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- Add file_parser component with default implementation - Introduce SearchFilter schema for path and tag filtering - Implement filter functionality in BaseFileStore and LocalFileStore - Update file watcher to use parser-based filtering instead of suffix filters - Register new FILE_PARSER component enum - Add test_data directory to gitignore refactor: improve component imports and initialization - Fix relative imports in application.py - Add file_parser import to component init - Initialize registry dict when component type doesn't exist - Remove circular import in HttpService by using string annotation - Update config yaml to use proper component names refactor: enhance file watcher architecture - Replace MdFileWatcher with more flexible FullFileWatcher and LightFileWatcher - Remove suffix-based filtering in favor of parser-based approach - Update BaseFileWatcher to resolve parsers from app context - Remove unused watch_filter method refactor: update ReMe core functionality - Remove memory_path creation - Simplify dream and proactive methods to return empty strings - Update config defaults for HTTP service and component backends docs: update component configuration in paw.yaml - Change service backend from cmd to http - Rename components to use correct singular forms - Add default file parser and file watcher configurations - Set up local file store with default settings ``` Co-authored-by: huangsen <huangsen.huang@alibaba-inc.com>
324 lines
12 KiB
Python
324 lines
12 KiB
Python
"""Summarizer module for memory summarization operations."""
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import datetime
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import json
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import zoneinfo
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from agentscope.agent import ReActAgent
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from agentscope.message import Msg
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from agentscope.token import HuggingFaceTokenCounter
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from agentscope.tool import Toolkit
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from loguru import logger
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from ..component import BaseStep
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from ..schema import AsMsgStat, AsBlockStat
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class Summarizer(BaseStep):
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"""Summarizer step for summarizing memory messages."""
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def __init__(
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self,
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working_dir: str,
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memory_dir: str,
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memory_compact_threshold: int,
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toolkit: Toolkit | None = None,
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console_enabled: bool = False,
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timezone: str | None = None,
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add_thinking_block: bool = True,
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as_token_counter: HuggingFaceTokenCounter | None = None,
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**kwargs,
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):
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"""Initialize the summarizer step.
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Args:
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working_dir: Working directory path.
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memory_dir: Memory directory path for storing summaries.
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memory_compact_threshold: Token threshold for memory compaction.
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toolkit: Optional toolkit for the agent.
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console_enabled: Whether to enable console output.
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timezone: Optional timezone string for date formatting.
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add_thinking_block: Whether to include thinking blocks in output.
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as_token_counter: Optional token counter instance.
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**kwargs: Additional keyword arguments passed to BaseStep.
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"""
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super().__init__(**kwargs)
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self.working_dir: str = working_dir
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self.memory_dir: str = memory_dir
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self.memory_compact_threshold: int = memory_compact_threshold
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self.toolkit: Toolkit | None = toolkit
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self.console_enabled: bool = console_enabled
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self.timezone: str | None = timezone
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self.add_thinking_block: bool = add_thinking_block
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self._as_token_counter: HuggingFaceTokenCounter | None = as_token_counter
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def _get_current_datetime(self) -> datetime.datetime:
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"""Get current datetime with timezone, fallback to local time if timezone is invalid."""
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if self.timezone:
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try:
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return datetime.datetime.now(zoneinfo.ZoneInfo(self.timezone))
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except Exception as e:
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self.logger.error(f"Invalid timezone: {self.timezone}, falling back to local time error={e}")
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return datetime.datetime.now()
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async def _count_str_token(self, text: str) -> int:
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"""Count tokens in a string."""
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return await self.as_token_counter.count(messages=[], text=text)
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async def _format_tool_result_output(self, output: str | list[dict]) -> tuple[str, int]:
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"""Convert tool result output to string."""
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if isinstance(output, str):
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return output, await self._count_str_token(output)
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textual_parts = []
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total_token_count = 0
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for block in output:
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try:
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if not isinstance(block, dict) or "type" not in block:
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logger.warning(
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f"Invalid block: {block}, expected a dict with 'type' key, skipped.",
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)
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continue
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block_type = block["type"]
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if block_type == "text":
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textual_parts.append(block.get("text", ""))
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total_token_count += await self._count_str_token(textual_parts[-1])
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elif block_type in ["image", "audio", "video"]:
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source = block.get("source", {})
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if source.get("type") == "base64":
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data = source.get("data", "")
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total_token_count += len(data) // 4 if data else 10
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else:
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url = source.get("url", "")
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total_token_count += await self._count_str_token(url) if url else 10
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textual_parts.append(f"[{block_type}] {url}")
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elif block_type == "file":
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file_path = block.get("path", "") or block.get("url", "")
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file_name = block.get("name", file_path)
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textual_parts.append(f"[file] {file_name}: {file_path}")
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total_token_count += await self._count_str_token(file_path)
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else:
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logger.warning(
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f"Unsupported block type '{block_type}' in tool result, skipped.",
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)
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except Exception as e:
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logger.warning(
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f"Failed to process block {block}: {e}, skipped.",
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)
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return "\n".join(textual_parts), total_token_count
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async def _stat_message(self, message: Msg) -> AsMsgStat:
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"""Analyze a message and generate block statistics."""
