first commit

relay/relay.py

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+
+import os, json, httpx
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+
+AGENT_URL = os.getenv("AGENT_URL", "http://ai-agent:8080")
+OPENAI_MODEL = os.getenv("OPENAI_MODEL", "gpt-4o-mini")
+
+def _read_api_key():
+    # Prefer file from Docker secret if present
+    path = os.getenv("OPENAI_API_KEY_FILE", "/run/secrets/openai_api_key")
+    if os.path.exists(path):
+        return open(path, "r").read().strip()
+    return os.getenv("OPENAI_API_KEY", "")
+
+SYSTEM_PROMPT = (
+    "You are an ops command planner. Convert the user's intent into a STRICT JSON object "
+    "with fields: action (scale|restart_service), params (dict). No prose. Examples: "
+    '{"action":"scale","params":{"service":"weblabs_php","replicas":3}} '
+    'or {"action":"restart_service","params":{"service":"weblabs_php"}}. '
+    "Only produce valid JSON. If unclear, choose the safest no-op."
+)
+
+class ChatIn(BaseModel):
+    prompt: str
+
+app = FastAPI(title="AI Relay (LLM -> Agent)")
+
+@app.get("/health")
+def health():
+    return {"ok": True}
+
+@app.post("/chat")
+async def chat(inp: ChatIn):
+    api_key = _read_api_key()
+    if not api_key:
+        raise HTTPException(500, "Missing OPENAI_API_KEY (env or secret).")
+
+    # Call OpenAI Responses API
+    url = "https://api.openai.com/v1/responses"
+    headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
+    body = {
+        "model": OPENAI_MODEL,
+        "input": f"{SYSTEM_PROMPT}\nUSER: {inp.prompt}",
+        "max_output_tokens": 300,
+        "temperature": 0.1
+    }
+
+    async with httpx.AsyncClient(timeout=30) as client:
+        r = await client.post(url, headers=headers, json=body)
+        if r.status_code >= 400:
+            raise HTTPException(502, f"OpenAI error: {r.text}")
+        data = r.json()
+        # Responses API returns output in 'output_text' (or tool messages). Try common fields.
+        content = data.get("output_text") or data.get("content") or ""
+        if isinstance(content, list):
+            # Some responses return a list of content parts; take text from first text part
+            for part in content:
+                if part.get("type") in ("output_text", "text") and part.get("text"):
+                    content = part["text"]
+                    break
+        if not isinstance(content, str):
+            content = str(content)
+
+    # Parse JSON from the model output
+    try:
+        cmd = json.loads(content)
+    except Exception as e:
+        raise HTTPException(500, f"Failed to parse model JSON: {e}; content={content[:200]}")
+
+    # Forward to the agent
+    async with httpx.AsyncClient(timeout=15) as client:
+        r = await client.post(f"{AGENT_URL}/command", json=cmd)
+        if r.status_code >= 400:
+            raise HTTPException(r.status_code, f"Agent error: {r.text}")
+        return r.json()