konnect 2.4.1, Mar 13 25

konnect 2.4.1 published on Thursday, Mar 13, 2025 by kong

konnect.getGatewayPluginAiResponseTransformer

Explore with Pulumi AI

konnect 2.4.1 published on Thursday, Mar 13, 2025 by kong

Using getGatewayPluginAiResponseTransformer

Two invocation forms are available. The direct form accepts plain arguments and either blocks until the result value is available, or returns a Promise-wrapped result. The output form accepts Input-wrapped arguments and returns an Output-wrapped result.

function getGatewayPluginAiResponseTransformer(args: GetGatewayPluginAiResponseTransformerArgs, opts?: InvokeOptions): Promise<GetGatewayPluginAiResponseTransformerResult>
function getGatewayPluginAiResponseTransformerOutput(args: GetGatewayPluginAiResponseTransformerOutputArgs, opts?: InvokeOptions): Output<GetGatewayPluginAiResponseTransformerResult>

def get_gateway_plugin_ai_response_transformer(control_plane_id: Optional[str] = None,
                                               opts: Optional[InvokeOptions] = None) -> GetGatewayPluginAiResponseTransformerResult
def get_gateway_plugin_ai_response_transformer_output(control_plane_id: Optional[pulumi.Input[str]] = None,
                                               opts: Optional[InvokeOptions] = None) -> Output[GetGatewayPluginAiResponseTransformerResult]

func LookupGatewayPluginAiResponseTransformer(ctx *Context, args *LookupGatewayPluginAiResponseTransformerArgs, opts ...InvokeOption) (*LookupGatewayPluginAiResponseTransformerResult, error)
func LookupGatewayPluginAiResponseTransformerOutput(ctx *Context, args *LookupGatewayPluginAiResponseTransformerOutputArgs, opts ...InvokeOption) LookupGatewayPluginAiResponseTransformerResultOutput

> Note: This function is named LookupGatewayPluginAiResponseTransformer in the Go SDK.

public static class GetGatewayPluginAiResponseTransformer 
{
    public static Task<GetGatewayPluginAiResponseTransformerResult> InvokeAsync(GetGatewayPluginAiResponseTransformerArgs args, InvokeOptions? opts = null)
    public static Output<GetGatewayPluginAiResponseTransformerResult> Invoke(GetGatewayPluginAiResponseTransformerInvokeArgs args, InvokeOptions? opts = null)
}

public static CompletableFuture<GetGatewayPluginAiResponseTransformerResult> getGatewayPluginAiResponseTransformer(GetGatewayPluginAiResponseTransformerArgs args, InvokeOptions options)
public static Output<GetGatewayPluginAiResponseTransformerResult> getGatewayPluginAiResponseTransformer(GetGatewayPluginAiResponseTransformerArgs args, InvokeOptions options)

fn::invoke:
  function: konnect:index/getGatewayPluginAiResponseTransformer:getGatewayPluginAiResponseTransformer
  arguments:
    # arguments dictionary

The following arguments are supported:

ControlPlaneId string

ControlPlaneId string

controlPlaneId String

controlPlaneId string

control_plane_id str

controlPlaneId String

getGatewayPluginAiResponseTransformer Result

The following output properties are available:

config Property Map
consumer Property Map
consumerGroup Property Map
controlPlaneId String
createdAt Number
enabled Boolean
id String
instanceName String
ordering Property Map
protocols List<String>
route Property Map
service Property Map
tags List<String>
updatedAt Number

Supporting Types

GetGatewayPluginAiResponseTransformerConfig

HttpProxyHost string: A string representing a host name, such as example.com.
HttpProxyPort double: An integer representing a port number between 0 and 65535, inclusive.
HttpTimeout double: Timeout in milliseconds for the AI upstream service.
HttpsProxyHost string: A string representing a host name, such as example.com.
HttpsProxyPort double: An integer representing a port number between 0 and 65535, inclusive.
HttpsVerify bool: Verify the TLS certificate of the AI upstream service.
Llm GetGatewayPluginAiResponseTransformerConfigLlm
MaxRequestBodySize double: max allowed body size allowed to be introspected
ParseLlmResponseJsonInstructions bool: Set true to read specific response format from the LLM, and accordingly set the status code / body / headers that proxy back to the client. You need to engineer your LLM prompt to return the correct format, see plugin docs 'Overview' page for usage instructions.
Prompt string: Use this prompt to tune the LLM system/assistant message for the returning proxy response (from the upstream), adn what response format you are expecting.
TransformationExtractPattern string: Defines the regular expression that must match to indicate a successful AI transformation at the response phase. The first match will be set as the returning body. If the AI service's response doesn't match this pattern, a failure is returned to the client.

