_kwargs): - """Handle execution errors by returning original messages. - - This method is called when an exception occurs during async_execute. It preserves - the original messages and marks the operation as unsuccessful. - - Args: - e: The exception that occurred during execution, if any. - **_kwargs: Additional keyword arguments (unused but required by interface). - """ - self.context.response.answer = self.context.messages - self.context.response.success = False - self.context.response.metadata["error"] = str(e) diff --git a/reme_ai/enumeration/__init__.py b/reme_ai/enumeration/__init__.py index 482d29e1..d62d01fd 100644 --- a/reme_ai/enumeration/__init__.py +++ b/reme_ai/enumeration/__init__.py @@ -4,10 +4,10 @@ This module provides enumerations used throughout the ReMe system, including language enumerations and other type definitions. """ -from reme_ai.enumeration.context_manage_enum import ContextManageEnum +from reme_ai.enumeration.working_summary_mode import WorkingSummaryMode from reme_ai.enumeration.language_enum import LanguageEnum __all__ = [ - "ContextManageEnum", + "WorkingSummaryMode", "LanguageEnum", ] diff --git a/reme_ai/enumeration/context_manage_enum.py b/reme_ai/enumeration/context_manage_enum.py deleted file mode 100644 index 3c290369..00000000 --- a/reme_ai/enumeration/context_manage_enum.py +++ /dev/null @@ -1,21 +0,0 @@ -"""Context management enumeration module. - -This module provides enumerations for context management strategies in the ReMe system. -""" - -from enum import Enum - - -class ContextManageEnum(str, Enum): - """ - An enumeration representing context management strategies. - - Members: - - COMPACT: Represents the compact context management strategy. - - COMPRESS: Represents the compress context management strategy. - - AUTO: Represents the automatic context management strategy. - """ - - COMPACT = "compact" - COMPRESS = "compress" - AUTO = "auto" diff --git a/reme_ai/enumeration/working_summary_mode.py b/reme_ai/enumeration/working_summary_mode.py new file mode 100644 index 00000000..a1dde18b --- /dev/null +++ b/reme_ai/enumeration/working_summary_mode.py @@ -0,0 +1,22 @@ +"""Working summary mode enumeration module. + +This module defines the strategies for working-memory style summarization in the +ReMe system. +""" + +from enum import Enum + + +class WorkingSummaryMode(str, Enum): + """ + Enumeration representing working summary strategies. + + Members: + - COMPACT: Only compact verbose tool messages into previews. + - COMPRESS: Only apply LLM-based compression over the history. + - AUTO: First compact messages, then optionally compress if needed. + """ + + COMPACT = "compact" + COMPRESS = "compress" + AUTO = "auto" diff --git a/reme_ai/main.py b/reme_ai/main.py index fea9e6c0..74410035 100644 --- a/reme_ai/main.py +++ b/reme_ai/main.py @@ -224,12 +224,6 @@ def main(): Command-line arguments are passed directly to ReMeApp.__init__(), allowing configuration via command line: - Example: - python -m reme_ai.app --llm_api_key=sk-xxx --config_path=config.yaml - - The app runs as a context manager, ensuring proper cleanup of resources - (database connections, API clients, etc.) on shutdown. - Note: Press Ctrl+C to gracefully shutdown the service. """ diff --git a/reme_ai/context/file_tool/__init__.py b/reme_ai/retrieve/working/__init__.py similarity index 63% rename from reme_ai/context/file_tool/__init__.py rename to reme_ai/retrieve/working/__init__.py index d9d5cfb9..ab1181a8 100644 --- a/reme_ai/context/file_tool/__init__.py +++ b/reme_ai/retrieve/working/__init__.py @@ -1,7 +1,7 @@ -"""File tool package for ReMe framework. +"""Working operations package for ReMe retrieve framework. -This package provides file-related operations that can be used in LLM-powered flows. -It includes ready-to-use operations for: +This package provides working/file-related operations that can be used in LLM-powered +flows. It currently includes ready-to-use operations for: - BatchWriteFileOp: Batch write multiple files operation - GrepOp: Text search operation for finding patterns in files diff --git a/reme_ai/context/file_tool/batch_write_file_op.py b/reme_ai/retrieve/working/batch_write_file_op.py similarity index 89% rename from reme_ai/context/file_tool/batch_write_file_op.py rename to reme_ai/retrieve/working/batch_write_file_op.py index 535b05cb..44bc9425 100644 --- a/reme_ai/context/file_tool/batch_write_file_op.py +++ b/reme_ai/retrieve/working/batch_write_file_op.py @@ -7,9 +7,10 @@ and returning a combined result of all write operations. from flowllm.core.context import C from flowllm.core.op import BaseAsyncOp -from flowllm.extensions.file_tool import WriteFileOp from loguru import logger +from .write_file_op import WriteFileOp + @C.register_op() class BatchWriteFileOp(BaseAsyncOp): @@ -34,13 +35,10 @@ class BatchWriteFileOp(BaseAsyncOp): # Get write file dictionary from context write_file_dict: dict = self.context.get("write_file_dict", {}) if not write_file_dict: - self.context.response.answer = "No write file task." logger.info("No write file task.") return # Process each file in the dictionary - result = [] for file_path, content in write_file_dict.items(): write_op = WriteFileOp(save_answer=self.save_answer) await write_op.async_call(file_path=file_path, content=content) - result.append(write_op.output) diff --git a/reme_ai/retrieve/working/grep_op.py b/reme_ai/retrieve/working/grep_op.py new file mode 100644 index 00000000..3d20df18 --- /dev/null +++ b/reme_ai/retrieve/working/grep_op.py @@ -0,0 +1,114 @@ +"""Grep text search operation module. + +This module provides a tool operation for searching text patterns in files. +It enables efficient content-based search using regular expressions, with support +for glob pattern filtering and result limiting. +""" + +import asyncio +from pathlib import Path +from typing import List + +from flowllm.core.context import C +from flowllm.core.op import BaseAsyncToolOp +from flowllm.core.schema import ToolCall +from loguru import logger + + +@C.register_op() +class GrepOp(BaseAsyncToolOp): + """Grep text search operation. + + This operation searches for text patterns in files using regular expressions. + Supports glob pattern filtering and result limiting. + """ + + file_path = __file__ + + def __init__(self, **kwargs): + kwargs.setdefault("raise_exception", False) + super().__init__(**kwargs) + + def build_tool_call(self) -> ToolCall: + """Build and return the tool call schema for this operator.""" + tool_params = { + "name": "Grep", + "description": self.get_prompt("tool_desc"), + "input_schema": { + "file_path": { + "type": "string", + "description": self.get_prompt("file_path"), + "required": True, + }, + "pattern": { + "type": "string", + "description": self.get_prompt("pattern"), + "required": True, + }, + "limit": { + "type": "number", + "description": self.get_prompt("limit"), + "required": False, + }, + }, + } + + return ToolCall(**tool_params) + + async def async_execute(self): + """Execute the grep search operation.""" + pattern: str = self.input_dict.get("pattern", "").strip() + file_path: str | None | Path = self.input_dict.get("file_path", "") + limit: int = self.input_dict.get("limit", 50) + + # Validate pattern + if not pattern: + raise ValueError("The 'pattern' parameter cannot be empty.") + + # Determine search directory + if file_path: + search_dir = Path(file_path).expanduser().resolve() + if not search_dir.exists(): + raise ValueError(f"Search file_path does not exist: {search_dir}") + else: + search_dir = Path.cwd() + + # Build grep command + cmd: List[str] = ["grep", "-RIni"] + if limit: + cmd.extend(["-m", str(limit)]) + cmd.extend(["--", pattern, str(search_dir)]) + + logger.info(f"Running grep command: {' '.join(cmd)}") + + # Execute grep using an async subprocess + process = await asyncio.create_subprocess_exec( + *cmd, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE, + ) + stdout, stderr = await process.communicate() + + if process.returncode not in (0, 1): + # grep returns 1 when no matches are found; treat other codes as errors + err_msg = stderr.decode("utf-8", errors="ignore").strip() + raise RuntimeError(f"grep failed with code {process.returncode}: {err_msg}") + + output_text = stdout.decode("utf-8", errors="ignore").strip() + + # Return raw grep output + if not output_text: + search_location = f'in file_path "{file_path}"' if file_path else "in the workspace directory" + result_msg = f'No matches found for pattern "{pattern}" {search_location}.' + else: + result_msg = output_text + + self.set_output(result_msg) + + async def async_default_execute(self, e: Exception = None, **_kwargs): + """Fill outputs with a default failure message when execution fails.""" + pattern: str = self.input_dict.get("pattern", "").strip() + error_msg = f'Failed to search for pattern "{pattern}"' + if e: + error_msg += f": {str(e)}" + self.set_output(error_msg) diff --git a/reme_ai/context/file_tool/grep_prompt.yaml b/reme_ai/retrieve/working/grep_prompt.yaml similarity index 70% rename from reme_ai/context/file_tool/grep_prompt.yaml rename to reme_ai/retrieve/working/grep_prompt.yaml index 0f49feac..11773cd7 100644 --- a/reme_ai/context/file_tool/grep_prompt.yaml +++ b/reme_ai/retrieve/working/grep_prompt.yaml @@ -3,21 +3,16 @@ tool_desc: | tool_desc_zh: | A powerful search tool for finding patterns in files using regular expressions. Supports full regex syntax (e.g., "log.*Error", "function\\s+\\w+"), glob pattern filtering, and result limiting. Ideal for searching code or text content across multiple files. +file_path: | + 要执行搜索的文件路径。 +file_path_zh: | + The file path to perform the search on. + pattern: | 需要在文件内容中匹配的正则表达式模式。 pattern_zh: | The regular expression pattern to search for in file contents. -path: | - 可选：要执行搜索的目录，默认为当前工作目录。 -path_zh: | - Optional: The directory to search in. Defaults to current working directory. - -glob: | - 可选：用于过滤目标文件的 glob 模式（如 "*.js"、"*.{ts,tsx}"）。 -glob_zh: | - Optional: Glob pattern to filter files (e.g., "*.js", "*.{ts,tsx}"). - limit: | 可选：设置最多返回的匹配行数；未指定时会返回所有匹配。 limit_zh: | diff --git a/reme_ai/retrieve/working/read_file_op.py b/reme_ai/retrieve/working/read_file_op.py new file mode 100644 index 00000000..6819e5d4 --- /dev/null +++ b/reme_ai/retrieve/working/read_file_op.py @@ -0,0 +1,120 @@ +"""Read file operation module. + +This module provides a tool operation for reading file contents. +It supports reading entire files or specific line ranges for large files. +""" + +from pathlib import Path +from typing import Optional + +from flowllm.core.context import C +from flowllm.core.op import BaseAsyncToolOp +from flowllm.core.schema import ToolCall + + +@C.register_op() +class ReadFileOp(BaseAsyncToolOp): + """Read file operation. + + This operation reads and returns the content of a specified file. + For text files, it can read specific line ranges using offset and limit. + """ + + file_path = __file__ + + def __init__(self, **kwargs): + kwargs.setdefault("raise_exception", False) + super().__init__(**kwargs) + + def build_tool_call(self) -> ToolCall: + """Build and return the tool call schema for this operator.""" + tool_params = { + "name": "ReadFile", + "description": self.get_prompt("tool_desc"), + "input_schema": { + "file_path": { + "type": "string", + "description": self.get_prompt("file_path"), + "required": True, + }, + "offset": { + "type": "number", + "description": self.get_prompt("offset"), + "required": True, + }, + "limit": { + "type": "number", + "description": self.get_prompt("limit"), + "required": True, + }, + }, + } + + return ToolCall(**tool_params) + + async def async_execute(self): + """Execute the read file operation.""" + file_path: str = self.input_dict.get("file_path", "").strip() + offset: Optional[int] = self.input_dict.get("offset") + limit: Optional[int] = self.input_dict.get("limit") + + # Validate file_path + if not file_path: + raise ValueError("The 'file_path' parameter cannot be empty.") + + # Resolve file path + file_path_obj = Path(file_path).expanduser().resolve() + + # Check if file exists + if not file_path_obj.exists(): + raise FileNotFoundError(f"File not found: {file_path_obj}") + + if not file_path_obj.is_file(): + raise ValueError(f"Path is not a file: {file_path_obj}") + + # Read file content + content = file_path_obj.read_text(encoding="utf-8") + lines = content.split("\n") + + # Handle line range if specified + if offset is not None or limit is not None: + if offset is None: + offset = 0 + if limit is None: + limit = len(lines) + + # Validate offset and limit + if offset < 0: + raise ValueError("Offset must be a non-negative number") + if limit <= 0: + raise ValueError("Limit must be a positive number") + + total_lines = len(lines) + start = offset + end = min(offset + limit, total_lines) + + if start >= total_lines: + raise ValueError( + f"Offset {offset} is beyond file length ({total_lines} lines)", + ) + + selected_lines = lines[start:end] + result_content = "\n".join(selected_lines) + + # Format output with range information + if end < total_lines: + result = f"Showing lines {start}-{end - 1} of {total_lines} total lines.\n\n---\n\n{result_content}" + else: + result = result_content + else: + result = content + + self.set_output(result) + + async def async_default_execute(self, e: Exception = None, **_kwargs): + """Fill outputs with a default failure message when execution fails.""" + file_path: str = self.input_dict.get("file_path", "").strip() + error_msg = f'Failed to read file "{file_path}"' + if e: + error_msg += f": {str(e)}" + self.set_output(error_msg) diff --git a/reme_ai/retrieve/working/read_file_prompt.yaml b/reme_ai/retrieve/working/read_file_prompt.yaml new file mode 100644 index 00000000..360fb55a --- /dev/null +++ b/reme_ai/retrieve/working/read_file_prompt.yaml @@ -0,0 +1,19 @@ +tool_desc: | + 读取指定文件内容的工具；对文本文件可通过 offset 与 limit 获取特定行区间，便于分页浏览大文件。 +tool_desc_zh: | + Reads and returns the content of a specified file. For text files, it can read specific line ranges using the 'offset' and 'limit' parameters. Use offset and limit to paginate through large files. + +file_path: | + 待读取文件的路径。 +file_path_zh: | + The path to the file to read. + +offset: | + 文本文件起始读取的 0 基行号；需与 limit 同时使用，适合分页查看大文件。 +offset_zh: | + For text files, the 0-based line number to start reading from. Requires 'limit' to be set. Use for paginating through large files. + +limit: | + 文本文件最多读取的行数；与 offset 配合实现分页，若仅设 offset 则会读到文件末尾。 +limit_zh: | + For text files, maximum number of lines to read. Use with 'offset' to paginate through large files. If omitted when offset is provided, reads from offset to the end of the file. diff --git a/reme_ai/retrieve/working/write_file_op.py b/reme_ai/retrieve/working/write_file_op.py new file mode 100644 index 00000000..20097c79 --- /dev/null +++ b/reme_ai/retrieve/working/write_file_op.py @@ -0,0 +1,89 @@ +"""Write file operation module. + +This module provides a tool operation for writing content to files. +It supports creating new files or overwriting existing files, and automatically +creates parent directories if they don't exist. +""" + +from pathlib import Path + +from flowllm.core.context import C +from flowllm.core.op import BaseAsyncToolOp +from flowllm.core.schema import ToolCall + + +@C.register_op() +class WriteFileOp(BaseAsyncToolOp): + """Write file operation. + + This operation writes content to a specified file. If the file doesn't exist, + it will be created. If parent directories don't exist, they will be created automatically. + """ + + file_path = __file__ + + def __init__(self, **kwargs): + kwargs.setdefault("raise_exception", False) + super().__init__(**kwargs) + + def build_tool_call(self) -> ToolCall: + """Build and return the tool call schema for this operator.""" + tool_params = { + "name": "WriteFile", + "description": self.get_prompt("tool_desc"), + "input_schema": { + "file_path": { + "type": "string", + "description": self.get_prompt("file_path"), + "required": True, + }, + "content": { + "type": "string", + "description": self.get_prompt("content"), + "required": True, + }, + }, + } + + return ToolCall(**tool_params) + + async def async_execute(self): + """Execute the write file operation.""" + file_path: str = self.input_dict.get("file_path", "").strip() + content: str = self.input_dict.get("content", "") + + # Validate file_path + if not file_path: + raise ValueError("The 'file_path' parameter cannot be empty.") + + # Resolve file path + file_path_obj = Path(file_path).expanduser().resolve() + + # Check if path is a directory + if file_path_obj.exists() and file_path_obj.is_dir(): + raise ValueError(f"Path is a directory, not a file: {file_path_obj}") + + # Create parent directories if they don't exist + file_path_obj.parent.mkdir(parents=True, exist_ok=True) + + # Check if file exists + file_exists = file_path_obj.exists() and file_path_obj.is_file() + + # Write content to file + file_path_obj.write_text(content, encoding="utf-8") + + # Format success message + if file_exists: + result = f"Successfully overwrote file: {file_path_obj}" + else: + result = f"Successfully created and wrote to new file: {file_path_obj}" + + self.set_output(result) + + async def async_default_execute(self, e: Exception = None, **_kwargs): + """Fill outputs with a default failure message when execution fails.""" + file_path: str = self.input_dict.get("file_path", "").strip() + error_msg = f'Failed to write file "{file_path}"' + if e: + error_msg += f": {str(e)}" + self.set_output(error_msg) diff --git a/reme_ai/retrieve/working/write_file_prompt.yaml b/reme_ai/retrieve/working/write_file_prompt.yaml new file mode 100644 index 00000000..02740d04 --- /dev/null +++ b/reme_ai/retrieve/working/write_file_prompt.yaml @@ -0,0 +1,9 @@ +tool_desc: | + Writes content to a specified file in the local filesystem. If the file doesn't exist, it will be created. If parent directories don't exist, they will be created automatically. If the file already exists, it will be overwritten with the new content. + +file_path: | + The absolute path to the file to write to (e.g., '/home/user/project/file.txt'). Relative paths are not supported. You must provide an absolute path. + +content: | + The content to write to the file. + diff --git a/reme_ai/summary/working/__init__.py b/reme_ai/summary/working/__init__.py new file mode 100644 index 00000000..ceca9dd7 --- /dev/null +++ b/reme_ai/summary/working/__init__.py @@ -0,0 +1,23 @@ +"""Working-memory summary package for the ReMe framework. + +This package provides *working-memory* oriented summary operations that can be used in +LLM-powered flows to reduce token usage and keep the active message window small, while +preserving access to detailed historical information when needed. It includes: + +- MessageCompactOp: Compact verbose tool messages by storing full content in external files + and keeping short previews in the working context. +- MessageCompressOp: Compress conversation history using an LLM to generate dense summaries + that represent the agent's state snapshot. +- MessageOffloadOp: Orchestrate compaction and optional compression as a unified + working-memory offload pipeline. +""" + +from .message_compact_op import MessageCompactOp +from .message_compress_op import MessageCompressOp +from .message_offload_op import MessageOffloadOp + +__all__ = [ + "MessageCompactOp", + "MessageCompressOp", + "MessageOffloadOp", +] diff --git a/reme_ai/context/offload/context_compact_op.py b/reme_ai/summary/working/message_compact_op.py similarity index 78% rename from reme_ai/context/offload/context_compact_op.py rename to reme_ai/summary/working/message_compact_op.py index 6e4031b8..b20488fb 100644 --- a/reme_ai/context/offload/context_compact_op.py +++ b/reme_ai/summary/working/message_compact_op.py @@ -1,9 +1,9 @@ -""" -Context compaction module for reducing token usage in conversation contexts. +"""Working-memory compaction module for reducing token usage. -This module provides functionality to compress large tool messages by storing -their full content in external files and keeping only previews in the context. -This helps manage context window limits while preserving important information. +This module provides functionality to compact large tool messages for +*working memory summary* by storing their full content in external files and +keeping only short previews in the active message list. This reduces token +usage while preserving access to detailed information when needed. """ from pathlib import Path @@ -17,13 +17,14 @@ from loguru import logger @C.register_op() -class ContextCompactOp(BaseAsyncOp): +class MessageCompactOp(BaseAsyncOp): """ - Context compaction operation that reduces token usage by compressing tool messages. + Working-memory compaction operation that reduces token usage by compacting tool messages. - When the total token count exceeds the threshold, this operation compresses large tool - messages by truncating their content and storing the full content in external files. - This helps manage context window limits while preserving recent tool messages. + When the total token count exceeds the threshold, this operation truncates large tool + messages and stores the full content in external files. This is intended for + working-memory style summarization: the agent sees a short preview while the + complete result remains available out-of-band. """ async def async_execute(self): @@ -41,7 +42,7 @@ class ContextCompactOp(BaseAsyncOp): # Get configuration from context max_total_tokens: int = self.context.get("max_total_tokens", 20000) max_tool_message_tokens: int = self.context.get("max_tool_message_tokens", 2000) - preview_char_length: int = self.context.get("preview_char_length", 100) + preview_char_length: int = self.context.get("preview_char_length", 0) keep_recent_count: int = self.context.get("keep_recent_count", 1) store_dir: Path = Path(self.context.get("store_dir", "")) @@ -50,12 +51,22 @@ class ContextCompactOp(BaseAsyncOp): assert preview_char_length >= 0, "preview_char_length must be greater than 0" assert keep_recent_count >= 0, "keep_recent_count must be greater than 0" - # Convert context messages to Message objects messages = [Message(**x) for x in self.context.messages] + + # Convert context messages to Message objects messages_to_compress = [x for x in messages if x.role is not Role.SYSTEM] if keep_recent_count > 0: messages_to_compress = messages_to_compress[:-keep_recent_count] + # Extract system message (should be exactly one) + system_message = [x for x in messages if x.role is Role.SYSTEM] + assert len(system_message) <= 1, f"Expected at most one system message, got {len(system_message)}" + + if len(system_message) == 0: + system_message = Message(role=Role.SYSTEM, content="") + else: + system_message = system_message[0] + # If nothing to compress after filtering, return original messages if not messages_to_compress: self.context.response.answer = self.context.messages @@ -103,18 +114,18 @@ class ContextCompactOp(BaseAsyncOp): # Store the full content for batch writing write_file_dict[store_path.as_posix()] = original_content - # Create compressed preview of the tool message content - compact_result = original_content[:preview_char_length] + "..." - # Log the compaction action logger.info( f"Compacting tool message (tool_call_id={tool_message.tool_call_id}): " - f"token count={tool_token_cnt}, saving full content to {store_path}", + f"token count={tool_token_cnt}, saving full content to {store_path.as_posix()}", ) # Update tool message content with preview and file reference - compact_result += f" (detailed result is stored in {store_path})" + compact_result = f"tool call={file_name} result is stored in file path=`{store_path.as_posix()}`" + if preview_char_length > 0: + compact_result += f"\npreview: {original_content[:preview_char_length]}..." tool_message.content = compact_result + system_message.content += f"\n\n{compact_result}" # Store write_file_dict in context for potential batch writing if write_file_dict: diff --git a/reme_ai/context/offload/context_compress_op.py b/reme_ai/summary/working/message_compress_op.py similarity index 93% rename from reme_ai/context/offload/context_compress_op.py rename to reme_ai/summary/working/message_compress_op.py index 7713ee18..3eac31da 100644 --- a/reme_ai/context/offload/context_compress_op.py +++ b/reme_ai/summary/working/message_compress_op.py @@ -1,14 +1,14 @@ -""" -Context compression module for reducing token usage in conversation contexts using LLM. +"""Working-memory compression module using LLM. -This module provides functionality to compress conversation history by using a language -model to generate concise summaries of older messages while preserving recent messages. -This helps manage context window limits while maintaining conversation coherence. +This module compresses long conversation history for *working memory summary* by +using a language model to generate concise summaries of older messages while +preserving more recent ones. It is designed to keep the agent's short-term +context small, while still retaining the essential information from past turns. The compression process: 1. Identifies messages that exceed token thresholds -2. Splits messages into groups if needed -3. Uses LLM to generate compressed summaries of older message groups +2. Optionally splits messages into groups based on token budget +3. Uses an LLM to generate compressed summaries of older message groups 4. Stores original messages to files for potential retrieval 5. Appends compressed summaries to the system message while preserving recent messages """ @@ -29,16 +29,17 @@ from reme_ai.utils.op_utils import extract_xml_tag_content @C.register_op() -class ContextCompressOp(BaseAsyncOp): +class MessageCompressOp(BaseAsyncOp): """ - Context compression operation that uses LLM to reduce token usage. + Working-memory compression operation that uses an LLM to reduce token usage. - When the total token count exceeds the threshold, this operation uses a language - model to compress older messages into a concise summary while keeping recent - messages intact. This preserves conversation context while reducing token usage. + When the total token count of older messages exceeds the threshold, this operation + calls a language model to compress them into a concise summary, while keeping + recent messages intact. This provides a compact *state snapshot* that the agent + can rely on for subsequent steps. Attributes: - file_path: Path to the operation file, used for configuration. + file_path: Path to the operation file, used for configuration (e.g. prompts). Context Parameters: max_total_tokens (int): Maximum token count threshold for compression. @@ -205,8 +206,8 @@ class ContextCompressOp(BaseAsyncOp): continue compress_content = ( - f"[Compressed conversation history - Part {g_idx}/{len(message_groups)}]\n{group_summary}\n\n" - f"(Original {len(messages)} messages are stored in: {store_path.as_posix()})\n" + f"[Compressed conversation history - Part {g_idx}/{len(message_groups)}]\n{group_summary}\n" + f"(Original {len(messages)} messages are stored in: {store_path.as_posix()})" ) compressed_tokens = self.token_count([Message(content=compress_content)]) diff --git a/reme_ai/context/offload/context_compress_prompt.yaml b/reme_ai/summary/working/message_compress_prompt.yaml similarity index 100% rename from reme_ai/context/offload/context_compress_prompt.yaml rename to reme_ai/summary/working/message_compress_prompt.yaml diff --git a/reme_ai/summary/working/message_offload_op.py b/reme_ai/summary/working/message_offload_op.py new file mode 100644 index 00000000..cdfa7697 --- /dev/null +++ b/reme_ai/summary/working/message_offload_op.py @@ -0,0 +1,112 @@ +"""Working-memory offload module based on compaction and compression. + +This module implements a high-level *working summary* operation that orchestrates +message compaction and LLM-based compression to reduce token usage for long +conversations. It first compacts verbose tool messages, and, if the reduction +ratio is not sufficient, it further compresses the history with an LLM. + +The offload process: +1. Compacts tool messages by storing full content in external files and keeping previews +2. Evaluates compaction effectiveness by comparing token counts before/after +3. If the compaction ratio exceeds a configurable threshold, applies LLM-based compression +""" + +from flowllm.core.context import C +from flowllm.core.op import BaseAsyncOp +from flowllm.core.schema import Message +from loguru import logger + +from reme_ai.enumeration import WorkingSummaryMode + + +@C.register_op() +class MessageOffloadOp(BaseAsyncOp): + """ + Working-memory offload operation that orchestrates compaction and compression. + + This operation is designed specifically for *working memory summary* in long-running + conversations. Its behavior is controlled by ``working_summary_mode``: + + - ``COMPACT`` – only compact verbose tool messages by storing full content externally + and keeping short previews in the context. + - ``COMPRESS`` – only apply LLM-based compression to generate a compact state snapshot. + - ``AUTO`` – first run compaction, then optionally run compression if the + compaction ratio is not sufficient. + + Context Parameters: + working_summary_mode (WorkingSummaryMode | str): Working summary strategy to use. + One of ``COMPACT``, ``COMPRESS`` or ``AUTO``. Defaults to ``AUTO``. + compact_ratio_threshold (float): Only used in ``AUTO`` mode. Threshold for + compaction ratio (compressed tokens divided by original tokens) above which + LLM compression is applied. Defaults to 0.75. + """ + + async def async_execute(self): + """ + Execute the working-memory offload operation. + + The behavior is selected via ``working_summary_mode``: + + - COMPACT: only compaction is executed. + - COMPRESS: only compression is executed. + - AUTO: compaction is executed first; if the reduction is insufficient, a + compression pass is executed. + """ + from .message_compact_op import MessageCompactOp + from .message_compress_op import MessageCompressOp + + message_compact_op = MessageCompactOp() + message_compress_op = MessageCompressOp() + + # Resolve working summary mode (string or enum) with AUTO as default. + working_summary_mode = self.context.get("working_summary_mode", WorkingSummaryMode.AUTO) + if isinstance(working_summary_mode, str): + working_summary_mode = WorkingSummaryMode(working_summary_mode) + + if working_summary_mode == WorkingSummaryMode.COMPACT: + logger.info("Working-memory offload mode: COMPACT (only compaction)") + await message_compact_op.async_call(context=self.context) + return + + if working_summary_mode == WorkingSummaryMode.COMPRESS: + logger.info("Working-memory offload mode: COMPRESS (only compression)") + await message_compress_op.async_call(context=self.context) + return + + # AUTO mode: compaction first, then compression if reduction is not sufficient. + logger.info("Working-memory offload mode: AUTO (compaction then optional compression)") + await message_compact_op.async_call(context=self.context) + + origin_messages = [Message(**x) for x in self.context.messages] + origin_token_cnt = self.token_count(origin_messages) + + result_messages = [Message(**x) for x in self.context.response.answer] + answer_token_cnt = self.token_count(result_messages) + + if origin_token_cnt <= 0: + logger.warning("Origin token count is 0 after compaction; skip compression stage") + return + + compact_ratio = answer_token_cnt / origin_token_cnt + compact_ratio_threshold: float = self.context.get("compact_ratio_threshold", 0.75) + + if compact_ratio > compact_ratio_threshold: + logger.info( + f"Working-memory offload: compact ratio {compact_ratio:.2f} > " + f"{compact_ratio_threshold:.2f}, applying compression stage", + ) + await message_compress_op.async_call(context=self.context) + + async def async_default_execute(self, e: Exception = None, **_kwargs): + """Handle execution errors by returning original messages. + + This method is called when an exception occurs during async_execute. It preserves + the original messages and marks the operation as unsuccessful. + + Args: + e: The exception that occurred during execution, if any. + **_kwargs: Additional keyword arguments (unused but required by interface). + """ + self.context.response.answer = self.context.messages + self.context.response.success = False + self.context.response.metadata["error"] = str(e) diff --git a/test_op/test_agentic_retrieve_op.py b/test_op/test_agentic_retrieve_op.py new file mode 100644 index 00000000..d3fa1f4a --- /dev/null +++ b/test_op/test_agentic_retrieve_op.py @@ -0,0 +1,112 @@ +"""Test script for AgenticRetrieveOp. + +This script provides a simple end-to-end test case for AgenticRetrieveOp. +It can be run directly with: python test_agentic_retrieve_op.py +""" + +import asyncio +import json + +from flowllm.core.enumeration import Role +from flowllm.core.schema import Message, ToolCall +from loguru import logger + +from reme_ai.agent.react.agentic_retrieve_op import AgenticRetrieveOp +from reme_ai.main import ReMeApp + + +async def test_agentic_retrieve_basic(): + """Basic test for AgenticRetrieveOp with a short conversation history.""" + logger.info("\n" + "=" * 60) + logger.info("Test: AgenticRetrieveOp basic behavior") + logger.info("=" * 60) + + tool_call_id = "call_6596dafa2a6a46f7a217da" + f = open("README.md", encoding="utf-8") + readme_content = f.read() + f.close() + + messages = [ + Message( + role=Role.SYSTEM, + content=( + "You are a helpful assistant. " + "请先使用`Grep`匹配关键词或者正则表达式所在行数，然后通过`ReadFile`读取位置附近的代码。" + "如果没有找到匹配项，永远不要放弃尝试，尝试其他的参数，比如只搜索部分关键词。" + "`Grep`之后通过 `ReadFile` 命令，你可以从指定偏移位置`offset`+长度`limit`开始查看内容，不要超过50行。" + "如果当前内容不足，`ReadFile` 命令也可以不断尝试不同的`offset`和`limit`参数" + ), + ), + Message( + role=Role.USER, + content="搜索下reme项目的的README文件", + ), + Message( + role=Role.ASSISTANT, + content="", + tool_calls=[ + ToolCall( + **{ + "index": 0, + "id": tool_call_id, + "function": { + "arguments": '{"query": "readme"}', + "name": "web_search", + }, + "type": "function", + }, + ), + ], + ), + Message( + role=Role.TOOL, + content=readme_content, + tool_call_id=tool_call_id, + ), + Message( + role=Role.USER, + content="根据readme回答task memory在appworld的效果是多少，需要具体的数值", + ), + ] + + # llm = "qwen3_coder_plus" + llm = "qwen3_30b_instruct" + # llm = "qwen3_coder_30b_instruct" + # llm = "qwen3_max_instruct" + op = AgenticRetrieveOp(llm=llm) + + await op.async_call( + messages=[m.model_dump() for m in messages], + working_summary_mode="auto", + compact_ratio_threshold=0.75, + max_total_tokens=6000, + max_tool_message_tokens=2000, + group_token_threshold=None, + keep_recent_count=1, + store_dir="./test_working_memory", + chat_id="c123", + ) + + answer = op.context.response.answer + messages = op.context.response.metadata["messages"] + logger.info(f"✓ AgenticRetrieveOp result answer: {answer}") + logger.info(f"✓ AgenticRetrieveOp result messages: {json.dumps(messages, ensure_ascii=False, indent=2)}") + logger.info(f" Success: {op.context.response.success}") + + +async def async_main(): + """Entry point for running AgenticRetrieveOp test.""" + async with ReMeApp(): + logger.info("=" * 80) + logger.info("Testing AgenticRetrieveOp - ReAct Retrieval Workflow") + logger.info("=" * 80) + + await test_agentic_retrieve_basic() + + logger.info("\n" + "=" * 80) + logger.info("All AgenticRetrieveOp tests completed!") + logger.info("=" * 80) + + +if __name__ == "__main__": + asyncio.run(async_main()) diff --git a/test_op/test_context_compact_op.py b/test_op/test_message_compact_op.py similarity index 88% rename from test_op/test_context_compact_op.py rename to test_op/test_message_compact_op.py index 9cc5727b..cd8e4edc 100644 --- a/test_op/test_context_compact_op.py +++ b/test_op/test_message_compact_op.py @@ -1,6 +1,6 @@ -"""Test script for ContextCompactOp. +"""Test script for MessageCompactOp. -This script provides test cases for ContextCompactOp class. +This script provides test cases for MessageCompactOp class. It can be run directly with: python test_context_compact_op.py """ @@ -9,13 +9,13 @@ import asyncio from flowllm.core.enumeration import Role from flowllm.core.schema import Message -from reme_ai.context.file_tool import BatchWriteFileOp -from reme_ai.context.offload import ContextCompactOp from reme_ai.main import ReMeApp +from reme_ai.retrieve.working import BatchWriteFileOp +from reme_ai.summary.working import MessageCompactOp async def async_main(): - """Test function for ContextCompactOp.""" + """Test function for MessageCompactOp.""" async with ReMeApp(): # Create test messages with system, user, assistant, tool sequence messages = [ @@ -60,7 +60,7 @@ async def async_main(): ] # Create op with lower thresholds for testing - op = ContextCompactOp() >> BatchWriteFileOp() + op = MessageCompactOp() >> BatchWriteFileOp() # Execute the compaction await op.async_call( diff --git a/test_op/test_context_compress_op.py b/test_op/test_message_compress_op.py similarity index 97% rename from test_op/test_context_compress_op.py rename to test_op/test_message_compress_op.py index e35ed71c..51423c3a 100644 --- a/test_op/test_context_compress_op.py +++ b/test_op/test_message_compress_op.py @@ -1,7 +1,7 @@ """ -Test script for ContextCompressOp. +Test script for MessageCompressOp. -This script demonstrates how to use the context compression operation to reduce +This script demonstrates how to use the message compression operation to reduce token usage in conversation histories using language models. """ @@ -9,16 +9,16 @@ import asyncio from loguru import logger -from reme_ai.context.offload.context_compress_op import ContextCompressOp from reme_ai.main import ReMeApp +from reme_ai.summary.working import MessageCompressOp async def main(): - """Main function to test ContextCompressOp.""" + """Main function to test MessageCompressOp.""" async with ReMeApp(): logger.info("=" * 80) - logger.info("Testing ContextCompressOp - LLM-based Context Compression") + logger.info("Testing MessageCompressOp - LLM-based Context Compression") logger.info("=" * 80) # Create a mock conversation with multiple messages @@ -220,7 +220,7 @@ async def main(): logger.info("Test 1: Messages below threshold (should skip compression)") logger.info("=" * 60) - compress_op1 = ContextCompressOp() + compress_op1 = MessageCompressOp() await compress_op1.async_call( messages=messages, @@ -236,7 +236,7 @@ async def main(): logger.info("Test 2: Messages above threshold (should