Snowflake · Schema
EmbedRequest
Request to calculate text embeddings using a specified model.
DataData WarehouseData LakehouseCloud Data PlatformAnalyticsArtificial IntelligenceData EngineeringData GovernanceSQLApache IcebergModel Context ProtocolT1
Properties
| Name | Type | Description |
|---|---|---|
| model | string | Identifier of the model to use for generating embeddings. Refer to Snowflake documentation for the list of supported models. Examples: - `snowflake-arctic-embed-m` - `snowflake-arctic-embed-m-v1.5` |
| text | array | An array of input texts for which vector embeddings will be calculated. Example: ["Hello world", "Machine learning is fascinating"] |
| provisioned_throughput_id | stringnull | The provisioned throughput ID to be used with the request. |
JSON Schema
{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"title": "EmbedRequest",
"type": "object",
"description": "Request to calculate text embeddings using a specified model.",
"properties": {
"model": {
"type": "string",
"description": "Identifier of the model to use for generating embeddings.\nRefer to Snowflake documentation for the list of supported models.\n\nExamples:\n - `snowflake-arctic-embed-m`\n - `snowflake-arctic-embed-m-v1.5`\n"
},
"text": {
"type": "array",
"items": {
"type": "string"
},
"description": "An array of input texts for which vector embeddings will be calculated.\nExample: [\"Hello world\", \"Machine learning is fascinating\"]",
"minItems": 1
},
"provisioned_throughput_id": {
"type": [
"string",
"null"
],
"description": "The provisioned throughput ID to be used with the request."
}
},
"required": [
"model",
"text"
]
}
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