ReMe/reme/reme.py
jinliyl 2612d25959
feat(lme): add cli execution and agentic search tooling (#334)
* feat(service): add CLI service for local job execution

- Introduce CliService to execute single jobs locally without serving ports
- Add prepare_start_config and should_precheck_start functions for CLI job setup
- Update reme start command to use CLI service when job argument is provided
- Change default service backend from http to cli in jinli_lme config
- Modify SearchStep to use constants and rename configuration parameters
- Add unit tests for CLI service functionality and configuration handling
- Update file extension support to include json format in addition to md and jsonl

* feat(search): add BM25 and vector search steps with configuration updates

- Add Bm25SearchStep and VectorSearchStep classes with tool context deduplication
- Register new search step components in index module
- Update configuration to use separate vector_search and bm25_search endpoints
- Modify LLM models from qwen3.7-plus/glm-5.1 to glm-5.2 variants
- Adjust search parameters and remove hybrid search implementation
- Configure embedding store as default in storage settings
- Remove auto-memory and file catalog configurations
- Update watch directories from multiple paths to session_dir only

* feat(agent): add tool result offloading and workspace management

- Add tool_results_dir configuration option for offloaded tool results storage
- Implement ToolResultOffloadMiddleware to persist large tool results to files
- Create WorkspaceBackend to standardize file operations across tools
- Add configurable builtin tools selection with sequential execution option
- Integrate middleware support for agent wrapper with offloading capability
- Update application initialization to create tool results directory
- Add safety mechanisms for filesystem operations with sanitized filenames
- Enhance agent wrapper with configurable working directory handling
- Upgrade agentscope dependency to version 2.0.4 for improved features

# Conflicts:
#	reme/application.py

* feat(benchmark): add LongMemEval agentic search and result management

- Introduce AgenticAnswerStep for agent-based history search
- Add LmePrepareJudgeStep and LmeSaveResultStep for evaluation pipeline
- Implement AddDraftStep and ReadAllDraftStep for evidence accumulation
- Update configuration with new agent wrapper and search parameters
- Add comparison script for analyzing agent run differences
- Include documentation for LongMemEval failure analysis
- Enhance tool result offloading with skip options
- Modify search defaults and indexing behavior

* feat(agent): implement tool result offloading with system reminders

- Added tool_result_offload_message parameter to agent wrapper reply method
- Implemented configurable reminder template for offloaded tool results
- Created system reminder messages when tool results are offloaded to files
- Added Chinese user message template for agentic answer step
- Updated tool result offloading middleware to use custom reminder templates
- Enhanced agentic answer instructions to handle long tool results via draft storage

* feat(scripts): add LongMemEval results summarization tool

- Create summarize_lme_results.py script to analyze result JSON files
- Implement command line interface with answer id and dataset root options
- Add support for specifying index range with start and end parameters
- Include option to show failure details and non-successful completions
- Calculate completion statistics and accuracy metrics
- Display detailed breakdown of yes/no/other judgements
- Handle missing and unreadable result files gracefully
- Format output with percentages and comprehensive summary statistics

* feat(summarize_lme_results): add question type breakdown to result summary

- Import defaultdict from collections module
- Add by_type dictionary to track statistics by question type
- Count completed, yes, no, and other responses for each question type
- Display detailed breakdown table showing accuracy by question type
- Include question type column when processing judgements
- Print comprehensive summary with question type distribution
- Calculate and display accuracy percentage for each question type category

* feat(lme): switch to qwen3.7-max model and add shuffle functionality

- Changed default LLM model from glm-5.1 to qwen3.7-max in jinli_lme.yaml
- Added random module import for shuffle functionality
- Implemented --shuffle argument with BooleanOptionalAction for dataset shuffling
- Added --seed argument to control random seed for reproducible shuffling
- Applied random shuffle to dataset indices when shuffle is enabled
- Added console output showing shuffle operation and seed information

* fix(cli): set default random seed for shuffle functionality

- Changed default seed value from None to 42 for consistent shuffling behavior
- Ensures reproducible results when using shuffle option without explicit seed
- Maintains backward compatibility while providing deterministic defaults

* refactor(benchmark): update agentic answer guidelines for grounding

- Updated English instruction to emphasize strict grounding in retrieved context
- Modified Chinese instruction to stress evidence-based responses without inference
- Removed redundant conciseness requirement in both language versions
- Enhanced clarity on proper use of draft saving and retrieval mechanisms
- Strengthened emphasis against hallucination of unsupported facts

* refactor(benchmark): update agentic search instructions and configuration

- Replace separate vector_search and bm25_search with unified search tool
- Update agent instructions to use single search tool with multiple strategies
- Simplify Chinese instructions for search methodology
- Add comprehensive search tool configuration with hybrid vector/BM25 capabilities
- Increase model retry attempts from 1 to 3 for better reliability
- Remove redundant tool references from job_tools list

* feat(search): add configurable search limit with environment variable support

- Remove hardcoded limit and min_score parameters from config schema
- Increase LLM context size from 200000 to 1000000
- Add REME_SEARCH_LIMIT environment variable support for search configuration
- Implement command line argument --search-limit to override default search limit
- Add input validation to ensure search limit is positive
- Modify subprocess execution to pass environment variables
- Update search step to use dynamic default limit from environment or fallback to 5

* refactor(benchmark): remove agentic answer step and related configurations

- Removed AgenticAnswerStep class and its registration
- Deleted agentic_answer.yaml prompt configuration file
- Removed agentic answer related job definitions from jinli_lme.yaml
- Cleaned up tool result offloading middleware implementation
- Removed tool_results_dir configuration field from application config
- Deleted comparison and analysis scripts for agent runs
- Removed agentic answer step from LME init module exports
- Updated agent wrapper to remove tool result offloading functionality
- Removed unused imports and dependencies in agent wrapper module