compress)") logger.info("=" * 60) - compress_op2 = ContextCompressOp() + compress_op2 = MessageCompressOp() await compress_op2.async_call( messages=messages, @@ -260,7 +260,7 @@ async def main(): logger.info("Test 3: Messages above micro threshold (should compress)") logger.info("=!" * 30) - compress_op2 = ContextCompressOp() + compress_op2 = MessageCompressOp() await compress_op2.async_call( messages=messages, diff --git a/test_op/test_context_offload_op.py b/test_op/test_message_offload_op.py similarity index 89% rename from test_op/test_context_offload_op.py rename to test_op/test_message_offload_op.py index b5b87117..403e89fd 100644 --- a/test_op/test_context_offload_op.py +++ b/test_op/test_message_offload_op.py @@ -1,6 +1,6 @@ -"""Test script for ContextOffloadOp. +"""Test script for MessageOffloadOp. -This script provides test cases for ContextOffloadOp class. +This script provides test cases for MessageOffloadOp class. It can be run directly with: python test_context_offload_op.py """ @@ -10,14 +10,14 @@ from flowllm.core.enumeration import Role from flowllm.core.schema import Message from loguru import logger -from reme_ai.context.file_tool import BatchWriteFileOp -from reme_ai.context.offload.context_offload_op import ContextOffloadOp -from reme_ai.enumeration import ContextManageEnum +from reme_ai.enumeration import WorkingSummaryMode from reme_ai.main import ReMeApp +from reme_ai.retrieve.working import BatchWriteFileOp +from reme_ai.summary.working import MessageOffloadOp async def test_compact_mode(): - """Test COMPACT mode - Only apply compaction.""" + """Test COMPACT mode - Only apply compaction with MessageOffloadOp.""" logger.info("\n" + "=" * 60) logger.info("Test: COMPACT mode - Only apply compaction") logger.info("=" * 60) @@ -64,11 +64,11 @@ async def test_compact_mode(): ), ] - op = ContextOffloadOp() >> BatchWriteFileOp() + op = MessageOffloadOp() >> BatchWriteFileOp() await op.async_call( messages=[m.model_dump() for m in messages], - context_manage_mode=ContextManageEnum.COMPACT, + context_manage_mode=WorkingSummaryMode.COMPACT, max_total_tokens=1000, # Low threshold to trigger compaction max_tool_message_tokens=100, # Low threshold to compact tool messages preview_char_length=50, # Keep 50 chars in preview @@ -82,7 +82,7 @@ async def test_compact_mode(): async def test_compress_mode(): - """Test COMPRESS mode - Only apply compression.""" + """Test COMPRESS mode - Only apply compression with MessageOffloadOp.""" logger.info("\n" + "=" * 60) logger.info("Test: COMPRESS mode - Only apply compression") logger.info("=" * 60) @@ -129,11 +129,11 @@ async def test_compress_mode(): ), ] - op = ContextOffloadOp() >> BatchWriteFileOp() + op = MessageOffloadOp() >> BatchWriteFileOp() await op.async_call( messages=[m.model_dump() for m in messages], - context_manage_mode=ContextManageEnum.COMPRESS, + context_manage_mode=WorkingSummaryMode.COMPRESS, max_total_tokens=2000, # Low threshold to trigger compression keep_recent_count=2, store_dir="./test_compact_storage", @@ -145,7 +145,7 @@ async def test_compress_mode(): async def test_auto_mode(): - """Test AUTO mode - Apply compaction first, then compression if needed.""" + """Test AUTO mode - Apply compaction first, then compression if needed using MessageOffloadOp.""" logger.info("\n" + "=" * 60) logger.info("Test: AUTO mode - Apply compaction first, then compression if needed") logger.info("=" * 60) @@ -209,11 +209,11 @@ async def test_auto_mode(): ), ] - op = ContextOffloadOp() >> BatchWriteFileOp() + op = MessageOffloadOp() >> BatchWriteFileOp() await op.async_call( messages=[m.model_dump() for m in auto_messages], - context_manage_mode=ContextManageEnum.AUTO, + context_manage_mode=WorkingSummaryMode.AUTO, compact_ratio_threshold=0.2, # Low threshold, should trigger compression after compact max_total_tokens=1000, max_tool_message_tokens=100, @@ -228,14 +228,14 @@ async def test_auto_mode(): async def async_main(): - """Test function for ContextOffloadOp.""" + """Test function for MessageOffloadOp.""" async with ReMeApp(): logger.info("=" * 80) - logger.info("Testing ContextOffloadOp - Context Management Orchestration") + logger.info("Testing MessageOffloadOp - Context Management Orchestration") logger.info("=" * 80) - # await test_compact_mode() - # await test_compress_mode() + await test_compact_mode() + await test_compress_mode() await test_auto_mode() logger.info("\n" + "=" * 80) From af0fbeaf8bdd617b17b9f80c75f6481c73f59535 Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Mon, 24 Nov 2025 21:19:45 +0800 Subject: [PATCH 29/39] fix(test): update search query in agentic retrieve test --- test_op/test_agentic_retrieve_op.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test_op/test_agentic_retrieve_op.py b/test_op/test_agentic_retrieve_op.py index d3fa1f4a..17bc026c 100644 --- a/test_op/test_agentic_retrieve_op.py +++ b/test_op/test_agentic_retrieve_op.py @@ -39,7 +39,7 @@ async def test_agentic_retrieve_basic(): ), Message( role=Role.USER, - content="搜索下reme项目的的README文件", + content="搜索下reme项目的的README内容", ), Message( role=Role.ASSISTANT, From 8791de6d6f9097850248aee5804926dbc25fd709 Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Tue, 25 Nov 2025 10:59:38 +0800 Subject: [PATCH 30/39] refactor(working_memory): rename context offload operations to working memory summarization --- reme_ai/config/default.yaml | 14 ++--- reme_ai/retrieve/working/read_file_op.py | 65 +++++++----------------- test_op/test_agentic_retrieve_op.py | 6 +-- 3 files changed, 27 insertions(+), 58 deletions(-) diff --git a/reme_ai/config/default.yaml b/reme_ai/config/default.yaml index ef73afc1..153866e1 100644 --- a/reme_ai/config/default.yaml +++ b/reme_ai/config/default.yaml @@ -161,11 +161,11 @@ flow: summary_working_memory: flow_content: MessageOffloadOp() >> BatchWriteFileOp() - description: "Manages context window limits by compacting tool messages and compressing conversation history. First compacts large tool messages by storing full content in external files, then applies LLM-based compression if compaction ratio exceeds threshold. This helps reduce token usage while preserving important information." + description: "Summarizes working memory by compacting tool messages and compressing conversation history. First compacts large tool messages by storing full content in external files, then applies LLM-based compression if compaction ratio exceeds threshold. This helps reduce token usage while preserving important information." input_schema: messages: type: array - description: "List of conversation messages to process for context offloading" + description: "List of conversation messages to process for working memory summarization" required: true working_summary_mode: type: string @@ -194,7 +194,7 @@ flow: required: false store_dir: type: string - description: "Directory path for storing offloaded message content. Full tool message content and compressed message groups are saved as files in this directory. Required for compaction and compression operations." + description: "Directory path for storing summarized message content. Full tool message content and compressed message groups are saved as files in this directory. Required for compaction and compression operations." required: false chat_id: type: string @@ -207,13 +207,13 @@ flow: read_working_memory: flow_content: ReadFileOp() - summary_working_memory_for_agentscope: + summary_working_memory_for_as: flow_content: MessageOffloadOp() - description: "Context offload operation for AgentScope integration. Manages context window limits by compacting tool messages and compressing conversation history without batch file writing. Same functionality as context_offload but without the BatchWriteFileOp step." + description: "Working memory summary operation for AgentScope integration. Summarizes working memory by compacting tool messages and compressing conversation history without batch file writing. Same functionality as summary_working_memory but without the BatchWriteFileOp step." input_schema: messages: type: array - description: "List of conversation messages to process for context offloading" + description: "List of conversation messages to process for working memory summarization" required: true working_summary_mode: type: string @@ -242,7 +242,7 @@ flow: required: false store_dir: type: string - description: "Directory path for storing offloaded message content. Full tool message content and compressed message groups are saved as files in this directory. Required for compaction and compression operations." + description: "Directory path for storing summarized message content. Full tool message content and compressed message groups are saved as files in this directory. Required for compaction and compression operations." required: false chat_id: type: string diff --git a/reme_ai/retrieve/working/read_file_op.py b/reme_ai/retrieve/working/read_file_op.py index 6819e5d4..adb1fb4f 100644 --- a/reme_ai/retrieve/working/read_file_op.py +++ b/reme_ai/retrieve/working/read_file_op.py @@ -4,6 +4,7 @@ This module provides a tool operation for reading file contents. It supports reading entire files or specific line ranges for large files. """ +import subprocess from pathlib import Path from typing import Optional @@ -58,58 +59,26 @@ class ReadFileOp(BaseAsyncToolOp): offset: Optional[int] = self.input_dict.get("offset") limit: Optional[int] = self.input_dict.get("limit") - # Validate file_path - if not file_path: - raise ValueError("The 'file_path' parameter cannot be empty.") - - # Resolve file path + # Validate and resolve file path + assert file_path, "The 'file_path' parameter