aiml/02_models/nova/plane/api/gateway.py

#!/usr/bin/env python3
"""
OpenAI-compatible gateway and route registry.
Proxies /v1/chat/completions and /v1/completions to an upstream (e.g., vLLM).
Also exposes /routes with configured external URLs for client discovery.
"""

import os
import json
from typing import Dict, Any

import requests
import yaml
from fastapi import FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
import uvicorn
from prometheus_client import Counter, Histogram, make_asgi_app
from opentelemetry import trace
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
from opentelemetry.sdk.trace.export import ConsoleSpanExporter


def load_routes() -> Dict[str, Any]:
    path = os.getenv("EXTERNAL_ROUTES_FILE", "nova/config/external_routes.yaml")
    try:
        with open(path, "r", encoding="utf-8") as f:
            return yaml.safe_load(f) or {}
    except Exception:
        # Default routes with provided URLs
        return {
            "services": {
                "primary_api": {
                    "candidates": [
                        "https://localhost:8080",
                        "https://charter-enjoying-manufacturers-fifteen.trycloudflare.com",
                    ]
                },
                "tensorboard": {
                    "candidates": [
                        "http://localhost:6006",
                        "https://heel-thumbnails-dans-liked.trycloudflare.com",
                    ]
                },
                "syncthing": {
                    "candidates": [
                        "http://localhost:8384",
                        "https://treasure-cash-integer-mae.trycloudflare.com",
                    ]
                },
                "jupyter": {
                    "candidates": [
                        "http://localhost:1111",
                        "https://cottage-angels-blair-greeting.trycloudflare.com",
                    ]
                },
            }
        }


UPSTREAM = os.getenv("UPSTREAM_OPENAI_BASE", "http://127.0.0.1:8000").rstrip("/")
GRAPHRAG_BASE = os.getenv("GRAPHRAG_BASE", "http://127.0.0.1:7012").rstrip("/")
EMBED_BASE = os.getenv("EMBED_BASE", "http://127.0.0.1:7013").rstrip("/")
PORT = int(os.getenv("PORT", "8088"))

app = FastAPI(title="Nova Gateway", version="0.1.0")
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

REQUESTS = Counter("gateway_requests_total", "Gateway requests", ["route"])
LATENCY = Histogram("gateway_request_latency_seconds", "Latency", ["route"])

# Basic OTel tracer to console (can be swapped for OTLP)
resource = Resource.create({"service.name": "nova-gateway"})
provider = TracerProvider(resource=resource)
provider.add_span_processor(SimpleSpanProcessor(ConsoleSpanExporter()))
trace.set_tracer_provider(provider)
tracer = trace.get_tracer(__name__)


@app.get("/health")
def health() -> Dict[str, Any]:
    REQUESTS.labels(route="health").inc()
    return {"status": "ok", "upstream": UPSTREAM, "port": PORT}


@app.get("/routes")
def routes() -> Dict[str, Any]:
    REQUESTS.labels(route="routes").inc()
    return load_routes()


def proxy_post(path: str, payload: Dict[str, Any], headers: Dict[str, str]) -> JSONResponse:
    url = f"{UPSTREAM}{path}"
    try:
        with tracer.start_as_current_span("proxy_post"):
            resp = requests.post(url, json=payload, headers=headers, timeout=120)
        content_type = resp.headers.get("content-type", "application/json")
        return JSONResponse(status_code=resp.status_code, content=resp.json() if 'json' in content_type else resp.text)
    except Exception as e:
        return JSONResponse(status_code=502, content={"error": f"upstream error: {e}"})


@app.post("/v1/chat/completions")
async def chat_completions(req: Request) -> JSONResponse:
    with LATENCY.labels(route="chat").time():
        REQUESTS.labels(route="chat").inc()
        payload = await req.json()
        headers = dict(req.headers)
        return proxy_post("/v1/chat/completions", payload, headers)


@app.post("/v1/completions")
async def completions(req: Request) -> JSONResponse:
    with LATENCY.labels(route="completions").time():
        REQUESTS.labels(route="completions").inc()
        payload = await req.json()
        headers = dict(req.headers)
        return proxy_post("/v1/completions", payload, headers)


