Lucy
82328b0a45
REST API for per-agent semantic memory retrieval.
- FastAPI server with /api/{agent}/save and /api/{agent}/query
- ChromaDB for persistent vector storage
- all-MiniLM-L6-v2 via sentence-transformers for embeddings
- Per-agent collections for clean separation
- Config through env vars or config.py
- .venv ready with all dependencies
110 lines
3.0 KiB
Python
110 lines
3.0 KiB
Python
"""Vector Memory Server — REST API.
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Endpoints
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---------
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- ``POST /api/{agent_name}/save`` — Save a memory
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- ``POST /api/{agent_name}/query`` — Query memories by semantic similarity
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- ``GET /health`` — Health check
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"""
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from __future__ import annotations
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from typing import Optional
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import uvicorn
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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from config import Config
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from store import MemoryStore
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# ---------------------------------------------------------------------------
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# App
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# ---------------------------------------------------------------------------
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app = FastAPI(
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title="Vector Memory Server",
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description="Semantic memory retrieval via REST. "
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"Per-agent collections, powered by ChromaDB + all-MiniLM-L6-v2.",
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version="0.1.0",
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)
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store = MemoryStore(Config.DATA_DIR)
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# ---------------------------------------------------------------------------
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# Request / Response models
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# ---------------------------------------------------------------------------
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class SaveRequest(BaseModel):
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text: str
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type: Optional[str] = "fact"
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tags: Optional[list[str]] = None
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date: Optional[str] = None
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class SaveResponse(BaseModel):
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success: bool
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id: str
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class QueryRequest(BaseModel):
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query: str
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top_n: Optional[int] = Config.DEFAULT_TOP_N
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class QueryResponse(BaseModel):
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results: list[dict]
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# ---------------------------------------------------------------------------
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# Routes
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# ---------------------------------------------------------------------------
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@app.post("/api/{agent_name}/save", response_model=SaveResponse)
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async def save_memory(agent_name: str, request: SaveRequest) -> SaveResponse:
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"""Embed and store a memory for the given agent."""
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try:
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metadata: dict = {}
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if request.type:
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metadata["type"] = request.type
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if request.tags:
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metadata["tags"] = ",".join(request.tags)
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if request.date:
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metadata["date"] = request.date
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memory_id = store.save(agent_name, request.text, metadata)
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return SaveResponse(success=True, id=memory_id)
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except Exception as exc:
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raise HTTPException(status_code=500, detail=str(exc))
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@app.post("/api/{agent_name}/query", response_model=QueryResponse)
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async def query_memory(
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agent_name: str, request: QueryRequest
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) -> QueryResponse:
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"""Retrieve semantically similar memories for the given agent."""
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try:
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results = store.query(agent_name, request.query, request.top_n)
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return QueryResponse(results=results)
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except Exception as exc:
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raise HTTPException(status_code=500, detail=str(exc))
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@app.get("/health")
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async def health() -> dict:
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return {"status": "ok"}
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# ---------------------------------------------------------------------------
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# Entrypoint
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# ---------------------------------------------------------------------------
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if __name__ == "__main__":
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uvicorn.run(
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"main:app",
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host=Config.HOST,
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port=Config.PORT,
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reload=False,
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)
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