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from pydantic import BaseModel, Field
from langchain_core.messages import SystemMessage, HumanMessage, ToolMessage, filter_messages
from src.utils.prompts import execution_agent_prompt, compress_execution_system_prompt, compress_execution_human_message
from src.utils.utils import tools, tools_by_name # Import the new MongoDB tools
import logging
class ExecutorNode:
"""
Executor node for handling tasks:
1. LLM reasoning
2. Tool invocation
3. Final compression
"""
def __init__(self, llm):
self.llm = llm
self.tools = tools # Use the MongoDB tools
self.tools_by_name = tools_by_name # Use the MongoDB tools by name
self.model_with_tools = llm.bind_tools(tools) # Bind the MongoDB tools
self.MAX_ITERATIONS = 3
self.execution_agent_prompt = execution_agent_prompt
self.compress_execution_system_prompt = compress_execution_system_prompt
self.compress_execution_human_message = compress_execution_human_message
# Debug tool binding
print(f"Available MongoDB tools: {list(self.tools_by_name.keys())}")
def llm_call(self, state: dict) -> dict:
"""Calls the LLM with the executor message history and returns updated state."""
try:
# Ensure we have the execution job in the messages
execution_job = state.get("execution_job", "")
existing_messages = state.get("executor_messages", [])
print("EXECUTOR MESSAGES MESSAGES", existing_messages)
print("EXECUTION JOB", execution_job)
# If no existing messages, add the execution job as initial human message
if not existing_messages and execution_job:
existing_messages = [HumanMessage(content=execution_job)]
messages = [SystemMessage(content=self.execution_agent_prompt)] + existing_messages
print(f"Calling LLM with {len(messages)} messages")
print(f"Last message: {messages[-1] if messages else 'No messages'}")
response = self.model_with_tools.invoke(messages)
print(f"LLM Response type: {type(response)}")
print(f"LLM Response content: {response.content[:100] if response.content else 'No content'}...")
print(f"Tool calls in response: {getattr(response, 'tool_calls', 'No tool_calls attribute')}")
return {
**state,
"executor_messages": existing_messages + [response]
}
except Exception as e:
return {
**state,
"error": str(e),
"executor_messages": state.get("executor_messages", [])
}
def tool_node(self, state: dict) -> dict:
"""Executes any tools requested by the LLM and appends ToolMessages."""
try:
executor_messages = state.get("executor_messages", [])
if not executor_messages:
print("No executor messages found")
return state
last_message = executor_messages[-1]
print(f"Last message type: {type(last_message)}")
# Get tool calls
tool_calls = getattr(last_message, "tool_calls", [])
print(f"Found {len(tool_calls)} tool calls: {tool_calls}")
if not tool_calls:
print("No tool calls found in last message")
return state
tool_outputs, new_data = [], []
for call in tool_calls:
print(f"Processing tool call: {call}")
tool_name = call.get("name")
args = call.get("args", {})
tool_id = call.get("id")
print(f"Tool: {tool_name}, Args: {args}, ID: {tool_id}")
if tool_name in self.tools_by_name:
try:
print(f"Invoking MongoDB tool {tool_name} with args {args}")
result = self.tools_by_name[tool_name].invoke(args)
print(f"MongoDB tool {tool_name} result: {result}")
tool_message = ToolMessage(
content=str(result),
name=tool_name,
tool_call_id=tool_id
)
tool_outputs.append(tool_message)
new_data.append(str(result))
except Exception as e:
error_msg = f"MongoDB tool {tool_name} failed: {e}"
print(f"Tool error: {error_msg}")
tool_outputs.append(
ToolMessage(
content=error_msg,
name=tool_name,
tool_call_id=tool_id
)
)
new_data.append(error_msg)
else:
error_msg = f"Tool {tool_name} not found. Available MongoDB tools: {list(self.tools_by_name.keys())}"
print(error_msg)
tool_outputs.append(
ToolMessage(
content=error_msg,
name=tool_name,
tool_call_id=tool_id
)
)
print(f"Returning {len(tool_outputs)} tool outputs")
return {
**state,
"executor_messages": executor_messages + tool_outputs,
"executor_data": state.get("executor_data", []) + new_data
}
except Exception as e:
return {
**state,
"error": f"Tool execution failed: {str(e)}"
}
def compress_execution(self, state: dict) -> dict:
"""Summarizes the execution and returns final structured output."""
try:
execution_job = state.get("execution_job", "Complete the assigned task")
executor_messages = state.get("executor_messages", [])
messages = [
SystemMessage(content=self.compress_execution_system_prompt),
*executor_messages,
HumanMessage(content=self.compress_execution_human_message.format(
shipment_request=execution_job
))
]
response = self.llm.invoke(messages)
executor_data = [
str(m.content) for m in executor_messages
if hasattr(m, 'content') and m.content
]
return {
"output": str(response.content),
"executor_data": executor_data,
"executor_messages": executor_messages
}
except Exception as e:
return {
"output": f"Execution completed with errors: {str(e)}",
"executor_data": state.get("executor_data", []),
"executor_messages": state.get("executor_messages", [])
}
def route_after_llm(self, state: dict) -> str:
"""Route: decide whether to call a tool or finalize."""
try:
executor_messages = state.get("executor_messages", [])
if not executor_messages:
return "compress_execution"
last_msg = executor_messages[-1]
has_tool_calls = bool(getattr(last_msg, "tool_calls", None))
print(f"Routing decision - Has tool calls: {has_tool_calls}")
return "tool_node" if has_tool_calls else "compress_execution"
except Exception as e:
return "compress_execution"
def guard_llm(self, state: dict) -> str:
"""Prevent infinite loops by limiting iterations."""
iteration_count = state.get("iteration_count", 0) + 1
state["iteration_count"] = iteration_count
print(f"Iteration count: {iteration_count}/{self.MAX_ITERATIONS}")
if iteration_count > self.MAX_ITERATIONS:
print("Max iterations reached, finalizing...")
return "compress_execution"
return self.route_after_llm(state) |