mirror of
https://github.com/agentscope-ai/ReMe.git
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* 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
264 lines
11 KiB
Python
264 lines
11 KiB
Python
"""Main application entry point."""
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import asyncio
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import heapq
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from concurrent.futures import ThreadPoolExecutor
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from pathlib import Path
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from typing import AsyncGenerator, TypeVar
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from . import __version__
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from .components import BaseComponent, ApplicationContext
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from .components.job import BackgroundJob, BaseJob, CronJob, StreamJob
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from .components.service import BaseService
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from .enumeration import ComponentEnum
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from .schema import ComponentConfig, Response, StreamChunk
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from .utils import execute_stream_task, print_logo, get_logger
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T = TypeVar("T", bound=BaseComponent)
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_NodeKey = tuple[ComponentEnum, str]
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class Application(BaseComponent):
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"""Wires components from config and runs jobs against them."""
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def __init__(self, **kwargs) -> None:
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self.context = ApplicationContext(**kwargs)
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self._started_components: list[BaseComponent] = []
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self._setup_workspace_directories()
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if self.config.enable_logo:
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print_logo(self.config)
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logger = get_logger(
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log_to_console=self.config.log_to_console,
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log_to_file=self.config.log_to_file,
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force_init=True,
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)
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logger.info(f"Initializing {self.config.app_name} Application v{__version__}")
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super().__init__()
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self._init_service()
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self._init_components()
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self._init_jobs()
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@property
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def config(self):
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"""Typed view onto the application config held by the context."""
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return self.context.app_config
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# ----- Wiring (called once during __init__) --------------------------
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def _setup_workspace_directories(self) -> None:
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"""Ensure the workspace root and configured subdirectories exist on disk."""
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cfg = self.config
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workspace_path = Path(cfg.workspace_dir).absolute()
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workspace_path.mkdir(parents=True, exist_ok=True)
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for subdir in [
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cfg.metadata_dir,
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cfg.session_dir,
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cfg.mem_session_dir,
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cfg.resource_dir,
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cfg.daily_dir,
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cfg.digest_dir,
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]:
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if subdir:
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(workspace_path / subdir).mkdir(parents=True, exist_ok=True)
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def _init_service(self) -> None:
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"""Instantiate the single service backend declared in config.service."""
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self.context.service = self._instantiate(
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ComponentEnum.SERVICE,
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self.config.service,
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label="Service",
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expected_type=BaseService,
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)
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def _init_components(self) -> None:
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"""Instantiate every component declared under config.components."""
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for ctype, group in self.config.components.items():
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self.context.components[ctype] = {}
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for name, cfg in group.items():
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self.context.components[ctype][name] = self._instantiate(
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ctype,
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cfg,
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label=f"Component '{name}'",
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expected_type=BaseComponent,
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name=name,
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)
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def _init_jobs(self) -> None:
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"""Instantiate every job declared under config.jobs."""
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for name, cfg in self.config.jobs.items():
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self.context.jobs[name] = self._instantiate(
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ComponentEnum.JOB,
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cfg,
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label=f"Job '{name}'",
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expected_type=BaseJob,
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name=name,
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)
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def _instantiate(
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self,
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ctype: ComponentEnum,
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cfg: ComponentConfig,
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*,
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label: str,
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expected_type: type[T],
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name: str | None = None,
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) -> T:
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"""Resolve cfg.backend through the registry and construct the instance.
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`label` is the human-readable identifier used only in error messages.
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`expected_type` narrows the return type and guards against a backend
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registered under the wrong ComponentEnum.
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`name` is forwarded to the constructor for named components/jobs;
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leave it None for the service, which is keyed solely by type.
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"""
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# Lazy import: the registry self-populates as component modules load.
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from .components import R
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if not cfg.backend:
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raise ValueError(f"{label} is missing the required 'backend' field")
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backend_cls = R.get(ctype, cfg.backend)
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if backend_cls is None:
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raise ValueError(f"Unregistered backend '{cfg.backend}' for {label}")
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params = cfg.model_dump()
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params["app_context"] = self.context
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if name is not None:
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params.setdefault("name", name)
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instance = backend_cls(**params)
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if not isinstance(instance, expected_type):
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got, want = type(instance).__name__, expected_type.__name__
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raise TypeError(f"{label} backend '{cfg.backend}' produced {got}, expected {want} subclass")
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return instance
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# ----- Dependency ordering ------------------------------------------
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def _topological_order(self) -> list[BaseComponent]:
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"""Return components in dependency order via Kahn's algorithm; raise on missing dep or cycle."""