cannot be empty." file_path_obj = Path(file_path).expanduser().resolve() + assert file_path_obj.exists(), f"File not found: {file_path_obj}" + assert file_path_obj.is_file(), f"Path is not a file: {file_path_obj}" - # Check if file exists - if not file_path_obj.exists(): - raise FileNotFoundError(f"File not found: {file_path_obj}") + # Set default values and validate + offset = offset or 0 + limit = limit or 1000000 + assert offset >= 0, "Offset must be a non-negative number" + assert limit > 0, "Limit must be a positive number" - if not file_path_obj.is_file(): - raise ValueError(f"Path is not a file: {file_path_obj}") + # Use sed for efficient line reading (1-indexed) + start_line = offset + 1 + end_line = offset + limit - # Read file content - content = file_path_obj.read_text(encoding="utf-8") - lines = content.split("\n") - - # Handle line range if specified - if offset is not None or limit is not None: - if offset is None: - offset = 0 - if limit is None: - limit = len(lines) - - # Validate offset and limit - if offset < 0: - raise ValueError("Offset must be a non-negative number") - if limit <= 0: - raise ValueError("Limit must be a positive number") - - total_lines = len(lines) - start = offset - end = min(offset + limit, total_lines) - - if start >= total_lines: - raise ValueError( - f"Offset {offset} is beyond file length ({total_lines} lines)", - ) - - selected_lines = lines[start:end] - result_content = "\n".join(selected_lines) - - # Format output with range information - if end < total_lines: - result = f"Showing lines {start}-{end - 1} of {total_lines} total lines.\n\n---\n\n{result_content}" - else: - result = result_content - else: - result = content - - self.set_output(result) + cmd = ["sed", "-n", f"{start_line},{end_line}p", str(file_path_obj)] + result = subprocess.run(cmd, capture_output=True, text=True, timeout=30, check=True) + content = result.stdout.rstrip("\n") + self.set_output(content) async def async_default_execute(self, e: Exception = None, **_kwargs): """Fill outputs with a default failure message when execution fails.""" diff --git a/test_op/test_agentic_retrieve_op.py b/test_op/test_agentic_retrieve_op.py index 17bc026c..7b0f0c1c 100644 --- a/test_op/test_agentic_retrieve_op.py +++ b/test_op/test_agentic_retrieve_op.py @@ -33,7 +33,7 @@ async def test_agentic_retrieve_basic(): "You are a helpful assistant. " "请先使用`Grep`匹配关键词或者正则表达式所在行数，然后通过`ReadFile`读取位置附近的代码。" "如果没有找到匹配项，永远不要放弃尝试，尝试其他的参数，比如只搜索部分关键词。" - "`Grep`之后通过 `ReadFile` 命令，你可以从指定偏移位置`offset`+长度`limit`开始查看内容，不要超过50行。" + "`Grep`之后通过 `ReadFile` 命令，你可以从指定偏移位置`offset`+长度`limit`开始查看内容，不要超过100行。" "如果当前内容不足，`ReadFile` 命令也可以不断尝试不同的`offset`和`limit`参数" ), ), @@ -60,7 +60,7 @@ async def test_agentic_retrieve_basic(): ), Message( role=Role.TOOL, - content=readme_content, + content=readme_content * 4, tool_call_id=tool_call_id, ), Message( @@ -79,7 +79,7 @@ async def test_agentic_retrieve_basic(): messages=[m.model_dump() for m in messages], working_summary_mode="auto", compact_ratio_threshold=0.75, - max_total_tokens=6000, + max_total_tokens=20000, max_tool_message_tokens=2000, group_token_threshold=None, keep_recent_count=1, From a6ba93ccf368c00bc17e6dd210b022f6d6a698ed Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Tue, 25 Nov 2025 11:23:02 +0800 Subject: [PATCH 31/39] refactor(read_file_op): replace subprocess with asyncio for sed command execution --- reme_ai/retrieve/working/read_file_op.py | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/reme_ai/retrieve/working/read_file_op.py b/reme_ai/retrieve/working/read_file_op.py index adb1fb4f..c6d4aaaa 100644 --- a/reme_ai/retrieve/working/read_file_op.py +++ b/reme_ai/retrieve/working/read_file_op.py @@ -4,7 +4,7 @@ This module provides a tool operation for reading file contents. It supports reading entire files or specific line ranges for large files. """ -import subprocess +import asyncio from pathlib import Path from typing import Optional @@ -76,8 +76,15 @@ class ReadFileOp(BaseAsyncToolOp): end_line = offset + limit cmd = ["sed", "-n", f"{start_line},{end_line}p", str(file_path_obj)] - result = subprocess.run(cmd, capture_output=True, text=True, timeout=30, check=True) - content = result.stdout.rstrip("\n") + process = await asyncio.create_subprocess_exec( + *cmd, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE, + ) + stdout, stderr = await asyncio.wait_for(process.communicate(), timeout=30) + + assert process.returncode == 0, f"sed command failed: {stderr.decode()}" + content = stdout.decode().rstrip("\n") self.set_output(content) async def async_default_execute(self, e: Exception = None, **_kwargs): From e69019623c5f76bc36c752fa7f7b4a28537b1f96 Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Tue, 25 Nov 2025 11:40:57 +0800 Subject: [PATCH 32/39] refactor(grep_op): replace asyncio subprocess with run_shell_command utility --- reme_ai/retrieve/working/grep_op.py | 18 ++++-------- reme_ai/retrieve/working/read_file_op.py | 13 +++------ reme_ai/utils/op_utils.py | 37 +++++++++++++++++++++++- test_op/test_agentic_retrieve_op.py | 5 ++-- 4 files changed, 48 insertions(+), 25 deletions(-) diff --git a/reme_ai/retrieve/working/grep_op.py b/reme_ai/retrieve/working/grep_op.py index 3d20df18..e995723a 100644 --- a/reme_ai/retrieve/working/grep_op.py +++ b/reme_ai/retrieve/working/grep_op.py @@ -5,7 +5,6 @@ It enables efficient content-based search using regular expressions, with suppor for glob pattern filtering and result limiting. """ -import asyncio from pathlib import Path from typing import List @@ -13,6 +12,7 @@ from flowllm.core.context import C from flowllm.core.op import BaseAsyncToolOp from flowllm.core.schema import ToolCall from loguru import logger +from reme_ai.utils.op_utils import run_shell_command @C.register_op() @@ -81,20 +81,12 @@ class GrepOp(BaseAsyncToolOp): logger.info(f"Running grep command: {' '.join(cmd)}") - # Execute grep using an async subprocess - process = await asyncio.create_subprocess_exec( - *cmd, - stdout=asyncio.subprocess.PIPE, - stderr=asyncio.subprocess.PIPE, - ) - stdout, stderr = await process.communicate() + # Execute grep using run_shell_command + stdout, stderr, returncode = await run_shell_command(cmd, timeout=None) - if process.returncode not in (0, 1): - # grep returns 1 when no matches are found; treat other codes as errors - err_msg = stderr.decode("utf-8", errors="ignore").strip() - raise RuntimeError(f"grep failed with code {process.returncode}: {err_msg}") + assert returncode in (0, 1), f"grep failed with code {returncode}: {stderr.strip()}" - output_text = stdout.decode("utf-8", errors="ignore").strip() + output_text = stdout.strip() # Return raw grep output if not output_text: diff --git a/reme_ai/retrieve/working/read_file_op.py b/reme_ai/retrieve/working/read_file_op.py index c6d4aaaa..9516798d 100644 --- a/reme_ai/retrieve/working/read_file_op.py +++ b/reme_ai/retrieve/working/read_file_op.py @@ -4,13 +4,13 @@ This module provides a tool operation for reading file contents. It supports reading entire files or specific line ranges for large files. """ -import asyncio from pathlib import Path from typing import Optional from flowllm.core.context import C from flowllm.core.op import BaseAsyncToolOp from flowllm.core.schema import ToolCall +from reme_ai.utils.op_utils import run_shell_command @C.register_op() @@ -76,15 +76,10 @@ class ReadFileOp(BaseAsyncToolOp): end_line = offset + limit cmd = ["sed", "-n", f"{start_line},{end_line}p", str(file_path_obj)] - process = await asyncio.create_subprocess_exec( - *cmd, - stdout=asyncio.subprocess.PIPE, - stderr=asyncio.subprocess.PIPE, - ) - stdout, stderr = await asyncio.wait_for(process.communicate(), timeout=30) + stdout, stderr, returncode = await run_shell_command(cmd, timeout=30) - assert process.returncode == 0, f"sed command failed: {stderr.decode()}" - content = stdout.decode().rstrip("\n") + assert returncode == 0, f"sed command failed: {stderr}" + content = stdout.rstrip("\n") self.set_output(content) async def async_default_execute(self, e: Exception = None, **_kwargs): diff --git a/reme_ai/utils/op_utils.py b/reme_ai/utils/op_utils.py index a4d86645..c6e439b7 100644 --- a/reme_ai/utils/op_utils.py +++ b/reme_ai/utils/op_utils.py @@ -4,15 +4,50 @@ This module provides helper functions for merging messages, parsing JSON respons extracting trajectory context, and parsing insight updates. """ +import asyncio import json import re -from typing import List +from typing import List, Optional, Tuple from flowllm.core.schema import Message, Trajectory from flowllm.core.utils import merge_messages_content as merge_messages_content_flowllm from loguru import logger +async def run_shell_command( + cmd: List[str], + timeout: Optional[float] = 30, +) -> Tuple[str, str, int]: + """Run a shell command asynchronously. + + Args: + cmd: Command and arguments as a list. + timeout: Timeout in seconds. None for no timeout. + + Returns: + Tuple of (stdout, stderr, returncode). + + Raises: + asyncio.TimeoutError: If command execution exceeds timeout. + """ + process = await asyncio.create_subprocess_exec( + *cmd, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE, + ) + + if timeout: + stdout, stderr = await asyncio.wait_for(process.communicate(), timeout=timeout) + else: + stdout, stderr = await process.communicate() + + return ( + stdout.decode("utf-8", errors="ignore"), + stderr.decode("utf-8", errors="ignore"), + process.returncode, + ) + + def merge_messages_content(messages: List[Message | dict]) -> str: """Merge content from a list of messages into a single string. diff --git a/test_op/test_agentic_retrieve_op.py