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112 lines
4.3 KiB
Python
112 lines
4.3 KiB
Python
"""Memory search tool for semantic search in memory files."""
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import json
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from loguru import logger
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from ....core.enumeration import MemorySource
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from ....core.op import BaseTool
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from ....core.runtime_context import RuntimeContext
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from ....core.schema import ToolCall
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class MemorySearch(BaseTool):
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"""Semantically search MEMORY.md and memory files."""
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def __init__(
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self,
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sources: list[MemorySource] | None = None,
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min_score: float = 0.1,
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max_results: int = 5,
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vector_weight: float = 0.7,
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candidate_multiplier: float = 3.0,
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**kwargs,
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):
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"""Initialize memory search tool."""
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assert 0.0 <= vector_weight <= 1.0, f"vector_weight must be between 0 and 1, got {vector_weight}"
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kwargs.setdefault("max_retries", 1)
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kwargs.setdefault("raise_exception", False)
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super().__init__(**kwargs)
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self.sources = sources or [MemorySource.MEMORY]
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self.min_score = min_score
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self.max_results = max_results
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self.vector_weight = vector_weight
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self.candidate_multiplier = candidate_multiplier
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def _build_tool_call(self) -> ToolCall:
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return ToolCall(
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**{
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"description": (
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"Mandatory recall step: semantically search MEMORY.md + memory/*.md "
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"(and optional session transcripts) before answering questions about "
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"prior work, decisions, dates, people, preferences, or todos; returns "
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"top snippets with path + lines."
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),
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "The semantic search query to find relevant memory snippets",
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},
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"max_results": {
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"type": "integer",
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"description": "Maximum number of search results to return (optional), default 5",
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},
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"min_score": {
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"type": "number",
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"description": "Minimum similarity score threshold for results (optional), default 0.1",
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},
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},
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"required": ["query"],
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},
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},
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)
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async def execute(self) -> str:
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"""Execute the memory search operation."""
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query: str = self.context.query.strip()
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min_score: float = self.context.get("min_score", self.min_score)
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max_results: int = self.context.get("max_results", self.max_results)
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assert query, "Query cannot be empty"
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assert (
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isinstance(min_score, float) 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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# Use hybrid_search from file_store
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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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sources=self.sources,
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vector_weight=self.vector_weight,
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candidate_multiplier=self.candidate_multiplier,
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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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async def call(self, context: RuntimeContext = None, **kwargs):
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"""Execute the tool with unified error handling.
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This method catches all exceptions and returns error messages
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to the LLM instead of raising them.
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"""
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self.context = RuntimeContext.from_context(context, **kwargs)
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try:
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await self.before_execute()
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response = await self.execute()
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response = await self.after_execute(response)
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return response
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except Exception as e:
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# Return error message to LLM instead of raising
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error_msg = f"{self.__class__.__name__} failed: {str(e)}"
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logger.error(error_msg)
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return await self.after_execute(error_msg)
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