Native embeddings

Native embeddings let LambdaDB generate vector values from a source text field and store them in a vector field. Choose the provider and model in the field’s embedding configuration. LambdaDB handles:

provider credentials
vector generation during document writes
query embedding generation for vector search

Native embedding generation consumes inference usage. Inference usage is measured in LIU (LambdaDB Inference Unit). See Understanding costs.

Supported providers

LambdaDB currently supports the following embedding provider:

Provider	Status
`openai`	Supported

Supported models

The following OpenAI embedding models are currently supported for native embedding vector fields.

Model	Default dimensions	Dimensions parameter	Similarity
`text-embedding-3-small`	`1536`	Optional, from `1` to `1536`	`cosine`
`text-embedding-3-large`	`3072`	Optional, from `1` to `3072`	`cosine`
`text-embedding-ada-002`	`1536`	Fixed at `1536`	`cosine`

Collection schema

Define a vector field with an embedding block to let LambdaDB generate its vectors.

{
  "indexConfigs": {
    "body": {
      "type": "text",
      "analyzers": ["english"]
    },
    "bodyEmbedding": {
      "type": "vector",
      "embedding": {
        "provider": "openai",
        "model": "text-embedding-3-small",
        "sourceField": "body"
      }
    }
  }
}

Schema rules

Use the recommended SDK versions, CLI, MCP server, or Migration CLI for native configuration.

embedding.provider is required
embedding.model is required
embedding.sourceField is required
embedding.sourceField must reference a text field in the same collection
native embedding vector fields must not use top-level dimensions
native embedding vector fields must not use top-level similarity
set optional dimensions and similarity inside embedding; omitted values use the model defaults
LambdaDB resolves and stores the effective embedding.dimensions and embedding.similarity

Write behavior

For native embedding vector fields, send the source text field and let LambdaDB generate the vector value. Do not send direct vector values for native embedding fields in:

Example upsert payload:

{
  "docs": [
    {
      "id": "doc-1",
      "body": "Refunds are available within 7 days of purchase."
    }
  ]
}

Query behavior

For native embedding vector fields, use knn.queryText instead of knn.queryVector.

{
  "query": {
    "knn": {
      "field": "bodyEmbedding",
      "queryText": "refund policy",
      "k": 10
    }
  }
}

For full query examples, see Vector query.

Bulk upsert

bulk upsert is not supported for collections that contain native embedding vector fields. Use the regular document write flow instead:

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Migrations

Collections

Data versioning

Documents

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Manage Costs

Supported providers

Supported models

Collection schema

Schema rules

Write behavior

Query behavior

Bulk upsert

​Supported providers

​Supported models

​Collection schema

​Schema rules

​Write behavior

​Query behavior

​Bulk upsert

Supported providers

Supported models

Collection schema

Schema rules

Write behavior

Query behavior

Bulk upsert