RAG Application using Streamlit and Langchain

This project is a Retrieval-Augmented Generation (RAG) application built using Streamlit and Langchain. The application allows users to retrieve information from various document formats including PDF, DOC, XLS, and TXT files.

Project Structure

my-rag-app
├── app.py                # Main entry point of the Streamlit application
├── data                  # Directory containing sample data files
│   ├── sample.pdf        # Sample PDF file for testing
│   ├── sample.doc        # Sample DOC file for testing
│   ├── sample.xls        # Sample XLS file for testing
│   └── sample.txt        # Sample TXT file for testing
├── requirements.txt      # List of dependencies for the project
├── utils                 # Directory containing utility functions
│   └── retriever.py      # Functions for retrieving content from files
└── README.md             # Documentation for the project

Setup Instructions

1. Clone the repository:

   git clone <repository-url>
   cd my-rag-app
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

Usage Guidelines

1. Run the Streamlit application:

   streamlit run app.py
   

2. Open your web browser and navigate to http://localhost:8501 to access the application.

Application Functionality

• The application allows users to upload documents in PDF, DOC, XLS, and TXT formats.
• It utilizes Langchain for processing and retrieving relevant information from the uploaded documents.
• Users can input queries and receive generated responses based on the content of the documents.

Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue for any enhancements or bug fixes.