HttpProxyHost string: A string representing a host name, such as example.com.
HttpProxyPort float64: An integer representing a port number between 0 and 65535, inclusive.
HttpTimeout float64: Timeout in milliseconds for the AI upstream service.
HttpsProxyHost string: A string representing a host name, such as example.com.
HttpsProxyPort float64: An integer representing a port number between 0 and 65535, inclusive.
HttpsVerify bool: Verify the TLS certificate of the AI upstream service.
Llm GetGatewayPluginAiResponseTransformerConfigLlm
MaxRequestBodySize float64: max allowed body size allowed to be introspected
ParseLlmResponseJsonInstructions bool: Set true to read specific response format from the LLM, and accordingly set the status code / body / headers that proxy back to the client. You need to engineer your LLM prompt to return the correct format, see plugin docs 'Overview' page for usage instructions.
Prompt string: Use this prompt to tune the LLM system/assistant message for the returning proxy response (from the upstream), adn what response format you are expecting.
TransformationExtractPattern string: Defines the regular expression that must match to indicate a successful AI transformation at the response phase. The first match will be set as the returning body. If the AI service's response doesn't match this pattern, a failure is returned to the client.

httpProxyHost String: A string representing a host name, such as example.com.
httpProxyPort Double: An integer representing a port number between 0 and 65535, inclusive.
httpTimeout Double: Timeout in milliseconds for the AI upstream service.
httpsProxyHost String: A string representing a host name, such as example.com.
httpsProxyPort Double: An integer representing a port number between 0 and 65535, inclusive.
httpsVerify Boolean: Verify the TLS certificate of the AI upstream service.
llm GetGatewayPluginAiResponseTransformerConfigLlm
maxRequestBodySize Double: max allowed body size allowed to be introspected
parseLlmResponseJsonInstructions Boolean: Set true to read specific response format from the LLM, and accordingly set the status code / body / headers that proxy back to the client. You need to engineer your LLM prompt to return the correct format, see plugin docs 'Overview' page for usage instructions.
prompt String: Use this prompt to tune the LLM system/assistant message for the returning proxy response (from the upstream), adn what response format you are expecting.
transformationExtractPattern String: Defines the regular expression that must match to indicate a successful AI transformation at the response phase. The first match will be set as the returning body. If the AI service's response doesn't match this pattern, a failure is returned to the client.

httpProxyHost string: A string representing a host name, such as example.com.
httpProxyPort number: An integer representing a port number between 0 and 65535, inclusive.
httpTimeout number: Timeout in milliseconds for the AI upstream service.
httpsProxyHost string: A string representing a host name, such as example.com.
httpsProxyPort number: An integer representing a port number between 0 and 65535, inclusive.
httpsVerify boolean: Verify the TLS certificate of the AI upstream service.
llm GetGatewayPluginAiResponseTransformerConfigLlm
maxRequestBodySize number: max allowed body size allowed to be introspected
parseLlmResponseJsonInstructions boolean: Set true to read specific response format from the LLM, and accordingly set the status code / body / headers that proxy back to the client. You need to engineer your LLM prompt to return the correct format, see plugin docs 'Overview' page for usage instructions.
prompt string: Use this prompt to tune the LLM system/assistant message for the returning proxy response (from the upstream), adn what response format you are expecting.
transformationExtractPattern string: Defines the regular expression that must match to indicate a successful AI transformation at the response phase. The first match will be set as the returning body. If the AI service's response doesn't match this pattern, a failure is returned to the client.

