LightRAG

LightRAG is a Retrieval-Augmented Generation (RAG) system designed for speed and simplicity. It leverages a graph-based approach inspired by nano-graphrag and supports various backend LLMs and embedding models, including Ollama, OpenAI, and Azure OpenAI. LightRAG is particularly well-suited for applications requiring large context windows (at least 32k tokens), which can be configured, even on resource-constrained GPUs. A key feature is its ability to handle diverse file formats like TXT, DOCX, PPTX, CSV, and PDF.

This flexible system offers robust API support through FastAPI, enabling seamless integration of RAG capabilities into existing LLM services. It facilitates operations such as querying with different search modes, streaming responses, and managing document insertions. Moreover, LightRAG provides intelligent caching for faster performance. The project's focus on developer accessibility is highlighted through clear setup instructions, example use cases, and a permissive MIT license.