This repository contains an implementation of a Retrieval-Augmented Generation (RAG) system powered by Large Language Models (LLMs). The project bridges the gap between state-of-the-art generative AI models and precise information retrieval systems, enabling robust and context-aware question-answering.
🧠** Key Features**
- Retrieval-Augmented Pipeline:
Combines dense vector search with LLMs to retrieve the most relevant context for a query. Ensures accurate and factually grounded responses by referencing external knowledge sources.
- Generative AI Integration:
Employs advanced LLMs to generate human-like answers based on retrieved context. Handles nuanced queries across diverse domains.
- Customizable & Scalable:
Supports integration with custom datasets or APIs for domain-specific applications. Scalable architecture for both small and large datasets.
- Real-World Applications:
Customer Support: Automated, intelligent assistance for FAQs or troubleshooting. Knowledge Management: Efficiently querying vast document repositories. Education: Personalized learning through detailed and contextual responses.
Technologies Used Large Language Models: GPT, T5. Vector Search: FAISS, Pinecone, ElasticSearch. Frameworks: PyTorch, Hugging Face Transformers.
🙌 Contributions Contributions, issues, and feature requests are welcome! Feel free to fork this repository and submit a pull request.
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