Add Support for LLM Agentic Model in VM Library

notebooks/agents/langchain_utils.py

@@ -0,0 +1,92 @@
+from typing import Dict, List, Any
+from langchain_core.messages import ToolMessage, AIMessage
+
+
+def capture_tool_output_messages(agent_result: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Capture all tool outputs and metadata from agent results.
+
+    Args:
+        agent_result: The result from the LangChain agent execution
+
+    Returns:
+        Dictionary containing tool outputs and metadata
+    """
+    messages = agent_result.get('messages', [])
+    tool_outputs = []
+
+    for message in messages:
+        if isinstance(message, ToolMessage):
+            tool_outputs.append({
+                'tool_name': 'unknown',  # ToolMessage doesn't directly contain tool name
+                'content': message.content,
+                'tool_call_id': getattr(message, 'tool_call_id', None)
+            })
+
+    return {
+        'tool_outputs': tool_outputs,
+        'total_messages': len(messages),
+        'tool_message_count': len(tool_outputs)
+    }
+
+
+def extract_tool_results_only(agent_result: Dict[str, Any]) -> List[str]:
+    """
+    Extract just the tool results in a simple format.
+
+    Args:
+        agent_result: The result from the LangChain agent execution
+
+    Returns:
+        List of tool result strings
+    """
+    messages = agent_result.get('messages', [])
+    tool_results = []
+
+    for message in messages:
+        if isinstance(message, ToolMessage):
+            tool_results.append(message.content)
+
+    return tool_results
+
+
+def get_final_agent_response(agent_result: Dict[str, Any]) -> str:
+    """
+    Get the final agent response from the conversation.
+
+    Args:
+        agent_result: The result from the LangChain agent execution
+
+    Returns:
+        The final response content as a string
+    """
+    messages = agent_result.get('messages', [])
+
+    # Look for the last AI message
+    for message in reversed(messages):
+        if isinstance(message, AIMessage):
+            return message.content
+
+    return "No final response found"
+
+
+def format_tool_outputs_for_display(captured_data: Dict[str, Any]) -> str:
+    """
+    Format tool outputs for readable display.
+
+    Args:
+        captured_data: Data from capture_tool_output_messages
+
+    Returns:
+        Formatted string for display
+    """
+    output = "Tool Execution Summary:\n"
+    output += f"Total messages: {captured_data['total_messages']}\n"
+    output += f"Tool messages: {captured_data['tool_message_count']}\n\n"
+
+    for i, tool_output in enumerate(captured_data['tool_outputs'], 1):
+        output += f"Tool {i}: {tool_output['tool_name']}\n"
+        output += f"Output: {tool_output['content']}\n"
+        output += "-" * 30 + "\n"
+
+    return output