File size: 3,711 Bytes
effde1c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 |
from fastapi import APIRouter, HTTPException, Depends
from typing import List, Optional
from pydantic import BaseModel
from backend.core.vector_store import get_global_vector_store
router = APIRouter()
class AddTextPayload(BaseModel):
id: str
text: str
metadata: Optional[dict] = None
class QueryPayload(BaseModel):
text: str
k: Optional[int] = 5
@router.post("/vectors/add", summary="Add text as vector")
def add_text(payload: AddTextPayload):
store = get_global_vector_store()
try:
store.add_text(payload.id, payload.text, payload.metadata)
return {"status": "ok", "id": payload.id}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@router.post("/vectors/query", summary="Query nearest vectors by text")
def query_text(payload: QueryPayload):
store = get_global_vector_store()
try:
results = store.query_text(payload.text, k=payload.k or 5)
# convert numpy arrays to lists for JSON
out = [{"id": r[0], "distance": r[1], "metadata": r[2]} for r in results]
return {"results": out}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
"""API routes for vector store operations (add/search)."""
from fastapi import APIRouter, Depends, HTTPException
from typing import List, Optional
from pydantic import BaseModel, Field
import numpy as np
from backend.core.vector_store import get_default_store, VectorStore
router = APIRouter(prefix="/vector", tags=["vector-store"])
class VectorAddRequest(BaseModel):
ids: List[str]
vectors: List[List[float]]
metas: Optional[List[dict]] = None
class VectorSearchRequest(BaseModel):
query: List[float] = Field(..., min_items=1)
top_k: int = 5
class VectorSearchResult(BaseModel):
id: str
score: float
meta: Optional[dict]
@router.post("/add")
def add_vectors(payload: VectorAddRequest):
store = get_default_store(dim=len(payload.vectors[0]) if payload.vectors else 128)
try:
vecs = np.array(payload.vectors, dtype=np.float32)
except Exception as e:
raise HTTPException(status_code=400, detail=f"invalid vectors: {e}")
count = store.add(payload.ids, vecs, payload.metas)
return {"indexed": count}
@router.post("/search", response_model=List[VectorSearchResult])
def search_vectors(payload: VectorSearchRequest):
store = get_default_store(dim=len(payload.query))
q = np.array(payload.query, dtype=np.float32)
results = store.search(q, top_k=payload.top_k)
return results
from fastapi import APIRouter, Depends, HTTPException
from typing import List, Optional
from backend.api.schemas import VectorUpsert, VectorQuery, VectorOut
from backend.core.vector_store import default_store, VectorStore
router = APIRouter(prefix="/vectors", tags=["vectors"])
@router.post("/upsert", response_model=VectorOut, summary="Upsert a single vector")
def upsert_vector(payload: VectorUpsert):
"""Add or update a single vector in the default store."""
try:
default_store.add(payload.id, payload.vector, metadata=payload.metadata or {})
return {"id": payload.id, "vector": payload.vector, "metadata": payload.metadata or {}}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@router.post("/query", response_model=List[VectorOut], summary="Query nearest vectors")
def query_vectors(payload: VectorQuery):
results = default_store.search(payload.vector, k=payload.k or 5)
return [{"id": r[0], "score": r[1], "metadata": r[2], "vector": None} for r in results]
|