update workflow error

app.py

@@ -182,8 +182,8 @@ class GAIAAgent:
         
         # Import tools from our tools.py file
         try:
-            from tools import create_all_tools
-            tool_list = create_all_tools()
+            from tools import get_all_tools
+            tool_list = get_all_tools()
             tools.extend(tool_list)
             logger.info(f"✅ Loaded {len(tool_list)} tools from tools.py")
         except ImportError as e:
@@ -191,18 +191,6 @@ class GAIAAgent:
         except Exception as e:
             logger.warning(f"⚠️ Error loading tools from tools.py: {e}")
         
-        # Import RAG tool from our retriever.py file
-        try:
-            from retriever import create_persona_tool
-            persona_tool = create_persona_tool()
-            if persona_tool:
-                tools.append(persona_tool)
-                logger.info("✅ Loaded persona RAG tool from retriever.py")
-        except ImportError as e:
-            logger.error(f"❌ Could not import retriever.py: {e}")
-        except Exception as e:
-            logger.warning(f"⚠️ Error loading RAG tool from retriever.py: {e}")
-        
         # Check if we have any tools
         if not tools:
             error_msg = "❌ No tools available! Check tools.py and retriever.py"
@@ -213,18 +201,18 @@ class GAIAAgent:
         for tool in tools:
             logger.info(f"   - {tool.metadata.name}: {tool.metadata.description[:50]}...")
         
-        # Step 3: Create the agent workflow
+        # Step 3: Create the agent using AgentWorkflow (course approach)
         try:
-            from llama_index.core.agent.workflow import AgentWorkflow
+            from llama_index.core.agent.workflow import AgentWorkflow, ToolCallResult, AgentStream
             
-            # Create the agent with a GAIA-optimized system prompt
+            # Create the agent with a GAIA-optimized system prompt (exactly like course)
             self.agent = AgentWorkflow.from_tools_or_functions(
                 tools_or_functions=tools,
                 llm=self.llm,
                 system_prompt=self._create_system_prompt()
             )
             
-            logger.info("✅ Agent workflow created successfully")
+            logger.info("✅ AgentWorkflow created successfully")
             
         except ImportError as e:
             error_msg = f"❌ Could not import AgentWorkflow: {e}"
@@ -285,14 +273,13 @@ class GAIAAgent:
     
     def __call__(self, question: str) -> str:
         """
-        Process a GAIA question and return an answer.
+        Process a GAIA question and return an answer using course approach.
         
-        This is the main method that the evaluation system calls.
-        It handles the entire question-answering pipeline:
-        1. Logs the incoming question
-        2. Runs the agent workflow asynchronously  
-        3. Extracts and cleans the response
-        4. Returns a properly formatted answer
+        This follows the exact pattern from the course notebook:
+        1. Run the agent to get a handler
+        2. Stream events asynchronously  
+        3. Extract the final response
+        4. Clean and return the answer
         
         Args:
             question (str): The GAIA question to answer
@@ -303,16 +290,35 @@ class GAIAAgent:
         logger.info(f"📝 Processing GAIA question: {question[:100]}...")
         
         try:
-            # Run the agent asynchronously
-            # GAIA questions can be complex and may require multiple tool calls
+            # Import event types for processing
+            from llama_index.core.agent.workflow import ToolCallResult, AgentStream
+            
+            # Run the agent asynchronously following course pattern
             loop = asyncio.new_event_loop()
             asyncio.set_event_loop(loop)
             
             try:
-                # Execute the agent workflow
-                result = loop.run_until_complete(
-                    self.agent.run(user_msg=question)
-                )
+                async def run_agent():
+                    # Start the agent run (course pattern)
+                    handler = self.agent.run(user_msg=question)
+                    
+                    # Stream events and collect reasoning (optional for debugging)
+                    reasoning_steps = []
+                    async for ev in handler.stream_events():
+                        if isinstance(ev, ToolCallResult):
+                            step = f"Tool: {ev.tool_name}({ev.tool_kwargs}) => {ev.tool_output}"
+                            reasoning_steps.append(step)
+                            logger.info(f"🔧 {step}")
+                        elif isinstance(ev, AgentStream):
+                            # This is the agent's thought process
+                            pass  # We could log this for debugging
+                    
+                    # Get the final response
+                    resp = await handler
+                    return resp
+                
+                # Execute the agent
+                result = loop.run_until_complete(run_agent())
                 
                 # Extract the response from the result object
                 answer = self._extract_response(result)
@@ -335,10 +341,10 @@ class GAIAAgent:
     
     def _extract_response(self, result: Any) -> str:
         """
-        Extract the text response from the agent workflow result.
+        Extract the text response from the AgentWorkflow result.
         
