LLM Slack Chat Bot

Functions

• Agentic work flow by Langgraph
• RAG for the personal data
• Short-term Context Retention
• Multi modal (image, text only for now)
• use OpenAI model
• permission for each user
• custom prompt for each user
• fetch youtube transcription by Tor proxy

Usage

1. ask the bot on DM or by tagging the bot
2. whenever LLM model create next token, it's updated on the answer message in realtime
3. if you ask again on the thread of the slack message, chatbot keep the short-term context
4. attach image if you want to ask about the image

Requirements

• python3.11
• install requirements.txt
• create slack app and add proper permissions
• set .env file by referring to .env_sample

Slack configuration

slack-config

Run

python chatbot.py

Deploy to server

configure deploy/config.sh and run the below this requires docker and docker-compose on the server. tested on ec2 server

cd deploy
sh deploy_server.sh

Plan

• local chatbot for device automation
• personal AI assistant
• company chatbot (not updated here)
• RAG for source code. let user can decide scope like cursor IDE

Reference

• Langchain-kr
• amazon-bedrock-aistylist-lab
• Slack Bolt for Python
• Langchain