mirror of
https://github.com/agentscope-ai/ReMe.git
synced 2026-10-09 03:20:54 +00:00
24 commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
jinliyl
|
cae613d1c4
|
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
|
||
|
WQS
|
6ed97f033b
|
feat(auto_resource): reuse parsed resource ownership per batch (#529)
* perf(auto_resource): reuse historical ownership lookup per batch Share a lazy ownership index across routed processors and expire it at the end of each resource batch. Refresh changed days and their safe aliases after writes, deletes, and partial failures. Add regression coverage for scan counts, ownership, lookup scope isolation, and retry behavior. * fix(auto_resource): reconcile shared lookup across active batches * fix(auto_resource): refresh batch ownership from filesystem metadata * refactor(auto_resource): simplify batch ownership lookup Keep per-day ownership caches paired with pre-read metadata and refresh changed days before subsequent resources. Remove reverse-index bookkeeping, post-read stabilization retries, and recursive scope fallback. Handle late ELOOP errors on Python 3.13 while preserving other OS errors. Update regression tests for cross-resource freshness and retain safety and lifecycle coverage. * refactor(auto_resource): streamline batch lookup refresh * refactor(auto_resource): scope ownership cache to batch mutations Replace filesystem snapshots with one lazy historical lookup shared by resource processors. Reload only days changed by this invocation and discard the cache when it ends. Reuse canonical daily-note scans for symlink safety and Python 3.13 compatibility. Consolidate regression tests around the narrowed batch contract. Validation: Python 3.11, 3.12 and 3.13 core/plugin suites each passed 1407 tests; pre-commit --all-files and offline lookup/deletion comparison passed. * refactor(auto_resource): reuse initial ownership without refreshes * test(auto_resource): streamline ownership lookup regression coverage |
||
|
xyf2020
|
06fb46fa48
|
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> |
||
|
WQS
|
5c17874f73
|
feat(auto_resource): interpret image resources into daily notes (#500)
* feat: add auto_image step for image resource caption notes * feat: wire image resources into the resource watch loop * refactor: split auto resource processors behind router * refactor: align resource processor module names * refactor: preserve auto resource compatibility * refactor: clarify auto resource routing structure * fix: address auto resource review concerns * test: scope auto resource fixtures * docs: align auto resource processor wording * test: cover image resize failures * fix: harden image resource lifecycle * fix: preserve resource image detail and linked daily ownership * style(file-graph): stabilize multiline docstring formatting |
||
|
imrewce
|
58276f740b
|
fix(file_io): auto appending suffix for all related steps (#430)
* fix(file_io): auto appending suffix for all related steps * fix(file_io): covering boundary cases of potential directory input |
||
|
jinliyl
|
e05b201da9
|
feat(backend): improve workspace support for web clients (#420)
* feat(backend): improve workspace support for web clients * fix(config): preserve the default workspace directory * chore(reme): bump version to 0.4.1.5 - Update __version__ from 0.4.1.4 to 0.4.1.5 in initialization file * fix(chat): disable builtin tools in read-only mode * fix(agent): make builtin tools opt-in * fix(list): tolerate files removed during mtime sort * fix(chat): expose complete read-only job set |
||
|
lichen2015
|
e7b9274190
|
fix(stat): return text/markdown for .md files regardless of OS mime registry (#433)
On macOS, mimetypes.guess_type() may not recognize .md files, causing stat to report application/octet-stream and breaking test assertions. Explicitly map .md files to text/markdown so the behavior is stable across platforms. |
||
|
jinliyl
|
9218a2d0e3
|
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 |
||
|
jinliyl
|
f31daf1949
|
Revert "feat(backend): improve workspace support for web clients (#417)" (#419)
Some checks are pending
Pre-commit / run (ubuntu-latest) (push) Waiting to run
Tests ReMe / Unit Tests - py3.11 (push) Waiting to run
Tests ReMe / Unit Tests - py3.12 (push) Waiting to run
Tests ReMe / Unit Tests - py3.13 (push) Waiting to run
Windows Smoke / CLI smoke - py3.11 (push) Waiting to run
This reverts commit
|
||
|
jinliyl
|
b00eb0a9ea
|
feat(backend): improve workspace support for web clients (#417) | ||
|
xyf2020
|
5bc46c88b6
|
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>
|
||
|
jinliyl
|
e256c556ca
|
feat: add workspace web APIs and star growth report (#416) | ||
|
jinliyl
|
eac8223387
|
feat: add frontend-ready wikilink graph APIs (#414) | ||
|
jinliyl
|
a9ec334adc
|
feat: simplify wikilink semantics and support line anchors (#412)
