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]