fix(llm): save intermediate thinking — DeepSeek reasoning_content fallback, force non-empty thought, increase API truncation to 50K

analysis/views/auto.py

@@ -191,8 +191,10 @@ def run_llm_analysis_view(request):
                 save_fields = ['progress_pct', 'progress_msg']
                 if tool_name == 'thinking':
                     # Accumulate LLM thinking text (appended to llm_thinking)
-                    if result:
-                        run.llm_thinking = (run.llm_thinking or '') + f'\n[{step}] {result}'
+                    thought = (result or '').strip()
+                    if not thought:
+                        thought = f'步骤 {step} 分析中...'
+                    run.llm_thinking = (run.llm_thinking or '') + f'\n[{step}] {thought}'
                     save_fields = ['progress_pct', 'progress_msg', 'llm_thinking']
                 elif tool_name == 'complete':
                     pass  # No extra tracking needed

analysis/views/dashboard.py

@@ -53,7 +53,7 @@ def run_status_api(request, display_id):
         'progress_pct': run.progress_pct,
         'progress_msg': run.progress_msg,
         'run_log': run.run_log[-2000:],  # last 2000 chars
-        'llm_thinking': run.llm_thinking[-5000:],  # last 5K chars
+        'llm_thinking': run.llm_thinking[-50000:] if run.llm_thinking else '',
         'tool_calls': run.tool_calls_json,
     })
 

tianxuan/llm_orchestrator.py

@@ -328,6 +328,9 @@ def run_llm_pipeline(dataset_id: str, entity_col: str = "",
 
         msg = resp["choices"][0]["message"]
         content = msg.get("content", "")
+        # DeepSeek reasoning fallback (reasoning_content for chain-of-thought)
+        if not content:
+            content = msg.get("reasoning_content", "") or ""
 
         # Pass thinking text to callback
         if callback: