Kong · Schema
PartialEmbeddings
API GatewayAI GatewayAI ConnectivityAgent GatewayEvent GatewayMCP RegistryService MeshLLMKafkaKonnectOpen Source
Properties
| Name | Type | Description |
|---|---|---|
| config | object | |
| created_at | integer | Unix epoch when the resource was created. |
| id | string | A string representing a UUID (universally unique identifier). |
| name | string | A unique string representing a UTF-8 encoded name. |
| tags | array | A set of strings representing tags. |
| type | string | |
| updated_at | integer | Unix epoch when the resource was last updated. |
JSON Schema
{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"$id": "#/components/schemas/PartialEmbeddings",
"title": "PartialEmbeddings",
"type": "object",
"properties": {
"config": {
"type": "object",
"properties": {
"auth": {
"type": "object",
"properties": {
"allow_override": {
"description": "If enabled, the authorization header or parameter can be overridden in the request by the value configured in the plugin.",
"type": "boolean",
"default": false
},
"aws_access_key_id": {
"description": "Set this if you are using an AWS provider (Bedrock) and you are authenticating using static IAM User credentials. Setting this will override the AWS_ACCESS_KEY_ID environment variable for this plugin instance.",
"type": "string",
"x-encrypted": true,
"x-referenceable": true
},
"aws_secret_access_key": {
"description": "Set this if you are using an AWS provider (Bedrock) and you are authenticating using static IAM User credentials. Setting this will override the AWS_SECRET_ACCESS_KEY environment variable for this plugin instance.",
"type": "string",
"x-encrypted": true,
"x-referenceable": true
},
"azure_client_id": {
"description": "If azure_use_managed_identity is set to true, and you need to use a different user-assigned identity for this LLM instance, set the client ID.",
"type": "string",
"x-referenceable": true
},
"azure_client_secret": {
"description": "If azure_use_managed_identity is set to true, and you need to use a different user-assigned identity for this LLM instance, set the client secret.",
"type": "string",
"x-encrypted": true,
"x-referenceable": true
},
"azure_tenant_id": {
"description": "If azure_use_managed_identity is set to true, and you need to use a different user-assigned identity for this LLM instance, set the tenant ID.",
"type": "string",
"x-referenceable": true
},
"azure_use_managed_identity": {
"description": "Set true to use the Azure Cloud Managed Identity (or user-assigned identity) to authenticate with Azure-provider models.",
"type": "boolean",
"default": false
},
"gcp_metadata_url": {
"description": "Custom metadata URL for GCP authentication. Useful for restricted network environments or custom GCP endpoints. If null, Kong will use the default Google metadata endpoint.",
"type": "string",
"x-referenceable": true
},
"gcp_oauth_token_url": {
"description": "Custom OAuth token URL for GCP authentication. Useful for restricted network environments or custom GCP endpoints. If null, Kong will use the default Google OAuth token endpoint.",
"type": "string",
"x-referenceable": true
},
"gcp_service_account_json": {
"description": "Set this field to the full JSON of the GCP service account to authenticate, if required. If null (and gcp_use_service_account is true), Kong will attempt to read from environment variable `GCP_SERVICE_ACCOUNT`.",
"type": "string",
"x-encrypted": true,
"x-referenceable": true
},
"gcp_use_service_account": {
"description": "Use service account auth for GCP-based providers and models.",
"type": "boolean",
"default": false
},
"header_name": {
"description": "If AI model requires authentication via Authorization or API key header, specify its name here.",
"type": "string",
"x-referenceable": true
},
"header_value": {
"description": "Specify the full auth header value for 'header_name', for example 'Bearer key' or just 'key'.",
"type": "string",
"x-encrypted": true,
"x-referenceable": true
},
"param_location": {
"description": "Specify whether the 'param_name' and 'param_value' options go in a query string, or the POST form/JSON body.",
"type": "string",
"enum": [
"body",
"query"
]
},
"param_name": {
"description": "If AI model requires authentication via query parameter, specify its name here.",
"type": "string",
"x-referenceable": true
