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from fastapi import APIRouter, HTTPException, Depends
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from typing import List, Optional
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from pydantic import BaseModel
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from backend.core.vector_store import get_global_vector_store
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router = APIRouter()
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class AddTextPayload(BaseModel):
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id: str
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text: str
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metadata: Optional[dict] = None
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class QueryPayload(BaseModel):
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text: str
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k: Optional[int] = 5
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@router.post("/vectors/add", summary="Add text as vector")
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def add_text(payload: AddTextPayload):
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store = get_global_vector_store()
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try:
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store.add_text(payload.id, payload.text, payload.metadata)
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return {"status": "ok", "id": payload.id}
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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@router.post("/vectors/query", summary="Query nearest vectors by text")
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def query_text(payload: QueryPayload):
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store = get_global_vector_store()
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try:
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results = store.query_text(payload.text, k=payload.k or 5)
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out = [{"id": r[0], "distance": r[1], "metadata": r[2]} for r in results]
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return {"results": out}
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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"""API routes for vector store operations (add/search)."""
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from fastapi import APIRouter, Depends, HTTPException
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from typing import List, Optional
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from pydantic import BaseModel, Field
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import numpy as np
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from backend.core.vector_store import get_default_store, VectorStore
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router = APIRouter(prefix="/vector", tags=["vector-store"])
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class VectorAddRequest(BaseModel):
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ids: List[str]
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vectors: List[List[float]]
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metas: Optional[List[dict]] = None
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class VectorSearchRequest(BaseModel):
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query: List[float] = Field(..., min_items=1)
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top_k: int = 5
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class VectorSearchResult(BaseModel):
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id: str
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score: float
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meta: Optional[dict]
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@router.post("/add")
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def add_vectors(payload: VectorAddRequest):
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store = get_default_store(dim=len(payload.vectors[0]) if payload.vectors else 128)
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try:
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vecs = np.array(payload.vectors, dtype=np.float32)
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except Exception as e:
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raise HTTPException(status_code=400, detail=f"invalid vectors: {e}")
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count = store.add(payload.ids, vecs, payload.metas)
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return {"indexed": count}
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@router.post("/search", response_model=List[VectorSearchResult])
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def search_vectors(payload: VectorSearchRequest):
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store = get_default_store(dim=len(payload.query))
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q = np.array(payload.query, dtype=np.float32)
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results = store.search(q, top_k=payload.top_k)
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return results
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from fastapi import APIRouter, Depends, HTTPException
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from typing import List, Optional
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from backend.api.schemas import VectorUpsert, VectorQuery, VectorOut
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from backend.core.vector_store import default_store, VectorStore
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router = APIRouter(prefix="/vectors", tags=["vectors"])
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@router.post("/upsert", response_model=VectorOut, summary="Upsert a single vector")
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def upsert_vector(payload: VectorUpsert):
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"""Add or update a single vector in the default store."""
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try:
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default_store.add(payload.id, payload.vector, metadata=payload.metadata or {})
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return {"id": payload.id, "vector": payload.vector, "metadata": payload.metadata or {}}
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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@router.post("/query", response_model=List[VectorOut], summary="Query nearest vectors")
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def query_vectors(payload: VectorQuery):
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results = default_store.search(payload.vector, k=payload.k or 5)
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return [{"id": r[0], "score": r[1], "metadata": r[2], "vector": None} for r in results]
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