* feat: simplify local links and support line anchors * fix: align line anchor tests with CI lint * fix: preserve local links across file moves * fix: encode markdown paths when rewriting links * refactor(read): keep explicit line range parameters * fix: simplify legacy link predicate compatibility * docs: align local link behavior with implementation * fix: skip unsupported markdown destination escapes * fix: normalize workspace link paths across platforms * fix: bound markdown link scanning * fix: keep local link processing linear * docs: clarify permissive markdown link parsing * fix: handle local link processing failures * refactor: limit file links to wikilink syntax * docs: align wikilink contract with implementation * fix: normalize dream and neighbor paths on Windows * fix: resolve workspace path for neighbor expansion |
||
|
xyf2020
|
f34dcdb09b
|
feat(Step tools): add white/black path prefix permission filtering to read, edit, write (#391)
* feat(read): add white/black path prefix permission filtering to ReadStep * feat: add PrefixCheck mixin for path-prefix permission in file I/O steps * feat: add injected_job_kwargs mechanism and refine path-prefix permission * refactor(file_io): consolidate prefix_check into _path module |
||
|
Amir Fathi
|
0522135791
|
fix(file_io): stop ReadStep small-file path over-counting total lines by 1 (#389)
content.split("\n") yields a trailing empty element for any file ending in a
newline, inflating total by 1 and letting a start_line one past real EOF be
silently accepted instead of rejected. Mirrors the trailing-newline correction
default_file_chunker already applies, and matches the large-file path's
line-by-line count.
Fixes #388
|
||
|
jinliyl
|
1687179f84
|
feat: add Auto Fin cookbook and managed outbound proxy support (#392)
Some checks are pending
Pre-commit / run (ubuntu-latest) (push) Waiting to run
Tests ReMe / Unit Tests - py3.13 (push) Waiting to run
Tests ReMe / Unit Tests - py3.11 (push) Waiting to run
Tests ReMe / Unit Tests - py3.12 (push) Waiting to run
Windows Smoke / CLI smoke - py3.11 (push) Waiting to run
* feat: add ssh proxy * feat: add ssh proxy * feat: add ssh proxy * feat: add ssh proxy * feat: add prompt * feat: add agent wrapper * feat: add agent wrapper * feat: add agent wrapper * feat: add tushare skill * feat: add tushare skill * feat: add tushare skill * feat: add none stream * chore(deps): update dependency versions in pyproject.toml - Bump claude-agent-sdk from 0.2.123 to 0.2.126 - Upgrade pre-commit to version 4.6.1 or higher - Upgrade pytest to version 9.1.1 or higher * feat(agent_wrapper): add session compaction support and unify session commands - Introduce compact_session method to BaseAgentWrapper and implement it in AsAgentWrapper, CcAgentWrapper, and CodexAgentWrapper - Add session_command module with SessionCommandResult dataclass and handle_session_command function for /clear and /compact commands - Update __init__.py exports to include session_command handlers - Modify DingTalkWaitStep to handle session commands via handle_session_command function - Remove streaming mode from DingTalkWaitStep and simplify reply handling to final Markdown replies only - Add unit tests for session compaction methods and session command handling across wrappers and DingTalk integration - Clean up and remove obsolete streaming and card rendering code from DingTalk wait step - Adjust daily_cookbook.yaml to remove stream and card_update_interval config entries for DingTalk wait step * feat(auto_fin): add Auto Fin simulated portfolio cookbook workflow - Add comprehensive Auto Fin schema exports for multiple models and enums - Implement base class and helpers for Auto Fin analysis steps - Create file, state, and formatting utilities for Auto Fin with atomic file writes and locking - Define Auto Fin pipeline with four analysis agents: backtest, event, portfolio, and US correlation - Register Auto Fin package in cookbook workflows and schema initialization - Add detailed documentation in markdown describing the system design, workflow, and data contracts * feat(outbound_proxy): add application-scoped outbound HTTP proxy components - Introduce BaseOutboundProxy and OutboundProxyEndpoint as core contracts - Implement FixedHttpOutboundProxy for external HTTP proxy integration - Add SshHttpOutboundProxy providing SSH-backed local HTTP proxy tunnels - Register outbound proxy components in component registry and enumeration - Update components package to include outbound_proxy module - Add dependency on pproxy for SSH HTTP proxy bridging - Include comprehensive unit tests covering proxy lifecycle, validation, environment merging, error handling, readiness, and monitoring mechanisms * refactor(network): replace SSH proxy with explicit HTTP outbound proxy - Remove SSH proxy helper implementation and references in codebase - Add support for explicit HTTP proxy URL in arXiv and