https://github.com/davramenko89/streamlit-rag
Simple Streamlit RAG App to chat with your data (PDF, DOC, XLS, TXT)
https://github.com/davramenko89/streamlit-rag
langchain python rag streamlit
Last synced: about 1 year ago
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Simple Streamlit RAG App to chat with your data (PDF, DOC, XLS, TXT)
- Host: GitHub
- URL: https://github.com/davramenko89/streamlit-rag
- Owner: davramenko89
- Created: 2025-03-31T20:03:19.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-03T16:45:55.000Z (about 1 year ago)
- Last Synced: 2025-04-15T01:18:23.682Z (about 1 year ago)
- Topics: langchain, python, rag, streamlit
- Language: Python
- Homepage: https://simple-rag-langchain-openai.streamlit.app/
- Size: 4.09 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 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
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.