feat(llm-proxy): in-cluster FastAPI shim with nginx pass-through
Closes the 'fork Pi coding agent' integration loop with a tiny
broker that mirrors the @earendil-works/pi-ai request shape:
POST /internal/canvas-llm/chat
body: { messages: [{role, content}], max_tokens }
The proxy speaks to OpenAI or Anthropic depending on which env key
is set. With no key, returns 503 so the frontend's deterministic
fallback in routeAgentInput fires gracefully.
- llm-proxy/main.py: FastAPI + httpx (~150 LOC). System messages get
split out for Anthropic (separate 'system' field) and inlined for
OpenAI.
- llm-proxy/Dockerfile: python:3.12-slim, uvicorn on :8080.
- Deployment + Service in demo namespace (verified Running).
- nginx.conf: /internal/canvas-llm/ proxies to the in-cluster service.
Provider keys are set to empty in the manifest by design: customer
flip is a single 'kubectl set env' or sealed-secret update.
This commit is contained in:
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"""Minimal LLM proxy for canvas.flow-master.ai.
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Brokers POST /internal/canvas-llm/chat to one of:
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- OPENAI_API_KEY -> https://api.openai.com/v1/chat/completions
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- ANTHROPIC_API_KEY -> https://api.anthropic.com/v1/messages
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Returns {content, provider}. Returns 503 when no key is configured so the
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frontend's deterministic fallback fires.
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Request shape (mirrors @earendil-works/pi-ai):
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POST /internal/canvas-llm/chat
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body: {"messages": [{"role": "system|user|assistant", "content": "..."}], "max_tokens": 320}
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No streaming, no tool calls, no images — keep it tiny. The frontend already
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calls deterministic tools on the EA2 backend; this proxy just synthesises
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fluent natural-language responses when no tool matches.
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"""
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import json
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import os
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from typing import Any
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import httpx
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from fastapi import FastAPI, HTTPException, Request
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from fastapi.responses import JSONResponse
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OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY", "").strip()
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OPENAI_MODEL = os.environ.get("OPENAI_MODEL", "gpt-4o-mini").strip()
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ANTHROPIC_API_KEY = os.environ.get("ANTHROPIC_API_KEY", "").strip()
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ANTHROPIC_MODEL = os.environ.get("ANTHROPIC_MODEL", "claude-3-5-haiku-latest").strip()
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app = FastAPI(title="canvas-llm-proxy", version="0.1.0")
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@app.get("/health")
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async def health() -> dict[str, Any]:
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return {
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"ok": True,
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"providers_available": {
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"openai": bool(OPENAI_API_KEY),
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"anthropic": bool(ANTHROPIC_API_KEY),
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},
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}
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def _split_system_messages(messages: list[dict]) -> tuple[str, list[dict]]:
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"""Anthropic uses a separate `system` field; OpenAI accepts inline."""
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system_chunks: list[str] = []
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non_system: list[dict] = []
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for m in messages:
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if m.get("role") == "system":
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system_chunks.append(str(m.get("content", "")))
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else:
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non_system.append(m)
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return ("\n\n".join(system_chunks), non_system)
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@app.post("/internal/canvas-llm/chat")
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async def chat(request: Request) -> JSONResponse:
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try:
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body = await request.json()
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except Exception as e:
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raise HTTPException(status_code=400, detail=f"invalid json: {e}")
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messages = body.get("messages", [])
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if not isinstance(messages, list) or not messages:
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raise HTTPException(status_code=400, detail="messages[] required")
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max_tokens = int(body.get("max_tokens", 320))
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if ANTHROPIC_API_KEY:
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system, rest = _split_system_messages(messages)
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anth_messages = [
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{"role": m.get("role", "user"), "content": [{"type": "text", "text": str(m.get("content", ""))}]}
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for m in rest
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if m.get("role") in ("user", "assistant")
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]
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if not anth_messages:
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anth_messages = [{"role": "user", "content": [{"type": "text", "text": "Hello."}]}]
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payload = {
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"model": ANTHROPIC_MODEL,
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"max_tokens": max_tokens,
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"messages": anth_messages,
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}
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if system:
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payload["system"] = system
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async with httpx.AsyncClient(timeout=30.0) as client:
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r = await client.post(
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"https://api.anthropic.com/v1/messages",
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headers={
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"x-api-key": ANTHROPIC_API_KEY,
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"anthropic-version": "2023-06-01",
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"content-type": "application/json",
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},
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json=payload,
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)
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if r.status_code >= 400:
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return JSONResponse(
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status_code=502,
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content={"error": "anthropic upstream", "status": r.status_code, "body": r.text[:500]},
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)
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data = r.json()
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chunks = data.get("content") or []
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content = "".join(c.get("text", "") for c in chunks if c.get("type") == "text").strip()
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return JSONResponse(content={"content": content, "provider": "anthropic"})
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if OPENAI_API_KEY:
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payload = {
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"model": OPENAI_MODEL,
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"max_tokens": max_tokens,
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"messages": [
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{"role": m.get("role", "user"), "content": str(m.get("content", ""))}
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for m in messages
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],
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}
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async with httpx.AsyncClient(timeout=30.0) as client:
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r = await client.post(
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"https://api.openai.com/v1/chat/completions",
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headers={
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"Authorization": f"Bearer {OPENAI_API_KEY}",
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"content-type": "application/json",
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},
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json=payload,
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)
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if r.status_code >= 400:
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return JSONResponse(
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status_code=502,
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content={"error": "openai upstream", "status": r.status_code, "body": r.text[:500]},
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)
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data = r.json()
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choices = data.get("choices") or []
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content = ""
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if choices:
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msg = choices[0].get("message") or {}
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content = str(msg.get("content", "")).strip()
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return JSONResponse(content={"content": content, "provider": "openai"})
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return JSONResponse(
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status_code=503,
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content={"error": "no provider configured", "hint": "set OPENAI_API_KEY or ANTHROPIC_API_KEY"},
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)
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