Update src/chimera/core/orchestrator.py

src/chimera/core/orchestrator.py

@@ -29,22 +29,25 @@ async def run_analysis(user_query: str) -> str:
     #    logger.info("Adding Weather task.")
     #    tasks.append(asyncio.create_task(external_apis.get_weather(location)))
 
+    # --- Start of Corrected Section ---
     if not tasks:
-        logger.warning("No relevant APIs identified for the query.")
+        logger.warning("No relevant APIs identified for the query. Proceeding without external API data.")
         # Fallback: Just send the raw query to Gemini? Or ask user for clarification?
         # For now, just send the query directly for general knowledge analysis
-         pass # Let Gemini handle the query directly without external data
+        pass # This 'pass' is now correctly indented under the 'if not tasks:' block.
+             # It signifies doing nothing specific if no tasks were added.
 
+    # --- Lines below are now correctly dedented ---
     # Step 2 & 3: Call APIs Concurrently and Gather Data
-    api_results = {}
-    if tasks:
+    api_results = {} # Initialize api_results regardless of whether tasks were added
+    if tasks: # Only execute gather if there are tasks
         logger.info(f"Gathering data from {len(tasks)} API(s)...")
         results = await asyncio.gather(*tasks, return_exceptions=True) # Collect all results/exceptions
         logger.info("API data gathering complete.")
 
         # Process results (basic example)
-        # Check result types or assigned task names if needed
-        if isinstance(results[0], dict) and "organic_results" in results[0]:
+        # Make sure results list is not empty before accessing results[0]
+        if results and isinstance(results[0], dict) and "organic_results" in results[0]:
              api_results["serp"] = results[0]
         # Add checks and assignments for other potential API results
 
@@ -57,9 +60,11 @@ async def run_analysis(user_query: str) -> str:
             elif isinstance(result, dict) and "error" in result:
                  logger.error(f"API call task {i} reported an error: {result['error']}")
 
+    # --- End of Corrected Section ---
+
     # Step 4: Format Data and Create Gemini Prompt
     # Process the gathered data into a readable format for the LLM
-    formatted_data = data_processing.format_api_data_for_llm(api_results)
+    formatted_data = data_processing.format_api_data_for_llm(api_results) # Pass potentially empty api_results
 
     # Construct the final prompt
     # This is CRITICAL - prompt engineering is key here!