8 commits

Author	SHA1	Message	Date
jinliyl	efcc2b34d1	feat: simplify plugin setup and add management CLI (#485) ... * feat: simplify plugin setup and add management CLI * fix: isolate plugin CLI import side effects * refactor: streamline plugin validation * fix: route plugin CLI arguments independently * fix: support standard plugin source layouts	2026-08-23 17:58:20 +08:00
jinliyl	618e8cec66	feat: add entry-point plugin system and extract Auto Fin (#459) ... * feat: add entry-point plugin system * fix: harden plugin config and client loading * docs(workflow): add detailed manual for publishing reme-auto-fin to PyPI - Provide step-by-step instructions for updating project.version and merging branches - Explain dependency verification for reme-ai on PyPI during build - Specify requirements for GitHub Actions secret configuration and version uniqueness - Describe manual workflow triggering and input of version number - Recommend publishing order for related projects - Clarify that only manual dispatch triggers publishing, no automatic triggers on push or tag * feat: support plugin-defined component types * refactor: simplify plugin configuration * fix: isolate plugin loading and defer client fallback * refactor: freeze built-in component registry * fix: isolate config entry point loading * fix: complete auto-fin package metadata	2026-08-19 17:23:23 +08:00
jinliyl	29eb51d7ba	fix: show resolved service URL and shrink Studio preview asset (#453) ... * fix: show resolved service address in startup banner * perf(website): reduce social preview image size * fix: resolve MCP transport in startup banner	2026-08-12 19:37:31 +08:00
jinliyl	bf7ca17705	feat(benchmark): add LongMemEval golden answer validation (#335) ... * feat(benchmark): add golden answer validation and session review for LongMemEval - Introduce GoldenCheckStep to validate LongMemEval golden answers using structured verdicts - Add SessionReviewStep to extract query/answer-relevant evidence from all sessions - Implement concurrent session processing with configurable concurrency limits - Create check_golden job configuration with lme_review and lme_judge agent wrappers - Add Qwen3.7-plus model configuration for enhanced processing capabilities - Include python_execute tool integration for agent-based reasoning and date validation - Generate comprehensive JSON output with session summaries and validation verdicts - Add run_check_golden.py script for batch processing across all LongMemEval samples - Configure proper logging initialization with console and file output options - Update component registry and file I/O modules to support new benchmark features * feat(scripts): add script to summarize LongMemEval check_golden verdicts - Parse check_golden.json files across all LongMemEval samples - Calculate accuracy metrics for golden answers and session IDs - Provide breakdown by question type with percentage calculations - Add command line options for listing bad samples and JSON output - Include progress tracking showing completed vs pending samples - Display confidence scores and date sanity checks statistics * refactor(benchmark): move golden check scripts to longmemeval directory - Moved run_check_golden.py from scripts/ to benchmark/longmemeval/ - Moved stats_check_golden.py from scripts/ to benchmark/longmemeval/ - Updated path resolution to use parents[2] instead of parent.parent - Added new --list-run-failed option to stats script - Added logging directory constant and functions for tracking launched samples - Enhanced stats output with launched count and run failure information - Improved error reporting with run failure details and log file paths * feat(benchmark): add LongMemEval agentic answer workflow with session extraction - Add LmeAgenticAnswerStep, LmeAutoMemoryStep, and LmeExtractSessionStep to __init__.py - Create shared helper render_with_source for displaying search results with session_id - Implement agentic_answer step with vector_search, bm25_search, and extract_session_by_id tools - Add auto_memory step to convert each session into search-friendly daily notes - Create extract_session step to retrieve and analyze raw session content by session_id - Update jinli_lme.yaml with auto_memory, vector_search, bm25_search, and agentic_answer jobs - Configure lme_memory, lme_extract, and lme_agentic_answer agent wrappers - Enhance search steps with include_source option to show session_id metadata - Add proper session_id tracking and collision handling in daily note generation * feat(benchmark): add LongMemEval agentic answer evaluation pipeline - Add session_id tracking to agentic_answer.py result metadata - Introduce run_agentic_answer.py driver for complete pipeline execution - Implement auto_memory, update_index, and agentic_answer job orchestration - Add concurrent execution with configurable limits and staggering - Create aggregation script for collecting tool-call trails and results - Add stats_agentic_answer.py for comprehensive result analysis - Implement resume capability with existing output detection - Generate aggregate.json with per-sample breakdown and tool call summaries * feat(steps): add ClearPathsStep for cleaning workspace outputs before rebuild - Introduce ClearPathsStep to remove stale workspace files/directories - Add support for