@app.post("/v1/rag/completions")
async def rag_completions(req: Request) -> JSONResponse:
    """RAG-augmented completions. Expects body with {model, prompt, rag:{...}}.
    rag: {collection, query_vector, seed_ids, top_k, depth}
    """
    with LATENCY.labels(route="rag_completions").time():
        REQUESTS.labels(route="rag_completions").inc()
        body = await req.json()
        model = body.get("model")
        prompt = body.get("prompt", "")
        rag = body.get("rag", {})
        headers = dict(req.headers)

        # Optional Dragonfly/Redis cache and embedding for query_text
        cache_key = None
        ctx = None
        try:
            import hashlib, json as _json
            cache_key = "rag:" + hashlib.sha256(_json.dumps(rag, sort_keys=True).encode()).hexdigest()
            import redis
            r = redis.from_url(os.getenv("REDIS_URL", "redis://127.0.0.1:6379/0"))
            cached = r.get(cache_key)
            if cached:
                ctx = _json.loads(cached)
            else:
                # Derive query_vector if only query_text is present
                qv = rag.get("query_vector")
                if qv is None and rag.get("query_text"):
                    try:
                        er = requests.post(f"{EMBED_BASE}/embed", json={"text": rag.get("query_text")}, timeout=5)
                        qv = er.json().get("vector") if er.status_code == 200 else None
                    except Exception:
                        qv = None
                gr = requests.post(f"{GRAPHRAG_BASE}/graphrag", json={
                    "collection": rag.get("collection", "default"),
                    "query_vector": qv,
                    "seed_ids": rag.get("seed_ids", []),
                    "top_k": int(rag.get("top_k", 5)),
                    "depth": int(rag.get("depth", 1)),
                }, timeout=10)
                if gr.status_code == 200:
                    ctx = gr.json()
                    r.setex(cache_key, int(os.getenv("RAG_CACHE_TTL", "60")), _json.dumps(ctx).encode())
        except Exception:
            # Fallback without cache
            try:
                qv = rag.get("query_vector")
                if qv is None and rag.get("query_text"):
                    try:
                        er = requests.post(f"{EMBED_BASE}/embed", json={"text": rag.get("query_text")}, timeout=5)
                        qv = er.json().get("vector") if er.status_code == 200 else None
                    except Exception:
                        qv = None
                gr = requests.post(f"{GRAPHRAG_BASE}/graphrag", json={
                    "collection": rag.get("collection", "default"),
                    "query_vector": qv,
                    "seed_ids": rag.get("seed_ids", []),
                    "top_k": int(rag.get("top_k", 5)),
                    "depth": int(rag.get("depth", 1)),
                }, timeout=10)
                if gr.status_code == 200:
                    ctx = gr.json()
            except Exception:
                ctx = None

        context_block = f"\n\n[CONTEXT]\n{json.dumps(ctx) if ctx else 'null'}\n\n"
        augmented = {"model": model, "prompt": context_block + prompt}
        return proxy_post("/v1/completions", augmented, headers)


@app.post("/v1/rag/chat/completions")
async def rag_chat_completions(req: Request) -> JSONResponse:
    """RAG-augmented chat. Adds a system message with context.
    Expects body with {model, messages, rag:{...}}.
    """
    with LATENCY.labels(route="rag_chat").time():
        REQUESTS.labels(route="rag_chat").inc()
        body = await req.json()
        model = body.get("model")
        messages = body.get("messages", [])
        rag = body.get("rag", {})
        headers = dict(req.headers)

        ctx = None
        try:
            qv = rag.get("query_vector")
            if qv is None and rag.get("query_text"):
                try:
                    er = requests.post(f"{EMBED_BASE}/embed", json={"text": rag.get("query_text")}, timeout=5)
                    qv = er.json().get("vector") if er.status_code == 200 else None
                except Exception:
                    qv = None
            gr = requests.post(f"{GRAPHRAG_BASE}/graphrag", json={
                "collection": rag.get("collection", "default"),
                "query_vector": qv,
                "seed_ids": rag.get("seed_ids", []),
                "top_k": int(rag.get("top_k", 5)),
                "depth": int(rag.get("depth", 1)),
            }, timeout=10)
            if gr.status_code == 200:
                ctx = gr.json()
        except Exception:
            ctx = None

        sys_msg = {"role": "system", "content": f"Context: {json.dumps(ctx) if ctx else 'null'}"}
        augmented = {"model": model, "messages": [sys_msg] + messages}
        return proxy_post("/v1/chat/completions", augmented, headers)

# 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)