This repository contains a collection of small projects designed to explore the application of RAG (Retrieval-Augmented Generation) in real-world scenarios.
The primary objective is to acquire hands-on experience and develop a deeper understanding of how to effectively utilize RAG in tackling practical problems
pip install -r requirements.txt
A simple question and answer application using LangChain framework and Chroma as the vector database.
The documents need to be in PDF format.
- Put your PDF files in the
pdfs
folder. - Execute the script running the command
python chatter.py
. - Make questions about the document's context.
Run the command python chatter.py
to start the application.
Using QA LangChain module.
Works the same way as chatter.py
, but using a different module.
Run the command python chatter-qa.py
to start the application.
This is a example of how to use RAG to answer questions in a SQL database.
There are tow database in the db
folder:
chinook.db
: A SQLite database about music store.movies.db
: A SQLite database about movies store.
Run the command python chatter-sql.py
to start the application.
A simples question and anwser application using LlamaIndex framework.
This application works the same way as chatter.py
, but using a different framework keeping the chat history.
Run the command python chatter-llama-index.py
to start the application.