http_proxy_host str: A string representing a host name, such as example.com.
http_proxy_port float: An integer representing a port number between 0 and 65535, inclusive.
http_timeout float: Timeout in milliseconds for the AI upstream service.
https_proxy_host str: A string representing a host name, such as example.com.
https_proxy_port float: An integer representing a port number between 0 and 65535, inclusive.
https_verify bool: Verify the TLS certificate of the AI upstream service.
llm GetGatewayPluginAiResponseTransformerConfigLlm
max_request_body_size float: max allowed body size allowed to be introspected
parse_llm_response_json_instructions bool: Set true to read specific response format from the LLM, and accordingly set the status code / body / headers that proxy back to the client. You need to engineer your LLM prompt to return the correct format, see plugin docs 'Overview' page for usage instructions.
prompt str: Use this prompt to tune the LLM system/assistant message for the returning proxy response (from the upstream), adn what response format you are expecting.
transformation_extract_pattern str: Defines the regular expression that must match to indicate a successful AI transformation at the response phase. The first match will be set as the returning body. If the AI service's response doesn't match this pattern, a failure is returned to the client.

httpProxyHost String: A string representing a host name, such as example.com.
httpProxyPort Number: An integer representing a port number between 0 and 65535, inclusive.
httpTimeout Number: Timeout in milliseconds for the AI upstream service.
httpsProxyHost String: A string representing a host name, such as example.com.
httpsProxyPort Number: An integer representing a port number between 0 and 65535, inclusive.
httpsVerify Boolean: Verify the TLS certificate of the AI upstream service.
llm Property Map
maxRequestBodySize Number: max allowed body size allowed to be introspected
parseLlmResponseJsonInstructions Boolean: Set true to read specific response format from the LLM, and accordingly set the status code / body / headers that proxy back to the client. You need to engineer your LLM prompt to return the correct format, see plugin docs 'Overview' page for usage instructions.
prompt String: Use this prompt to tune the LLM system/assistant message for the returning proxy response (from the upstream), adn what response format you are expecting.
transformationExtractPattern String: Defines the regular expression that must match to indicate a successful AI transformation at the response phase. The first match will be set as the returning body. If the AI service's response doesn't match this pattern, a failure is returned to the client.

GetGatewayPluginAiResponseTransformerConfigLlm

Auth GetGatewayPluginAiResponseTransformerConfigLlmAuth
Logging GetGatewayPluginAiResponseTransformerConfigLlmLogging
Model GetGatewayPluginAiResponseTransformerConfigLlmModel
RouteType string: The model's operation implementation, for this provider. Set to preserve to pass through without transformation.

Auth GetGatewayPluginAiResponseTransformerConfigLlmAuth
Logging GetGatewayPluginAiResponseTransformerConfigLlmLogging
Model GetGatewayPluginAiResponseTransformerConfigLlmModel
RouteType string: The model's operation implementation, for this provider. Set to preserve to pass through without transformation.

auth GetGatewayPluginAiResponseTransformerConfigLlmAuth
logging GetGatewayPluginAiResponseTransformerConfigLlmLogging
model GetGatewayPluginAiResponseTransformerConfigLlmModel
routeType String: The model's operation implementation, for this provider. Set to preserve to pass through without transformation.

auth GetGatewayPluginAiResponseTransformerConfigLlmAuth
logging GetGatewayPluginAiResponseTransformerConfigLlmLogging
model GetGatewayPluginAiResponseTransformerConfigLlmModel
routeType string: The model's operation implementation, for this provider. Set to preserve to pass through without transformation.

auth GetGatewayPluginAiResponseTransformerConfigLlmAuth
logging GetGatewayPluginAiResponseTransformerConfigLlmLogging
model GetGatewayPluginAiResponseTransformerConfigLlmModel
route_type str: The model's operation implementation, for this provider. Set to preserve to pass through without transformation.

auth Property Map
logging Property Map
model Property Map
routeType String: The model's operation implementation, for this provider. Set to preserve to pass through without transformation.

GetGatewayPluginAiResponseTransformerConfigLlmAuth

AllowOverride bool: If enabled, the authorization header or parameter can be overridden in the request by the value configured in the plugin.
AwsAccessKeyId string: 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.
AwsSecretAccessKey string: 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.
AzureClientId string: 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.
AzureClientSecret string: 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.
AzureTenantId string: 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.
AzureUseManagedIdentity bool: Set true to use the Azure Cloud Managed Identity (or user-assigned identity) to authenticate with Azure-provider models.
GcpServiceAccountJson string: 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.
GcpUseServiceAccount bool: Use service account auth for GCP-based providers and models.
HeaderName string: If AI model requires authentication via Authorization or API key header, specify its name here.
HeaderValue string: Specify the full auth header value for 'header_name', for example 'Bearer key' or just 'key'.
ParamLocation string: Specify whether the 'param_name' and 'param_value' options go in a query string, or the POST form/JSON body.
ParamName string: If AI model requires authentication via query parameter, specify its name here.
ParamValue string: Specify the full parameter value for 'param_name'.

