Integrating Slack Bot with an LLM-based Chatbot using Gemini Model and ngrok

This project demonstrates how to integrate a Slack bot with a Large Language Model (LLM)-based chatbot using Google's Gemini model. The integration allows for intelligent and context-aware communication directly within Slack.

Project Overview

The Slack bot is built using Python and Flask and is integrated with an LLM-powered chatbot utilizing the Gemini model. This setup enables the bot to process user inputs in Slack channels and respond intelligently based on the latest data engineering information.

Key Components

1. Slack Bot Setup

Libraries Used: slack_sdk, slack_bolt Functionality: The bot listens for app_mention events triggered when someone mentions the bot in a Slack channel. Configuration: Tokens and secrets are securely loaded from a .env file using environment variables

2. Flask Integration

 Flask App: The Flask app handles incoming HTTP requests from Slack.
 Event Processing: Slack events are processed through Flask using SlackRequestHandler from the slack_bolt.adapter.flask module.

3. Gemini Model Integration

 Gemini Model: The Gemini model, provided by Google's Generative AI platform, processes and responds to queries.
 Python Package: The google.generativeai package is used to interact with the Gemini model, allowing for prompt-based queries.
 Configuration: Tokens are securely loaded from a .env file using environment variables for geminiai api.

4. ngrok Setup

ngrok Usage: ngrok provides a public URL that forwards requests to the local Flask application, enabling Slack to communicate with the bot. Setup Command: ngrok http 6060 is used to expose the Flask app to the internet.

Result

The integration results in a Slack bot capable of performing advanced data analysis and responding intelligently to user queries within Slack.