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4 commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
jinliyl
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9218a2d0e3
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refactor: derive dialog paths from session_dir (#421)
* refactor: derive dialog paths from session directory * fix: normalize configured session paths * fix: align dialog watch paths with writers * fix: reject absolute session directories |
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xyf2020
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5bc46c88b6
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feat(benchmark): enhance session memory retrieval and isolate benchmark assets (#409)
* chore(benchmark): isolate dataset/workspaces/results per benchmark
- Move shared benchmark/{datasets,memory_workspaces,results} into per-benchmark subdirs benchmark/<name>/{dataset,workspaces,results}
- Update beam/longmemeval config.yaml and run.py path defaults
- Relocate longmemeval download.py to benchmark/longmemeval/ (downloads into dataset/ subdir); inline dataset download docs into README
- Update .gitignore: benchmark/*/{dataset,workspaces,results}/
- Move result-{beam,longmemeval}.md to benchmark/results_md/ and drop result- prefix; update README links
- Fix stale path refs in llm_judge.py and logs/demo_search_format.py
* feat(benchmark): add read tool to agentic answer and update BEAM results
- Add 'read' to job_tools in BaseAgenticAnswerStep for file reading capability
- Document read tool usage in lme/agentic_answer.yaml system prompt
- Update result-beam.md with latest evaluation scores (OVERALL: 0.623/0.580)
* feat(auto_memory): add source line-number markers for note traceability
- Add _format_history hook in AutoMemoryStep with line-number annotation
- Override in BeamAutoMemoryStep to prefix each turn with [Ln] for citation
- Add session_file variable to prompt templates for source marker paths
- Simplify repeated extraction rules by referencing system prompt
- Enhance agentic_answer search strategy (multi-search, read tool hint)
- Add warning log on ReadStep failure
* feat(beam): enhance auto_memory with source markers and pilot ingest tooling
* refactor(beam): rename max_chunk_words to max_segment_words, drop one-off pilot scripts
* feat: add CompressorStep and search_v2 dual-mode session compression
- Add CompressorStep (reme/steps/evolve/compressor.py) for direct LLM
text compression with optional query-guided relevance filtering
- Extend search_v2_step to support query-aware and query-independent
session transcript compression via _compress injected kwargs
- Refactor _source_format.py: split into render_chunk_entries +
join_chunk_entries; session chunks now render line-aligned with
L<n>: prefixes for verbatim/compressed parity
- Add JOB_TOOLS and INJECTED_JOB_KWARGS to BaseAgenticAnswerStep for
per-subclass tool and parameter injection
- LmeAgenticAnswerStep injects _search._compress payload to enable
query-aware compression during benchmark evaluation
- Record compression ablation results in result-longmemeval.md
- Add unit tests for CompressorStep and search compression paths
* refactor(compress): relax session compression to lenient format-preserving strategy and update LME results
* refactor(benchmark): make session compression config-driven via compress_session flag
Move session-transcript compression from LME hard-coded injection to a
runtime context flag set by evaluation.compress_session in each
benchmark config. Compression is off by default for both BEAM and LME,
and BaseAgenticAnswerStep now conditionally injects the _search compress
payload only when the flag is truthy.
* feat(lme/auto_memory): add source attribution markers with line numbers
Add _format_history to annotate each turn with [Ln] line numbers and
expose {session_file} in prompts so the agent can emit bare wikilink-style
source markers like [[session/dialog/s1.jsonl#L1-L2,L5-L6]] at the end
of factual entries. Consolidate the per-prompt body/format rules into
references to the system prompt to avoid drift, and add frontmatter-
protection guidance for the edit tool.
* feat: improve agentic answer prompt and update beam 100K results
- Strengthen abstention rule: prohibit extrapolation from related but
non-direct evidence
- Add multi-angle search after preliminary answer to check for
conflicting/supplementary/updated information
- Add max-iteration fallback to 'Information not found'
- Update beam.md with 100K results (agentscope 2.0.4.post1, from scratch)
including per-type token consumption and memory construction stats
- config.yaml: 100K dataset, 20 workers for BEAM evaluation
- run.py: add memory construction token usage tracking (default agent)
- Overall: 0.635 → 0.654 (+0.019), contradiction_resolution: 0.338 → 0.478
(+0.140), abstention: 0.500 → 0.525 (+0.025)
* feat(read): add session-aware formatting for read tool and update BEAM eval
- Add truncate_session_output in _file_io.py to render jsonl session
lines as [speaker @ time] content before byte-budget truncation
- Add read_step_format_session flag to ReadStep, honoring injected
job kwargs (precedence) and YAML fallback
- Inject read_step_format_session=True into BaseAgenticAnswerStep
so agentic answer reads render session transcripts human-readably
- Refine BEAM agentic_answer prompt: continue multi-angle search
after preliminary answer, forbid fabrication/extrapolation
- Update BEAM config to 1M variant and add sequential 100K-eval /
1M-build shell script
- Refresh benchmark/results_md/beam.md with latest results
* chore(config): disable expand_links in beam and lme search_v2 configs
* refactor(beam): drop one-off sequential 100K-eval-then-1M-build script
* fix(benchmark): add compressor job to beam config and fix BEAM clone instructions
- Add compressor job and compressor as_llm component to reme/config/beam.yaml
(aligned with lme.yaml) so that compress_session: true works for BEAM
- Add graceful degradation guard in search_v2._compress_session_entries:
when the compressor job is missing from the active config, log a warning
and skip compression instead of raising 'Job compressor not found'.