AllowOverride bool: If enabled, the authorization header or parameter can be overridden in the request by the value configured in the plugin.
AwsAccessKeyId string: 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.
AwsSecretAccessKey string: 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.
AzureClientId string: 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.
AzureClientSecret string: 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.
AzureTenantId string: 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.
AzureUseManagedIdentity bool: Set true to use the Azure Cloud Managed Identity (or user-assigned identity) to authenticate with Azure-provider models.
GcpServiceAccountJson string: 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.
GcpUseServiceAccount bool: Use service account auth for GCP-based providers and models.
HeaderName string: If AI model requires authentication via Authorization or API key header, specify its name here.
HeaderValue string: Specify the full auth header value for 'header_name', for example 'Bearer key' or just 'key'.
ParamLocation string: Specify whether the 'param_name' and 'param_value' options go in a query string, or the POST form/JSON body.
ParamName string: If AI model requires authentication via query parameter, specify its name here.
ParamValue string: Specify the full parameter value for 'param_name'.

allowOverride Boolean: If enabled, the authorization header or parameter can be overridden in the request by the value configured in the plugin.
awsAccessKeyId String: 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.
awsSecretAccessKey String: 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.
azureClientId String: 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.
azureClientSecret String: 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.
azureTenantId String: 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.
azureUseManagedIdentity Boolean: Set true to use the Azure Cloud Managed Identity (or user-assigned identity) to authenticate with Azure-provider models.
gcpServiceAccountJson String: 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.
gcpUseServiceAccount Boolean: Use service account auth for GCP-based providers and models.
headerName String: If AI model requires authentication via Authorization or API key header, specify its name here.
headerValue String: Specify the full auth header value for 'header_name', for example 'Bearer key' or just 'key'.
paramLocation String: Specify whether the 'param_name' and 'param_value' options go in a query string, or the POST form/JSON body.
paramName String: If AI model requires authentication via query parameter, specify its name here.
paramValue String: Specify the full parameter value for 'param_name'.

allowOverride boolean: If enabled, the authorization header or parameter can be overridden in the request by the value configured in the plugin.
awsAccessKeyId string: 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.
awsSecretAccessKey string: 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.
azureClientId string: 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.
azureClientSecret string: 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.
azureTenantId string: 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.
azureUseManagedIdentity boolean: Set true to use the Azure Cloud Managed Identity (or user-assigned identity) to authenticate with Azure-provider models.
gcpServiceAccountJson string: 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.
gcpUseServiceAccount boolean: Use service account auth for GCP-based providers and models.
headerName string: If AI model requires authentication via Authorization or API key header, specify its name here.
headerValue string: Specify the full auth header value for 'header_name', for example 'Bearer key' or just 'key'.
paramLocation string: Specify whether the 'param_name' and 'param_value' options go in a query string, or the POST form/JSON body.
paramName string: If AI model requires authentication via query parameter, specify its name here.
paramValue string: Specify the full parameter value for 'param_name'.

allow_override bool: If enabled, the authorization header or parameter can be overridden in the request by the value configured in the plugin.
aws_access_key_id str: 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.
aws_secret_access_key str: 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.
azure_client_id str: 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.
azure_client_secret str: 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.
azure_tenant_id str: 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.
azure_use_managed_identity bool: Set true to use the Azure Cloud Managed Identity (or user-assigned identity) to authenticate with Azure-provider models.
gcp_service_account_json str: 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.
gcp_use_service_account bool: Use service account auth for GCP-based providers and models.
header_name str: If AI model requires authentication via Authorization or API key header, specify its name here.
header_value str: Specify the full auth header value for 'header_name', for example 'Bearer key' or just 'key'.
param_location str: Specify whether the 'param_name' and 'param_value' options go in a query string, or the POST form/JSON body.
param_name str: If AI model requires authentication via query parameter, specify its name here.
param_value str: Specify the full parameter value for 'param_name'.

