https://github.com/itz-me-nvs/streamlit_doc_rag
Streamlit-powered Document Retrieval QA (RAG) Web App: A dynamic application that answers user queries based solely on the uploaded documents, ensuring accurate, context-driven responses tailored to your data
https://github.com/itz-me-nvs/streamlit_doc_rag
groq huggingface rag streamlit
Last synced: 2 months ago
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Streamlit-powered Document Retrieval QA (RAG) Web App: A dynamic application that answers user queries based solely on the uploaded documents, ensuring accurate, context-driven responses tailored to your data
- Host: GitHub
- URL: https://github.com/itz-me-nvs/streamlit_doc_rag
- Owner: itz-me-nvs
- Created: 2025-01-15T08:00:13.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-16T20:05:55.000Z (over 1 year ago)
- Last Synced: 2025-07-29T01:43:25.530Z (11 months ago)
- Topics: groq, huggingface, rag, streamlit
- Language: Python
- Homepage:
- Size: 10.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Streamlit Document Retrieval QA (RAG) Web App
A powerful and interactive web application that answers user queries based solely on the uploaded documents. This app uses cutting-edge NLP techniques to retrieve and process information, ensuring accurate, context-driven responses tailored to your data. Built with Streamlit, LangChain, and HuggingFace, it allows seamless integration of your documents for a custom question-answering experience.
# Live Playground
Feel free to explore my Streamlit web application built for retrieval-augmented generation (RAG): [click here](https://workmate-doc-rag.streamlit.app/)
## Features
- Upload PDF documents for analysis.
- Ask questions based on the content of the uploaded documents.
- Accurate, concise, and context-aware responses.
- Responses are restricted to the context of the provided documents.
- User-friendly interface built with Streamlit.
---
## Prerequisites
Ensure you have the following installed on your system:
- Python 3.8 or higher
- pip (Python package manager)
- Virtual environment (optional but recommended)
---
## Installation
1. **Clone the Repository**
```bash
git clone https://github.com/itz-me-nvs/streamlit_doc_rag.git
cd streamlit_doc_rag