RAG Playground#


A live version of the application is hosted on Streamlit, try it out yourself using the link below: RAG Playground on Streamlit


The RAG Playground is an application designed to facilitate question-answering tasks based on uploaded PDF documents. It leverages LLamaIndex for RAG functionalities and utilizes Streamlit for the user interface.

Key Features#

  • PDF Upload: Easily upload PDF files to the application.

  • Questioning: Ask questions about the uploaded PDF documents.

  • RAG Integration: Utilize LLamaIndex for RAG capabilities.

  • Embeddings: Convert text to embeddings using the BAAI/bge-small-en-v1.5 model.

  • Reranker: Reorder search results based on relevance to queries.

  • Streamlit Optimization: Enhance performance using @st.experimental_fragment and @st.cache_resource.

Project Workflow#

  1. PDF Processing:

    • Load PDF files and extract text using PDFReader.

    • Load data into Documents in LLamaIndex.

  2. Chunking and Conversion:

    • Chunk text and convert it into nodes using VectorStoreIndex.from_documents.

    • Convert text to embeddings using the BAAI/bge-small-en-v1.5 model.

  3. Search Optimization:

    • Implement a reranker to reorder search results based on query relevance.

    • Display top-ranked results after reranking.

  4. Interface Optimization:

    • Build the user interface using Streamlit.

    • Optimize Streamlit performance with @st.experimental_fragment and @st.cache_resource.

Tech Stack Used#

  • LLamaIndex

  • Streamlit

  • BAAI/bge-small-en-v1.5 model

Repository and Deployment#

Github - https://github.com/abhi2596/UnifyAI_RAG_playground/tree/main Streamlit App - https://unifyai-rag-playground.streamlit.app/

Instructions to run locally:

  1. First create a virtual environment in python

python -m venv <virtual env name>
  1. Activate it and install poetry

source <virtual env name>/Scripts/activate - Windows
source <virtual env name>/bin/activate - Linux/Unix
pip install poetry
  1. Clone the repo

git clone https://github.com/abhi2596/UnifyAI_RAG_playground/tree/main
  1. Run the following commands

poetry install 
cd rag
streamlit run app.py



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Abhijeet Chintakunta