feat(01-02): LLM Backend Pool — LiteLLM Router with Ollama + Anthropic + OpenAI fallback

- Create llm_pool/router.py: LiteLLM Router with fast (Ollama) and quality (Anthropic/OpenAI) model groups
- Configure fallback chain: quality providers fail -> fast group
- Pin LiteLLM to ==1.82.5 (avoid September 2025 OOM regression in later releases)
- Create llm_pool/main.py: FastAPI service on port 8004 with /complete and /health endpoints
- Add providers/__init__.py: reserved for future per-provider customization
- Update docker-compose.yml: add llm-pool and celery-worker service stubs

packages/llm-pool/llm_pool/router.py

@@ -0,0 +1,99 @@
+"""
+LiteLLM Router — multi-provider LLM backend with automatic fallback.
+
+Provider groups:
+  "fast"    → Ollama (local, low-latency, no cost)
+  "quality" → Anthropic claude-sonnet-4 (primary), OpenAI gpt-4o (fallback)
+
+Fallback chain:
+  quality providers fail → fall back to "fast" group
+
+NOTE: LiteLLM is pinned to ==1.82.5 in pyproject.toml.
+      Do NOT upgrade without testing — a September 2025 OOM regression exists
+      in releases between 1.83.x and later versions.
+"""
+
+from __future__ import annotations
+
+import logging
+
+from litellm import Router
+
+from shared.config import settings
+
+logger = logging.getLogger(__name__)
+
+# ---------------------------------------------------------------------------
+# Model list — three entries across two groups
+# ---------------------------------------------------------------------------
+_model_list: list[dict] = [
+    # fast group — local Ollama, no API cost
+    {
+        "model_name": "fast",
+        "litellm_params": {
+            "model": "ollama/qwen3:8b",
+            "api_base": settings.ollama_base_url,
+        },
+    },
+    # quality group — Anthropic primary
+    {
+        "model_name": "quality",
+        "litellm_params": {
+            "model": "anthropic/claude-sonnet-4-20250514",
+            "api_key": settings.anthropic_api_key,
+        },
+    },
+    # quality group — OpenAI fallback (within the same group)
+    {
+        "model_name": "quality",
+        "litellm_params": {
+            "model": "openai/gpt-4o",
+            "api_key": settings.openai_api_key,
+        },
+    },
+]
+
+# ---------------------------------------------------------------------------
+# Router — latency-based, 2 retries per provider, then cross-group fallback
+# ---------------------------------------------------------------------------
+llm_router = Router(
+    model_list=_model_list,
+    # If all quality providers fail, fall back to the fast group
+    fallbacks=[{"quality": ["fast"]}],
+    routing_strategy="latency-based-routing",
+    num_retries=2,
+    set_verbose=False,
+)
+
+
+async def complete(
+    model_group: str,
+    messages: list[dict],
+    tenant_id: str,
+) -> str:
+    """
+    Request a completion from the LiteLLM Router.
+
+    Args:
+        model_group: "quality" or "fast" — selects the provider group.
+        messages:    OpenAI-format message list, e.g.
+                     [{"role": "system", "content": "..."}, {"role": "user", "content": "..."}]
+        tenant_id:   Konstruct tenant UUID, attached to LiteLLM metadata for
+                     per-tenant cost tracking.
+
+    Returns:
+        The model's response content as a plain string.
+
+    Raises:
+        Exception: Propagated if all providers in the group (and fallbacks) fail.
+    """
+    logger.info("LLM request", extra={"model_group": model_group, "tenant_id": tenant_id})
+
+    response = await llm_router.acompletion(
+        model=model_group,
+        messages=messages,
+        metadata={"tenant_id": tenant_id},
+    )
+
+    content: str = response.choices[0].message.content or ""
+    return content