aiml/02_models/nova/plane/memory/embed_service.py

#!/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)