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- Add file_parser component with default implementation - Introduce SearchFilter schema for path and tag filtering - Implement filter functionality in BaseFileStore and LocalFileStore - Update file watcher to use parser-based filtering instead of suffix filters - Register new FILE_PARSER component enum - Add test_data directory to gitignore refactor: improve component imports and initialization - Fix relative imports in application.py - Add file_parser import to component init - Initialize registry dict when component type doesn't exist - Remove circular import in HttpService by using string annotation - Update config yaml to use proper component names refactor: enhance file watcher architecture - Replace MdFileWatcher with more flexible FullFileWatcher and LightFileWatcher - Remove suffix-based filtering in favor of parser-based approach - Update BaseFileWatcher to resolve parsers from app context - Remove unused watch_filter method refactor: update ReMe core functionality - Remove memory_path creation - Simplify dream and proactive methods to return empty strings - Update config defaults for HTTP service and component backends docs: update component configuration in paw.yaml - Change service backend from cmd to http - Rename components to use correct singular forms - Add default file parser and file watcher configurations - Set up local file store with default settings ``` Co-authored-by: huangsen <huangsen.huang@alibaba-inc.com>
62 lines
2.4 KiB
Python
62 lines
2.4 KiB
Python
"""Memory search step for semantic search in memory files."""
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import json
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from ..component import R
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from ..component.base_step import BaseStep
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from ..enumeration import ComponentEnum
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from ..schema import SearchFilter
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@R.register("memory_search")
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class MemorySearch(BaseStep):
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"""Semantically search MEMORY.md and memory files."""
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component_type = ComponentEnum.STEP
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def __init__(self, vector_weight: float = 0.7, candidate_multiplier: float = 3.0, **kwargs):
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"""Initialize memory search step.
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Args:
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vector_weight: Weight for vector search vs keyword search.
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candidate_multiplier: Multiplier for candidate count before filtering.
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**kwargs: Additional arguments passed to BaseStep.
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"""
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super().__init__(**kwargs)
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self.vector_weight = vector_weight
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self.candidate_multiplier = candidate_multiplier
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async def execute(self):
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"""Execute the memory search operation."""
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assert self.context is not None, "Context is not set"
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query: str = self.context.get("query", "").strip()
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min_score: float = self.context.get("min_score", 0.1)
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max_results: int = self.context.get("max_results", 5)
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assert query, "Query cannot be empty"
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assert (
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isinstance(min_score, float | int) and 0.0 <= min_score <= 1.0
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), f"min_score must be between 0 and 1, got {min_score}"
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assert (
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isinstance(max_results, int) and max_results > 0
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), f"max_results must be a positive integer, got {max_results}"
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filter_paths: list[str] | None = self.context.get("paths") or None
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filter_tags: list[str] | None = self.context.get("tags") or None
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exclude_paths: list[str] | None = self.context.get("exclude_paths") or None
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search_filter = None
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if filter_paths or filter_tags or exclude_paths:
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search_filter = SearchFilter(paths=filter_paths, tags=filter_tags, exclude_paths=exclude_paths)
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results = await self.file_store.hybrid_search(
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query=query,
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limit=max_results,
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vector_weight=self.vector_weight,
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candidate_multiplier=self.candidate_multiplier,
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search_filter=search_filter,
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)
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# Filter by min_score
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results = [r for r in results if r.score >= min_score]
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return json.dumps([result.model_dump(exclude_none=True) for result in results], indent=2, ensure_ascii=False)
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