raglab-easygenius 0.1.0

A modular RAG library with embeddings and vector store support

🧠 MyRAGProject

Now with interactive setup!

• On first use, you'll be prompted to choose your embedding model (HuggingFace or Gemini) and vector database (Qdrant or FAISS).
• The setup wizard will create a .env file for your configuration and install only the dependencies you need.
• Supports Gemini API key and Qdrant port via .env.

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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 🚀