specifying paths and config_keys as targets to clear - Implement safety checks to prevent deletion of files outside workspace - Add logging for cleared paths and warnings for invalid paths - Configure clear_paths_step in jinli_lme.yaml to clean daily_dir - Add clear_paths_step to clean mem_answer.json before rebuilds * feat(benchmark): add resume functionality to agentic answer runner - Replace --force flag with --resume flag for controlling job execution - By default every job reruns with clean rebuild behavior using config clear steps - Add --resume option to skip samples whose output already exists and continue interrupted batches - Update documentation to reflect new default clean rebuild behavior - Modify job skipping logic to honor resume flag instead of force flag - Update dry-run output to show correct todo jobs based on resume status - Change default example command to use --resume for continuing interrupted runs * feat(benchmark): generate JSONL output for check golden records - Add write_check_golden_list function to create JSONL file - Write all readable check_golden records as JSONL format - Include check_golden_list path in stats output - Display generated JSONL file path in summary report - Maintain UTF-8 encoding with non-ASCII character support * refactor(benchmark): rename answer judge step and integrate LME LLM judge - Rename AnswerJudgeStep to LmeLlmJudgeStep and update imports - Add new llm_judge configuration in jinli_lme.yaml - Update run_agentic_answer.py to include llm_judge in pipeline - Modify LmeLlmJudgeStep to read from query.json and answer.json - Write LLM judgement results back to mem_answer.json - Add command line options for start/end sample range selection - Update aggregate.json generation to include LLM judgement data - Add resume capability for llm_judge job based on judgement presence * refactor(benchmark): rename answer judge step and integrate LME LLM judge - Rename AnswerJudgeStep to LmeLlmJudgeStep and update imports - Add new llm_judge configuration in jinli_lme.yaml - Update run_agentic_answer.py to include llm_judge in pipeline - Modify LmeLlmJudgeStep to read from query.json and answer.json - Write LLM judgement results back to mem_answer.json - Add command line options for start/end sample range selection - Update aggregate.json generation to include LLM judgement data - Add resume capability for llm_judge job based on judgement presence * feat(steps): add wait_for_paths_step to block until workspace files exist - Introduce WaitForPathsStep class that polls for required workspace-relative paths - Add step registration with 'wait_for_paths_step' backend identifier - Implement path validation to ensure targets are within workspace boundaries - Add polling mechanism with configurable intervals via poll_seconds parameter - Include logging functionality with log_every_seconds parameter for status updates - Add metadata tracking of waited paths and duration in response object - Register step in index module and expose in public API - Configure step in jinli_lme.yaml to wait for session_review.json before golden check - Add script rename from run_check_golden.py to run_golden_check.py with enhanced options * feat(benchmark): enhance longmemeval benchmarking with concurrency and progress tracking - Add benchmark extra dependency group with portalocker requirement - Introduce concurrent execution support for golden_check and session_review workflows - Add progress reporting interval option with real-time status updates - Implement global throttling mechanism for session review requests using file locks - Enhance golden check validation with current schema verification - Add active task tracking and graceful shutdown handling - Rename check_golden scripts to golden_check for consistency - Update statistics reporting with correct/incorrect terminology instead of reasonable - Add stale format detection and compatibility handling for verdict fields - Include both_correct rate calculation in accuracy metrics - Add concurrency and staggering options for better resource management * ci(workflow): add Windows smoke test workflow - Create new workflow file .github/workflows/windows-smoke.yml - Configure workflow to trigger on push and pull request events - Set up Python environment with version 3.11 - Install package dependencies using pip - Run version job as smoke test for CLI functionality - Enable concurrency control to prevent duplicate runs - Use matrix strategy for Python version testing * feat(benchmark): add retry mechanism and health check for session review - Added retry configuration options (retry_initial_seconds, retry_max_seconds, retry_max_attempts) to jinli_lme.yaml - Implemented exponential backoff retry logic with configurable parameters in session_review step - Added output_is_healthy function to verify session_review.json integrity and absence of failed reviews - Updated resume functionality to skip only healthy outputs instead of all existing files - Integrated JSON parsing and validation to check for failed reviews in output files - Enhanced error handling and logging for retry attempts and recovery scenarios * feat(benchmark): add LongMemEval session review statistics script - Create stats_session_review.py to summarize session_review.json artifacts - Add command