-        Agent workflows can return different types of objects.
-        This method handles various result formats robustly.
+        Based on the course notebook, AgentWorkflow returns an AgentOutput with this structure:
+        AgentOutput(response=ChatMessage(...), tool_calls=[], raw={...})
         
         Args:
             result: The result object from the agent workflow
@@ -346,18 +352,35 @@ class GAIAAgent:
         Returns:
             str: Extracted response text
         """
-        # Try different ways to extract the response
-        if hasattr(result, 'response'):
-            return str(result.response)
-        elif hasattr(result, 'content'):
-            return str(result.content)
-        elif hasattr(result, 'message'):
-            if hasattr(result.message, 'content'):
-                return str(result.message.content)
+        try:
+            # Handle AgentOutput format from course (most likely)
+            if hasattr(result, 'response'):
+                chat_message = result.response
+                if hasattr(chat_message, 'blocks'):
+                    # Extract text from TextBlock(s)
+                    for block in chat_message.blocks:
+                        if hasattr(block, 'text'):
+                            return str(block.text)
+                elif hasattr(chat_message, 'content'):
+                    return str(chat_message.content)
+                else:
+                    return str(chat_message)
+            
+            # Fallback to other common formats
+            elif hasattr(result, 'content'):
+                return str(result.content)
+            elif hasattr(result, 'message'):
+                if hasattr(result.message, 'content'):
+                    return str(result.message.content)
+                else:
+                    return str(result.message)
             else:
-                return str(result.message)
-        else:
-            # Fallback: convert whatever we got to string
+                # Final fallback: convert whatever we got to string
+                return str(result)
+                
+        except Exception as e:
+            logger.warning(f"⚠️ Error extracting response: {e}")
+            # If extraction fails, try simple string conversion
             return str(result)
     
     def _clean_answer(self, answer: str) -> str:

requirements.txt

@@ -45,13 +45,10 @@ llama-index-llms-huggingface-api
 # Requires HF_TOKEN in your Space secrets
 
 # ============================================================================
-# AGENT WORKFLOW SYSTEM
+# AGENT SYSTEM - Course Approach
 # ============================================================================
-# This enables the agent functionality from the course
-
-llama-index-agent-workflow
-# Agent workflow system - allows creating agents that can use multiple tools
-# This is what orchestrates the web search, calculator, and RAG tools
+# AgentWorkflow is part of llama-index-core, no separate package needed
+# This matches exactly what the course notebook uses
 
 # ============================================================================
 # RETRIEVAL SYSTEMS (RAG) - Enhanced with Vector Embeddings
@@ -104,54 +101,4 @@ python-dotenv
 nest-asyncio
 # Allows running async code in environments that already have an event loop
 # Required for running LlamaIndex query engines in Jupyter/Gradio
-# Fixes "RuntimeError: This event loop is already running" errors
-
-# ============================================================================
-# OPTIONAL: ADDITIONAL USEFUL PACKAGES
-# ============================================================================
-# These might be helpful for specific GAIA questions but aren't required
-
-# numpy
-# For numerical computations if needed for advanced math questions
-
-# matplotlib
-# For creating charts/graphs if GAIA questions require visualizations
-
-# beautifulsoup4
-# For parsing HTML if web search results need detailed extraction
-
-# ============================================================================
-# DEVELOPMENT AND DEBUGGING (Optional)
-# ============================================================================
-# Uncomment these if you want enhanced debugging capabilities
-
-# jupyter
-# For interactive development and testing
-
-# ipywidgets
-# For enhanced Jupyter notebook widgets
-
-# rich
-# For beautiful terminal output and better logging
-
-# ============================================================================
-# INSTALLATION NOTES
-# ============================================================================
-# 
-# To install all dependencies:
-#   pip install -r requirements.txt
-#
-# For HuggingFace Spaces:
-#   - This file should be in your Space root directory
-#   - Dependencies are automatically installed when you deploy
-#   - No manual installation needed
-#
-# API Keys Setup:
-#   - Go to your Space Settings → Repository secrets
-#   - Add OPENAI_API_KEY (for OpenAI) or HF_TOKEN (for HuggingFace)
-#   - At least one is required for the agent to work
-#
-# Troubleshooting:
-#   - If imports fail, check that all packages installed correctly
-#   - Some packages may require specific versions for compatibility
-#   - Check the Space logs for detailed error messages
+# Fixes "RuntimeError: This event loop is already running" errors

tools.py

@@ -653,4 +653,5 @@ if __name__ == "__main__":
     print("\n=== Tools Testing Complete ===")
     print("\nTo use these tools in your agent:")
     print("from tools import get_all_tools")
-    print("tools = get_all_tools()")
+    print("tools = get_all_tools()")
+