LLM RAG

what is Easy RAG? Easy RAG is a Retrieval Augmented Generation (RAG) implementation that utilizes LlamaIndex for document processing, Gemini for embeddings, and LanceDB for vector storage, aimed at enhancing knowledge retrieval and generation. how to use Easy RAG? To use Easy RAG, set up the environment by installing dependencies and configuring your Google API key. Then, you can ingest data and run a search server using the provided commands. key features of Easy RAG? Integration with LlamaIndex for efficient document processing Use of Gemini for advanced embeddings LanceDB for scalable vector storage Command-line interface for data ingestion and search use cases of Easy RAG? Enhancing document retrieval for AI applications Building knowledge bases that require efficient data processing Implementing search functionalities in applications using vector databases FAQ from Easy RAG? What is RAG? RAG stands for Retrieval Augmented Generation, a method that combines retrieval of documents with generative models to improve the quality of generated content. Is Easy RAG free to use? Yes! Easy RAG is open-source and available for anyone to use. What programming language is Easy RAG written in? Easy RAG is primarily written in Python.