QnA-With-RAG

📄 Project Description:

A full-stack application that enables you to turn any text document or piece of content into context that any LLM can use as references during chatting. This application allows you to add and delete documents and can create multiple knowledge bases.

🎯 Purpose and Achievement:

This project is more than just a chatbot. I want to learn how to code a production-ready chatbot using the OpenAI API. Through my research, I found my way to accomplish this goal. Additionally, I've been learning about VectorDB and Langsmith, which I have extensively incorporated into this project.

⚙️🚀 Technologies and Frameworks Utilized

💡Features

• Chat with text files
• Chat streaming
• Upload multiple Text file
• Maintainable vector DB (add, delete files)
• Static API Token Authentication for ChromaDB
• Create more than one knowledge base

🛠️ App flow

🎬 Demo

demo-qna-with-rag-1.mp4

🔜 Future Development and Features

For future development, I would like to use React.js for the frontend and enhance the functionality of ChromDB. This includes features such as filtering data, deleting files through search, and most importantly, adding memory capabilities for the chatbot.

▶️ Run

Clone the repository

git clone git@github.com:Ja-yy/QnA-With-RAG.git

Set up this environment variable in .env file

OPENAI_API_KEY='<open_ai_key>'
EMBEDDING_MODEL='text-embedding-ada-002'
CHAT_MODEL='gpt-3.5-turbo'
TEMPERATURE=0
MAX_RETRIES=2
REQUEST_TIMEOUT=15

CHROMADB_HOST="chromadb"
CHROMADB_PORT="8000"

CHROMA_SERVER_AUTH_CREDENTIALS="<test-token>"
CHROMA_SERVER_AUTH_CREDENTIALS_PROVIDER="chromadb.auth.token.TokenConfigServerAuthCredentialsProvider"
CHROMA_SERVER_AUTH_PROVIDER="chromadb.auth.token.TokenAuthServerProvider"
CHROMA_SERVER_AUTH_TOKEN_TRANSPORT_HEADER="AUTHORIZATION"

Build and run docker image

docker-compose  up -d --build

Now, go to localhost:8501

Enjoy the app :)