What is this?

Milvus DB Studio is a visual dashboard for Milvus — an open-source database built for storing and searching AI vectors (embeddings).

Instead of typing long curl commands to inspect your data, you get a clean workbench docked right in VS Code's Activity Bar. Think of it like a database explorer (such as the SQL or MongoDB tools you may already use), but built for Milvus.

Everything runs locally inside your editor — no separate app, no browser tab, no extra setup.

What you can do with it

• Browse collections — see all your collections, their schema, and load status
• Manage records — insert, view, and delete data without writing code
• Vector search — find the most similar vectors (the core of AI/RAG apps)
• Scalar query — filter records by metadata fields
• Raw REST console — send any Milvus API request directly
• Local or Cloud — connect to a Milvus server on your machine or Zilliz Cloud

Install

From the VS Code Marketplace

1. Open the Extensions view (Cmd+Shift+X / Ctrl+Shift+X)
2. Search for Milvus DB Studio
3. Click Install

From a .vsix file (manual install)

1. Download the milvus-db-studio-x.y.z.vsix file
2. In VS Code, open the Extensions view → ··· menu → Install from VSIX…
3. Select the downloaded file

Or from the terminal:

code --install-extension milvus-db-studio-0.1.0.vsix

Before you start (Prerequisites)

You need one thing: a running Milvus server.

Don't have one? No problem — the next section shows you how to start one for free in under a minute.

Step 1 — Start a Milvus server (local)

If you already have a Milvus server running, skip to Step 2.

The easiest way to run Milvus locally is with Docker. If you don't have Docker yet, install Docker Desktop first.

# 1. Download the official Milvus setup file
wget https://github.com/milvus-io/milvus/releases/download/v3.0-beta/milvus-standalone-docker-compose.yml -O docker-compose.yml

# 2. Start Milvus in the background
docker compose up -d

That's it! Milvus is now running and listening at:

http://localhost:19530

Tip: To stop Milvus later, run docker compose down in the same folder.

Step 2 — Open the workbench

There are two ways to open Milvus DB Studio:

• Activity Bar — click the Milvus DB Studio icon in the left Activity Bar to open the Workbench panel.
• Command Palette — press Cmd+Shift+P / Ctrl+Shift+P, then run Milvus DB Studio: Open Workbench to open it in an editor tab.

Step 3 — Connect to your server

In the workbench, enter these settings:

Click Connect and you're in.

Using Zilliz Cloud? Choose Cloud as the connection type, paste your cluster URL, and add your API key.

Step 4 — Create your first collection

A collection is like a table in a normal database. Let's create one called demo_collection that stores a small 3-dimension vector plus some text.

You can do this two ways:

Option A — Use the workbench (recommended for beginners): open the Resources tab and fill in the create-collection form.

Option B — Use the terminal (copy & paste this):

curl -X POST http://localhost:19530/v2/vectordb/collections/create \
  -H "Content-Type: application/json" \
  -d '{
    "collectionName": "demo_collection",
    "schema": {
      "autoId": false,
      "enabledDynamicField": true,
      "fields": [
        { "fieldName": "id", "dataType": "Int64", "isPrimary": true },
        { "fieldName": "vector", "dataType": "FloatVector", "elementTypeParams": { "dim": "3" } },
        { "fieldName": "text", "dataType": "VarChar", "elementTypeParams": { "max_length": 512 } }
      ]
    },
    "indexParams": [
      { "fieldName": "vector", "metricType": "COSINE", "indexName": "vector", "indexType": "AUTOINDEX" }
    ]
  }'

What this means: id is the unique key, vector holds the AI embedding (3 numbers here for simplicity), and text stores a label. COSINE is how similarity is measured.

Step 5 — Add some data

Now insert a few example rows:

curl -X POST http://localhost:19530/v2/vectordb/entities/insert \
  -H "Content-Type: application/json" \
  -d '{
    "collectionName": "demo_collection",
    "data": [
      { "id": 1, "vector": [0.1, 0.2, 0.3], "text": "hello milvus" },
      { "id": 2, "vector": [0.2, 0.3, 0.4], "text": "vector search" },
      { "id": 3, "vector": [0.9, 0.8, 0.7], "text": "ai native database" }
    ]
  }'

Switch back to the workbench, refresh, and open demo_collection — your records are there, ready to browse, query, and search. ✅

Commands

You can also open the workbench any time from the Milvus DB Studio icon in the Activity Bar.

Troubleshooting

I can't connect. Make sure your Milvus server is running (docker compose up -d) and that the Endpoint URI matches http://localhost:19530.

The Activity Bar icon isn't showing. Reload VS Code (Cmd+Shift+P / Ctrl+Shift+P → Developer: Reload Window) after installing.

Is my data safe? Everything runs locally inside VS Code. Connection details are stored only in the extension's local storage, and requests go only to the Milvus server you point at.

License

MIT © Harish Kaparwan