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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.

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Install OpenData Vector

Download and install the Vector binary: 
curl https://www.opendata.dev/install.sh | sh
This places the opendata-vector binary in the current directory.

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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.

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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.

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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
}

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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).

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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"
    }
  }
}

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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.