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How to build a RAG pipeline on Milvus with Milvus

Store document chunks in Milvus and retrieve them for an LLM, following the Build RAG tutorial.

Short answer: Install pip install -U pymilvus and set up Milvus Lite or connect to a server; then follow the Build RAG with Milvus tutorial linked in the README; Embed your chunks and insert them with their text as fields. All steps.

Who it is forA developer adding chat-with-your-docs to a product, using LangChain or LlamaIndex.

The problemWithout a proper vector store, retrieval is slow and does not scale past a demo.

The outcomeA RAG app that retrieves relevant chunks from Milvus and passes them to your LLM.

What you need first

  • Python and the pymilvus SDK
  • Milvus Lite for a local file, or a deployed Milvus server or Zilliz Cloud with credentials
  • An embedding model to create vectors

Steps

  1. Install pip install -U pymilvus and set up Milvus Lite or connect to a server
  2. Follow the Build RAG with Milvus tutorial linked in the README
  3. Embed your chunks and insert them with their text as fields
  4. Retrieve with client.search and pass the returned text to your LLM
  5. Use the LangChain or LlamaIndex integration if you already use those frameworks

Prompt for your agent

Build a RAG pipeline for <documents> on milvus-io/milvus following the Build RAG with Milvus tutorial: chunk, embed with <model>, insert, retrieve top 3 with client.search and answer with <LLM>. Test it on <questions>.

Caveat. The README links to the tutorial for full code; answer quality depends on your chunking and embedding model.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for milvus-io/milvus and "Build a RAG pipeline on Milvus"

Add Deduped to Claude Code once

claude mcp add --transport http deduped https://dedupedai.com/mcp

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