https://github.com/itsaidi/ragify
RAGify is a Retrieval-Augmented Generation (RAG) application designed to enhance the way you interact with PDF documents. Powered by Streamlit, LangChain, ChromaDB, and local LLMs via Ollama, this app allows you to query PDF files intelligently in both English and Arabic.
https://github.com/itsaidi/ragify
langchain ollama python rag readthedocs streamlit
Last synced: 13 days ago
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RAGify is a Retrieval-Augmented Generation (RAG) application designed to enhance the way you interact with PDF documents. Powered by Streamlit, LangChain, ChromaDB, and local LLMs via Ollama, this app allows you to query PDF files intelligently in both English and Arabic.
- Host: GitHub
- URL: https://github.com/itsaidi/ragify
- Owner: ITSAIDI
- Created: 2024-11-30T18:01:06.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-30T23:46:35.000Z (over 1 year ago)
- Last Synced: 2025-02-28T01:24:23.164Z (over 1 year ago)
- Topics: langchain, ollama, python, rag, readthedocs, streamlit
- Language: Jupyter Notebook
- Homepage:
- Size: 8.3 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# RAGify
**RAGify** is a Retrieval-Augmented Generation (RAG) application designed to enhance the way you interact with PDF documents. Powered by **Streamlit**, **LangChain**, **ChromaDB**, and local **LLMs via Ollama**, this app allows you to query PDF files intelligently in both **English/French** and **Arabic**.
---
## 🚀 Key Features
- **PDF Querying**: Upload PDFs and ask questions to extract insights quickly and accurately.
- **Multilingual Support**: Seamless handling of both **English** and **Arabic** text for querying and responses.
- **Local LLMs**: Ensures privacy by using local language models via **Ollama**—no external API required.
- **Efficient Retrieval**: Employs **ChromaDB** for fast and accurate document embeddings and retrieval.
- **Streamlit UI**: User-friendly interface for easy document interaction.
---
## 📷 Screenshots
---
## 🛠️ Installation
### Clone the Repository
```bash
git clone https://github.com/ITSAIDI/RAGify.git
cd RAGify
cd Code
```
### Install Dependencies
- Install first Ollama server in your machine.
- In a new **cmd** run the commands bellow to install some models :
```bash
ollama pull hf.co/nomic-ai/nomic-embed-text-v1.5-GGUF:F32
ollama pull llama3.2:3b
ollama pull llama3.1:8b
ollama pull qwen:7b
```
- Then in a new Conda env or venv install some python libraries with :
```bash
pip install -r requirements.txt
```
### Start the Application
```bash
streamlit run main.py
```
---
## 📝 How to Use
1. Upload a PDF file(s) via the Streamlit interface.
2. Choose your query language (Arabic or other).
3. Ask questions about the document.
4. Get precise answers powered by the RAG pipeline.
---
## 🌐 Technologies Used
- **Streamlit**: Frontend interface for user interaction.
- **LangChain**: Framework for building RAG pipelines.
- **ChromaDB**: Vector database for document embeddings and retrieval.
- **Ollama LLMs**: Local language model server for secure and private inference.
---
## 🤝 Contributing
Contributions are welcome! Please fork the repository and submit a pull request.
---
## 🌟 Acknowledgments
Special thanks to the developers of Streamlit, LangChain, ChromaDB, and Ollama for their fantastic tools that made this app possible.
---