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13 commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
jinliyl
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cae613d1c4
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refactor(auto-fin): one note per topic, merged by a separate digest step (#558)
* fix(auto-fin): parse topic IDs from fenced JSON replies
* fix(auto-fin): limit report agent tool calls in prompt
* refactor(auto-fin): research news by topic before market open
* fix(config): update default model version for claude_code backend
- Change model version from qwen3.8-max to qwen3.7-plus
- Use environment variable LLM_MODEL_NAME to allow override
- Ensure backend configuration reflects updated model setting
* refactor(auto-fin): write one note per topic before the daily digest
The merge step did two jobs at once: it researched every topic and
combined the results into a single report. Split it the way daily-paper
separates analysis from its brief, so each topic earns a durable note of
its own.
- auto_fin_research_step writes one note per topic that had relevant
news, tagged `kind: auto-fin-topic` and `topic` in frontmatter
- auto_fin_digest_step merges those notes into the day's brief with no
tools of its own and appends a `## 主题详解` section linking back to
each note
- a same-day rerun finds a topic's note by its `topic` frontmatter and
replaces it in place, deleting the old file when the title changed
- base.py now owns the shared Markdown layer: title sanitizing, report
normalizing, wikilink validation, note lookup, atomic frontmatter
writes, and change tracking, so both steps share one write path
- the DingTalk step maps `auto_fin_digest_path` to `markdown_path`
explicitly instead of relying on whichever step ran last
- drop the unused AutoFinTopicOutput schema and read `job_tools` from
the step config rather than hardcoding `search`
Co-Authored-By: Claude <noreply@anthropic.com>
* fix(dingtalk): surface rejection details when delivery fails
A failed group send only reported HTTPStatusError, so an operator had to
reproduce the request by hand to learn why DingTalk refused it. Include
the status code and the whitelisted error keys from the response body in
both the log line and the raised RuntimeError.
Only `code`, `message`, and `requestid` are reported: the request body
carries the message content and credentials, so an error response that
echoes it back must not reach the log. Detail is truncated to 200 chars.
Co-Authored-By: Claude <noreply@anthropic.com>
* fix(test): make the suite green on CI
- Point the cookbook claude_code model assertion at qwen3.7-plus, the
default commit
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xyf2020
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06fb46fa48
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feat(tags): add configurable tag indexing and filtered hybrid search (#530)
* Add optional tag generation and normalization to auto memory * Add tag index components and clean up temporary JSONL files * Preserve tag index state when reconciliation fails * Refactor and streamline application implementation * Fix pylint C1803 warnings in tag normalization tests * Document optional tag index configuration * Make tag index failures non-blocking and disable auto-memory tags * Add configurable tag indexing and tag listing * Add tag-filtered hybrid search with exact candidate ranking * Remove obsolete generated files * Rename tag index key to tag_key and reject reserved fields * Extract automatic tagging into a dedicated step * Restrict frontmatter updates to authorized keys * Refine tag filtering and automatic memory tagging * Require underscore-separated tags in auto-tag prompts - Forbid spaces in tags and require underscores (e.g. sam_altman) in both English and Chinese auto_tag prompts, with English examples switched to English entities (OpenAI, gold) - Drop prompt-string assertions superseded by the new tagging rule - Merge construction/runtime tag_key validation tests into one parametrized case * Make max_tags_per_file configurable in auto-tag step * Consolidate tag index tests * Fix search test fixture lint warnings * Align tag contracts and index health behavior * Fall back when tag index is unavailable --------- Co-authored-by: jinli.yl <jinli.yl@alibaba-inc.com> |
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xyf2020
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3f2eb6235f
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feat(benchmark): extract BEAM and LongMemEval into standalone plugins (#512)
* Simplify project implementation * Centralize benchmark agentic answer base class * Remove bundled ReMe source snapshots * Preserve local benchmark configs and plugin discovery behavior * docs: enrich job parameter descriptions in beam and lme plugin configs * Simplify project structure and remove obsolete code * Move benchmark search step configuration into BEAM and LME plugins * Rename benchmark judge packages to avoid import collisions * Remove explicit plugin package loading in favor of entry-point discovery * Export benchmark plugin Steps from public packages |
