Create src/chimera/core/orchestrator.py (The MCP)

src/chimera/core/orchestrator.py (The MCP)

@@ -0,0 +1,85 @@
+# 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)))
+
+    if not tasks:
+        logger.warning("No relevant APIs identified for the query.")
+        # 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
+
+    # Step 2 & 3: Call APIs Concurrently and Gather Data
+    api_results = {}
+    if 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]:
+             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']}")
+
+    # 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)
+
+    # 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