raggenius 0.2.1

A modular RAG library with embeddings and vector store support

🧠 MyRAGProject

A lightweight, developer-friendly Retrieval-Augmented Generation (RAG) engine in Python.

This project allows you to ingest .txt files, generate embeddings, store them in Qdrant (a vector database), and later query them to build intelligent AI applications (like chatbots, document Q&A, etc.).

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📦 Features

• 🔍 Smart chunking of input documents
• 🤖 Embedding generation via HuggingFace
• 🧠 Vector storage using Qdrant
• 💬 Ready for question-answering and search
• 🚀 Modular and easy to extend

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🗂️ Folder Structure

myragproject/ ├── main.py ├── data/ │ └── sample.txt ├── app/ │ ├── config.py │ └── ingest.py ├── raglib/ │ ├── chunking/ │ │ └── smart_chunker.py │ ├── embeddings/ │ │ └── huggingface.py │ └── vector_stores/ │ └── qdrant_store.py

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🔧 Setup Instructions

1. Clone the repository

git clone https://github.com/your-username/myragproject.git
cd myragproject
2. Install dependencies
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pip install -r requirements.txt
Or manually install:

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pip install sentence-transformers qdrant-client requests
3. Start Qdrant (vector DB)
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docker run -p 6333:6333 -v qdrant_data:/qdrant/storage qdrant/qdrant
This ensures persistent vector storage.

4. Add your document
Place a .txt file inside the data/ folder, e.g.:

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data/sample.txt
🚀 Run the RAG Ingest Pipeline
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python main.py
It will:

Read the file

Split it into chunks

Generate embeddings

Store in Qdrant with payloads

📖 Example Output
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📄 Starting ingestion for: data/sample.txt
🧩 Created 4 chunks
🔢 Generated embeddings
✅ Stored 4 points in Qdrant.
✅ Embeddings stored successfully in Qdrant.
🔍 Coming Soon
❓ Ask questions and retrieve top relevant chunks

📚 Support for PDFs and multiple file types

🧪 FastAPI-based chatbot API

🧠 Tech Stack
Python 3.10+

HuggingFace (sentence-transformers)

Qdrant vector store

Docker (for Qdrant server)

🧩 Credits
Built with ❤️ by [Abhishek]

Inspired by modern RAG systems like LangChain, LlamaIndex

📄 License
MIT License

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## ✅ To Use It Now

Just create a `README.md` file in your root folder and paste the above content.

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Would you like me to generate:

- `requirements.txt` for you?
- Add FastAPI interface?
- Publish this to GitHub with a badge?

Let’s make this production-ready 🚀