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jinliyl
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ebcb154e37
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fix(search): isolate range dedup state (#465) | ||
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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 |
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Ziyang Guo
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e41b1673ad
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fix(search): honor min_score in plain search steps (#338) | ||
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Ziyang Guo
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c5eefe4da3
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fix(search): expose markdown frontmatter on chunks (#314)
* fix(search): expose markdown frontmatter on chunks * style(search): apply pre-commit formatting * fix(search): make frontmatter chunk metadata opt-in * fixup! fix(search): expose markdown frontmatter on chunks * feat(markdown): add include_frontmatter_keys_in_metadata allow-list opt-in --------- Co-authored-by: RerankerGuo <1875366113@qq.com> Co-authored-by: Ziyang Guo <121015044+RunMarshal@users.noreply.github.com> |
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jinliyl
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2612d25959
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feat(lme): add cli execution and agentic search tooling (#334)
* feat(service): add CLI service for local job execution - Introduce CliService to execute single jobs locally without serving ports - Add prepare_start_config and should_precheck_start functions for CLI job setup - Update reme start command to use CLI service when job argument is provided - Change default service backend from http to cli in jinli_lme config - Modify SearchStep to use constants and rename configuration parameters - Add unit tests for CLI service functionality and configuration handling - Update file extension support to include json format in addition to md and jsonl * feat(search): add BM25 and vector search steps with configuration updates - Add Bm25SearchStep and VectorSearchStep classes with tool context deduplication - Register new search step components in index module - Update configuration to use separate vector_search and bm25_search endpoints - Modify LLM models from qwen3.7-plus/glm-5.1 to glm-5.2 variants - Adjust search parameters and remove hybrid search implementation - Configure embedding store as default in storage settings - Remove auto-memory and file catalog configurations - Update watch directories from multiple paths to session_dir only * feat(agent): add tool result offloading and workspace management - Add tool_results_dir configuration option for offloaded tool results storage - Implement ToolResultOffloadMiddleware to persist large tool results to files - Create WorkspaceBackend to standardize file operations across tools - Add configurable builtin tools selection with sequential execution option - Integrate middleware support for agent wrapper with offloading capability - Update application initialization to create tool results directory - Add safety mechanisms for filesystem operations with sanitized filenames - Enhance agent wrapper with configurable working directory handling - Upgrade agentscope dependency to version 2.0.4 for improved features # Conflicts: # reme/application.py * feat(benchmark): add LongMemEval agentic search and result management - Introduce AgenticAnswerStep for agent-based history search - Add LmePrepareJudgeStep and LmeSaveResultStep for evaluation pipeline - Implement AddDraftStep and ReadAllDraftStep for evidence accumulation - Update configuration with new agent wrapper and search parameters - Add comparison script for analyzing agent run differences - Include documentation for LongMemEval failure analysis - Enhance tool result offloading with skip options - Modify search defaults and indexing behavior * feat(agent): implement tool result offloading with system reminders - Added tool_result_offload_message parameter to agent wrapper reply method - Implemented configurable reminder template for offloaded tool results - Created system reminder messages when tool results are offloaded to files - Added Chinese user message template for agentic answer step - Updated tool result offloading middleware to use custom reminder templates - Enhanced agentic answer instructions to handle long tool results via draft storage * feat(scripts): add LongMemEval results summarization tool - Create summarize_lme_results.py script to analyze result JSON files - Implement command line interface with answer id and dataset root options - Add support for specifying index range with start and end parameters - Include option to show failure details and non-successful completions - Calculate completion statistics and accuracy metrics - Display detailed breakdown of yes/no/other judgements - Handle missing and unreadable result files gracefully - Format output with percentages and comprehensive summary statistics * feat(summarize_lme_results): add question type breakdown to result summary - Import defaultdict from collections module - Add by_type dictionary to track statistics by question type - Count completed, yes, no, and other responses for each question type - Display detailed breakdown table showing accuracy by question type - Include question type column when processing judgements - Print comprehensive summary with question type distribution - Calculate and display accuracy percentage for each question type category * feat(lme): switch to qwen3.7-max model and add shuffle functionality - Changed default LLM model from glm-5.1 to qwen3.7-max in jinli_lme.yaml - Added random module import for shuffle functionality - Implemented --shuffle argument with BooleanOptionalAction for dataset shuffling - Added --seed argument to control random seed for reproducible shuffling - Applied random shuffle to dataset indices when shuffle is enabled - Added console output showing shuffle operation and seed information * fix(cli): set default random seed for shuffle functionality - Changed default seed value from None to 42 for consistent shuffling behavior - Ensures reproducible results when using shuffle option without explicit seed - Maintains backward compatibility while providing deterministic defaults * refactor(benchmark): update agentic answer guidelines for grounding - Updated English instruction to emphasize strict grounding in retrieved context - Modified Chinese instruction to stress evidence-based responses without inference - Removed redundant conciseness requirement in both language versions - Enhanced clarity on proper use of draft saving and retrieval mechanisms - Strengthened emphasis against hallucination of unsupported facts * refactor(benchmark): update agentic search instructions and configuration - Replace separate vector_search and bm25_search with unified search tool - Update agent instructions to use single search tool with multiple strategies - Simplify Chinese instructions for search methodology - Add comprehensive search tool configuration with hybrid vector/BM25 capabilities - Increase model retry attempts from 1 to 3 for better reliability - Remove redundant tool references from job_tools list * feat(search): add configurable search limit with environment variable support - Remove hardcoded limit and min_score parameters from config schema - Increase LLM context size from 200000 to 1000000 - Add REME_SEARCH_LIMIT environment variable support for search configuration - Implement command line argument --search-limit to override default search limit - Add input validation to ensure search limit is positive - Modify subprocess execution to pass environment variables - Update search step to use dynamic default limit from environment or fallback to 5 * refactor(benchmark): remove agentic answer step and related configurations - Removed AgenticAnswerStep class and its registration - Deleted agentic_answer.yaml prompt configuration file - Removed agentic answer related job definitions from jinli_lme.yaml - Cleaned up tool result offloading middleware implementation - Removed tool_results_dir configuration field from application config - Deleted comparison and analysis scripts for agent runs - Removed agentic answer step from LME init module exports - Updated agent wrapper to remove tool result offloading functionality - Removed unused imports and dependencies in agent wrapper module * refactor(benchmark): remove unused LME result processing components - Removed LmePrepareJudgeStep and LmeSaveResultStep classes from benchmark module - Cleaned up imports and exports in lme module initialization - Removed unused middleware configuration from agent wrapper - Deleted obsolete result.py file containing deprecated result processing logic - Simplified agent instantiation by removing middleware parameter - Updated import statements to reflect removed dependencies * refactor(index): remove unused search steps and update imports - Remove Bm25SearchStep and VectorSearchStep from index steps module - Remove unused prepare_start_config and should_precheck_start exports - Move import statements to proper location in reme.py - Update test module to use direct import path for CliService - Remove vector_search and bm25_search configurations from jinli_lme.yaml - Add workspace directory environment variable configuration - Add docstring to getcwd method in agent wrapper - Remove empty middleware list from agent wrapper initialization * feat(index): add BM25 and vector search steps with tool context deduplication - Add Bm25SearchStep for plain BM25 keyword search with tool_context deduplication - Add VectorSearchStep for plain vector search with tool_context deduplication - Implement tool context state management with TTL-based deduplication - Add support for chunk deduplication across tool contexts within TTL window - Update index steps module to include new search step classes - Add test coverage for CLI metadata output functionality - Refactor CLI