HuggingFace clients - Modify clients to use async context manager for consistent resource handling - Update daily paper steps to forward outbound proxy configuration explicitly - Change tests to cover new proxy usage model and remove SSH proxy mocks - Add outbound proxy component configuration in daily_cookbook.yaml - Ensure proxy URL usage disables environment trust in HTTP clients - Fix app context component enum access to be defensive against missing keys * feat(agent_wrapper): add managed proxy support for command environments - Introduce BaseOutboundProxy binding in BaseAgentWrapper for outbound proxy management - Add bash_environment and command_proxy_environment properties to apply proxy settings - Update WorkspaceBackend instantiation in AsAgentWrapper to use bash_environment - Inject managed proxy export commands into Claude Code Bash commands via hooks - Enhance CodexAgentWrapper to include managed proxy in shell environment policy - Modify daily_cookbook.yaml steps to specify outbound_proxy as default where needed - Add comprehensive unit tests verifying managed proxy injection and environment isolation - Ensure subprocess_environment remains unchanged while proxy is applied selectively to commands * refactor(memory): replace search job_tools with memory in daily cookbook config - Change workspace_dir default from .reme to reme_workspace - Replace search job_tools with memory across multiple components and jobs - Update descriptions to reflect long-term memory retrieval instead of search - Modify system prompts to instruct using memory for retrieving notes - Adjust unit tests to verify memory job_tools and job presence instead of search - Ensure consistency in configuration and tests for memory backend usage * refactor(config): rename memory to memory_search in daily cookbook config - Change all occurrences of "memory" to "memory_search" in job_tools and job definitions - Update related system prompts to reflect the new memory_search terminology - Modify unit tests to assert the presence of memory_search instead of memory - Ensure consistency across skills, job tools, and backend configurations in multiple components * feat(auto_fin): add deterministic quantitative research and ranking fusion - Introduce new schema models: EtfScore, RankingMetrics, ExtremeAnalysis, DimensionRanking, and FusionRanking to represent deterministic research outputs - Add ranking data to event, backtest, us_correlation, and portfolio analysis outputs - Implement ranking_section renderer to format Top20 scores and diagnostics in Markdown - Develop AutoFinQuantStep for deterministic ETF ranking using TuShare data, Polars, and a custom extremely randomized tree ensemble - Integrate quantitative rankings into backtest and portfolio analysis steps and reports - Extend auto_fin pipeline with new quant_enabled and quant_required config options - Enforce ranking constraints like unique codes, contiguous ranks, and normalized fusion weights - Update analysis YAMLs with rules limiting data freshness, universe, and ranking usage - Incorporate ranking outputs into all major markdown report bodies in Auto Fin pipeline - Add concurrency-limited asynchronous TuShare client to fetch required market data - Introduce cross-sectional rank correlation and NDCG metrics for ranking quality evaluation * feat(auto_fin): implement stage-wise notification and reporting for analysis pipeline - Refactor notification config in daily_cookbook.yaml to support dispatch steps - Update AutoFinNotificationStep to deduplicate notifications per run stage - Add _notify_stage method in pipeline to send notifications for each analysis stage - Implement persistence and notification for event, backtest, US correlation, and portfolio stages - Modify pipeline flow to persist reports and notify after each stage completion - Adjust metadata to track notifications and errors per stage - Update tests to verify stage-wise notification sending and deduplication - Remove older combined report persistence in favor of modular stage handling * feat(auto_fin): add outbound proxy support for Tushare API usage - Introduce BaseOutboundProxy reference in AutoFinPipelineStep and AutoFinQuantStep - Update TushareResearchClient and trade calendar fetch to accept and use proxy URL - Create _ProxiedTushareApi adapter to route Tushare requests via explicit HTTP proxy - Modify create_tushare_api utility to optionally return proxied API client - Add unit tests covering proxy forwarding and client behavior with managed proxies - Ensure proxy usage respects explicit proxy URL over environment fallback - Integrate outbound proxy into data fetching and quantitative research steps * feat(auto_fin): enforce checkpoint time validation and add state models - Introduce AnalysisState base class and specific states for event, backtest, and US correlation analyses - Replace analysis output types