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LLM API with FastAPI

This repository connects the LLM API to Slack. It currently supports implementations using OpenAI's ChatGPT and Google's Gemini model. The basic structure is straightforward. When a message arrives through Slack, we generate a response using the LLM's API. It has multimodal capabilities, enabling us to process and analyze images.

All settings are set via environment variables. See here.

envrionment	description	default
slack_token	A Slack token that begins with XOXB	required
openai_token	An OpenAI token that begins with sk	required
number_of_messages_to_keep	Set how many conversation histories to keep	5
max_token	The maximum number of tokens	2048
system_content	Enter the system content for ChatGPT	N/A
gpt_model	GPT Model	gpt-3.5-turbo
gemini_model	Gemini Model	gemini-1.5-pro-001
claude_model	Claude Model	claude-3-5-sonnet@20240620

Supported LLMs

• OpenAI GPT
• Google Gemini
• Anthropic Claude

Prerequisite

• Docker

Before running the application, make sure that Docker is installed and running on your system.

important: Set and use all the environment variables in app/config/constants.py.

Local Execution Guide

1. First, to run this application in your local environment, please execute the following command to install the required libraries.

pip install -r requirements.txt

2. Once the necessary libraries have been installed, execute the following command to run the application.

uvicorn app.main:app --reload

This command will run the application based on the app object in the main module of the app package. You can use the --reload option to automatically reload the application when file changes are detected.

Installation

1. Clone the repository:

https://github.com/jybaek/llm-with-slack.git
cd llm-with-slack

2. Build the Docker image:

docker build -t llm-api .

3. Run the Docker container:

docker run --rm -it -p8000:8000 llm-api

4. Open your web browser and go to http://localhost:8000/docs to access the Swagger UI and test the API.

Sample

Gemini	GPT

API Documentation

The API documentation can be found at http://localhost:8000/docs once the Docker container is running.

License

This project is licensed under the terms of the Apache license. See LICENSE for more information.