#!/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)