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nodes: dict[_NodeKey, BaseComponent] = {
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(ctype, name): comp for ctype, group in self.context.components.items() for name, comp in group.items()
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}
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in_degree, dependents = self._build_dependency_graph(nodes)
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ready = [k for k, d in in_degree.items() if d == 0]
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heapq.heapify(ready)
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ordered: list[BaseComponent] = []
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while ready:
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key = heapq.heappop(ready)
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ordered.append(nodes[key])
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for downstream in dependents[key]:
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in_degree[downstream] -= 1
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if in_degree[downstream] == 0:
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heapq.heappush(ready, downstream)
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if len(ordered) != len(nodes):
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unresolved = [f"{k[0].value}:{k[1]}" for k, d in in_degree.items() if d > 0]
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raise ValueError(f"Circular dependency detected among: {unresolved}")
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return ordered
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@staticmethod
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def _build_dependency_graph(
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nodes: dict[_NodeKey, BaseComponent],
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) -> tuple[dict[_NodeKey, int], dict[_NodeKey, list[_NodeKey]]]:
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"""Compute in-degree and adjacency lists; raise if a required dep is missing."""
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in_degree: dict[_NodeKey, int] = dict.fromkeys(nodes, 0)
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dependents: dict[_NodeKey, list[_NodeKey]] = {k: [] for k in nodes}
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for key, comp in nodes.items():
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for dep in comp.dependencies:
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dep_key = (dep.ctype, dep.name)
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if dep_key in nodes:
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dependents[dep_key].append(key)
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in_degree[key] += 1
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elif not dep.optional:
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raise ValueError(
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f"Component {key[0].value}:{key[1]} depends on unregistered {dep.ctype.value}:{dep.name}",
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)
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return in_degree, dependents
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# ----- Lifecycle -----------------------------------------------------
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async def _start(self) -> None:
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"""Start components, then jobs as base > stream > background > cron."""
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pool_size = self.config.thread_pool_max_workers
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if pool_size > 0:
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self.context.thread_pool = ThreadPoolExecutor(max_workers=pool_size)
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self.logger.info(f"Thread pool created with max_workers={pool_size}")
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try:
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components = self._topological_order()
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jobs = list(self.context.jobs.values())
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base_jobs = [j for j in jobs if not isinstance(j, (StreamJob, BackgroundJob))]
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stream_jobs = [j for j in jobs if isinstance(j, StreamJob)]
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background_jobs = [j for j in jobs if isinstance(j, BackgroundJob) and not isinstance(j, CronJob)]
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cron_jobs = [j for j in jobs if isinstance(j, CronJob)]
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for c in components + base_jobs + stream_jobs + background_jobs + cron_jobs:
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await self._start_one(c)
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except Exception:
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await self._close()
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raise
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async def _start_one(self, c: BaseComponent) -> None:
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"""Start one component and record it for ordered shutdown."""
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try:
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if isinstance(c, BackgroundJob):
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self.logger.info(f"Starting background job: {c.name}")
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await c.start()
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self._started_components.append(c)
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except Exception as e:
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self.logger.exception(f"Failed to start {c.component_type.value}:{c.name}: {e}")
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raise
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async def _close(self) -> None:
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"""Close in reverse start order so every peer outlives its dependents."""
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for c in reversed(self._started_components):
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try:
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await c.close()
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except Exception as e:
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self.logger.exception(f"Failed to close {c.component_type.value}:{c.name}: {e}")
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self._started_components.clear()
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if self.context.thread_pool is not None:
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self.context.thread_pool.shutdown(wait=True)
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self.context.thread_pool = None
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async def update_component(self, component_enum: ComponentEnum | str, name: str, /, **kwargs) -> BaseComponent:
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"""Update an existing component by type/name; never creates missing components."""
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component_enum = ComponentEnum(component_enum)
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group = self.context.components.get(component_enum)
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if not group or name not in group:
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raise KeyError(f"Component '{name}' not found in {component_enum.value}")
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component = group[name]
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for key, value in kwargs.items():
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if not hasattr(component, key):
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raise AttributeError(f"Component {component_enum.value}:{name} has no attribute '{key}'")
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setattr(component, key, value)
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return component
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# ----- Job execution -------------------------------------------------
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async def run_job(self, name: str, /, **kwargs) -> Response:
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"""Execute a registered job by name and return its final Response."""
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if name not in self.context.jobs:
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raise KeyError(f"Job '{name}' not found")
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return await self.context.jobs[name](**kwargs)
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async def run_stream_job(self, name: str, /, **kwargs) -> AsyncGenerator[StreamChunk, None]:
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"""Execute a streaming job, yielding chunks as they are produced."""
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if name not in self.context.jobs:
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raise KeyError(f"Job '{name}' not found")
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stream_queue: asyncio.Queue = asyncio.Queue()
|
|
task = asyncio.create_task(self.context.jobs[name](stream_queue=stream_queue, **kwargs))
|
|
async for chunk in execute_stream_task(
|
|
stream_queue=stream_queue,
|
|
task=task,
|
|
task_name=name,
|
|
output_format="chunk",
|
|
):
|
|
assert isinstance(chunk, StreamChunk)
|
|
yield chunk
|
|
|
|
def run_app(self):
|
|
"""Serve the application through the configured service backend."""
|
|
assert isinstance(self.context.service, BaseService)
|
|
self.context.service.run_app(app=self)
|