Free RAG Examples 🚀

A comprehensive collection of Retrieval-Augmented Generation (RAG) implementations using different methods, embedding models, and data sources.

📋 Overview

This repository contains various RAG implementation examples that demonstrate different approaches to building question-answering systems with document retrieval. Each example showcases a different combination of:

• Embedding Models: Google Gemini, HuggingFace, OpenAI
• Data Sources: PDFs, Web pages, Local documents
• Vector Stores: In-memory vector stores
• LLM Models: Google Gemini, OpenAI GPT

🏗️ Project Structure

free-RAG/
├── app/
│   ├── gemini-localembed.py      # RAG with Gemini embeddings + local docs
│   ├── gemini+huggingapi.py      # RAG with Gemini + HuggingFace embeddings
│   ├── gemini+websedeta.py       # RAG with web data using Gemini
│   └── normal-openai.py          # RAG implementation with OpenAI
├── retreival.py                  # Main retrieval example with PDF
├── indexing.py                   # Document indexing utilities
├── AI_rec.pdf                    # Sample PDF document
├── example.env                   # Environment variables template
├── pyproject.toml               # Project dependencies
└── README.md                    # This file

🛠️ Setup

Prerequisites

• Python 3.13+
• UV package manager

Installation

1. Clone the repository:

   git clone <your-repo-url>
   cd free-RAG

2. Install dependencies using UV:

   uv sync

3. Set up environment variables:

   cp example.env .env

   Edit .env and add your API keys:

   GOOGLE_API_KEY="your-google-api-key"
   HUGGINGFACEHUB_API_TOKEN="your-huggingface-token"
   OPENAI_API_KEY="your-openai-key"

📚 Examples

1. PDF-based RAG (retreival.py)

• Description: Basic RAG implementation using PDF documents
• Embedding: HuggingFace Sentence Transformers
• Model: Supports both Gemini and local embeddings
• Usage:

  uv run python retreival.py

2. Web-based RAG (app/gemini+websedeta.py)

• Description: RAG system that loads data from multiple web pages
• Data Source: Multiple documentation URLs
• Embedding: Google Gemini embeddings
• Usage:

  uv run python app/gemini+websedeta.py

3. Gemini + HuggingFace RAG (app/gemini+huggingapi.py)

• Description: Hybrid approach using both Gemini and HuggingFace
• Features: Combines different embedding approaches
• Usage:

  uv run python app/gemini+huggingapi.py

4. Local Embedding RAG (app/gemini-localembed.py)

• Description: RAG with local Gemini embeddings
• Features: Optimized for local deployment
• Usage:

  uv run python app/gemini-localembed.py

5. OpenAI RAG (app/normal-openai.py)

• Description: Traditional RAG implementation using OpenAI
• Embedding: OpenAI text embeddings
• Model: GPT models for generation
• Usage:

  uv run python app/normal-openai.py

🔧 Dependencies

The project uses the following key dependencies:

• langchain-community - Document loaders and utilities
• langchain-core - Core LangChain functionality
• langchain-google-genai - Google Gemini integration
• langchain-huggingface - HuggingFace integration
• langchain-text-splitters - Text chunking utilities
• sentence-transformers - Local embedding models
• pypdf - PDF processing
• python-dotenv - Environment variable management

🚀 Key Features

• Multiple Embedding Options: Compare different embedding models
• Flexible Data Sources: Support for PDFs, web pages, and local documents
• Vector Store Integration: In-memory vector storage for fast retrieval
• Interactive Querying: Command-line interfaces for testing
• Environment Configuration: Easy setup with environment variables

📖 Usage Examples

Basic Query Example

# Load your documents
loader = PyPDFLoader("your_document.pdf")
docs = loader.load()

# Create embeddings and vector store
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
vector_store = InMemoryVectorStore(embeddings)
vector_store.add_documents(documents=docs)

# Query the system
query = "Your question here"
results = vector_store.similarity_search(query)

Web Data Loading Example

from langchain_community.document_loaders import WebBaseLoader

# Load multiple web pages
loader = WebBaseLoader([
    "https://example.com/page1",
    "https://example.com/page2"
])
docs = loader.load()

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

📝 License

This project is open source and available under the MIT License.

🔗 Resources

• LangChain Documentation
• Google Gemini API
• HuggingFace Transformers
• OpenAI API

⚠️ Notes

• Make sure to set up your API keys before running the examples
• Some examples require internet connectivity for web scraping
• The USER_AGENT environment variable is recommended for web scraping
• Each example is designed to be self-contained and runnable independently

📞 Support

If you encounter any issues or have questions, please:

1. Check the existing issues in the repository
2. Create a new issue with a detailed description
3. Include error messages and environment information

Happy coding! 🎉