with corresponding state classes in run schemas - Add require_checkpoint_reached method to validate decision_at/data_cutoff against current time - Enforce checkpoint time checks before analysis steps in event, backtest, portfolio, and quant analyses - Refactor quant data loading to include adjustment factors and apply price adjustments without fallback - Update analysis YAML docs to require real-time checkpoint validation and forbid using future data - Improve portfolio run serialization by excluding redundant legacy fields and nested proposed actions - Add helper to extract readable sections from persisted checkpoint documents - Fix event analysis output validation to reject events and sources with future timestamps * feat(auto_fin): auto-select latest reached checkpoint if none specified - Extend checkpoint config to accept empty string for auto selection - Add static method to compute latest checkpoint reached by current time - Modify pipeline step to auto-select checkpoint based on trade calendar and time - Adjust force flag default depending on whether checkpoint is explicit or auto - Log details when checkpoint is auto-selected to improve observability - Add comprehensive tests for auto checkpoint selection logic and edge cases - Remove deprecated default and required constraints from force parameter in config * refactor(auto_fin): unify datetime comparison with compare_datetimes utility - Replace direct datetime comparisons with compare_datetimes function calls - Use cmp_to_key with compare_datetimes for sorting datetime tuples and lists - Update validation logic in backtest, event, analysis, and ledger modules for consistent datetime handling - Add unit tests to verify handling of naive and aware datetime comparisons in event and backtest validations - Ensure marked_at and interval_end timestamps are set and compared consistently using compare_datetimes - Improve correctness of ordering and conditional checks related to timestamps throughout auto_fin steps and ledger code * feat(auto_fin): add datetime comparison helper for mixed timezone data - Implement compare_datetimes function to handle naive and aware datetimes - Ensure naive datetime is interpreted in the known timezone of the counterpart - Facilitate comparisons between legacy and timezone-aware Auto Fin data - Add module docstring explaining purpose of the helpers * docs(auto_fin): enforce unique ETF representative per sub-theme in analysis rules - Update backtest.yaml to recommend or highlight only one ETF per sub-theme for ETF analyses - Modify event.yaml to map only one representative ETF per sub-theme, avoiding duplicate recommendations - Revise portfolio.yaml to restrict holdings/buys to a single ETF per sub-theme, preventing repeated buys of highly overlapping ETFs - Adjust us_correlation.yaml to retain only one representative A-share ETF per sub-theme for mapping or recommendation - Add test to verify presence of new sub-theme uniqueness guidance in step prompts * feat(auto_fin): separate draft model and include deterministic fusion ranking - Introduce _PortfolioProposalDraft pydantic model for agent-authored fields before ranking - Discard any "fusion_ranking" data from draft to prevent conflicts with canonical ranking - Modify AutoFinPortfolioStep to receive draft, enrich with fusion_ranking, and produce final output - Update tests to use _PortfolioProposalDraft and validate deterministic fusion ranking propagation - Add async test verifying fusion ranking is correctly set in portfolio output with no errors * refactor(auto_fin): rewrite and simplify Auto Fin schema and steps - Remove legacy Auto Fin analysis step modules and helpers - Replace complex ranking and portfolio models with simplified current-news models - Update schema to focus on news-case workflow with new domain models - Remove A-share decision checkpoints and backtest details from schema - Simplify recommendation and decision output structures - Clean up deprecated state and utility functions - Update Auto Fin steps initialization to new pipeline steps only - Improve uniqueness validation for themes and ETFs in research plan * feat(auto_fin): implement full local cache and analysis workflow for Auto Fin - Add AutoFinDataStep to prepare and cache daily TuShare data with lookback - Add AutoFinAnalysisStep to analyze cached data and generate Markdown report - Implement detailed time window, ETF filtering, and historical case validation - Introduce YAML prompts for planning and decision-making steps - Update .gitignore to include reme_workspace/ - Clean up config and import structure for auto_fin steps - Remove old pipeline.py and consolidate functionality into new modules - Use polars for efficient CSV reading and data processing - Ensure atomic writes and strict JSON serialization for cache files - Enforce rules on news timing, ETF universe, and historical case usage * fix(auto_fin): restrict news data source to '财联社' in analysis and cache - Update analysis templates to specify current news as from '财联社' only - Modify news fetching functions to filter by source '财联社' - Add validation method to check cached news source correctness - Update news caching logic to exclude non-'财联社' news - Enhance unit tests with multiple sources to ensure filtering works - Confirm news API calls include source filter parameter as '财联社' * refactor(auto_fin): convert I/O methods to asynchronous implementations - Change _news, _dataset, and _theme_data methods to async for improved concurrency - Move JSONL and CSV reading operations to asynchronous wrappers using asyncio.to_thread - Remove synchronous _read_jsonl and _read_csv functions, integrate them as static async class methods - Update cache validation methods to async, awaiting I/O operations accordingly - Adjust usage of dataset and news retrieval in analysis step to await asynchronous methods - Add async unit test to validate JSONL reading with unicode line separators - Preserve existing functionality while enabling non-blocking file and data access * fix(nx_file_graph): defer networkx import and improve dependency handling - Move networkx import inside NxFileGraph constructor for lazy loading - Raise ImportError with original exception context if networkx is missing - Remove module-level fallback assignment of nx to None - Expand test to block loading of multiple optional core dependencies eagerly - Change exception type in test from ModuleNotFoundError to AssertionError - Update test comments to reflect broader optional dependency checks * feat(embedding_store): add quota retry delay mechanism for embedding requests - Introduce quota_retry_delay parameter to configure wait time before retry on quota exhaustion - Implement detection of insufficient quota errors in LocalEmbeddingStore without external SDK - Add retry logic with custom delay when quota is insufficient during embedding requests - Update configuration to set max_retries and quota_retry_delay defaults for embedding store - Add unit tests covering quota exhaustion retry behavior with delay and opt-in control - Ensure existing retry behavior remains unchanged if quota_retry_delay is not set * feat(auto_fin): add detailed logging to analysis and data fetching steps - Add _preview static method for bounded diagnostic output in analysis.py - Log prompt start, completion, errors, and validation details in _reply method - Add info logs for major processing steps in execute method of analysis.py - Add debug and info logs for cache validation, data fetching, and pagination in data.py - Log conditions for skipping reports and cache plans in data.py execute method - Log download summaries and cache writes for news and ETF data - Improve error logging with exception details in cache validation functions - Ensure all logs include context such as record counts, paths, and parameters * refactor(auto_fin): overhaul Auto Fin workflow and schema contracts - Replace old Auto Fin schema models with comprehensive new data classes - Remove legacy Auto Fin analysis step in favor of modular agent-based steps - Introduce AutoFinAgentStep for validating structured agent replies - Simplify data cleaning and JSONL writing utilities for news cache - Remove synchronous and asynchronous dataset methods from analysis step - Redefine Auto Fin analysis configuration for 360-day news retention and multi-step pipeline - Remove embedded analysis prompt templates and replace with agent-driven logic - Update __init__.py exports to match new step implementations and remove deprecated classes - Improve error handling and validation in agent step reply processing - Clean up redundant imports and unused code in analysis and data preparation modules * feat(auto_fin): add detailed logging for analysis and data processing steps - Add timing logs to measure agent prompt processing duration in analysis.py - Log news cache hits and news write paths with record counts in data.py - Include detailed info logs for news download start and completion in data.py - Add start, progress, and completion logs with topic and event counts in history.py - Log start and completion of merge step including path and ETF count in merge.py - Add start and done logs with window and news counts in topic.py * feat(auto_fin): enhance schema and steps with detailed ETF and event modeling - Replace and add multiple AutoFin schema classes to support detailed ETF selection, historical research, market analysis, forecast models, and report output with validation - Implement Shanghai timezone normalization and strict validation in schema models - Remove deprecated AutoFin analysis agent step and consolidate reply handling in base step - Introduce AutoFinStep base class with shared helpers for prompt handling, data fetching, logging, and JSONL file