Easyrag
Facilitates Retrieval Augmented Generation (RAG) using LlamaIndex, Gemini embeddings, and LanceDB for local knowledge storage.
Acerca de
Easyrag simplifies the process of building RAG applications by integrating LlamaIndex for document handling, Gemini for generating embeddings, and LanceDB for efficient vector storage. It provides scripts for data ingestion from various sources (code, URLs, PDFs) and a search server for querying the knowledge base, making it easy to create local, embedded RAG systems.
Características Principales
- Leverages LanceDB for vector storage
- Data ingestion from code, URLs, and PDFs
- Uses LlamaIndex for document processing
- Employs Gemini for generating embeddings
- Includes a search server for querying the knowledge base
- 1 GitHub stars
Casos de Uso
- Developing a search engine for web content
- Creating a local knowledge base for code repositories
- Building a RAG-based question answering system for PDF documents
Sponsored