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blocks = []
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if isinstance(message.content, str):
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blocks.append(
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AsBlockStat(
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block_type="text",
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text=message.content,
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token_count=await self._count_str_token(message.content),
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),
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)
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return AsMsgStat(
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name=message.name or message.role,
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role=message.role,
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content=blocks,
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timestamp=message.timestamp or "",
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metadata=message.metadata or {},
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)
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for block in message.content:
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block_type = block.get("type", "unknown")
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if block_type == "text":
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text = block.get("text", "")
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token_count = await self._count_str_token(text)
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blocks.append(
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AsBlockStat(
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block_type=block_type,
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text=text,
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token_count=token_count,
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),
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)
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elif block_type == "thinking":
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thinking = block.get("thinking", "")
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token_count = await self._count_str_token(thinking)
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blocks.append(
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AsBlockStat(
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block_type=block_type,
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text=thinking,
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token_count=token_count,
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),
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)
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elif block_type in ("image", "audio", "video"):
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source = block.get("source", {})
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url = source.get("url", "")
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if source.get("type") == "base64":
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data = source.get("data", "")
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token_count = len(data) // 4 if data else 10
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else:
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token_count = await self._count_str_token(url) if url else 10
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blocks.append(
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AsBlockStat(
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block_type=block_type,
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text="",
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token_count=token_count,
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media_url=url,
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),
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)
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elif block_type == "tool_use":
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tool_name = block.get("name", "")
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tool_input = block.get("input", "")
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try:
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input_str = json.dumps(tool_input, ensure_ascii=False)
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except (TypeError, ValueError):
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input_str = str(tool_input)
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token_count = await self._count_str_token(tool_name + input_str)
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blocks.append(
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AsBlockStat(
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block_type=block_type,
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text="",
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token_count=token_count,
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tool_name=tool_name,
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tool_input=input_str,
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),
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)
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elif block_type == "tool_result":
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tool_name = block.get("name", "")
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output = block.get("output", "")
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formatted_output, token_count = await self._format_tool_result_output(output)
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blocks.append(
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AsBlockStat(
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block_type=block_type,
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text="",
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token_count=token_count,
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tool_name=tool_name,
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tool_output=formatted_output,
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),
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)
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else:
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logger.warning(f"Unsupported block type {block_type}, skipped.")
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return AsMsgStat(
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name=message.name or message.role,
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role=message.role,
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content=blocks,
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timestamp=message.timestamp or "",
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metadata=message.metadata or {},
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)
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async def _count_msgs_token(self, messages: list[Msg]) -> int:
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"""Count total token count of a list of messages."""
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total = 0
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for msg in messages:
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stat = await self._stat_message(msg)
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total += stat.total_tokens
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return total
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async def _format_msgs_to_str(
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self,
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messages: list[Msg],
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memory_compact_threshold: int,
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include_thinking: bool = True,
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) -> str:
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"""Format list of messages to a single formatted string.
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Messages are processed in reverse order (newest first) and older
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messages are skipped when token count exceeds memory_compact_threshold.
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Args:
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messages: List of Msg objects to format.
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memory_compact_threshold: Maximum token count before skipping older messages.
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include_thinking: Whether to include thinking blocks in output.
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"""
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if not messages:
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return ""
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formatted_parts: list[str] = []
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total_token_count = 0
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for i in range(len(messages) - 1, -1, -1):
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stat = await self._stat_message(messages[i])
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formatted_content = stat.format(include_thinking=include_thinking)
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content_token_count = await self._count_str_token(formatted_content)
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is_latest = i == len(messages) - 1
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if not is_latest and total_token_count + content_token_count > memory_compact_threshold:
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logger.info(
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f"Skipping older messages: adding {content_token_count} tokens would exceed threshold "
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f"{memory_compact_threshold} (current: {total_token_count})",
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)
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break
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if is_latest and content_token_count > memory_compact_threshold:
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logger.warning(
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f"Latest message alone ({content_token_count} tokens) exceeds threshold "
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f"{memory_compact_threshold}, including it anyway.",
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)
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formatted_parts.append(formatted_content)
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total_token_count += content_token_count
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formatted_parts.reverse()
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return "\n\n".join(formatted_parts)
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async def execute(self):
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"""Execute the summarization step."""
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messages: list[Msg] = self.context.data.get("messages", [])
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if not messages:
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return ""
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before_token_count = await self._count_msgs_token(messages)
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history_formatted_str: str = await self._format_msgs_to_str(
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messages=messages,
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memory_compact_threshold=self.memory_compact_threshold,
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include_thinking=self.add_thinking_block,
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)
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after_token_count = await self._count_str_token(history_formatted_str)
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logger.info(f"Summarizer before_token_count={before_token_count} after_token_count={after_token_count}")
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if not history_formatted_str:
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logger.warning(f"No history to summarize. messages={messages}")
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return ""
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agent = ReActAgent(
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name="reme_summarizer",
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model=self.as_llm.model,
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sys_prompt="You are a helpful assistant.",
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formatter=self.as_llm_formatter.formatter,
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toolkit=self.toolkit,
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)
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agent.set_console_output_enabled(self.console_enabled)
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user_message: str = f"# conversation\n{history_formatted_str}\n\n" + self.prompt_format(
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"user_message",
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date=self._get_current_datetime().strftime("%Y-%m-%d"),
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working_dir=self.working_dir,
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memory_dir=self.memory_dir,
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)
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summary_msg: Msg = await agent.reply(
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Msg(
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name="reme",
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role="user",
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content=user_message,
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),
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)
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for i, (msg, _) in enumerate(agent.memory.content):
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logger.info(f"Summarizer memory[{i}]: {msg.content}")
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history_summary: str = summary_msg.get_text_content()
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logger.info(f"Summarizer Result:\n{history_summary}")
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return history_summary
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