* refactor(benchmark): remove unused LME result processing components

- Removed LmePrepareJudgeStep and LmeSaveResultStep classes from benchmark module
- Cleaned up imports and exports in lme module initialization
- Removed unused middleware configuration from agent wrapper
- Deleted obsolete result.py file containing deprecated result processing logic
- Simplified agent instantiation by removing middleware parameter
- Updated import statements to reflect removed dependencies

* refactor(index): remove unused search steps and update imports

- Remove Bm25SearchStep and VectorSearchStep from index steps module
- Remove unused prepare_start_config and should_precheck_start exports
- Move import statements to proper location in reme.py
- Update test module to use direct import path for CliService
- Remove vector_search and bm25_search configurations from jinli_lme.yaml
- Add workspace directory environment variable configuration
- Add docstring to getcwd method in agent wrapper
- Remove empty middleware list from agent wrapper initialization

* feat(index): add BM25 and vector search steps with tool context deduplication

- Add Bm25SearchStep for plain BM25 keyword search with tool_context deduplication
- Add VectorSearchStep for plain vector search with tool_context deduplication
- Implement tool context state management with TTL-based deduplication
- Add support for chunk deduplication across tool contexts within TTL window
- Update index steps module to include new search step classes
- Add test coverage for CLI metadata output functionality
- Refactor CLI service to remove unused show_status parameter
- Update documentation comments to reflect internal service configuration

* feat(steps): add Python code execution capability

- Introduce PythonExecuteStep to run Python code in subprocess
- Add configuration for python_execute step in jinli_lme.yaml
- Register python_execute in available tools list
- Implement timeout handling with default 60 second limit
- Capture stdout/stderr output and return code metadata
- Add comprehensive unit tests for execution scenarios
- Support workspace directory context for code execution
- Handle timeout errors and runtime exceptions gracefully

* refactor(python_execute): replace subprocess with asyncio for Python code execution

- Replace subprocess.run with asyncio.create_subprocess_exec for non-blocking execution
- Add _PythonResult dataclass to encapsulate execution results and timeout status
- Implement proper timeout handling with asyncio.wait_for and process.kill()
- Update metadata to include returncode and stderr when timeout occurs
- Convert synchronous _run_python method to asynchronous implementation
- Maintain backward compatibility while improving execution reliability

* refactor(python_execute): replace subprocess with asyncio for Python code execution

- Replace subprocess.run with asyncio.create_subprocess_exec for non-blocking execution
- Add _PythonResult dataclass to encapsulate execution results and timeout status
- Implement proper timeout handling with asyncio.wait_for and process.kill()
- Update metadata to include returncode and stderr when timeout occurs
- Convert synchronous _run_python method to asynchronous implementation
- Maintain backward compatibility while improving execution reliability
2026-07-08 17:43:09 +09:00

78 lines
3.2 KiB
Python

"""ReMe memory management application entry point."""
import asyncio
import sys
from .application import Application
from .components import R
from .components.service.cli_service import prepare_start_config, should_precheck_start
from .config import parse_args, resolve_app_config
from .enumeration import ComponentEnum
from .utils import cli_find_reme, load_env, precheck_start, running_service_config
_CLIENT_KWARGS = {"host", "port", "timeout", "transport", "command", "args", "show_metadata"}
class ReMe(Application):
"""ReMe memory management application."""
async def call_server(action: str, **kwargs):
"""Call the running server with a client matching its *actual* service config.
The client backend and its transport/host/port are taken from the running
``reme start`` process — its start args replayed through the same
``resolve_app_config`` the server used — so a bare ``reme <action>`` reaches
the server however it was actually started (``http`` REST or ``mcp``
streamable-http / sse / stdio), including ``service.*`` overrides that never
touched the on-disk config file. Falls back to local config resolution when
no server is detected. Explicit ``backend=`` / ``transport=`` / ``host=`` /
``port=`` kwargs still win and never leak into the tool payload.
"""
# config-selecting keys steer client construction; they are not tool args.
resolve_kwargs = {}
if isinstance(kwargs.get("config"), str):
resolve_kwargs["config"] = kwargs.pop("config")
if isinstance(kwargs.get("service"), dict):
resolve_kwargs["service"] = kwargs.pop("service")
# Prefer the running server's real config; fall back to the local config file.
service = running_service_config()
if service is None:
service = resolve_app_config(**resolve_kwargs).get("service")
service = service if isinstance(service, dict) else {}
backend: str = kwargs.pop("backend", None) or service.get("backend", "http")
# Seed client kwargs from the service config only when we are actually using
# that service's backend — transport/host/port are backend-specific, so an
# explicit backend override must not inherit the other backend's settings.
seed = service if backend == service.get("backend") else {}
client_kwargs = {k: seed[k] for k in _CLIENT_KWARGS if k in seed}
client_kwargs.update({key: kwargs.pop(key) for key in list(kwargs) if key in _CLIENT_KWARGS})
client_cls = R.get(ComponentEnum.CLIENT, backend)
if client_cls is None:
raise ValueError(f"Unknown client backend: {backend!r}")
async with client_cls(**client_kwargs) as client:
async for chunk in client(action=action, **kwargs):
print(chunk, end="", flush=True)
print()
def main():
"""Parse CLI arguments and launch the appropriate mode."""
action, kwargs = parse_args(*sys.argv[1:])
if action == "start":
load_env()
kwargs = prepare_start_config(kwargs)
if should_precheck_start(kwargs) and not precheck_start(kwargs.get("service")):
return
ReMe(**kwargs).run_app()
elif action == "find_reme":
cli_find_reme()
else:
asyncio.run(call_server(action, **kwargs))
if __name__ == "__main__":
main()