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How to start a vector database in a single local file with Milvus

Install pymilvus with Milvus Lite and create a collection in a local file, no server needed.

Short answer: Install the SDK with pip install -U pymilvus, and pymilvus[milvus-lite] for Milvus Lite; then create a local database with MilvusClient("milvus_demo.db"); create a collection with client.create_collection using your embedding dimension, for example 768. All steps.

Who it is forA developer prototyping RAG in a notebook who does not want to run a database server yet.

The problemSetting up a database server just to try vector search slows down early experiments.

The outcomeA working vector collection persisted to a local .db file that you can later move to a server.

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 the SDK with pip install -U pymilvus, and pymilvus[milvus-lite] for Milvus Lite
  2. Create a local database with MilvusClient("milvus_demo.db")
  3. Create a collection with client.create_collection using your embedding dimension, for example 768
  4. Insert your embedded data with client.insert
  5. Search with client.search, setting limit and output_fields

Prompt for your agent

Using milvus-io/milvus with Milvus Lite, create milvus_demo.db, embed <documents> with <model>, create a collection with the right dimension, insert them, and search for <query>. Show me the top 3 results with their text.

Caveat. Milvus Lite is for quickstarts; move to a Milvus server or Zilliz Cloud for production scale.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for milvus-io/milvus and "Start a vector database in a single local file"

Add Deduped to Claude Code once

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

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