Using LlamaIndex with Elasticsearch for Enhanced Retrieval-Augmented Generation (RAG)

摘要

Retrieval-Augmented Generation (RAG) is a technique that combines retrieval and generation capabilities. It effectively addresses some issues of large language models (LLMs), such as hallucinations and knowledge limitations. With the evolution of RAG, vector technology involved in RAG has gained attention, and vector databases have become more widely recognized. Established database providers now support vector retrieval, including Elasticsearch, which recently added support for vector retrieval in its latest version. This article introduces the deployment of Elasticsearch and embedding models in RAG, as well as how to use Elasticsearch for document indexing and retrieval within the LLM framework LlamaIndex.