line options for listing failed, missing, and run failed samples - Implement JSON output mode for programmatic consumption - Calculate and display health statistics including total samples, healthy outputs, failed sessions - Provide detailed failure information with session IDs and error messages - Generate re-run commands for samples with failed reviews - Add percentage calculations for better statistical overview - Include support for multiple output formats and detailed logging * feat(benchmark): add LongMemEval output cleanup script and enhance golden check retry logic - Added clean_sample_outputs.py script to remove generated LongMemEval files while preserving source inputs - Implemented configurable retry mechanism in golden_check.py with exponential backoff strategy - Added retry parameters (initial/max seconds and max attempts) to control failure recovery behavior - Integrated asyncio support for asynchronous sleep during retry intervals - Configured default retry settings in jinli_lme.yaml with 5s initial and 300s maximum intervals - Preserved core files (query.json, answer.json, session/) while cleaning generated artifacts * feat(benchmark): add AppleDouble file cleanup to sample output cleaner - Remove AppleDouble files starting with '._' recursively including under session/ - Add is_under helper function to check if path is inside parent directory - Track targets in set to avoid duplicate processing - Include AppleDouble files in cleanup targets when not already covered by existing targets - Maintain dry-run mode as default behavior with --apply flag for actual deletion * refactor(benchmark): update LongMemEval sample output cleaning script - Add time and Iterator imports for enhanced functionality - Add --progress-every argument to control progress reporting frequency - Replace is_under function with iter_sample_targets generator - Implement detailed progress tracking with timing measurements - Add sample-by-sample processing with elapsed time reporting - Include AppleDouble file detection within session directory - Update target counting and deletion statistics display - Add conditional progress updates based on progress-every setting - Improve dry-run mode with would-delete indication * chore(benchmark): increase initial interval for session review step - Changed START_INTERVAL_SECONDS from 1.0 to 3.0 seconds - Adjusted timing parameters for better benchmark stability * refactor(benchmark): implement coordinated retry mechanism for session reviews - Add retry gate condition to coordinate concurrent review attempts - Implement wait_for_healthy_start_slot to handle sequential retries - Create mark_retrying and mark_recovered functions to track retry states - Update reply_with_retry to accept index parameter for coordination - Add has_prior_retry logic to prevent race conditions during recovery - Ensure proper cleanup of retry state on success or failure - Maintain backward compatibility while adding coordination features * chore(benchmark): adjust session review start interval timeout - Changed START_INTERVAL_SECONDS from 3.0 to 5.0 seconds - Increased initial delay for session review benchmark step - Updated timeout configuration for improved stability * refactor(benchmark): update session review concurrency and throttling mechanism - Replace global throttle with per-process concurrency control - Add concurrency parameter with default value of 30 in config - Add start_interval_seconds parameter with default value of 2 seconds - Change default concurrency from 3 to 1 in command line interface - Update documentation to reflect new throttling behavior - Implement semaphore-based concurrency limiting for review tasks - Modify retry mechanism to use local locking instead of global files - Remove portalocker dependency for cross-process throttling * refactor(config): update session review configuration and concurrency settings - Removed deprecated retry configuration parameters from jinli_lme.yaml - Increased MAX_CONCURRENCY from 30 to 60 in session_review.py - Reduced START_INTERVAL_SECONDS from 2.0 to 1.0 in session_review.py - Cleaned up redundant backend specifications in configuration file - Simplified agent wrapper configurations by removing obsolete retry settings * feat(benchmark): enhance LME auto memory step with advanced scheduling and error handling - Add datetime parsing functionality for LongMemEval timestamps with regex pattern - Implement configurable concurrency limits with MAX_CONCURRENCY of 60 - Introduce retry mechanism with exponential backoff for agent interactions - Add session filtering based on date comparison with question_date validation - Create rate limiting with start interval control between requests - Implement sophisticated retry coordination using asyncio conditions - Add comprehensive error tracking for failed and filtered session extracts - Remove deprecated concurrency parameter from jinli_lme.yaml configuration - Add structured output validation in session review step - Include detailed metadata reporting with session statistics and errors * fix(benchmark): adjust default concurrency for auto_memory job - Changed default concurrency from 3 to 1 for auto_memory job to prevent API overload - Updated help text to reflect new default value of 1 for concurrency parameter - Modified documentation to clarify concurrency behavior varies by job type * refactor(search): replace hardcoded candidate multiplier with constant - Introduced _CANDIDATE_MULTIPLIER constant set to 10 - Replaced hardcoded factor of 5 with _CANDIDATE_MULTIPLIER in BM25 search - Replaced hardcoded factor of 5 with _CANDIDATE_MULTIPLIER in vector search - Updated test to verify both search steps use ten times limit for candidates - Imported VectorSearchStep and Bm25SearchStep in test module - Added comprehensive test case for candidate count calculation logic * feat(lme): add data inspection error handling with fallback mechanism - Implemented non-retryable data inspection error markers detection - Added _is_data_inspection_error method to identify inspection failures - Created fallback handling for data inspection errors in auto memory extraction - Added fallback handling for data inspection errors in session review - Extended failed extracts tracking with non-retryable and fallback flags - Separated fallback extracts from regular failed extracts in reporting - Enhanced error logging with specific data inspection failure messages - Updated metrics to track fallback extractions and reviews separately - Maintained existing retry logic for other exception types * feat(benchmark): enhance session review statistics with fallback tracking - Add support for identifying and listing non-retryable fallback reviews - Introduce --list-fallback argument to display fallback review details - Separate retryable failures from non-retryable fallbacks in reporting - Track fallback samples and sessions separately from failed ones - Update console output to show both retryable and non-retryable categories - Include fallback details in JSON output with reasons and session info - Modify failure counting logic to distinguish between retryable and fallback reviews * feat(benchmark): add question_id tracking and enhanced fallback reporting - Add question_id function to extract query.question_id from data - Initialize question_id_by_id dictionary to store question IDs by index - Store question_id for each sample during data processing - Enhance fallback output to include question IDs and session information - Format sample labels with question IDs when available - Display session IDs associated with each fallback case * feat(benchmark): add question_id support and improve bad sample reporting - Add question_id_for function to extract question_id from multiple sources - Add sample_label function to format samples as idx(question_id) when available - Store question_id in data dictionary during processing - Change bad_golden and bad_sessions to store full records instead of just indices - Update list_bad output to show formatted labels with question_id information - Improve error reporting with more detailed sample identification * feat(benchmark): enhance golden check stats with structured output - Add related_session_ids function to extract session IDs from verdict records - Create grouped_records function to group records by question type - Replace flat list output with JSON-formatted grouped records in list_bad option - Replace flat list output with JSON-formatted grouped records in list_bad_sessions option - Maintain Chinese labels while adding structured data presentation - Improve readability of bad verdict record display with hierarchical grouping * feat(benchmark): update data structure for question indexing - Replace sample_label with _idx field for index tracking - Add question_id field to store _question_id values - Maintain backward compatibility with empty string defaults - Preserve existing session_id functionality - Update data mapping to include new fields in grouped results * refactor(benchmark): streamline golden answer verification process - Replace relevance filtering with comprehensive information extraction - Remove is_relevant field and simplify session summary structure - Change relevant_info to extracted_info for clarity - Update golden check logic to work with full extractions instead of filtered summaries - Simplify prompt instructions to focus on complete information extraction - Remove redundant schema validation and structured output requirements - Adjust statistics calculation to match new extraction approach - Update metadata field names to reflect extraction rather than relevance checking * feat(benchmark): add selective file deletion option to clean_sample_outputs - Add --filename argument to delete only specific root-level files - Modify iter_sample_targets function to accept optional filenames filter - Implement validation for root-level filename constraints - Update function calls to pass filenames parameter - Add example usage for selective file deletion in documentation * feat(benchmark): add error count metrics to golden check statistics - Added golden_bad, session_bad, and both_bad calculation fields - Updated console output format to include error counts per question type - Modified table display to show both accuracy rates and error numbers - Enhanced statistical summary with additional error breakdown metrics * test(search): update search step tests with include_source parameter - Added include_source=False parameter to VectorSearchStep initialization - Added include_source=False parameter to Bm25SearchStep initialization - Maintained existing RuntimeContext parameters for both search steps - Updated test calls to match new constructor signature with include_source option	2026-07-13 21:26:27 +08:00