allowOverride Boolean: If enabled, the authorization header or parameter can be overridden in the request by the value configured in the plugin.
awsAccessKeyId String: 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.
awsSecretAccessKey String: 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.
azureClientId String: 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.
azureClientSecret String: 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.
azureTenantId String: 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.
azureUseManagedIdentity Boolean: Set true to use the Azure Cloud Managed Identity (or user-assigned identity) to authenticate with Azure-provider models.
gcpServiceAccountJson String: 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.
gcpUseServiceAccount Boolean: Use service account auth for GCP-based providers and models.
headerName String: If AI model requires authentication via Authorization or API key header, specify its name here.
headerValue String: Specify the full auth header value for 'header_name', for example 'Bearer key' or just 'key'.
paramLocation String: Specify whether the 'param_name' and 'param_value' options go in a query string, or the POST form/JSON body.
paramName String: If AI model requires authentication via query parameter, specify its name here.
paramValue String: Specify the full parameter value for 'param_name'.

GetGatewayPluginAiResponseTransformerConfigLlmLogging

LogPayloads bool: If enabled, will log the request and response body into the Kong log plugin(s) output.
LogStatistics bool: If enabled and supported by the driver, will add model usage and token metrics into the Kong log plugin(s) output.

LogPayloads bool: If enabled, will log the request and response body into the Kong log plugin(s) output.
LogStatistics bool: If enabled and supported by the driver, will add model usage and token metrics into the Kong log plugin(s) output.

logPayloads Boolean: If enabled, will log the request and response body into the Kong log plugin(s) output.
logStatistics Boolean: If enabled and supported by the driver, will add model usage and token metrics into the Kong log plugin(s) output.

logPayloads boolean: If enabled, will log the request and response body into the Kong log plugin(s) output.
logStatistics boolean: If enabled and supported by the driver, will add model usage and token metrics into the Kong log plugin(s) output.

log_payloads bool: If enabled, will log the request and response body into the Kong log plugin(s) output.
log_statistics bool: If enabled and supported by the driver, will add model usage and token metrics into the Kong log plugin(s) output.

logPayloads Boolean: If enabled, will log the request and response body into the Kong log plugin(s) output.
logStatistics Boolean: If enabled and supported by the driver, will add model usage and token metrics into the Kong log plugin(s) output.

GetGatewayPluginAiResponseTransformerConfigLlmModel

Name string: Model name to execute.
Options GetGatewayPluginAiResponseTransformerConfigLlmModelOptions: Key/value settings for the model
Provider string: AI provider request format - Kong translates requests to and from the specified backend compatible formats.

Name string: Model name to execute.
Options GetGatewayPluginAiResponseTransformerConfigLlmModelOptions: Key/value settings for the model
Provider string: AI provider request format - Kong translates requests to and from the specified backend compatible formats.

name String: Model name to execute.
options GetGatewayPluginAiResponseTransformerConfigLlmModelOptions: Key/value settings for the model
provider String: AI provider request format - Kong translates requests to and from the specified backend compatible formats.

name string: Model name to execute.
options GetGatewayPluginAiResponseTransformerConfigLlmModelOptions: Key/value settings for the model
provider string: AI provider request format - Kong translates requests to and from the specified backend compatible formats.

name str: Model name to execute.
options GetGatewayPluginAiResponseTransformerConfigLlmModelOptions: Key/value settings for the model
provider str: AI provider request format - Kong translates requests to and from the specified backend compatible formats.

name String: Model name to execute.
options Property Map: Key/value settings for the model
provider String: AI provider request format - Kong translates requests to and from the specified backend compatible formats.

GetGatewayPluginAiResponseTransformerConfigLlmModelOptions

AnthropicVersion string: Defines the schema/API version, if using Anthropic provider.
AzureApiVersion string: 'api-version' for Azure OpenAI instances.
AzureDeploymentId string: Deployment ID for Azure OpenAI instances.
AzureInstance string: Instance name for Azure OpenAI hosted models.
Bedrock GetGatewayPluginAiResponseTransformerConfigLlmModelOptionsBedrock
Gemini GetGatewayPluginAiResponseTransformerConfigLlmModelOptionsGemini
Huggingface GetGatewayPluginAiResponseTransformerConfigLlmModelOptionsHuggingface
InputCost double: Defines the cost per 1M tokens in your prompt.
Llama2Format string: If using llama2 provider, select the upstream message format.
MaxTokens double: Defines the max_tokens, if using chat or completion models.
MistralFormat string: If using mistral provider, select the upstream message format.
OutputCost double: Defines the cost per 1M tokens in the output of the AI.
Temperature double: Defines the matching temperature, if using chat or completion models.
TopK double: Defines the top-k most likely tokens, if supported.
TopP double: Defines the top-p probability mass, if supported.
UpstreamPath string: Manually specify or override the AI operation path, used when e.g. using the 'preserve' route_type.
UpstreamUrl string: Manually specify or override the full URL to the AI operation endpoints, when calling (self-)hosted models, or for running via a private endpoint.

