This topic provides a guide to Retrieval-Augmented Generation (RAG)-related documents to help you quickly find the content that you require.
RAG-based LLM chatbot
This topic describes how to associate multiple types of vector databases with RAG services when you use Elastic Algorithm Service (EAS) to deploy the RAG services. The RAG architecture is designed for retrieval and generation.
For retrieval, EAS integrates with a range of vector database services, including open source Faiss and the following Alibaba Cloud services: Milvus, Elasticsearch, Hologres, and AnalyticDB for PostgreSQL.
For generation, EAS supports a diverse array of open source models such as Qwen, Meta Llama, Mistral, and Baichuan, while also integrating with ChatGPT.
After you deploy an RAG service, you can call the RAG service by using the web UI or API operations. The web UI provides various inference parameters and allows you to upload knowledge base files to develop personalized and precise large language models (LLMs).
Use EAS and Elasticsearch to deploy a RAG-based LLM chatbot
This topic describes how to associate Elasticsearch vector databases with RAG services when you use EAS to deploy the RAG services. This topic also describes the basic features of RAG chatbots and the features of Elasticsearch.