jinliyl	38cf16071b	refactor(embedding): update embedding model initialization and session storage paths (#329) ... * refactor(embedding): update embedding model initialization and session storage paths - Remove unused inspect import from as_embedding module - Pass dimensions directly to embedding model constructor instead of using parameters - Update session state file paths to use mem_session directory instead of resource - Add mem_session_dir configuration option to application config schema - Update workspace directory creation to include new mem_session directory - Change AgentScope and Claude Code session paths to use mem_session directory - Move embedding dimensions from parameters to top-level configuration - Update AgentScope dependency version from 2.0.3 to 2.0.4 - Update integration tests to reflect new session file location paths * chore(version): bump version to 0.4.0.8 - Update __version__ from 0.4.0.7 to 0.4.0.8 in __init__.py	2026-07-08 12:30:46 +09:00
jinliyl	ffb4d08c4f	feat(mem): Enhance daily note system with metadata handling and write functionality (#295) ... * feat(file_io): add daily_write step for creating daily notes with conversation metadata - Add DailyWriteStep class that delegates to write job for creating daily notes - Register daily_write job in default configuration with proper parameters - Include validation for name and session_id path components - Add test coverage for daily_write functionality including metadata handling - Preserve existing job execution method in application.py after repositioning - Update base_step.py to use positional-only parameter syntax for job methods - Import and expose DailyWriteStep in file_io module initialization - Override reserved metadata keys (name, description, session_id, source_conversation) with fixed values - Refresh daily index after successful write operation - Generate proper source conversation links in markdown format * feat(daily): refactor daily note system with enhanced metadata handling - Introduce validate_filename_component function and export it - Add _INDEX_HIDDEN_METADATA_KEYS to hide conversation metadata from index - Update scan_notes to exclude hidden metadata keys from index rendering - Modify auto_memory to use daily_write tool and manage session frontmatter - Implement session note lookup and renaming based on frontmatter name - Update daily_list to return flattened note metadata including session info - Change daily_write to dispatch write step instead of running job - Add test cases for updated daily note functionality and metadata handling - Update version from 0.4.0.2 to 0.4.0.3 * fix(evolve): correct metadata update in auto memory response - Fixed trailing comma issue in metadata dictionary update - Ensured proper formatting of response metadata structure - Maintained existing functionality while fixing syntax error * refactor(auto_resource): replace daily_create with dynamic note management - Remove DailyCreateStep and related exports from file_io module - Replace static daily note creation with dynamic resource-linked card system - Implement LLM-suggested naming with frontmatter-driven file management - Add source_resource linking for tracking original files - Introduce collision handling with hash-based suffixes - Update documentation to reflect new resource card workflow - Modify auto_resource prompts to use write/edit tools instead of daily_create - Adjust test fixture comments to match new agent behavior - Update framework diagrams and quick start examples accordingly * feat(app): add version info to app initialization and update auto-memory logic - Include version number in application startup logging - Remove tool result truncation logic from auto-memory step - Update auto-memory to exclude tool_result blocks from saved history - Add test case to verify tool results are filtered out from message saving - Update YAML prompts to clarify filename naming rules without dates - Modify configuration to support new dispatch steps format with persistence control * feat(auto_memory): add note modification tracking and optimize frontmatter updates - Add _note_bytes and _note_modified methods to track actual file changes - Optimize frontmatter updates by checking existing metadata before update - Add modified flag to response metadata indicating actual note changes - Update logging to include modified status in various operations - Add comprehensive tests for modified/unmodified detection scenarios - Enhance result hook logic to skip when no actual changes occur - Refactor metadata handling to properly track creation vs modification status	2026-06-25 21:54:56 +08:00
Sen Huang	e31db5fe19	docs: rename vault_dir to workspace_dir in documentation and examples (#286) ... * docs: rename vault_dir to workspace_dir in documentation and examples * refactor(extract): format long method call across multiple lines * refactor(extract): format system prompt parameters for better readability	2026-06-22 16:58:57 +08:00
jinliyl	206a53e5ed	init: reme version 0.4.0 (#284)	2026-06-22 15:41:19 +08:00

Renamed from reme4/application.py (Browse further)