operations - Add AutoFinDataStep to manage daily news data complete with schedule validation, caching, and source validation logic - Update cookbook configuration to customize auto_fin step parameters and simplify outbound proxy settings - Refactor imports and clean unused code for better maintainability * feat(auto_fin): introduce detailed historical event resolution and market similarity analysis - Add AutoFinHistoricalEventReference and AutoFinHistoricalSimilarity models for refined event referencing and similarity judgment - Implement validation to ensure non-empty critical fields and uniqueness of historical news IDs - Develop method to resolve Agent-selected historical event references from workspace files with strict path and existence checks - Enrich historical events with market entry and future returns data after resolution - Redesign market step to calculate similarity-weighted ETF forecasts based on matched historical event similarities - Enforce validation on matched historical events for uniqueness and proper weight summation - Simplify merge step output to final Markdown report without YAML frontmatter and redundant fields - Update user instructions for history search, market, and merge steps to reflect new data structures and responsibilities - Adjust test suite to cover new schema and step behavior changes, including enhanced validation and JSON output formats * feat(auto_fin): add new cron jobs and output analysis jsonl - Add new cron jobs auto_fin_1145_cron and auto_fin_1800_cron with auto_fin_steps - Change auto_fin_0930_cron schedule to run Monday to Sunday - Extend merge step to write analysis data to auto_fin_analysis.jsonl - Update unit tests to verify new cron jobs and their steps configuration * fix(auto_fin): improve atomic file write and refresh daily index - Change temporary file naming to include UUID for uniqueness and hidden prefix - Replace atomic write method from using Path.replace to os.replace with safe unlink - Add import and use os.replace for safer file replace operation - Refresh daily index after writing auto finance markdown and JSONL files - Import and call refresh_day_index in merge step to update file index asynchronously * docs(cookbook): add optional SSH proxy configuration in README files - Introduce optional SSH proxy setup in auto-fin and daily_paper cookbooks - Provide instructions to enable outbound proxy via `daily_cookbook.yaml` and environment variables - Add `REME_PROXY_IP` and `REME_PROXY_ACCOUNT` environment variables descriptions in multiple README files - Update English and Chinese README and README_ZH documents with proxy details - Maintain consistent formatting of environment variable tables across documents * fix(file_io): include schema_version in hidden metadata keys - Added "schema_version" to _INDEX_HIDDEN_METADATA_KEYS in _daily_index.py - Updated _render_notes_block to always include additional keys regardless of schema_version fix(deps): move pproxy dependency to later in pyproject.toml - Removed pproxy from early dependencies list - Added pproxy back near the end of dependency list for better ordering fix(outbound_proxy): require pproxy package for ssh_http proxy - Added importlib.util check for pproxy package presence - Raise RuntimeError if pproxy is not installed when using SSH HTTP outbound proxy - Improved error message suggests installing reme-ai with 'core' extra * docs(readme): update News section with new Cookbook workflows - Clarify introduction of optional Cookbooks with Daily Paper and Auto Fin workflows - Update English README to reflect both paper discovery and file-native ETF event research - Revise Chinese README to include financial news and historical market data research capability - Maintain announcement of paper acceptance at Findings of ACL 2026 * feat(auto_fin): add calculation results to final Markdown output - Implement _calculation_results to summarize forecast for each ETF analyzed - Include program-calculated results in the JSON input for the Markdown report - Update YAML template to incorporate calculation results and adjust recommendation rules - Refine recommendation logic to rely on event impact judgments combined with calculation outputs - Modify tests to verify presence of calculation results and updated report content and format * up prompt * fix(keyword_index): ignore non-indexable chunks during keyword sync - Add is_indexable method to base and BM25 keyword index classes to check text tokenizability - Update local file store to exclude non-indexable chunks from expected document IDs to prevent rebuild - Fix JSONL chunker to correctly handle Unicode line separator U+2028 inside JSON strings without splitting - Add test to ensure non-empty but non-indexable chunk does not trigger keyword index rebuild - Add test to verify U+2028 character does not cause incorrect JSONL record splitting |
||
|
jinliyl