AnthropicVersion string: Defines the schema/API version, if using Anthropic provider.
AzureApiVersion string: 'api-version' for Azure OpenAI instances.
AzureDeploymentId string: Deployment ID for Azure OpenAI instances.
AzureInstance string: Instance name for Azure OpenAI hosted models.
Bedrock GetGatewayPluginAiResponseTransformerConfigLlmModelOptionsBedrock
Gemini GetGatewayPluginAiResponseTransformerConfigLlmModelOptionsGemini
Huggingface GetGatewayPluginAiResponseTransformerConfigLlmModelOptionsHuggingface
InputCost float64: Defines the cost per 1M tokens in your prompt.
Llama2Format string: If using llama2 provider, select the upstream message format.
MaxTokens float64: Defines the max_tokens, if using chat or completion models.
MistralFormat string: If using mistral provider, select the upstream message format.
OutputCost float64: Defines the cost per 1M tokens in the output of the AI.
Temperature float64: Defines the matching temperature, if using chat or completion models.
TopK float64: Defines the top-k most likely tokens, if supported.
TopP float64: Defines the top-p probability mass, if supported.
UpstreamPath string: Manually specify or override the AI operation path, used when e.g. using the 'preserve' route_type.
UpstreamUrl string: Manually specify or override the full URL to the AI operation endpoints, when calling (self-)hosted models, or for running via a private endpoint.

anthropicVersion String: Defines the schema/API version, if using Anthropic provider.
azureApiVersion String: 'api-version' for Azure OpenAI instances.
azureDeploymentId String: Deployment ID for Azure OpenAI instances.
azureInstance String: Instance name for Azure OpenAI hosted models.
bedrock GetGatewayPluginAiResponseTransformerConfigLlmModelOptionsBedrock
gemini GetGatewayPluginAiResponseTransformerConfigLlmModelOptionsGemini
huggingface GetGatewayPluginAiResponseTransformerConfigLlmModelOptionsHuggingface
inputCost Double: Defines the cost per 1M tokens in your prompt.
llama2Format String: If using llama2 provider, select the upstream message format.
maxTokens Double: Defines the max_tokens, if using chat or completion models.
mistralFormat String: If using mistral provider, select the upstream message format.
outputCost Double: Defines the cost per 1M tokens in the output of the AI.
temperature Double: Defines the matching temperature, if using chat or completion models.
topK Double: Defines the top-k most likely tokens, if supported.
topP Double: Defines the top-p probability mass, if supported.
upstreamPath String: Manually specify or override the AI operation path, used when e.g. using the 'preserve' route_type.
upstreamUrl String: Manually specify or override the full URL to the AI operation endpoints, when calling (self-)hosted models, or for running via a private endpoint.

anthropicVersion string: Defines the schema/API version, if using Anthropic provider.
azureApiVersion string: 'api-version' for Azure OpenAI instances.
azureDeploymentId string: Deployment ID for Azure OpenAI instances.
azureInstance string: Instance name for Azure OpenAI hosted models.
bedrock GetGatewayPluginAiResponseTransformerConfigLlmModelOptionsBedrock
gemini GetGatewayPluginAiResponseTransformerConfigLlmModelOptionsGemini
huggingface GetGatewayPluginAiResponseTransformerConfigLlmModelOptionsHuggingface
inputCost number: Defines the cost per 1M tokens in your prompt.
llama2Format string: If using llama2 provider, select the upstream message format.
maxTokens number: Defines the max_tokens, if using chat or completion models.
mistralFormat string: If using mistral provider, select the upstream message format.
outputCost number: Defines the cost per 1M tokens in the output of the AI.
temperature number: Defines the matching temperature, if using chat or completion models.
topK number: Defines the top-k most likely tokens, if supported.
topP number: Defines the top-p probability mass, if supported.
upstreamPath string: Manually specify or override the AI operation path, used when e.g. using the 'preserve' route_type.
upstreamUrl string: Manually specify or override the full URL to the AI operation endpoints, when calling (self-)hosted models, or for running via a private endpoint.

