Try to get web search working

back/bots/models/chat.py

@@ -2,10 +2,9 @@
 from django.db import models
 import uuid
 from langchain_aws import ChatBedrock
-from langchain_core.messages import HumanMessage, SystemMessage, AIMessage
+from langchain_core.messages import HumanMessage, SystemMessage, AIMessage, ToolMessage
 from langchain_core.tools import tool
 from langchain_core.callbacks.base import BaseCallbackHandler
-from langchain.agents import create_agent
 from tavily import TavilyClient
 import logging
 import base64
@@ -116,47 +115,100 @@ def web_search(query: str) -> str:
                     return f"Error during search: {str(e)}"
 
 
-            # Create chat model
+            # Create chat model with tool binding
             chat_model = ChatBedrock(model_id=self.ai.model_id)
             tools = [web_search]
 
-            # Create modern agent with tool calling support
-            # This is the recommended approach per LangChain docs
-            agent = create_agent(
-                model=chat_model,
-                tools=tools,
-                system_prompt=self.get_system_message(),
-                debug=settings.DEBUG
-            )
+            # Bind tools to the model for proper tool calling
+            model_with_tools = chat_model.bind_tools(tools)
 
             # Extract text input from message_list for agent
             agent_input = self._extract_agent_input(message_list)
 
             logger.info(f"Invoking agent with input: {agent_input[:100]}...")
             logger.info("🤖 AGENT_INVOKE_START: web_search tool available")
 
-            # Invoke agent - the CompiledStateGraph handles tool loop internally
-            response = agent.invoke({"messages": [HumanMessage(content=agent_input)]})
+            # Build and run the agent loop manually
+            messages = [SystemMessage(content=self.get_system_message()), HumanMessage(content=agent_input)]
+
+            # Agentagent loop - keep invoking until no more tool calls
+            max_iterations = 5
+            iteration = 0
+
+            while iteration < max_iterations:
+                iteration += 1
+                logger.info(f"🤖 AGENT_LOOP_ITERATION: {iteration}")
+
+                # Call the model
+                response = model_with_tools.invoke(messages)
+                messages.append(response)
+
+                # Check if model wants to call a tool
+                if not response.tool_calls:
+                    logger.info(f"🤖 AGENT_LOOP_COMPLETE: no more tool calls after {iteration} iterations")
+                    break
+
+                # Process tool calls
+                for tool_call in response.tool_calls:
+                    tool_name = tool_call["name"]
+                    tool_args = tool_call["args"]
+                    logger.info(f"🔍 AGENT_TOOL_CALL: {tool_name} with args: {tool_args}")
+
+                    # Execute the tool
+                    if tool_name == "web_search":
+                        tool_result = web_search.invoke(tool_args)
+                    else:
+                        tool_result = f"Unknown tool: {tool_name}"
+
+                    logger.info(f"🔍 AGENT_TOOL_RESULT: {tool_result[:100]}")
+
+                    # Add tool result to messages
+                    messages.append(ToolMessage(
+                        content=tool_result,
+                        tool_call_id=tool_call["id"],
+                        name=tool_name
+                    ))
 
-            logger.info("🤖 AGENT_INVOKE_COMPLETE: got response")
+            logger.info("🤖 AGENT_LOOP_COMPLETE: extracting final response")
 
-            # Extract response text from the agent result
-            # The response is a dict with 'messages' key containing final messages
+            # Extract response text from the final message
+            # The messages list now contains: System, HumanMessage, AIMessage (with tool call), ToolMessage, AIMessage (final response)
             response_text = ""
             usage_metadata = {"input_tokens": 0, "output_tokens": 0}
 
-            if isinstance(response, dict) and "messages" in response:
-                for msg in reversed(response["messages"]):
-                    if isinstance(msg, AIMessage):
+            # Find the last AIMessage (should be the final response)
+            for msg in reversed(messages):
+                if isinstance(msg, AIMessage) and not msg.tool_calls:
+                    # This is the final response (no tool calls)
+                    if isinstance(msg.content, str):
                         response_text = msg.content
-                        # Extract token usage from the message metadata
-                        if hasattr(msg, 'usage_metadata') and msg.usage_metadata:
-                            usage_metadata = msg.usage_metadata
+                    elif isinstance(msg.content, list):
+                        # Extract text from content list
+                        text_parts = []
+                        for item in msg.content:
+                            if isinstance(item, dict) and item.get('type') == 'text':
+                                text_parts.append(item.get('text', ''))
+                            elif isinstance(item, str):
+                                text_parts.append(item)
+                        response_text = "".join(text_parts).strip()
+
+                    # Extract token usage
+                    if hasattr(msg, 'usage_metadata') and msg.usage_metadata:
+                        usage_metadata = msg.usage_metadata
+
+                    logger.info(f"🤖 FINAL_RESPONSE: {len(response_text)} chars")
+                    break
+
+            if not response_text:
+                # Fallback: get the last message
+                for msg in reversed(messages):
+                    if isinstance(msg, AIMessage):
+                        if isinstance(msg.content, str):
+                            response_text = msg.content
+                        elif isinstance(msg.content, list):
+                            text_parts = [item.get('text', '') for item in msg.content if isinstance(item, dict) and item.get('type') == 'text']
+                            response_text = "".join(text_parts).strip()
                         break
-            elif isinstance(response, dict) and "output" in response:
-                response_text = response["output"]
-            else:
-                response_text = str(response)
 
             message_order = self.messages.count()
 

back/test_integration.py

@@ -6,16 +6,17 @@
 import os
 import sys
 import django
-import requests
-from django.contrib.auth.models import User
-from rest_framework_simplejwt.tokens import RefreshToken
-from bots.models import Bot, AiModel
 
 os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'server.settings')
 sys.path.insert(0, '/home/ubuntu/repos/bots/back')
 
 django.setup()
 
+import requests
+from django.contrib.auth.models import User
+from rest_framework_simplejwt.tokens import RefreshToken
+from bots.models import Bot, AiModel
+
 
 def run_integration_test():
     print("=" * 60)