service to remove unused show_status parameter - Update documentation comments to reflect internal service configuration * feat(steps): add Python code execution capability - Introduce PythonExecuteStep to run Python code in subprocess - Add configuration for python_execute step in jinli_lme.yaml - Register python_execute in available tools list - Implement timeout handling with default 60 second limit - Capture stdout/stderr output and return code metadata - Add comprehensive unit tests for execution scenarios - Support workspace directory context for code execution - Handle timeout errors and runtime exceptions gracefully * refactor(python_execute): replace subprocess with asyncio for Python code execution - Replace subprocess.run with asyncio.create_subprocess_exec for non-blocking execution - Add _PythonResult dataclass to encapsulate execution results and timeout status - Implement proper timeout handling with asyncio.wait_for and process.kill() - Update metadata to include returncode and stderr when timeout occurs - Convert synchronous _run_python method to asynchronous implementation - Maintain backward compatibility while improving execution reliability * refactor(python_execute): replace subprocess with asyncio for Python code execution - Replace subprocess.run with asyncio.create_subprocess_exec for non-blocking execution - Add _PythonResult dataclass to encapsulate execution results and timeout status - Implement proper timeout handling with asyncio.wait_for and process.kill() - Update metadata to include returncode and stderr when timeout occurs - Convert synchronous _run_python method to asynchronous implementation - Maintain backward compatibility while improving execution reliability |
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jinliyl
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7369342115
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feat(search): add tool context deduplication and improve search configuration (#321)
* feat(search): add tool context deduplication and improve search configuration - Modify _make_tool methods to accept and inject tool_context_id parameter - Add tool_context_id handling in AS and CC agent wrappers - Increase search candidate multiplier from 3.0 to 5.0 in default config - Extend HTTP client timeout from 30s to 3600s - Add tool context deduplication logic to prevent duplicate search results - Implement TTL-based expiration for seen chunks in tool contexts - Add comprehensive unit tests for tool context deduplication behavior - Update .gitignore to exclude longmemeval directory - Add time import for timestamp functionality in search step * refactor(search): replace time module with datetime for timestamp generation - Removed unused time import - Added static method _now_ts using datetime.timestamp - Updated clock parameter to use _now_ts method instead of time.time - Maintained same timestamp precision and functionality |
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xyf2020
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43a407bc4f
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feat: add start_date/end_date time filter support for search job (#317)
* feat: add start_date/end_date time filter support for search job
- Add _extract_date_from_path to extract validated YYYY-MM-DD from chunk paths
- Add start_date/end_date filtering in _matches_search_filter
- Implement progressive recall in FaissLocalFileStore.vector_search
- Promote start_date/end_date from context to search_filter in SearchStep
- Add start_date/end_date parameters to search job in default.yaml
- Add unit tests for date filter functionality
* fix: validate/normalize date filters and harden _extract_date_from_path
Address three code-review comments on the time_filter search feature:
1. Validate/normalize start_date and end_date before string comparison.
_matches_search_filter does lexicographic comparison against path_date
(always canonical YYYY-MM-DD). Raw caller values like '2026-2-28' or
'abc' would produce silently wrong results. Now SearchStep normalizes
valid dates via extract_daily_date (with strptime fallback for
non-zero-padded input) and silently ignores invalid dates with a
logger.warning, removing them from the filter.
2. Clarify behavior for paths without embedded dates.
Added optional strict_date_filter parameter (default False). When True
and at least one date bound is active, chunks whose path yields no date
(e.g. digest/personal/topic.md) are excluded. When False (default),
the existing behavior is preserved — dateless paths pass through.
3. Harden _extract_date_from_path against non-standard suffixes.
Previously parts[1].split('.')[0] accepted '2026-05-18.anything' as a
valid date. Now only exact 'YYYY-MM-DD' (dir) and 'YYYY-MM-DD.md'
(day-index) forms are accepted.
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jinliyl
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206a53e5ed
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init: reme version 0.4.0 (#284) |
Renamed from tests4/unit/test_search_step.py (Browse further)