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#!/usr/bin/env python3
"""
Embedders service (deterministic hashing embeddings; no external deps).
Endpoint: POST /embed {text: str} or {texts: [str]} -> {vector: [...]} or {vectors: [[...]]}
Environment: PORT (default 7013), DIM (default 384)
"""
import os
import hashlib
from typing import List, Dict, Any
from fastapi import FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
from prometheus_client import Counter, Histogram, make_asgi_app
import uvicorn
PORT = int(os.getenv("PORT", "7013"))
DIM = int(os.getenv("DIM", "384"))
app = FastAPI(title="Nova Embedders", version="0.1.0")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
REQUESTS = Counter("embed_requests_total", "Embedders requests", ["route"])
LATENCY = Histogram("embed_request_latency_seconds", "Latency", ["route"])
def _embed_one(text: str) -> List[float]:
# Simple hashing TF-like vector with l2 normalization
vec = [0.0] * DIM
for tok in text.lower().split():
h = int(hashlib.sha256(tok.encode()).hexdigest(), 16)
idx = h % DIM
vec[idx] += 1.0
# l2 normalize
norm = sum(v * v for v in vec) ** 0.5
if norm > 0:
vec = [v / norm for v in vec]
return vec
@app.get("/health")
def health() -> Dict[str, Any]:
REQUESTS.labels(route="health").inc()
return {"status": "ok", "port": PORT, "dim": DIM}
@app.post("/embed")
async def embed(req: Request) -> JSONResponse:
with LATENCY.labels(route="embed").time():
REQUESTS.labels(route="embed").inc()
body = await req.json()
if "text" in body:
return JSONResponse(status_code=200, content={"vector": _embed_one(str(body["text"]))})
if "texts" in body:
return JSONResponse(status_code=200, content={"vectors": [_embed_one(str(t)) for t in body["texts"]]})
return JSONResponse(status_code=400, content={"error": "provide 'text' or 'texts'"})
# Prometheus metrics
metrics_app = make_asgi_app()
app.mount("/metrics", metrics_app)
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=PORT)
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