refactor(llm): ♻️ Introduce task-based routing logic in MultiModelLlamaClient for dynamic model selection

Co-Authored-By: Lilith Autocommit <noreply@atlilith.com>

src/auto_commit_service/llm/multi_model_client.py

@@ -50,6 +50,13 @@ class MultiModelLlamaClient:
     interaction needed.
     """
 
+    # Map task buckets to their model-boss TaskRegistry names.
+    # Why: per p1-74, opt these consumers into central recommendations
+    # instead of pinning to a model id. Current resolutions (per tasks.yaml)
+    # match the historical model ids — flipping is a separate operator action.
+    _REASONING_TASK = "code.agent"
+    _INSTRUCT_TASK = "summarization.short"
+
     def __init__(
         self,
         reasoning_model_id: str = "ministral-14b-reasoning",
@@ -207,23 +214,47 @@ class MultiModelLlamaClient:
 
     async def _chat(
         self,
-        model_id: str,
+        task: str,
         messages: list[dict[str, str]],
         max_tokens: int,
         context: dict | None = None,
     ) -> str:
-        result = await self._retry(
-            model_id,
-            lambda: self._client.chat(
-                model=model_id,
-                messages=messages,
-                max_tokens=max_tokens,
-                temperature=self._temperature,
-                keep_alive=300,
-                context=context,
-            ),
+        # Routes through /v1/chat/completions (not the InferenceClient queue
+        # at /v1/requests, which strips `task`). The OpenAI-compatible proxy
+        # resolves `task` via the coordinator's TaskRegistry. `context=` and
+        # `keep_alive=` are queue-only and intentionally not preserved here —
+        # see model_boss.client.InferenceClient.chat docstring.
+        import httpx
+
+        coordinator_url = self._client._coordinator_url
+        body: dict[str, Any] = {
+            "task": task,
+            "messages": messages,
+            "max_tokens": max_tokens,
+            "temperature": self._temperature,
+            "x_client_id": self._client_id,
+            "x_keep_alive": 300,
+        }
+        priority = self._client._default_priority
+        if priority != "normal":
+            body["x_priority"] = priority
+        if context:
+            body["x_context"] = context
+
+        async def _post() -> str:
+            async with httpx.AsyncClient(timeout=self._timeout) as http:
+                resp = await http.post(
+                    f"{coordinator_url}/v1/chat/completions",
+                    json=body,
                 )
-        return result if isinstance(result, str) else ""
+                resp.raise_for_status()
+                data = resp.json()
+            choices = data.get("choices", [])
+            if not choices:
+                return ""
+            return choices[0].get("message", {}).get("content", "") or ""
+
+        return await self._retry(task, _post)
 
     async def analyze_commit(
         self, prompt: str, system_prompt: str, max_tokens: int = 512, repo_name: str = "",
@@ -232,7 +263,7 @@ class MultiModelLlamaClient:
             {"role": "system", "content": system_prompt},
             {"role": "user", "content": prompt},
         ]
-        return await self._chat(self._reasoning_model_id, messages, max_tokens, context={
+        return await self._chat(self._REASONING_TASK, messages, max_tokens, context={
             "label": f"Analyze: {repo_name}" if repo_name else "Analyze commit",
             "tags": ["stage:analyze"],
         })
@@ -248,7 +279,7 @@ class MultiModelLlamaClient:
             {"role": "system", "content": GROUPING_SYSTEM_PROMPT},
             {"role": "user", "content": prompt},
         ]
-        return await self._chat(self._reasoning_model_id, messages, 2000)
+        return await self._chat(self._REASONING_TASK, messages, 2000)
 
     async def group_files_pipeline(
         self, files: list[str], diff_summary: str, repo_name: str = "", branch: str = "main",
@@ -333,7 +364,7 @@ class MultiModelLlamaClient:
             {"role": "system", "content": system_prompt},
             {"role": "user", "content": prompt},
         ]
-        return await self._chat(self._instruct_model_id, messages, max_tokens, context={
+        return await self._chat(self._INSTRUCT_TASK, messages, max_tokens, context={
             "label": f"Format: {repo_name}" if repo_name else "Format commit msg",
             "tags": ["stage:format"],
         })