n8n + Python + LangChain AI Agent

n8n-langchain-agent/n8n-langchain-agent.py

@@ -0,0 +1,119 @@
+from langchain_core.messages import SystemMessage, AIMessage, ToolMessage, HumanMessage
+from langchain_anthropic import ChatAnthropic
+from langchain_openai import ChatOpenAI
+from dotenv import load_dotenv
+from datetime import datetime
+import streamlit as st
+import asyncio
+import json
+import os
+
+from tools import available_functions
+
+load_dotenv()
+model = os.getenv('LLM_MODEL', 'gpt-4o')
+
+system_message = f"""
+You are a personal assistant who helps with research, managing Google Drive, and managing Slack. 
+You never give IDs to the user since those are just for you to keep track of.
+The link to any Google Doc is: https://docs.google.com/document/d/[document ID]
+The current date is: {datetime.now().date()}
+"""
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# ~~~~~~~~~~~~~~~~~~~~~~ AI Prompting Function ~~~~~~~~~~~~~~~~~~~~~~~~~
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+def get_chunk_text(chunk):
+    response_content = ""
+    chunk_content = chunk.content
+    if isinstance(chunk_content, str):
+        response_content += chunk_content
+    elif isinstance(chunk_content, list):
+        for chunk_text in chunk_content:
+            if "text" in chunk_text:
+                response_content += chunk_text["text"]
+
+    return response_content  
+
+def prompt_ai(messages):
+    # First, prompt the AI with the latest user message
+    tools = [tool for _, tool in available_functions.items()]
+    n8n_chatbot = ChatOpenAI(model=model) if "gpt" in model.lower() else ChatAnthropic(model=model)
+    n8n_chatbot_with_tools = n8n_chatbot.bind_tools(tools)
+
+    stream = n8n_chatbot_with_tools.stream(messages)
+    first = True
+    for chunk in stream:
+        if first:
+            gathered = chunk
+            first = False
+        else:
+            gathered = gathered + chunk
+
+        yield get_chunk_text(chunk)
+
+    has_tool_calls = len(gathered.tool_calls) > 0
+
+    # Second, see if the AI decided it needs to invoke a tool
+    if has_tool_calls:
+        # Add the tool request to the list of messages so the AI knows later it invoked the tool
+        messages.append(gathered)
+
+        # If the AI decided to invoke a tool, invoke it
+        # For each tool the AI wanted to call, call it and add the tool result to the list of messages
+        for tool_call in gathered.tool_calls:
+            tool_name = tool_call["name"].lower()
+            selected_tool = available_functions[tool_name]
+            print(f"\nInvoking tool: {tool_call['name']} with args {tool_call['args']}")
+            tool_output = selected_tool.invoke(tool_call["args"])
+            print(f"Result of invoking tool: {tool_output}\n")
+            messages.append(ToolMessage(tool_output, tool_call_id=tool_call["id"]))                
+
+        # Call the AI again so it can produce a response with the result of calling the tool(s)
+        additional_stream = prompt_ai(messages)
+        for additional_chunk in additional_stream:
+            yield additional_chunk
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# ~~~~~~~~~~~~~~~~~~ Main Function with UI Creation ~~~~~~~~~~~~~~~~~~~~
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+async def main():
+    st.title("n8n LangChain Agent")
+
+    # Initialize chat history
+    if "messages" not in st.session_state:
+        st.session_state.messages = [
+            SystemMessage(content=system_message)
+        ]    
+
+    # Display chat messages from history on app rerun
+    for message in st.session_state.messages:
+        message_json = json.loads(message.json())
+        message_type = message_json["type"]
+        if message_type in ["human", "ai", "system"]:
+            with st.chat_message(message_type):
+                st.markdown(message_json["content"])        
+
+    # React to user input
+    if prompt := st.chat_input("What would you like to do today?"):
+        # Display user message in chat message container
+        st.chat_message("user").markdown(prompt)
+        # Add user message to chat history
+        st.session_state.messages.append(HumanMessage(content=prompt))
+
+        # Display assistant response in chat message container
+        with st.chat_message("assistant"):
+            stream = prompt_ai(st.session_state.messages)
+            response = st.write_stream(stream)
+        
+            st.session_state.messages.append(AIMessage(content=response))
+
+
+if __name__ == "__main__":
+    asyncio.run(main())