Building an AI Agent for RAG with Milvus and LlamaIndex

In 2023, large language models (LLMs) gained immense popularity, leading to the development of two main types of LLM applications: retrieval augmented generation (RAG) and AI agents. RAG involves using a vector database like Milvus to inject contextual data, while AI Agents use LLMs to utilize other tools. This article combines these two concepts by building an AI Agent for RAG using Milvus and LlamaIndex. The tech stack includes Milvus, LlamaIndex, and OpenAI (or alternatively OctoAI or HuggingFace). The process involves spinning up Milvus, loading data into it via LlamaIndex, creating query engine tools for the AI Agent, and finally building the AI Agent for RAG. This architecture allows an AI Agent to perform RAG on documents by providing it with the necessary tools for querying a vector database.

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