|
cf22ef3b1d
|
feat: add codex auth modes, background embedding/index repair, and qwenpaw logging (#371)
* feat(codex): add authentication mode support with thread-safe logging - Implement _CodexAuthConfig dataclass for resolved auth settings - Add auth_mode parameter with auto/api_key/oauth options - Separate API key and OAuth authentication flows - Force specific login method based on auth mode - Add explicit API key validation requirement - Serialize concurrent logger initialization in thread lock - Close logging handlers properly during cleanup - Update default config with auth_mode presets for codex and codex_oauth - Add comprehensive tests for authentication modes and concurrent logging * feat(file_store): implement background embedding backfill and keyword index repair - Add _after_embedding_backfill hook in FAISS local file store - Schedule startup embedding repair without delaying component readiness - Cancel and collect embedding backfill task during component shutdown - Log progress at fixed percentage boundaries for long-running operations - Process embedding backfill in configurable batch sizes with progress reporting - Rebuild keyword index in bounded batches with detailed mismatch diagnostics - Format stdlib logs consistently with QwenPaw console output using relative paths - Run embedding backfill as background task that doesn't block component startup - Add comprehensive tests for background embedding and keyword index repair scenarios * fix(file-store): repair graph-chunk consistency on load - Add _repair_graph_chunk_consistency method to detect and fix mismatched graph/chunk states - Clear torn graph/chunk state when missing or orphaned chunks are detected - Ensure keyword index sync handles empty chunks properly - Add comprehensive tests for graph-chunk consistency scenarios - Update test utilities to properly seed graph/chunk snapshots - Increment version to 0.4.1.2 * feat(file_io): enhance list step response format and add comprehensive logging - Format list output with bullet points for better readability - Add explicit "No files found" message when directory is empty - Include detailed timing information for file store startup phases - Add logging for chunk loading, graph consistency checks, and keyword indexing - Provide detailed metrics for embedding backfill operations - Add comprehensive test coverage for empty directory scenarios - Include batch processing statistics for embedding operations * feat(logger): add QwenPaw logging integration with forwarding mechanism - Introduce _ForwardToLoggerHandler to forward log records to target logger - Add qwenpaw logger integration that forwards ReMe logs to QwenPaw handlers - Maintain ReMe logger stability for modules that cache it at import time - Enable QwenPaw handlers to take effect without ReMe reconfiguration - Add comprehensive tests for stdlib forwarding to QwenPaw sinks - Support explicit REME_DISABLE_LOGURU=false to keep original Loguru backend - Preserve existing logging behavior when QwenPaw is not configured * fix(file_store): serialize concurrent FAISS dump operations to prevent corruption - Add asyncio lock to ensure only one FAISS dump operation runs at a time - Generate unique temporary filenames using UUID tokens for atomic replacement - Implement proper cleanup of temporary files in finally block - Add comprehensive test to verify concurrent dumps are serialized - Ensure atomic writes by replacing both index and idmap files together - Prevent partial state writes during concurrent access scenarios |
||
|
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 |
||
|
imrewce
|
1e798d3b4e
|
fix(file_io): fix risk of out-workspace paths (#322)
* fix(file_io): fix risk of out-workspace paths * chore(file_io): remove unused unittest file |
||
|
Ziyang Guo
|
c060933e4d
|
fix(auto-memory): preserve message timestamps (#310)
* fix(auto-memory): preserve message timestamps * fix(auto-memory): infer daily date from messages * feat(file_io): add strict date parsing and improve daily date handling - Add new parse_daily_date function for strict YYYY-MM-DD validation - Replace extract_daily_date with parse_daily_date for explicit date validation - Change _messages_day to use max date instead of min for historical imports - Reorder imports to maintain consistent module ordering - Move session message saving after date validation in auto_memory - Add comprehensive tests for invalid date rejection before saving - Add tests for strict YYYY-MM-DD date format validation - Update test names to reflect latest date behavior --------- Co-authored-by: Ziyang Guo <121015044+RunMarshal@users.noreply.github.com> Co-authored-by: jinli.yl <jinli.yl@alibaba-inc.com> |
||
|
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 |
||
|
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 |
||
|
jinliyl
|
206a53e5ed
|
init: reme version 0.4.0 (#284) |