anthropic_version str: Defines the schema/API version, if using Anthropic provider.
azure_api_version str: 'api-version' for Azure OpenAI instances.
azure_deployment_id str: Deployment ID for Azure OpenAI instances.
azure_instance str: Instance name for Azure OpenAI hosted models.
bedrock GetGatewayPluginAiResponseTransformerConfigLlmModelOptionsBedrock
gemini GetGatewayPluginAiResponseTransformerConfigLlmModelOptionsGemini
huggingface GetGatewayPluginAiResponseTransformerConfigLlmModelOptionsHuggingface
input_cost float: Defines the cost per 1M tokens in your prompt.
llama2_format str: If using llama2 provider, select the upstream message format.
max_tokens float: Defines the max_tokens, if using chat or completion models.
mistral_format str: If using mistral provider, select the upstream message format.
output_cost float: Defines the cost per 1M tokens in the output of the AI.
temperature float: Defines the matching temperature, if using chat or completion models.
top_k float: Defines the top-k most likely tokens, if supported.
top_p float: Defines the top-p probability mass, if supported.
upstream_path str: Manually specify or override the AI operation path, used when e.g. using the 'preserve' route_type.
upstream_url str: Manually specify or override the full URL to the AI operation endpoints, when calling (self-)hosted models, or for running via a private endpoint.

anthropicVersion String: Defines the schema/API version, if using Anthropic provider.
azureApiVersion String: 'api-version' for Azure OpenAI instances.
azureDeploymentId String: Deployment ID for Azure OpenAI instances.
azureInstance String: Instance name for Azure OpenAI hosted models.
bedrock Property Map
gemini Property Map
huggingface Property Map
inputCost Number: Defines the cost per 1M tokens in your prompt.
llama2Format String: If using llama2 provider, select the upstream message format.
maxTokens Number: Defines the max_tokens, if using chat or completion models.
mistralFormat String: If using mistral provider, select the upstream message format.
outputCost Number: Defines the cost per 1M tokens in the output of the AI.
temperature Number: Defines the matching temperature, if using chat or completion models.
topK Number: Defines the top-k most likely tokens, if supported.
topP Number: Defines the top-p probability mass, if supported.
upstreamPath String: Manually specify or override the AI operation path, used when e.g. using the 'preserve' route_type.
upstreamUrl String: Manually specify or override the full URL to the AI operation endpoints, when calling (self-)hosted models, or for running via a private endpoint.

GetGatewayPluginAiResponseTransformerConfigLlmModelOptionsBedrock

AwsRegion string: If using AWS providers (Bedrock) you can override the AWS_REGION environment variable by setting this option.

AwsRegion string: If using AWS providers (Bedrock) you can override the AWS_REGION environment variable by setting this option.

awsRegion String: If using AWS providers (Bedrock) you can override the AWS_REGION environment variable by setting this option.

awsRegion string: If using AWS providers (Bedrock) you can override the AWS_REGION environment variable by setting this option.

aws_region str: If using AWS providers (Bedrock) you can override the AWS_REGION environment variable by setting this option.

awsRegion String: If using AWS providers (Bedrock) you can override the AWS_REGION environment variable by setting this option.

GetGatewayPluginAiResponseTransformerConfigLlmModelOptionsGemini

ApiEndpoint string: If running Gemini on Vertex, specify the regional API endpoint (hostname only).
LocationId string: If running Gemini on Vertex, specify the location ID.
ProjectId string: If running Gemini on Vertex, specify the project ID.

ApiEndpoint string: If running Gemini on Vertex, specify the regional API endpoint (hostname only).
LocationId string: If running Gemini on Vertex, specify the location ID.
ProjectId string: If running Gemini on Vertex, specify the project ID.

apiEndpoint String: If running Gemini on Vertex, specify the regional API endpoint (hostname only).
locationId String: If running Gemini on Vertex, specify the location ID.
projectId String: If running Gemini on Vertex, specify the project ID.

apiEndpoint string: If running Gemini on Vertex, specify the regional API endpoint (hostname only).
locationId string: If running Gemini on Vertex, specify the location ID.
projectId string: If running Gemini on Vertex, specify the project ID.

api_endpoint str: If running Gemini on Vertex, specify the regional API endpoint (hostname only).
location_id str: If running Gemini on Vertex, specify the location ID.
project_id str: If running Gemini on Vertex, specify the project ID.

apiEndpoint String: If running Gemini on Vertex, specify the regional API endpoint (hostname only).
locationId String: If running Gemini on Vertex, specify the location ID.
projectId String: If running Gemini on Vertex, specify the project ID.