},
"param_value": {
"description": "Specify the full parameter value for 'param_name'.",
"type": "string",
"x-encrypted": true,
"x-referenceable": true
}
}
},
"model": {
"type": "object",
"properties": {
"name": {
"description": "Model name to execute.",
"type": "string"
},
"options": {
"description": "Key/value settings for the model",
"type": "object",
"properties": {
"azure": {
"type": "object",
"properties": {
"api_version": {
"description": "'api-version' for Azure OpenAI instances.",
"type": "string",
"default": "2023-05-15"
},
"deployment_id": {
"description": "Deployment ID for Azure OpenAI instances.",
"type": "string"
},
"instance": {
"description": "Instance name for Azure OpenAI hosted models.",
"type": "string"
}
}
},
"bedrock": {
"type": "object",
"properties": {
"aws_assume_role_arn": {
"description": "If using AWS providers (Bedrock) you can assume a different role after authentication with the current IAM context is successful.",
"type": "string"
},
"aws_region": {
"description": "If using AWS providers (Bedrock) you can override the `AWS_REGION` environment variable by setting this option.",
"type": "string"
},
"aws_role_session_name": {
"description": "If using AWS providers (Bedrock), set the identifier of the assumed role session.",
"type": "string"
},
"aws_sts_endpoint_url": {
"description": "If using AWS providers (Bedrock), override the STS endpoint URL when assuming a different role.",
"type": "string"
},
"batch_bucket_prefix": {
"description": "S3 URI prefix (s3://bucket/prefix/) where Bedrock will get input files from and store results to for native batch API.",
"type": "string"
},
"batch_role_arn": {
"description": "AWS role arn used for calling batch API. Try to get the value from request if ommited.",
"type": "string"
},
"embeddings_normalize": {
"description": "If using AWS providers (Bedrock), set to true to normalize the embeddings.",
"type": "boolean",
"default": false
},
"performance_config_latency": {
"description": "Force the client's performance configuration 'latency' for all requests. Leave empty to let the consumer select the performance configuration.",
"type": "string"
},
"video_output_s3_uri": {
"description": "S3 URI (s3://bucket/prefix) where Bedrock will store generated video files. Required for video generation.",
"type": "string"
}
}
},
"gemini": {
"type": "object",
"properties": {
"api_endpoint": {
"description": "If running Gemini on Vertex, specify the regional API endpoint (hostname only).",
"type": "string"
},
"location_id": {
"description": "If running Gemini on Vertex, specify the location ID.",
"type": "string"
},
"project_id": {
"description": "If running Gemini on Vertex, specify the project ID.",
"type": "string"
}
}
},
"huggingface": {
"type": "object",
"properties": {
"use_cache": {
"description": "Use the cache layer on the inference API",
"type": "boolean"
},
"wait_for_model": {
"description": "Wait for the model if it is not ready",
"type": "boolean"
}
}
},
"upstream_url": {
"description": "upstream url for the embeddings",
"type": "string"
}
}
},
"provider": {
"description": "AI provider format to use for embeddings API",
"type": "string",
"enum": [
"azure",
"bedrock",
"gemini",
"huggingface",
"mistral",
"ollama",
"openai"
]
}
},
"required": [
"name",
"provider"
]
}
},
"required": [
"model"
]
},
"created_at": {
"description": "Unix epoch when the resource was created.",
"type": "integer",
"nullable": true
},
"id": {
"description": "A string representing a UUID (universally unique identifier).",
"type": "string",
"nullable": true
},
"name": {
"description": "A unique string representing a UTF-8 encoded name.",
"type": "string",
"nullable": true
},
"tags": {
"description": "A set of strings representing tags.",
"type": "array",
"items": {
"description": "A string representing a tag.",
"type": "string"
},
"nullable": true
},
"type": {
"type": "string",
"const": "embeddings",
"x-terraform-transform-const": true
},
"updated_at": {
"description": "Unix epoch when the resource was last updated.",
"type": "integer",
"nullable": true
}
},
"example": {
"config": {
"auth": {
"header_name": "Authorization",
"header_value": "Bearer openai-api-key"
},
"model": {
"name": "text-embedding-3-small",
"provider": "openai"
}
},
"type": "embeddings"
},
"additionalProperties": false,
"required": [
"type",
"config"
]
}