workflowstacks
ai-agent

Milvus: Fast Vector Search

Get scalable vector ANN search with Milvus, a cloud-native database for founders
advanced⏱ 1-2 days💵 Free + LLM API costs
46,040 stars4,238 forksGoGuide quality 9/10Updated 9/10/2026100% free · open source
What it is

Use Milvus to find similar data in seconds.

Real use case example

"A founder wants to add new users to a loyalty program based on their purchase history. She uses Milvus to quickly compare new users with existing members and sends personalized recommendations to those with similar buying behavior."

Best insideCodex CLIGPT (ChatGPT)Any AI Client
When to use it
  • •Building a recommendation system that requires fast and accurate similarity searches
  • •Developing a computer vision application that needs to efficiently query large datasets of image embeddings
  • •Creating a natural language processing model that requires scalable semantic search capabilities
Ready-to-paste prompt
python -c 'from milvus import Milvus, Client; client = Client(); client.connect(); client.create_collection('test'); client.insert('test', [[1.0, 2.0], [3.0, 4.0]])'
Heads up: Make sure to allocate sufficient GPU resources when running Milvus, as it requires significant GPU memory to perform efficient vector searches, and ensure that the Docker container has the necessary permissions to access the host machine's GPU
Saves to your device
Use with Claude
New

Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.

✅ Light setup

Installs with a command or two; your AI agent can do it for you.

Try it instantly — no install
Claude Code
mkdir -p ~/.claude/skills/milvus && curl -fsSL https://workflowstacks.com/api/skills/milvus/claude-skill -o ~/.claude/skills/milvus/SKILL.md
Open in another AI app

Opens the app with this repo with the prompt ready to go — no copy-paste needed.

Connect the whole catalog (MCP)
claude mcp add --transport http workflowstacks https://workflowstacks.com/api/mcp

Adds a WorkflowStacks connector to Claude Code: search and load any skill here by chatting.

How Milvus: Fast Vector Search works
Codeflow
Free to inspect

Milvus: Fast Vector Search is a very large Go project (~1.5M lines across 4328 code files, plus 519 test files). Setup is light: installs like a normal app. Reading the code is optional. Last commit this month, Apache-2.0 license, has a test suite.

Size
Very large codebase
~1.5M lines · 4328 code files · days to read — use, don't read
Setup
One-command install
Installs like a normal app. Reading the code is optional.
Runs on
Go · Docker
No API keys detected
Go 58%C++ 21%Python 20%
Where to start reading
  1. 1
    README.md
    Start here — what it does and how to install it
  2. 2
    AGENTS.md
    The instructions the AI actually follows
  3. 3
    cmd/main.go
    Where the program starts running
  4. 4
    go.mod
    Dependencies and the commands it exposes
  5. 5
    internal/datacoord/README.md
    Inside internal/ — the main logic begins here
What's in each folder
internal/Core code — the actual logic3358 files
pkg/Core code — the actual logic759 files
client/Frontend / UI204 files
cmd/Command-line entry points89 files
docs/Documentation226 files
deployments/Folder39 files
scripts/Helper scripts39 files
ci/Folder20 files
READMEHas testsDocumentedCI checksDocker readyApache-2.0 licenseUpdated this month
Quick Actions
Details
Creator
milvus-io
Language
Go
Category
ai-agent
Published
9/16/2019

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