fix(llm): add fetch-models endpoint, fix SQL Lab INSERT (client_id truncation, sync mode, target_column, timestamp normalization)

- Add POST /api/llm/providers/fetch-models route with LLMClient.fetch_models()
- Add target_column to TranslationJob model/schema/service/orchestrator
- Fix SQL Lab execute: truncate client_id to 11 chars (varchar(11))
- Switch SQL Lab to sync mode (runAsync: false) — no Celery workers
- Fix polling: unwrap nested result from Superset query API
- Fix ClickHouse timestamp: normalize float timestamps to YYYY-MM-DD

backend/src/plugins/llm_analysis/service.py

@@ -1,5 +1,4 @@
 # #region LLMAnalysisService [C:3] [TYPE Module] [SEMANTICS llm, screenshot, playwright, openai, tenacity]
-# @COMPLEXITY: 3
 # @BRIEF Services for LLM interaction and dashboard screenshots.
 # @LAYER: Domain
 # @RELATION DEPENDS_ON -> playwright
@@ -876,6 +875,29 @@ class LLMClient:
             return await self.get_json_completion(messages)
     # endregion LLMClient.test_runtime_connection
 
+    # region LLMClient.fetch_models [TYPE Function]
+    # @PURPOSE: Fetch available models from the provider's API.
+    # @PRE: Client is initialized with provider credentials.
+    # @POST: Returns a list of model ID strings.
+    # @SIDE_EFFECT: Calls external LLM API /v1/models endpoint.
+    async def fetch_models(self) -> List[str]:
+        with belief_scope("LLMClient.fetch_models"):
+            try:
+                response = await self.client.models.list()
+                model_ids = [m.id for m in response.data]
+                model_ids.sort()
+                logger.reason(
+                    f"[LLMClient.fetch_models] Fetched {len(model_ids)} models from {self.base_url}",
+                    extra={"src": "LLMClient.fetch_models"},
+                )
+                return model_ids
+            except Exception as e:
+                logger.warning(
+                    f"[LLMClient.fetch_models] Failed to fetch models: {e}",
+                )
+                raise
+    # endregion LLMClient.fetch_models
+
     # region LLMClient.analyze_dashboard [TYPE Function]
     # @PURPOSE: Sends dashboard data (screenshot + logs) to LLM for health analysis.
     # @PRE: screenshot_path exists, logs is a list of strings.