f9b405372d
* claude code first pass * rename routes * search_messages and list_messages * revert agents messagesearch * generate api * fix backend for list all messages * request for message search * return list of letta message * add tests * error in archive endpoint * archive delete return type wrong * optional params for archive creation * add passage to tpuf on create * fix archive manager * support global passage search * search by agent * just do basic org wide search for now * change message test to be about fresh data, cleanup after --------- Co-authored-by: Ari Webb <ari@letta.com>
109 lines
4.7 KiB
Python
109 lines
4.7 KiB
Python
from datetime import datetime
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from typing import List, Literal, Optional
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from fastapi import APIRouter, Body, Depends
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from pydantic import BaseModel, Field
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from letta.schemas.enums import TagMatchMode
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from letta.schemas.passage import Passage
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from letta.server.rest_api.dependencies import HeaderParams, get_headers, get_letta_server
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from letta.server.server import SyncServer
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router = APIRouter(prefix="/passages", tags=["passages"])
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class PassageSearchRequest(BaseModel):
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"""Request model for searching passages across archives."""
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query: str = Field(..., description="Text query for semantic search")
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agent_id: Optional[str] = Field(None, description="Filter passages by agent ID")
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archive_id: Optional[str] = Field(None, description="Filter passages by archive ID")
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tags: Optional[List[str]] = Field(None, description="Optional list of tags to filter search results")
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tag_match_mode: Literal["any", "all"] = Field(
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"any", description="How to match tags - 'any' to match passages with any of the tags, 'all' to match only passages with all tags"
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)
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limit: int = Field(50, description="Maximum number of results to return", ge=1, le=100)
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start_date: Optional[datetime] = Field(None, description="Filter results to passages created after this datetime")
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end_date: Optional[datetime] = Field(None, description="Filter results to passages created before this datetime")
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class PassageSearchResult(BaseModel):
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"""Result from a passage search operation with scoring details."""
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passage: Passage = Field(..., description="The passage object")
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score: float = Field(..., description="Relevance score")
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metadata: dict = Field(default_factory=dict, description="Additional metadata about the search result")
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@router.post("/search", response_model=List[PassageSearchResult], operation_id="search_passages")
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async def search_passages(
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request: PassageSearchRequest = Body(...),
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server: SyncServer = Depends(get_letta_server),
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headers: HeaderParams = Depends(get_headers),
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):
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"""
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Search passages across the organization with optional agent and archive filtering.
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Returns passages with relevance scores.
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This endpoint supports semantic search through passages:
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- If neither agent_id nor archive_id is provided, searches ALL passages in the organization
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- If agent_id is provided, searches passages across all archives attached to that agent
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- If archive_id is provided, searches passages within that specific archive
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- If both are provided, agent_id takes precedence
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"""
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actor = await server.user_manager.get_actor_or_default_async(actor_id=headers.actor_id)
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# Convert tag_match_mode to enum
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tag_mode = TagMatchMode.ANY if request.tag_match_mode == "any" else TagMatchMode.ALL
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# Determine which embedding config to use
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embedding_config = None
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if request.agent_id:
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# Search by agent
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agent_state = await server.agent_manager.get_agent_by_id_async(agent_id=request.agent_id, actor=actor)
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embedding_config = agent_state.embedding_config
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elif request.archive_id:
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# Search by archive_id
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archive = await server.archive_manager.get_archive_by_id_async(archive_id=request.archive_id, actor=actor)
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embedding_config = archive.embedding_config
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else:
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# Search across all passages in the organization
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# Get default embedding config from any agent or use server default
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agent_count = await server.agent_manager.size_async(actor=actor)
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if agent_count > 0:
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# Get first agent to derive embedding config
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agents = await server.agent_manager.list_agents_async(actor=actor, limit=1)
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if agents:
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embedding_config = agents[0].embedding_config
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if not embedding_config:
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# Fall back to server default
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embedding_config = server.default_embedding_config
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# Search passages
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passages_with_metadata = await server.agent_manager.query_agent_passages_async(
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actor=actor,
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agent_id=request.agent_id, # Can be None for organization-wide search
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archive_id=request.archive_id, # Can be None if searching by agent or org-wide
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query_text=request.query,
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limit=request.limit,
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embedding_config=embedding_config,
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embed_query=True,
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tags=request.tags,
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tag_match_mode=tag_mode,
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start_date=request.start_date,
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end_date=request.end_date,
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)
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# Convert to PassageSearchResult objects
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results = [
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PassageSearchResult(
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passage=passage,
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score=score,
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metadata=metadata,
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)
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for passage, score, metadata in passages_with_metadata
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]
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return results
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