from __future__ import annotations
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List, Optional
from sentence_transformers import SentenceTransformer

app = FastAPI(title="mindnet-embed", version="1.0")

MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"  # 384-dim
_model: SentenceTransformer | None = None

class EmbedIn(BaseModel):
    model: Optional[str] = None
    inputs: List[str]

class EmbedOut(BaseModel):
    embeddings: List[List[float]]

@app.on_event("startup")
def _load_model():
    global _model
    _model = SentenceTransformer(MODEL_NAME)

@app.get("/health")
def health():
    return {"ok": True, "model": MODEL_NAME, "dim": 384}

@app.post("/embed", response_model=EmbedOut)
def embed(payload: EmbedIn) -> EmbedOut:
    if _model is None:
        raise HTTPException(status_code=503, detail="Model not loaded")
    if not payload.inputs:
        return EmbedOut(embeddings=[])
    vecs = _model.encode(payload.inputs, normalize_embeddings=False).tolist()
    if any(len(v) != 384 for v in vecs):
        raise HTTPException(status_code=500, detail="Embedding size mismatch (expected 384)")
    return EmbedOut(embeddings=vecs)