Vector Quickstart

Get a vector search database running on your machine in under five minutes. You will install Vector, configure a collection, upsert a few records, and run a similarity search using curl.

Install OpenData Vector

Download and install the Vector binary:

curl -fsSL https://www.opendata.dev/install.sh | sh -s -- vector

This places the opendata-vector binary in the current directory.

Configure Vector

Create a file called vector.yaml with the following contents:

vector.yaml

storage:
  type: InMemory

flush_interval: 1
dimensions: 2
distance_metric: L2

metadata_fields:
  - name: label
    field_type: String
    indexed: true

This configures a collection with:

2 dimensions — each vector has two f32 components.
L2 distance — Euclidean distance, where lower scores mean higher similarity.
One indexed field — a label string field you can filter on during search.

Start the server

Run the binary with the configuration file you just created:

./opendata-vector --port 8080 vector --config vector.yaml

Vector starts and is ready to accept requests on port 8080.

Upsert records

Insert three records with 2-dimensional vectors. Requests use the application/protobuf+json content type:

curl -X POST http://localhost:8080/api/v1/vector/write \
  -H "Content-Type: application/protobuf+json" \
  -d '{
    "upsertVectors": [
      {
        "id": "north",
        "attributes": {
          "vector": [0.0, 1.0],
          "label": "north"
        }
      },
      {
        "id": "east",
        "attributes": {
          "vector": [1.0, 0.0],
          "label": "east"
        }
      },
      {
        "id": "northeast",
        "attributes": {
          "vector": [1.0, 1.0],
          "label": "northeast"
        }
      }
    ]
  }'

The server responds with a confirmation:

{
  "status": "success",
  "vectorsUpserted": 3
}

Search for nearest neighbors

Search for the 2 closest vectors to [0.0, 0.9] — a point near “north”:

curl -X POST http://localhost:8080/api/v1/vector/search \
  -H "Content-Type: application/protobuf+json" \
  -d '{
    "vector": [0.0, 0.9],
    "k": 2
  }'

The response returns the nearest records ranked by L2 distance:

{
  "status": "success",
  "results": [
    {
      "score": 0.01,
      "vector": {
        "id": "north",
        "attributes": {
          "vector": [0.0, 1.0],
          "label": "north"
        }
      }
    },
    {
      "score": 1.01,
      "vector": {
        "id": "northeast",
        "attributes": {
          "vector": [1.0, 1.0],
          "label": "northeast"
        }
      }
    }
  ]
}

north is closest because [0.0, 1.0] is only 0.1 away from the query [0.0, 0.9] in Euclidean distance (score = 0.01 = 0.1²).

Fetch a record by ID

Retrieve a specific record using its ID:

curl http://localhost:8080/api/v1/vector/vectors/east

{
  "status": "success",
  "vector": {
    "id": "east",
    "attributes": {
      "vector": [1.0, 0.0],
      "label": "east"
    }
  }
}

Next steps

To try a more realistic quickstart that indexes a large set of documents using a real embedding model, see the example on GitHub.
Understand how records are structured in Data Model.
Learn how the vector index works in Storage Design.
Browse the full REST API in the API reference section in the sidebar.

Concepts

Timeseries

Log

Vector

Buffer

Vector Quickstart

Install OpenData Vector

Configure Vector

Start the server

Upsert records

Search for nearest neighbors

Fetch a record by ID

Next steps

​Install OpenData Vector

​Configure Vector

​Start the server

​Upsert records

​Search for nearest neighbors

​Fetch a record by ID

​Next steps

Install OpenData Vector

Configure Vector

Start the server

Upsert records

Search for nearest neighbors

Fetch a record by ID

Next steps