GetGatewayPluginAiResponseTransformerConfigLlmModelOptionsHuggingface

UseCache bool: Use the cache layer on the inference API
WaitForModel bool: Wait for the model if it is not ready

UseCache bool: Use the cache layer on the inference API
WaitForModel bool: Wait for the model if it is not ready

useCache Boolean: Use the cache layer on the inference API
waitForModel Boolean: Wait for the model if it is not ready

useCache boolean: Use the cache layer on the inference API
waitForModel boolean: Wait for the model if it is not ready

use_cache bool: Use the cache layer on the inference API
wait_for_model bool: Wait for the model if it is not ready

useCache Boolean: Use the cache layer on the inference API
waitForModel Boolean: Wait for the model if it is not ready

GetGatewayPluginAiResponseTransformerConsumer

Id string

Id string

id String

id string

id str

id String

GetGatewayPluginAiResponseTransformerConsumerGroup

Id string

Id string

id String

id string

id str

id String

GetGatewayPluginAiResponseTransformerOrdering

After GetGatewayPluginAiResponseTransformerOrderingAfter
Before GetGatewayPluginAiResponseTransformerOrderingBefore

After GetGatewayPluginAiResponseTransformerOrderingAfter
Before GetGatewayPluginAiResponseTransformerOrderingBefore

after GetGatewayPluginAiResponseTransformerOrderingAfter
before GetGatewayPluginAiResponseTransformerOrderingBefore

after GetGatewayPluginAiResponseTransformerOrderingAfter
before GetGatewayPluginAiResponseTransformerOrderingBefore

after GetGatewayPluginAiResponseTransformerOrderingAfter
before GetGatewayPluginAiResponseTransformerOrderingBefore

after Property Map
before Property Map

GetGatewayPluginAiResponseTransformerOrderingAfter

Accesses List<string>

Accesses []string

accesses List<String>

accesses string[]

accesses Sequence[str]

accesses List<String>

GetGatewayPluginAiResponseTransformerOrderingBefore

Accesses List<string>

Accesses []string

accesses List<String>

accesses string[]

accesses Sequence[str]

accesses List<String>

GetGatewayPluginAiResponseTransformerRoute

Id string

Id string

id String

id string

id str

id String

GetGatewayPluginAiResponseTransformerService

Id string

Id string

id String

id string

id str

id String

Package Details

Repository: konnect kong/terraform-provider-konnect
License
Notes: This Pulumi package is based on the konnect Terraform Provider.

konnect 2.4.1 published on Thursday, Mar 13, 2025 by kong

kong/terraform-provider-konnect

konnect.getGatewayPluginAiResponseTransformer

On this page

On this page

Using getGatewayPluginAiResponseTransformer

getGatewayPluginAiResponseTransformer Result

Supporting Types

GetGatewayPluginAiResponseTransformerConfig

GetGatewayPluginAiResponseTransformerConfigLlm

GetGatewayPluginAiResponseTransformerConfigLlmAuth

GetGatewayPluginAiResponseTransformerConfigLlmLogging

GetGatewayPluginAiResponseTransformerConfigLlmModel

GetGatewayPluginAiResponseTransformerConfigLlmModelOptions

GetGatewayPluginAiResponseTransformerConfigLlmModelOptionsBedrock

GetGatewayPluginAiResponseTransformerConfigLlmModelOptionsGemini

GetGatewayPluginAiResponseTransformerConfigLlmModelOptionsHuggingface

GetGatewayPluginAiResponseTransformerConsumer

GetGatewayPluginAiResponseTransformerConsumerGroup

GetGatewayPluginAiResponseTransformerOrdering

GetGatewayPluginAiResponseTransformerOrderingAfter

GetGatewayPluginAiResponseTransformerOrderingBefore

GetGatewayPluginAiResponseTransformerRoute

GetGatewayPluginAiResponseTransformerService

Package Details

On this page

On this page