Build AI Semantic Search with TextEmbedding API - AnalyticDB for PostgreSQL - Alibaba Cloud - AnalyticDB

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RAM authorization

The table below describes the authorization required to call this API. You can define it in a Resource Access Management (RAM) policy. The table's columns are detailed below:

Action: The actions can be used in the Action element of RAM permission policy statements to grant permissions to perform the operation.
API: The API that you can call to perform the action.
Access level: The predefined level of access granted for each API. Valid values: create, list, get, update, and delete.
Resource type: The type of the resource that supports authorization to perform the action. It indicates if the action supports resource-level permission. The specified resource must be compatible with the action. Otherwise, the policy will be ineffective.
- For APIs with resource-level permissions, required resource types are marked with an asterisk (*). Specify the corresponding Alibaba Cloud Resource Name (ARN) in the Resource element of the policy.
- For APIs without resource-level permissions, it is shown as All Resources. Use an asterisk (*) in the Resource element of the policy.
Condition key: The condition keys defined by the service. The key allows for granular control, applying to either actions alone or actions associated with specific resources. In addition to service-specific condition keys, Alibaba Cloud provides a set of common condition keys applicable across all RAM-supported services.
Dependent action: The dependent actions required to run the action. To complete the action, the RAM user or the RAM role must have the permissions to perform all dependent actions.

Action

Access level

Resource type

Condition key

Dependent action

gpdb:TextEmbedding

none

*DBInstance

acs:gpdb:{#regionId}:{#accountId}:dbinstance/{#DBInstanceId}

None

Request parameters

Parameter	Type	Required	Description	Example
DBInstanceId	string	Yes	The cluster ID.	gp-xxxxxxxxx
RegionId	string	Yes	The region ID of the cluster.	cn-hangzhou
Model	string	No	The text embedding model. Valid values: text-embedding-v1:1536 dimensions text-embedding-v2:1536 dimensions text-embedding-v3 (default):1024, 768, and 512 dimensions text2vec: 1024 dimensions m3e-base: 768 dimensions m3e-small: 512 dimensions	text-embedding-v3
Input	array	Yes	The text strings to embed. Up to 100 items.
	string	No	The text to embed. Maximum length: 2,048 tokens.	ADBPGYesWhat?
Dimension	integer	No	The number of output dimensions. Defaults to the maximum dimension count supported by the model. Note The text-embedding-v3 supports 1024, 768, and 512 dimensions. Default value: 1024.	1024

Response elements

Element	Type	Description	Example
	object
RequestId	string	The request ID.	ABB39CC3-4488-4857-905D-2E4A051D0521
Message	string	The response message.	Successful
Status	string	The status of the operation. Valid values: success fail	success
TextTokens	integer	The total number of tokens consumed.	1000
Results	object
Results	array<object>	The embedding results.
	array<object>	The embedding result.
Embedding	object
Embedding	array	The embedding values.
	number	The embedding value.	0.1234
Index	integer	The zero-based index of the corresponding entry in the Input parameter.	0

Examples

Success response

JSON format

{
  "RequestId": "ABB39CC3-4488-4857-905D-2E4A051D0521",
  "Message": "Successful",
  "Status": "success",
  "TextTokens": 1000,
  "Results": {
    "Results": [
      {
        "Embedding": {
          "Embedding": [
            0.1234
          ]
        },
        "Index": 0
      }
    ]
  }
}

Error codes

See Error Codes for a complete list.

Release notes

See Release Notes for a complete list.