Retrieval-Augmented Generation (RAG) is a powerful technique that enhances language model responses by combining information retrieval with text generation.
-
Document Retrieval
RAG first searches a large corpus to retrieve the most relevant documents based on the user's query. -
Query Augmentation & Generation
These retrieved documents are then combined with the original query and passed to a language model (e.g., GPT, BERT) to generate a context-aware and accurate response.
- Allows models to answer questions using external or real-time knowledge
- No need to retrain the base language model on new data
- Ideal for open-domain QA, document-based chatbots, and knowledge-based systems
RAG enables smarter and more informed responses—perfect for scenarios where knowledge is vast, dynamic, or frequently updated.
Sure! Here's a GitHub-friendly README.md Features section formatted with markdown:
-
🔍 Document Embedding & Vector Store Creation
Automatically convert documents into embeddings and store them efficiently for retrieval. -
📄 Text Preprocessing Pipeline
Clean, tokenize, and normalize text data before embedding for better results. -
🧠 Integration-Ready for LLM-Based Generation
Easily connect with large language models (e.g., GPT, LLaMA) for generating contextual answers. -
🛠️ Postprocessing & Logging Utilities
Tools to refine outputs and log results for debugging, evaluation, and monitoring. -
🗂️ Modular Design
Swap or customize components like retrievers, embeddings, and models for rapid experimentation.
Absolutely! Here's the polished and properly formatted GitHub-style README.md section for Project Structure, Installation, Usage, and Customization:
RAG/
├── app_st.py # Main application script (entry point)
├── embedding.py # Handles embedding generation
├── logging_config.py # Custom logging configuration
├── postprocessing.py # Post-processing logic for model outputs
├── preprocessed.py # Document preprocessing pipeline
├── requirements.txt # Python dependencies
├── utils.py # Utility functions
├── vector_store.py # Code for storing and retrieving from vector store
└── README.md # Project documentation
Clone the repository:
git clone https://github.com/ravindrakush11/RAG.git
cd RAGCreate a virtual environment (optional but recommended):
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activateInstall dependencies:
pip install -r requirements.txtTo start the main RAG pipeline, run:
python app_st.pyNote: Make sure your document corpus or input data is properly prepared and formatted before running the script.
- ✨Embeddings: Modify
embedding.pyto switch or fine-tune the embedding model. - 🧹Preprocessing: Adjust
preprocessed.pyto add domain-specific cleaning or tokenization. - 🧠Post-processing: Tweak
postprocessing.pyto improve the quality and relevance of generated answers. - 💾Vector Store: Experiment with different vector storage options in
vector_store.py(e.g., FAISS, Chroma, etc.).