Phase 3: AI integration with Ollama via Python sidecar

- sidecar: FastAPI app with /embed, /generate, /rerank hitting Ollama
- sidecar: Dockerfile, env var config (EMBED_MODEL, GEN_MODEL, RERANK_MODEL)
- aibridge: reqwest HTTP client with typed request/response structs
- aibridge: Axum proxy endpoints (POST /ai/embed, /ai/generate, /ai/rerank)
- gateway: wires AiClient with SIDECAR_URL env var
- e2e verified: nomic-embed-text returns 768d vectors, qwen2.5 generates text

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

sidecar/sidecar/generate.py

@@ -0,0 +1,55 @@
+import os
+
+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel
+
+from .ollama import client
+
+router = APIRouter()
+
+GEN_MODEL = os.environ.get("GEN_MODEL", "qwen2.5")
+
+
+class GenerateRequest(BaseModel):
+    prompt: str
+    model: str | None = None
+    system: str | None = None
+    temperature: float = 0.7
+    max_tokens: int = 2048
+
+
+class GenerateResponse(BaseModel):
+    text: str
+    model: str
+    tokens_evaluated: int | None = None
+    tokens_generated: int | None = None
+
+
+@router.post("", response_model=GenerateResponse)
+async def generate(req: GenerateRequest):
+    model = req.model or GEN_MODEL
+
+    payload = {
+        "model": model,
+        "prompt": req.prompt,
+        "stream": False,
+        "options": {
+            "temperature": req.temperature,
+            "num_predict": req.max_tokens,
+        },
+    }
+    if req.system:
+        payload["system"] = req.system
+
+    async with client() as c:
+        resp = await c.post("/api/generate", json=payload)
+        if resp.status_code != 200:
+            raise HTTPException(502, f"Ollama error: {resp.text}")
+        data = resp.json()
+
+    return GenerateResponse(
+        text=data.get("response", ""),
+        model=model,
+        tokens_evaluated=data.get("prompt_eval_count"),
+        tokens_generated=data.get("eval_count"),
+    )