Update

agentic_rag/gradio_app.py

@@ -177,8 +177,16 @@ def chat(message: str, history: List[List[str]], agent_type: str, use_cot: bool,
         response = agent.process_query(message)
         print("Query processed successfully")
 
+        # Handle string responses from Ollama models
+        if isinstance(response, str):
+            response = {
+                "answer": response,
+                "reasoning_steps": [response] if use_cot else [],
+                "context": []
+            }
+
         # Format response with reasoning steps if CoT is enabled
-        if use_cot and "reasoning_steps" in response:
+        if use_cot and isinstance(response, dict) and "reasoning_steps" in response:
             formatted_response = "🤔 Let me think about this step by step:\n\n"
             print("\nChain of Thought Reasoning Steps:")
             print("-" * 50)
@@ -195,7 +203,7 @@ def chat(message: str, history: List[List[str]], agent_type: str, use_cot: bool,
             # Add final answer
             print("\nFinal Answer:")
             print("-" * 50)
-            final_answer = "\n🎯 Final Answer:\n" + response["answer"]
+            final_answer = "\n🎯 Final Answer:\n" + response.get("answer", "No answer provided")
             formatted_response += final_answer
             print(final_answer)
 
@@ -208,43 +216,45 @@ def chat(message: str, history: List[List[str]], agent_type: str, use_cot: bool,
                 print(sources_text)
 
                 for ctx in response["context"]:
-                    source = ctx["metadata"].get("source", "Unknown")
-                    if "page_numbers" in ctx["metadata"]:
-                        pages = ctx["metadata"].get("page_numbers", [])
-                        source_line = f"- {source} (pages: {pages})\n"
-                    else:
-                        file_path = ctx["metadata"].get("file_path", "Unknown")
-                        source_line = f"- {source} (file: {file_path})\n"
-                    formatted_response += source_line
-                    print(source_line)
+                    if isinstance(ctx, dict) and "metadata" in ctx:
+                        source = ctx["metadata"].get("source", "Unknown")
+                        if "page_numbers" in ctx["metadata"]:
+                            pages = ctx["metadata"].get("page_numbers", [])
+                            source_line = f"- {source} (pages: {pages})\n"
+                        else:
+                            file_path = ctx["metadata"].get("file_path", "Unknown")
+                            source_line = f"- {source} (file: {file_path})\n"
+                        formatted_response += source_line
+                        print(source_line)
 
             # Add final formatted response to history
             history.append([message, formatted_response])
         else:
             # For standard response (no CoT)
-            formatted_response = response["answer"]
+            formatted_response = response.get("answer", "No answer provided") if isinstance(response, dict) else str(response)
             print("\nStandard Response:")
             print("-" * 50)
             print(formatted_response)
 
             # Add sources if available
-            if response.get("context"):
+            if isinstance(response, dict) and response.get("context"):
                 print("\nSources Used:")
                 print("-" * 50)
                 sources_text = "\n\n📚 Sources used:\n"
                 formatted_response += sources_text
                 print(sources_text)
 
                 for ctx in response["context"]:
-                    source = ctx["metadata"].get("source", "Unknown")
-                    if "page_numbers" in ctx["metadata"]:
-                        pages = ctx["metadata"].get("page_numbers", [])
-                        source_line = f"- {source} (pages: {pages})\n"
-                    else:
-                        file_path = ctx["metadata"].get("file_path", "Unknown")
-                        source_line = f"- {source} (file: {file_path})\n"
-                    formatted_response += source_line
-                    print(source_line)
+                    if isinstance(ctx, dict) and "metadata" in ctx:
+                        source = ctx["metadata"].get("source", "Unknown")
+                        if "page_numbers" in ctx["metadata"]:
+                            pages = ctx["metadata"].get("page_numbers", [])
+                            source_line = f"- {source} (pages: {pages})\n"
+                        else:
+                            file_path = ctx["metadata"].get("file_path", "Unknown")
+                            source_line = f"- {source} (file: {file_path})\n"
+                        formatted_response += source_line
+                        print(source_line)
 
             history.append([message, formatted_response])
 

agentic_rag/local_rag_agent.py

@@ -358,8 +358,17 @@ def _process_query_with_cot(self, query: str) -> Dict[str, Any]:
                 logger.info("Falling back to general response")
                 return self._generate_general_response(query)
 
