src/chimera/core/orchestrator.py

# src/chimera/core/orchestrator.py
import asyncio
from ..api_clients import gemini_client, serp_client # , external_apis
from ..utils.logging_config import logger
from ..utils import data_processing

async def run_analysis(user_query: str) -> str:
    """
    Main orchestration logic for Project Chimera.
    1. Interprets user query (simple keyword check for now).
    2. Calls relevant APIs concurrently.
    3. Gathers data.
    4. Formats data and creates a prompt for Gemini.
    5. Calls Gemini for analysis.
    6. Returns the result.
    """
    logger.info(f"Received query: {user_query}")

    # Step 1: Basic Query Interpretation (Replace with more sophisticated logic/LLM call if needed)
    tasks = []
    if "news" in user_query.lower() or "search" in user_query.lower():
        # Extract keywords or use the whole query for SERP
        search_term = user_query # Or extract better term
        logger.info("Adding SERP task.")
        tasks.append(asyncio.create_task(serp_client.search_google(search_term, num_results=5)))
    # Add conditions for other APIs based on keywords (e.g., "weather", "stock", "earthquake")
    # if "weather" in user_query.lower():
    #    location = "New York" # Extract location from query
    #    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. 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 # 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 = {} # 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)
        # 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

        # Handle potential errors from asyncio.gather
        for i, result in enumerate(results):
            if isinstance(result, Exception):
                logger.error(f"API call task {i} failed: {result}")
                # Decide how to handle partial failures - inform Gemini? Return error?
                # For now, we'll just log it and proceed with whatever data we got.
            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) # Pass potentially empty api_results

    # Construct the final prompt
    # This is CRITICAL - prompt engineering is key here!
    prompt = f"""
    Analyze the following user query and synthesized real-time data to provide insights, identify patterns, potential solutions, or opportunities.

    User Query: "{user_query}"

    Synthesized Data:
    ---
    {formatted_data if formatted_data else "No additional real-time data was gathered for this query."}
    ---

    Based on the query and the data (if provided), please provide a comprehensive analysis. Consider potential implications, connections between data points, and answer the user's core question or request. If suggesting solutions or opportunities, be specific and justify your reasoning.
    """

    # Step 5: Call Gemini
    logger.info("Sending final prompt to Gemini for analysis.")
    analysis_result = await gemini_client.generate_analysis(prompt)

    # Step 6: Return Result
    logger.info("Analysis complete.")
    return analysis_result