Doc_Query_Genie

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Doc_Query_Genie

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Rag (Retreival Augmented Generation) Python solution with LLama3, LangChain, Ollama and ChromaDB in a Flask API based solution

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# 📜 Doc_Query_Genie

🚀 **A Powerful Retrieval-Augmented Generation (RAG) API using LLama3, LangChain, Ollama, and ChromaDB**

![PDF_Searcher](https://github.com/user-attachments/assets/053688a0-63e7-4005-a258-69bc4200913a)

## 🔍 About The Project

**Doc_Query_Genie** is a cutting-edge RAG-based Python solution that enhances information retrieval and generation using **Flask API**. With the power of **LLama3, LangChain, Ollama, and ChromaDB**, this tool provides a seamless experience for querying both general knowledge and custom document uploads.

### ✨ Key Features

✅ **AI-Powered Chat** – Use it like OpenAI's ChatGPT to ask any question.  
✅ **PDF Intelligence** – Upload a PDF and ask context-specific questions.  
✅ **Source Referencing** – Get precise answers with citations from the document (paragraph/line references).  
✅ **Fast & Efficient** – Optimized for quick and reliable response generation.  
✅ **Easy Integration** – Simple API setup to integrate with other applications.

This project brings the best of **AI-driven retrieval** and **context-aware generation**, making it a versatile tool for researchers, students, and professionals.

## 🚀 Getting Started

1. **Clone the repository**  
   ```sh  
   git clone https://github.com/yourusername/Doc_Query_Genie.git  
   cd Doc_Query_Genie  
   ```  
2. **Install dependencies**  
   ```sh  
   pip install -r requirements.txt  
   ```  
3. **Run the application**  
   ```sh  
   python app.py  
   ```  
4. **Use the API**  
   - Access it at `http://127.0.0.1:5000/`  
   - Upload PDFs and start querying

## 🤝 Contributing

We welcome contributions! Feel free to submit issues or pull requests.

---  
🌟 **If you find this project helpful, please consider giving it a star!**

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