Skipped when there is no app_context so unit tests mocking run_job still
drive compression behavior.
- Fix BEAM download instructions in README.md/README_ZH.md: add mkdir -p
before cd benchmark/beam/dataset (the directory is gitignored and absent
in a fresh clone)
* fix(steps): guard compressor exceptions and fix ReadStep boolean override
1. search_v2: catch per-entry exceptions from run_job('compressor') inside
compress() so asyncio.gather never propagates a compressor failure (e.g.
temporary LLM outage). The failing entry keeps its original body while
remaining entries are still compressed, preserving already-retrieved
search results.
2. read: replace 'context_value or yaml_value' with an existence check so
that a runtime-injected False can explicitly disable a YAML-true
read_step_format_session flag.
Add focused unit tests for both paths.
* fix(search_v2): use existence check for strict_date_filter boolean override
Replace 'context_value or yaml_value' with an existence-based check so
that a runtime-injected False can explicitly disable a YAML-true
strict_date_filter flag, consistent with the read_step_format_session fix.
* refactor(search): simplify strict_date_filter fallback to truthiness-or
* style(test): rename unused param to satisfy pylint W0613
---------
Co-authored-by: sa-buc <jiangniurou.xyf@dail-algo011164204033.ET135>
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xyf2020
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630f26b119
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feat(search): scoped dedup, session-chunk merge, and unified recall formatting (#384)
* feat(search): add tool_context-scoped chunk dedup with TTL
Introduce _ToolContextDedupMixin shared by search/vector_search/bm25_search
to skip already-seen chunks within one agent tool_context. Per-context state
lives in app_context.metadata with configurable TTL (default 24h).
* feat(search): unify chunk answer rendering with merge and explicit empty messages
- Refactor SearchStep/VectorSearchStep/Bm25SearchStep to share format_chunks_answer for consistent source rendering and adjacent session-chunk merging.
- Distinguish empty results: ALL_RETURNED_MESSAGE when dedup removes everything vs NO_RESULTS_MESSAGE when nothing matched.
- Bump JsonlFileChunker default max_chars to 4000.
- Add unit tests for source-format merge and empty-result messages.
* refactor(config): reorganize file_chunker components and move jsonl max_chars into config
- Register explicit markdown/json/jsonl chunkers in beam.yaml and lme.yaml with markdown options (embed_toc, max_ast_sections, frontmatter handling) and jsonl max_chars=4000.
- Restrict default chunker to txt/log extensions.
- Revert JsonlFileChunker code default max_chars back to 2000; the 4000 value now lives in config.
* chore(benchmark): increase longmemeval num_items from 64 to 500
* refactor(search): split SearchStep into simplified and v2 variants, extract counter utility
- Extract global_counter_next from ApplicationContext into reme/utils/counter.py
as a standalone function operating on metadata dict with lazy initialization.
- Split SearchStep into two variants:
- SearchStep (simplified): inline chunk.id dedup, single-branch vector/keyword
optimization based on vector_weight, inline answer formatting.
- SearchV2Step (full): preserves _ToolContextDedupMixin with interval-subset-aware
dedup and format_chunks_answer with session-aware chunk merging.
- Update beam.yaml and lme.yaml to use search_v2_step for benchmark jobs.
- Rename existing search tests to test_search_v2_step_* and add new
test_search_step_* tests covering the simplified variant.
* fix: normalise missing trailing newline in _build_union_chunk to prevent line collision
* refactor: lazy-init counter tree in ApplicationContext metadata
- Remove hardcoded _counter_tree and _counter_tree_lock initialization
from ApplicationContext.metadata; rely on lazy initialization in
reme.utils.counter.global_counter_next on first call
- Set longmemeval num_items back to 500
- Remove obsolete trailing-newline collision tests
---------
Co-authored-by: sa-buc <jiangniurou.xyf@dail-algo011164204033.ET135>
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jinliyl
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bf7ca17705
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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 |