Interface GenerateContentConfig

If enabled, audio timestamp will be included in the request to the model.

Resource name of a context cache that can be used in subsequent requests.

Number of response variations to return.

Positive values penalize tokens that repeatedly appear in the generated text, increasing the probability of generating more diverse content.

Used to override HTTP request options.

Labels with user-defined metadata to break down billed charges.

Number of top candidate tokens to return the log probabilities for at each generation step.

Maximum number of tokens that can be generated in the response.

If specified, the media resolution specified will be used.

Positive values penalize tokens that already appear in the generated text, increasing the probability of generating more diverse content.

Whether to return the log probabilities of the tokens that were chosen by the model at each step.

Output response media type of the generated candidate text.

The requested modalities of the response. Represents the set of modalities that the model can return.

Schema that the generated candidate text must adhere to.

Configuration for model router requests.

Safety settings in the request to block unsafe content in the response.

When seed is fixed to a specific number, the model makes a best effort to provide the same response for repeated requests. By default, a random number is used.

The speech generation configuration.

List of strings that tells the model to stop generating text if one of the strings is encountered in the response.

Instructions for the model to steer it toward better performance. For example, "Answer as concisely as possible" or "Don't use technical terms in your response".

Value that controls the degree of randomness in token selection. Lower temperatures are good for prompts that require a less open-ended or creative response, while higher temperatures can lead to more diverse or creative results.

The thinking features configuration.

Associates model output to a specific function call.

Code that enables the system to interact with external systems to perform an action outside of the knowledge and scope of the model.

For each token selection step, the top_k tokens with the highest probabilities are sampled. Then tokens are further filtered based on top_p with the final token selected using temperature sampling. Use a lower number for less random responses and a higher number for more random responses.

Tokens are selected from the most to least probable until the sum of their probabilities equals this value. Use a lower value for less random responses and a higher value for more random responses.