Scikit RAG + OpenAI

This sample demonstrates how to deploy a Flask-based Retrieval-Augmented Generation (RAG) chatbot using OpenAI's GPT model. The chatbot retrieves relevant documents from a knowledge base using scikit-learn and Sentence Transformers and then generates responses using OpenAI's GPT model.

Prerequisites

1. Download Defang CLI
2. (Optional) If you are using Defang BYOC authenticated with your AWS account
3. (Optional - for local development) Docker CLI

Deploying

1. Open the terminal and type defang login
2. Type defang compose up in the CLI.
3. Your app will be running within a few minutes.

Local Development

1. Clone the repository.
2. Create a .env file in the root directory and set your OpenAI API key or add the OPENAI_API_KEY into your .zshrc or .bashrc file:
3. Run the command docker compose -f compose.dev.yaml up --build to spin up a docker container for this RAG chatbot

Configuration

• The knowledge base is the all the markdown files in the defang docs website. The logic for parsing can be found in './app/get_knowledge_base.py'.
• The file get_knowledge_base.py parses every webpage as specified into paragraphs and writes to knowledge_base.json for the RAG retrieval.
• To obtain your own knowledge base, please feel free to implement your own parsing scheme.
• for local development, please use the compose.dev.yaml file where as for production, please use the compose.yaml.

Title: Scikit RAG + OpenAI

Description: An application demonstrating a GPT-4-based chatbot enhanced with a Retrieval-Augmented Generation (RAG) framework, leveraging scikit-learn for efficient contextual embeddings and dynamic knowledge retrieval.

Tags: Flask, Scikit, Python, RAG, OpenAI, GPT, Machine Learning

Languages: python