metadata

title: Base Rag q and A
emoji: 🌍
colorFrom: red
colorTo: purple
sdk: streamlit
sdk_version: 1.28.2
app_file: app.py
pinned: false
license: apache-2.0

LLM Evluation using Ragas and Langchain

Ragas is a framework that helps you evaluate an enterprise Retrieval Augmented Generation (RAG) pipelines. Ragas is very easy to use and evaluate the RAG since there is no additional data required. The Context used in the RAG pipeline and Question and Answers are used for evaluating the RAG.

Ragas can provide below metrics https://docs.ragas.io/en/latest/concepts/metrics/index.html

Faithfulness
Answer relevancy
Context recall
Context precision
Context relevancy
Context entity recall

We will use LangChain framework to implement the RAG and functions/chains provided within LangChain

Purpose

Evaluation or RAG approach using LangChain and OpenAI

Features

Usage

add your PDF files in the data folder
update the path in the vector_loader.py and run the file using python vector_loader.py
update the index name for the DB
this will generate local FAISS vector db files
update the index files in app.py
run the streamlit app using streamlit run app.py

Sample Output

Future Enhancements

Contributing

Contributions are welcome! If you have any ideas, suggestions, or bug fixes, please submit a pull request or open an issue in the GitHub repository.

License

This project is licensed under the MIT License.