b/test_op/test_agentic_retrieve_op.py index 7b0f0c1c..7233d2dd 100644 --- a/test_op/test_agentic_retrieve_op.py +++ b/test_op/test_agentic_retrieve_op.py @@ -70,8 +70,9 @@ async def test_agentic_retrieve_basic(): ] # llm = "qwen3_coder_plus" - llm = "qwen3_30b_instruct" - # llm = "qwen3_coder_30b_instruct" + # llm = "qwen3_30b_instruct" + # llm = "qwen3_30b_thinking" + llm = "qwen3_coder_30b_instruct" # llm = "qwen3_max_instruct" op = AgenticRetrieveOp(llm=llm) From 2143a7381942061ef086d4ba7f1a504b4e5bbd26 Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Tue, 25 Nov 2025 11:59:21 +0800 Subject: [PATCH 33/39] refactor(context): remove redundant main function definition --- cookbook/context_demo.py | 7 ------- 1 file changed, 7 deletions(-) delete mode 100644 cookbook/context_demo.py diff --git a/cookbook/context_demo.py b/cookbook/context_demo.py deleted file mode 100644 index c57eb933..00000000 --- a/cookbook/context_demo.py +++ /dev/null @@ -1,7 +0,0 @@ -def main(): - ... - - -if __name__ == "__main__": - def main(): - ... From 55eb1f87bc159761695100bed8c8b572a63086da Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Tue, 25 Nov 2025 12:13:22 +0800 Subject: [PATCH 34/39] refactor(summary): remove system message handling in compression logic --- reme_ai/summary/working/message_compact_op.py | 10 ---------- .../summary/working/message_compress_op.py | 20 ++++++------------- test_op/test_agentic_retrieve_op.py | 4 ++-- 3 files changed, 8 insertions(+), 26 deletions(-) diff --git a/reme_ai/summary/working/message_compact_op.py b/reme_ai/summary/working/message_compact_op.py index b20488fb..6fc5858c 100644 --- a/reme_ai/summary/working/message_compact_op.py +++ b/reme_ai/summary/working/message_compact_op.py @@ -58,15 +58,6 @@ class MessageCompactOp(BaseAsyncOp): if keep_recent_count > 0: messages_to_compress = messages_to_compress[:-keep_recent_count] - # Extract system message (should be exactly one) - system_message = [x for x in messages if x.role is Role.SYSTEM] - assert len(system_message) <= 1, f"Expected at most one system message, got {len(system_message)}" - - if len(system_message) == 0: - system_message = Message(role=Role.SYSTEM, content="") - else: - system_message = system_message[0] - # If nothing to compress after filtering, return original messages if not messages_to_compress: self.context.response.answer = self.context.messages @@ -125,7 +116,6 @@ class MessageCompactOp(BaseAsyncOp): if preview_char_length > 0: compact_result += f"\npreview: {original_content[:preview_char_length]}..." tool_message.content = compact_result - system_message.content += f"\n\n{compact_result}" # Store write_file_dict in context for potential batch writing if write_file_dict: diff --git a/reme_ai/summary/working/message_compress_op.py b/reme_ai/summary/working/message_compress_op.py index 3eac31da..99bad722 100644 --- a/reme_ai/summary/working/message_compress_op.py +++ b/reme_ai/summary/working/message_compress_op.py @@ -162,7 +162,6 @@ class MessageCompressOp(BaseAsyncOp): async def _compress_with_groups( self, - system_message: Message, message_groups: List[List[Message]], ) -> Tuple[dict, list]: """Compress multiple message groups and prepare them for storage. @@ -173,7 +172,6 @@ class MessageCompressOp(BaseAsyncOp): message. Otherwise, original messages are preserved in the return list. Args: - system_message: The system message to append compressed summaries to. message_groups: List of message groups, where each group is a list of Message objects to be compressed together. @@ -190,7 +188,7 @@ class MessageCompressOp(BaseAsyncOp): chat_id: str = self.context.get("chat_id", uuid4().hex) # Create a copy of system_message to avoid modifying the original - system_message_copy = Message(role=system_message.role, content=system_message.content) + compress_message = Message(role=Role.ASSISTANT, content="") for g_idx, messages in enumerate(message_groups): group_original_tokens = self.token_count(messages) @@ -218,7 +216,7 @@ class MessageCompressOp(BaseAsyncOp): ) return_messages.extend(messages) else: - system_message_copy.content += compress_content + "\n\n" + compress_message.content += compress_content + "\n\n" write_file_dict[store_path.as_posix()] = messages_str logger.info( f"Group {g_idx} compression successful: " @@ -227,7 +225,7 @@ class MessageCompressOp(BaseAsyncOp): f"{100 * (1 - compressed_tokens / group_original_tokens):.1f}%)", ) - return_messages = [system_message_copy] + return_messages + return_messages = [compress_message] + return_messages return write_file_dict, return_messages async def async_execute(self): @@ -257,13 +255,7 @@ class MessageCompressOp(BaseAsyncOp): messages = [Message(**x) for x in self.context.messages] # Extract system message (should be exactly one) - system_message = [x for x in messages if x.role is Role.SYSTEM] - assert len(system_message) <= 1, f"Expected at most one system message, got {len(system_message)}" - - if len(system_message) == 0: - system_message = Message(role=Role.SYSTEM, content="") - else: - system_message = system_message[0] + system_messages = [x for x in messages if x.role is Role.SYSTEM] messages_without_system = [x for x in messages if x.role is not Role.SYSTEM] if keep_recent_count > 0: @@ -296,13 +288,13 @@ class MessageCompressOp(BaseAsyncOp): else: message_groups = [messages_to_compress] - write_file_dict, return_messages = await self._compress_with_groups(system_message, message_groups) + write_file_dict, return_messages = await self._compress_with_groups(message_groups) # Store write_file_dict in context for potential batch writing if write_file_dict: self.context.write_file_dict = write_file_dict - self.context.response.answer = [x.simple_dump() for x in (return_messages + recent_messages)] + self.context.response.answer = [x.simple_dump() for x in (system_messages + return_messages + recent_messages)] self.context.response.metadata["write_file_dict"] = write_file_dict async def async_default_execute(self, e: Exception = None, **_kwargs): diff --git a/test_op/test_agentic_retrieve_op.py b/test_op/test_agentic_retrieve_op.py index 7233d2dd..3cb9d851 100644 --- a/test_op/test_agentic_retrieve_op.py +++ b/test_op/test_agentic_retrieve_op.py @@ -70,9 +70,9 @@ async def test_agentic_retrieve_basic(): ] # llm = "qwen3_coder_plus" - # llm = "qwen3_30b_instruct" + llm = "qwen3_30b_instruct" # llm = "qwen3_30b_thinking" - llm = "qwen3_coder_30b_instruct" + # llm = "qwen3_coder_30b_instruct" # llm = "qwen3_max_instruct" op = AgenticRetrieveOp(llm=llm) From 32bfef63f8c512e67199a0742d244dd08e90ecb3 Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Tue, 25 Nov 2025 13:19:37 +0800 Subject: [PATCH 35/39] feat(memory): add working memory operations to retrieve and summary modules --- reme_ai/retrieve/__init__.py | 3 +++ reme_ai/summary/__init__.py | 5 ++++- 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/reme_ai/retrieve/__init__.py b/reme_ai/retrieve/__init__.py index 9c95d946..142d2630 100644 --- a/reme_ai/retrieve/__init__.py +++ b/reme_ai/retrieve/__init__.py @@ -4,14 +4,17 @@ This module provides submodules for different types of memory retrieval: - personal: Personal memory retrieval operations - task: Task memory retrieval operations - tool: Tool memory retrieval operations +- working: Working-memory retrieval operations """ from . import personal from . import task from . import tool +from . import working __all__ = [ "personal", "task", "tool", + "working", ] diff --git a/reme_ai/summary/__init__.py b/reme_ai/summary/__init__.py index 0dd27ce5..47688a15 100644 --- a/reme_ai/summary/__init__.py +++ b/reme_ai/summary/__init__.py @@ -1,17 +1,20 @@ -"""Summary operations module. +"""Summary operations' module. This module provides summary operations for different types of memories: - Personal memory summary operations - Task memory summary operations - Tool memory summary operations +- Working memory summary operations """ from . import personal from . import task from . import tool +from . import working __all__ = [ "personal", "task", "tool", + "working", ] From ac6f0c56d9e6389b958feab08b97b629b5e85524 Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Wed, 26 Nov 2025 00:18:35 +0800 Subject: [PATCH 36/39] refactor(grep_op): replace shell grep with python regex search --- reme_ai/retrieve/working/grep_op.py | 47 +++++++++--------------- reme_ai/retrieve/working/read_file_op.py | 4 +- 2 files changed, 20 insertions(+), 31 deletions(-) diff --git a/reme_ai/retrieve/working/grep_op.py b/reme_ai/retrieve/working/grep_op.py index e995723a..eaa30642 100644 --- a/reme_ai/retrieve/working/grep_op.py +++ b/reme_ai/retrieve/working/grep_op.py @@ -5,14 +5,13 @@ It enables efficient content-based search using regular expressions, with suppor for glob pattern filtering and result limiting. """ +import re from pathlib import Path -from typing import List from flowllm.core.context import C from flowllm.core.op import BaseAsyncToolOp from flowllm.core.schema import ToolCall from loguru import logger -from reme_ai.utils.op_utils import run_shell_command @C.register_op() @@ -59,41 +58,31 @@ class GrepOp(BaseAsyncToolOp): """Execute the grep search operation.""" pattern: str = self.input_dict.get("pattern", "").strip() file_path: str | None | Path = self.input_dict.get("file_path", "") - limit: int = self.input_dict.get("limit", 50) + limit: int = int(self.input_dict.get("limit", 50)) - # Validate pattern - if not pattern: - raise ValueError("The 'pattern' parameter cannot be empty.") + assert pattern, "The 'pattern' parameter cannot be empty." + assert file_path, "The 'file_path' parameter is required." + target_file = Path(file_path).expanduser().resolve() + assert target_file.exists(), f"File does not exist: {target_file}" + assert target_file.is_file(), f"Path is not a file: {target_file}" - # Determine search directory - if file_path: - search_dir = Path(file_path).expanduser().resolve() - if not search_dir.exists(): - raise ValueError(f"Search file_path does not exist: {search_dir}") - else: - search_dir = Path.cwd() + logger.info(f"Searching for pattern '{pattern}' in {target_file}") - # Build grep command - cmd: List[str] = ["grep", "-RIni"] - if limit: - cmd.extend(["-m", str(limit)]) - cmd.extend(["--", pattern, str(search_dir)]) + regex = re.compile(re.escape(pattern), re.IGNORECASE) + results = [] - logger.info(f"Running grep command: {' '.join(cmd)}") + with target_file.open("r", encoding="utf-8", errors="ignore") as f: + for line_num, line in enumerate(f, 1): + if regex.search(line): + results.append(f"{target_file}:{line_num}:{line.rstrip()}") + if len(results) >= limit: + break - # Execute grep using run_shell_command - stdout, stderr, returncode = await run_shell_command(cmd, timeout=None) - - assert returncode in (0, 1), f"grep failed with code {returncode}: {stderr.strip()}" - - output_text = stdout.strip() - - # Return raw grep output - if not output_text: + if not results: search_location = f'in file_path "{file_path}"' if file_path else "in the workspace directory" result_msg = f'No matches found for pattern "{pattern}" {search_location}.' else: - result_msg = output_text + result_msg = "\n".join(results) self.set_output(result_msg) diff --git a/reme_ai/retrieve/working/read_file_op.py b/reme_ai/retrieve/working/read_file_op.py index 9516798d..0d2a8f93 100644 --- a/reme_ai/retrieve/working/read_file_op.py +++ b/reme_ai/retrieve/working/read_file_op.py @@ -56,8 +56,8 @@ class ReadFileOp(BaseAsyncToolOp): async def async_execute(self): """Execute the read file operation.""" file_path: str = self.input_dict.get("file_path", "").strip() - offset: Optional[int] = self.input_dict.get("offset") - limit: Optional[int] = self.input_dict.get("limit") + offset: Optional[int] = int(self.input_dict.get("offset")) + limit: Optional[int] = int(self.input_dict.get("limit")) # Validate and resolve file path assert file_path, "The 'file_path' parameter cannot be empty." From ed7f8e32efbc9550bb79417ebcee37fee43d6172 Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Wed, 26 Nov 2025 11:10:23 +0800 Subject: [PATCH 37/39] feat(summary): increase default keep_recent_count and optimize compression logic --- reme_ai/summary/working/message_compact_op.py | 5 +- .../summary/working/message_compress_op.py | 48 ++++++++----------- 2 files changed, 23 insertions(+), 30 deletions(-) diff --git a/reme_ai/summary/working/message_compact_op.py b/reme_ai/summary/working/message_compact_op.py index 6fc5858c..01505839 100644 --- a/reme_ai/summary/working/message_compact_op.py +++ b/reme_ai/summary/working/message_compact_op.py @@ -43,7 +43,7 @@ class MessageCompactOp(BaseAsyncOp): max_total_tokens: int = self.context.get("max_total_tokens", 20000) max_tool_message_tokens: int = self.context.get("max_tool_message_tokens", 2000) preview_char_length: int = self.context.get("preview_char_length", 0) - keep_recent_count: int = self.context.get("keep_recent_count", 1) + keep_recent_count: int = self.context.get("keep_recent_count", 10) store_dir: Path = Path(self.context.get("store_dir", "")) assert max_total_tokens > 0, "max_total_tokens must be greater than 0" @@ -118,8 +118,7 @@ class MessageCompactOp(BaseAsyncOp): tool_message.content = compact_result # Store write_file_dict in context for potential batch writing - if write_file_dict: - self.context.write_file_dict = write_file_dict + self.context.write_file_dict = write_file_dict # Return the compacted messages as JSON self.context.response.answer = [x.simple_dump() for x in messages] diff --git a/reme_ai/summary/working/message_compress_op.py b/reme_ai/summary/working/message_compress_op.py index 99bad722..7e975138 100644 --- a/reme_ai/summary/working/message_compress_op.py +++ b/reme_ai/summary/working/message_compress_op.py @@ -160,10 +160,7 @@ class MessageCompressOp(BaseAsyncOp): return result - async def _compress_with_groups( - self, - message_groups: List[List[Message]], - ) -> Tuple[dict, list]: + async def _compress_with_groups(self, message_groups: List[List[Message]]) -> Tuple[dict, list]: """Compress multiple message groups and prepare them for storage. This method processes each message group, compresses it using LLM, and determines @@ -184,8 +181,8 @@ class MessageCompressOp(BaseAsyncOp): compressed or didn't benefit from compression. """ write_file_dict = {} - return_messages = [] chat_id: str = self.context.get("chat_id", uuid4().hex) + success: bool = True # Create a copy of system_message to avoid modifying the original compress_message = Message(role=Role.ASSISTANT, content="") @@ -200,8 +197,8 @@ class MessageCompressOp(BaseAsyncOp): if not group_summary: logger.warning(f"Group {g_idx} compression returned empty summary, using original messages.") - return_messages.extend(messages) - continue + success = False + break compress_content = ( f"[Compressed conversation history - Part {g_idx}/{len(message_groups)}]\n{group_summary}\n" @@ -209,24 +206,22 @@ class MessageCompressOp(BaseAsyncOp): ) compressed_tokens = self.token_count([Message(content=compress_content)]) - if compressed_tokens >= group_original_tokens: - logger.warning( - f"Group {g_idx} compression did not reduce tokens: " - f"{group_original_tokens} -> {compressed_tokens}. Using original messages.", - ) - return_messages.extend(messages) - else: - compress_message.content += compress_content + "\n\n" - write_file_dict[store_path.as_posix()] = messages_str - logger.info( - f"Group {g_idx} compression successful: " - f"{group_original_tokens} -> {compressed_tokens} tokens " - f"(reduction: {group_original_tokens - compressed_tokens} tokens, " - f"{100 * (1 - compressed_tokens / group_original_tokens):.1f}%)", - ) + compress_message.content += compress_content + "\n\n" + write_file_dict[store_path.as_posix()] = messages_str + logger.info( + f"Group {g_idx} compression successful: " + f"{group_original_tokens} -> {compressed_tokens} tokens " + f"(reduction: {group_original_tokens - compressed_tokens} tokens, " + f"{100 * (1 - compressed_tokens / group_original_tokens):.1f}%)", + ) - return_messages = [compress_message] + return_messages - return write_file_dict, return_messages + if success: + return write_file_dict, [compress_message] + else: + return_messages = [] + for group in message_groups: + return_messages.extend(group) + return {}, return_messages async def async_execute(self): """ @@ -246,7 +241,7 @@ class MessageCompressOp(BaseAsyncOp): # Note: max_total_tokens does not include keep_recent_count messages or system messages max_total_tokens: int = self.context.get("max_total_tokens", 20000) group_token_threshold: int = self.context.get("group_token_threshold", None) - keep_recent_count: int = self.context.get("keep_recent_count", 2) + keep_recent_count: int = self.context.get("keep_recent_count", 10) assert max_total_tokens > 0, "max_total_tokens must be positive" assert keep_recent_count >= 0, "keep_recent_count must be non-negative" @@ -291,8 +286,7 @@ class MessageCompressOp(BaseAsyncOp): write_file_dict, return_messages = await self._compress_with_groups(message_groups) # Store write_file_dict in context for potential batch writing - if write_file_dict: - self.context.write_file_dict = write_file_dict + self.context.write_file_dict = write_file_dict self.context.response.answer = [x.simple_dump() for x in (system_messages + return_messages + recent_messages)] self.context.response.metadata["write_file_dict"] = write_file_dict From ef7e8052a2dfe6d8421bb180b4f9a9e599c080f7 Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Wed, 26 Nov 2025 11:39:03 +0800 Subject: [PATCH 38/39] chore(version): bump version to 0.2.0.2 --- pyproject.toml | 4 ++-- reme_ai/__init__.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 804e7f46..2d16f641 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,7 +32,7 @@ classifiers = [ keywords = ["llm", "memory", "experience", "memoryscope", "ai", "mcp", "http"] dependencies = [ - "flowllm[reme]>=0.2.0.3", + "flowllm[reme]>=0.2.0.4", ] [project.optional-dependencies] @@ -46,7 +46,7 @@ dev = [ ] token = [ - "flowllm[token]>=0.2.0.3" + "flowllm[token]>=0.2.0.4" ] full = [ diff --git a/reme_ai/__init__.py b/reme_ai/__init__.py index 912d0b64..eb3ed005 100644 --- a/reme_ai/__init__.py +++ b/reme_ai/__init__.py @@ -30,4 +30,4 @@ __all__ = [ "vector_store", ] -__version__ = "0.2.0.1" +__version__ = "0.2.0.2" From d99d31cbffbef94416afda12f389087124405c4c Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Thu, 27 Nov 2025 13:12:59 +0800 Subject: [PATCH 39/39] chore(version): bump package version to 0.2.0.3 --- pyproject.toml | 4 ++-- reme_ai/__init__.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 2d16f641..10086172 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,7 +32,7 @@ classifiers = [ keywords = ["llm", "memory", "experience", "memoryscope", "ai", "mcp", "http"] dependencies = [ - "flowllm[reme]>=0.2.0.4", + "flowllm[reme]>=0.2.0.5", ] [project.optional-dependencies] @@ -46,7 +46,7 @@ dev = [ ] token = [ - "flowllm[token]>=0.2.0.4" + "flowllm[token]>=0.2.0.5" ] full = [ diff --git a/reme_ai/__init__.py b/reme_ai/__init__.py index eb3ed005..04d0b476 100644 --- a/reme_ai/__init__.py +++ b/reme_ai/__init__.py @@ -30,4 +30,4 @@ __all__ = [ "vector_store", ] -__version__ = "0.2.0.2" +__version__ = "0.2.0.3"