Model predictions by use case

Headers

Authorization: Bearer ACCESS_TOKEN

string

The authentication and authorization access token.

Content-Type

string

application/json

Path Parameters

USE_CASE

string

required

The name of the use case for the model.

MODEL_ID

string

required

The unique identifier of the model.

Body

application/json

Request information varies based on the use case in the request. See the specific use case for valid information for that use case.

batch

object[]

The batch of key:value pairs used as inputs in the prediction. Up to 32 inputs per request are allowed.

Maximum array length: 32

Show child attributes

batch.text

string

The content the model analyzes.

Example:

"The content the model analyzes."

modelConfig

ModelConfig · object

Provides fields and values that specify ranges for tokens. Fields used for specific use cases and models are specified. The default values are used if other values are not specified.

Show child attributes

modelConfig.temperature

number<float>

A sampling temperature between 0 and 2. A higher sampling temperature such as 0.8, results in more random (creative) output. A lower value such as 0.2 results in more focused (conservative) output. A lower value does not guarantee the model returns the same response for the same input. We recommend staying at or below a temperature of 1.0. Values above 1.0 might return nonsense unless the topP value is lowered to be more deterministic.

Required range: 0 <= x <= 2

Example:

0.8

modelConfig.topP

number<float>

A floating-point number between 0 and 1 that controls the cumulative probability of the top tokens to consider, known as the randomness of the LLM's response. This parameter is also referred to as top probability. Set topP to 1 to consider all tokens. A higher value specifies a higher probability threshold and selects tokens whose cumulative probability is greater than the threshold. The higher the value, the more diverse the output.

Required range: 0 <= x <= 1

Example:

1

modelConfig.topK

integer

An integer that controls the number of top tokens to consider. Set top_k to -1 to consider all tokens.

Example:

-1

modelConfig.presencePenalty

number<float>

A floating-point number between -2.0 and 2.0 that penalizes new tokens based on whether they have already appeared in the text. This increases the model's use of diverse tokens. A value greater than zero (0) encourages the model to use new tokens. A value less than zero (0) encourages the model to repeat existing tokens. This is applicable for all OpenAI, Mistral, and Llama models.

Required range: -2 <= x <= 2

Example:

2

modelConfig.frequencyPenalty

number<float>

A floating-point number between -2.0 and 2.0 that penalizes new tokens based on their frequency in the generated text. A value greater than zero (0) encourages the model to use new tokens. A value less than zero (0) encourages the model to repeat existing tokens. This is applicable for all OpenAI, Mistral, and Llama models.

Required range: -2 <= x <= 2

Example:

1

modelConfig.maxTokens

integer<int32>

The maximum number of tokens to generate per output sequence. The value is different for each model. Review individual model specifications when the value exceeds 2048.

Example:

1

modelConfig.apiKey

string

This optional parameter is only required when using the model for prediction. You can find this value in your model's settings:

OpenAI: Copy and paste the API key found in your organization's settings. For more information, see OpenAI Authentication API keys.
Azure OpenAI: Copy and paste the API key found in your Azure portal. See Authenticate with API key.
Anthropic: Copy and paste the API key found in your Anthrophic console or by using the Anthropic API.
Google Vertex AI: Copy and paste the base64-encoded service account key JSON found in your Google Cloud console. This service account key must have the Vertex AI user role enabled. For more information, see generate service account key.

Example:

"API key specific to use case and model"

modelConfig.azureDeployment

string

This optional parameter is the name of the deployed Azure OpenAI model and is only required when a deployed Azure OpenAI model is used for prediction.

Example:

"DEPLOYMENT_NAME"

modelConfig.azureEndpoint

string

This optional parameter is the URL endpoint of the deployed Azure OpenAI model and is only required when a deployed Azure OpenAI model is used for prediction.

Example:

"https://azure.endpoint.com"

modelConfig.googleProjectId

string

This parameter is optional, and is only required when a Google Vertex AI model is used for prediction.

Example:

"[GOOGLE_PROJECT_ID]"

modelConfig.googleRegion

string

This parameter is optional, and is only required when a Google Vertex AI model is used for prediction. The possible region values are:

us-central1
us-west4
northamerica-northeast1
us-east4
us-west1
asia-northeast3
asia-southeast1
asia-northeast

Example:

"[GOOGLE_PROJECT_REGION_OF_MODEL_ACCESS]"

Response

IMPORTANT: This contains some of the information in a response to this use case. However, the response varies based on the use case and other information in the request. See the specific use case for valid information for that use case.

predictions

object[]

Show child attributes

predictions.response

string

The results returned from the request.

Example:

"The response varies based on the use case of the request. For valid information, see the individual use case documentation. "

Models

Authentication

Use Case

Predict

Async Chunking

Async Predict

Prompt Preview

Signals

Tokenization

Headers

Path Parameters

Body

Response