mirror of
https://github.com/coleam00/ai-agents-masterclass.git
synced 2025-11-29 00:23:14 +00:00
119 lines
4.7 KiB
Python
119 lines
4.7 KiB
Python
from langchain_core.messages import SystemMessage, AIMessage, ToolMessage, HumanMessage
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from langchain_anthropic import ChatAnthropic
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from langchain_openai import ChatOpenAI
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from dotenv import load_dotenv
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from datetime import datetime
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import streamlit as st
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import asyncio
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import json
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import os
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from tools import available_functions
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load_dotenv()
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model = os.getenv('LLM_MODEL', 'gpt-4o')
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system_message = f"""
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You are a personal assistant who helps with research, managing Google Drive, and managing Slack.
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You never give IDs to the user since those are just for you to keep track of.
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The link to any Google Doc is: https://docs.google.com/document/d/[document ID]
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The current date is: {datetime.now().date()}
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"""
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# ~~~~~~~~~~~~~~~~~~~~~~ AI Prompting Function ~~~~~~~~~~~~~~~~~~~~~~~~~
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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def get_chunk_text(chunk):
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response_content = ""
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chunk_content = chunk.content
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if isinstance(chunk_content, str):
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response_content += chunk_content
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elif isinstance(chunk_content, list):
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for chunk_text in chunk_content:
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if "text" in chunk_text:
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response_content += chunk_text["text"]
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return response_content
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def prompt_ai(messages):
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# First, prompt the AI with the latest user message
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tools = [tool for _, tool in available_functions.items()]
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n8n_chatbot = ChatOpenAI(model=model) if "gpt" in model.lower() else ChatAnthropic(model=model)
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n8n_chatbot_with_tools = n8n_chatbot.bind_tools(tools)
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stream = n8n_chatbot_with_tools.stream(messages)
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first = True
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for chunk in stream:
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if first:
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gathered = chunk
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first = False
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else:
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gathered = gathered + chunk
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yield get_chunk_text(chunk)
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has_tool_calls = len(gathered.tool_calls) > 0
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# Second, see if the AI decided it needs to invoke a tool
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if has_tool_calls:
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# Add the tool request to the list of messages so the AI knows later it invoked the tool
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messages.append(gathered)
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# If the AI decided to invoke a tool, invoke it
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# For each tool the AI wanted to call, call it and add the tool result to the list of messages
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for tool_call in gathered.tool_calls:
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tool_name = tool_call["name"].lower()
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selected_tool = available_functions[tool_name]
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print(f"\nInvoking tool: {tool_call['name']} with args {tool_call['args']}")
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tool_output = selected_tool.invoke(tool_call["args"])
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print(f"Result of invoking tool: {tool_output}\n")
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messages.append(ToolMessage(tool_output, tool_call_id=tool_call["id"]))
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# Call the AI again so it can produce a response with the result of calling the tool(s)
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additional_stream = prompt_ai(messages)
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for additional_chunk in additional_stream:
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yield additional_chunk
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# ~~~~~~~~~~~~~~~~~~ Main Function with UI Creation ~~~~~~~~~~~~~~~~~~~~
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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async def main():
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st.title("n8n LangChain Agent")
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# Initialize chat history
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if "messages" not in st.session_state:
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st.session_state.messages = [
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SystemMessage(content=system_message)
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]
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# Display chat messages from history on app rerun
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for message in st.session_state.messages:
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message_json = json.loads(message.json())
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message_type = message_json["type"]
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if message_type in ["human", "ai", "system"]:
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with st.chat_message(message_type):
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st.markdown(message_json["content"])
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# React to user input
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if prompt := st.chat_input("What would you like to do today?"):
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# Display user message in chat message container
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st.chat_message("user").markdown(prompt)
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# Add user message to chat history
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st.session_state.messages.append(HumanMessage(content=prompt))
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# Display assistant response in chat message container
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with st.chat_message("assistant"):
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stream = prompt_ai(st.session_state.messages)
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response = st.write_stream(stream)
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st.session_state.messages.append(AIMessage(content=response))
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if __name__ == "__main__":
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asyncio.run(main()) |