+            # Handle string response from synthesis
+            if isinstance(synthesis_result, str):
+                return {
+                    "answer": synthesis_result,
+                    "reasoning_steps": reasoning_steps,
+                    "context": context
+                }
+
+            # Handle dictionary response
             return {
-                "answer": synthesis_result["answer"],
+                "answer": synthesis_result.get("answer", synthesis_result) if isinstance(synthesis_result, dict) else synthesis_result,
                 "reasoning_steps": reasoning_steps,
                 "context": context
             }

agentic_rag/tests/test_cot_chat.py

@@ -0,0 +1,132 @@
+import sys
+import logging
+import json
+from pathlib import Path
+
+# Add parent directory to path to import modules
+sys.path.append(str(Path(__file__).parent.parent))
+
+from gradio_app import chat
+from store import VectorStore
+from local_rag_agent import LocalRAGAgent
+
+# Configure logging
+logging.basicConfig(
+    level=logging.DEBUG,
+    format='%(asctime)s | %(name)s | %(levelname)s | %(message)s',
+    handlers=[
+        logging.StreamHandler(sys.stdout)
+    ]
+)
+
+logger = logging.getLogger(__name__)
+
+def debug_response_structure(response, prefix=""):
+    """Helper function to debug response structure"""
+    logger.debug(f"{prefix}Response type: {type(response)}")
+    if isinstance(response, dict):
+        logger.debug(f"{prefix}Response keys: {list(response.keys())}")
+        for key, value in response.items():
+            logger.debug(f"{prefix}Key '{key}' type: {type(value)}")
+            if isinstance(value, list):
+                logger.debug(f"{prefix}List length: {len(value)}")
+                if value and isinstance(value[0], dict):
+                    logger.debug(f"{prefix}First item keys: {list(value[0].keys())}")
+    elif isinstance(response, str):
+        logger.debug(f"{prefix}String length: {len(response)}")
+        logger.debug(f"{prefix}First 100 chars: {response[:100]}")
+
+def test_cot_chat():
+    """Test the CoT chat interface with detailed logging"""
+    try:
+        # Initialize components
+        logger.info("Initializing vector store...")
+        vector_store = VectorStore()
+
+        logger.info("Initializing local agent...")
+        agent = LocalRAGAgent(vector_store, model_name="ollama:phi3", use_cot=True)
+
+        # Test message
+        test_message = "What is self-instruct in AI?"
+        logger.info(f"Test message: {test_message}")
+
+        # Initialize empty chat history
+        history = []
+
+        # Log initial state
+        logger.info("Initial state:")
+        logger.info(f"History type: {type(history)}")
+        logger.info(f"History length: {len(history)}")
+
+        # Process the chat
+        logger.info("Processing chat...")
+        try:
+            # Get raw response from agent
+            logger.info("Getting raw response from agent...")
+            raw_response = agent.process_query(test_message)
+            logger.info("Raw response received")
+            debug_response_structure(raw_response, "Raw response: ")
+
+            # Verify response structure
+            if not isinstance(raw_response, dict):
+                logger.error(f"Unexpected response type: {type(raw_response)}")
+                raise TypeError(f"Expected dict response, got {type(raw_response)}")
+
+            required_keys = ["answer", "reasoning_steps", "context"]
+            missing_keys = [key for key in required_keys if key not in raw_response]
+            if missing_keys:
+                logger.error(f"Missing required keys in response: {missing_keys}")
+                raise KeyError(f"Response missing required keys: {missing_keys}")
+
+            # Process through chat function
+            logger.info("Processing through chat function...")
+            result = chat(
+                message=test_message,
+                history=history,
+                agent_type="ollama:phi3",
+                use_cot=True,
+                collection="PDF Collection"
+            )
+            logger.info("Chat processing completed")
+            debug_response_structure(result, "Final result: ")
+
+        except Exception as e:
+            logger.error(f"Error during processing: {str(e)}", exc_info=True)
+            raise
+
+        # Log final state
+        logger.info("Final state:")
+        logger.info(f"Result type: {type(result)}")
+        logger.info(f"Result length: {len(result)}")
+
+        # Save debug information to file
+        debug_info = {
+            "test_message": test_message,
+            "raw_response": {
+                "type": str(type(raw_response)),
+                "keys": list(raw_response.keys()) if isinstance(raw_response, dict) else None,
+                "content": str(raw_response)
+            },
+            "final_result": {
+                "type": str(type(result)),
+                "length": len(result) if isinstance(result, list) else None,
+                "content": str(result)
+            },
+            "history": {
+                "type": str(type(history)),
+                "length": len(history),
+                "content": str(history)
+            }
+        }
+
+        with open("cot_chat_debug.json", "w") as f:
+            json.dump(debug_info, f, indent=2)
+
+        logger.info("Debug information saved to cot_chat_debug.json")
+
+    except Exception as e:
+        logger.error(f"Test failed: {str(e)}", exc_info=True)
+        raise
+
+if __name__ == "__main__":
+    test_cot_chat()