Retrieval-augmented generation (RAG) applications integrate private data with public data and improve large language models’ (LLMs) output, but building one is challenging as private data can be unstructured and siloed. You’ll also need a reliable and efficient way to retrieve relevant information from the knowledge base. This might seem like an uphill battle, but it’s doable with tools like Milvus and LlamaIndex, which can quickly handle big data and retrieve relevant information, especially when adopted together.
What Are Milvus and LlamaIndex?
To build an RAG application that optimizes query efficiency, you need a scalable, flexible vector database and an indexing algorithm. Before showing you how to build one, we’ll quickly discuss Milvus and LlamaIndex.
