Package com.google.genai.types

Class GenerationConfig

java.lang.Object
com.google.genai.JsonSerializable
com.google.genai.types.GenerationConfig
public abstract class GenerationConfig extends JsonSerializable
Generation config.

  • Constructor Details

    • GenerationConfig

      public GenerationConfig()

  • Method Details

    • modelSelectionConfig

      public abstract Optional<ModelSelectionConfig> modelSelectionConfig()
      Optional. Config for model selection.

    • responseJsonSchema

      public abstract Optional<Object> responseJsonSchema()
      Output schema of the generated response. This is an alternative to `response_schema` that accepts [JSON Schema](https://json-schema.org/).

    • audioTimestamp

      public abstract Optional<Boolean> audioTimestamp()
      Optional. If enabled, audio timestamps will be included in the request to the model. This can be useful for synchronizing audio with other modalities in the response. This field is not supported in Gemini API.

    • candidateCount

      public abstract Optional<Integer> candidateCount()
      Optional. The number of candidate responses to generate. A higher `candidate_count` can provide more options to choose from, but it also consumes more resources. This can be useful for generating a variety of responses and selecting the best one.

    • enableAffectiveDialog

      public abstract Optional<Boolean> enableAffectiveDialog()
      Optional. If enabled, the model will detect emotions and adapt its responses accordingly. For example, if the model detects that the user is frustrated, it may provide a more empathetic response. This field is not supported in Gemini API.

    • frequencyPenalty

      public abstract Optional<Float> frequencyPenalty()
      Optional. Penalizes tokens based on their frequency in the generated text. A positive value helps to reduce the repetition of words and phrases. Valid values can range from [-2.0, 2.0].

    • logprobs

      public abstract Optional<Integer> logprobs()
      Optional. The number of top log probabilities to return for each token. This can be used to see which other tokens were considered likely candidates for a given position. A higher value will return more options, but it will also increase the size of the response.

    • maxOutputTokens

      public abstract Optional<Integer> maxOutputTokens()
      Optional. The maximum number of tokens to generate in the response. A token is approximately four characters. The default value varies by model. This parameter can be used to control the length of the generated text and prevent overly long responses.

    • mediaResolution

      public abstract Optional<MediaResolution> mediaResolution()
      Optional. The token resolution at which input media content is sampled. This is used to control the trade-off between the quality of the response and the number of tokens used to represent the media. A higher resolution allows the model to perceive more detail, which can lead to a more nuanced response, but it will also use more tokens. This does not affect the image dimensions sent to the model.

    • presencePenalty

      public abstract Optional<Float> presencePenalty()
      Optional. Penalizes tokens that have already appeared in the generated text. A positive value encourages the model to generate more diverse and less repetitive text. Valid values can range from [-2.0, 2.0].

    • responseLogprobs

      public abstract Optional<Boolean> responseLogprobs()
      Optional. If set to true, the log probabilities of the output tokens are returned. Log probabilities are the logarithm of the probability of a token appearing in the output. A higher log probability means the token is more likely to be generated. This can be useful for analyzing the model's confidence in its own output and for debugging.

    • responseMimeType

      public abstract Optional<String> responseMimeType()
      Optional. The IANA standard MIME type of the response. The model will generate output that conforms to this MIME type. Supported values include 'text/plain' (default) and 'application/json'. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined. This is a preview feature.

    • responseModalities

      public abstract Optional<List<Modality>> responseModalities()
      Optional. The modalities of the response. The model will generate a response that includes all the specified modalities. For example, if this is set to `[TEXT, IMAGE]`, the response will include both text and an image.

    • responseSchema

      public abstract Optional<Schema> responseSchema()
      Optional. Lets you to specify a schema for the model's response, ensuring that the output conforms to a particular structure. This is useful for generating structured data such as JSON. The schema is a subset of the [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema) object. When this field is set, you must also set the `response_mime_type` to `application/json`.

    • routingConfig

      public abstract Optional<GenerationConfigRoutingConfig> routingConfig()
      Optional. Routing configuration. This field is not supported in Gemini API.

    • seed

      public abstract Optional<Integer> seed()
      Optional. A seed for the random number generator. By setting a seed, you can make the model's output mostly deterministic. For a given prompt and parameters (like temperature, top_p, etc.), the model will produce the same response every time. However, it's not a guaranteed absolute deterministic behavior. This is different from parameters like `temperature`, which control the *level* of randomness. `seed` ensures that the "random" choices the model makes are the same on every run, making it essential for testing and ensuring reproducible results.

    • speechConfig

      public abstract Optional<SpeechConfig> speechConfig()
      Optional. The speech generation config.

    • stopSequences

      public abstract Optional<List<String>> stopSequences()
      Optional. A list of character sequences that will stop the model from generating further tokens. If a stop sequence is generated, the output will end at that point. This is useful for controlling the length and structure of the output. For example, you can use ["\n", "###"] to stop generation at a new line or a specific marker.

    • temperature

      public abstract Optional<Float> temperature()
      Optional. Controls the randomness of the output. A higher temperature results in more creative and diverse responses, while a lower temperature makes the output more predictable and focused. The valid range is (0.0, 2.0].

    • thinkingConfig

      public abstract Optional<ThinkingConfig> thinkingConfig()
      Optional. Configuration for thinking features. An error will be returned if this field is set for models that don't support thinking.

    • topK

      public abstract Optional<Float> topK()
      Optional. Specifies the top-k sampling threshold. The model considers only the top k most probable tokens for the next token. This can be useful for generating more coherent and less random text. For example, a `top_k` of 40 means the model will choose the next word from the 40 most likely words.

    • topP

      public abstract Optional<Float> topP()
      Optional. Specifies the nucleus sampling threshold. The model considers only the smallest set of tokens whose cumulative probability is at least `top_p`. This helps generate more diverse and less repetitive responses. For example, a `top_p` of 0.9 means the model considers tokens until the cumulative probability of the tokens to select from reaches 0.9. It's recommended to adjust either temperature or `top_p`, but not both.

    • enableEnhancedCivicAnswers

      public abstract Optional<Boolean> enableEnhancedCivicAnswers()
      Optional. Enables enhanced civic answers. It may not be available for all models. This field is not supported in Vertex AI.

    • builder

      public static GenerationConfig.Builder builder()
      Instantiates a builder for GenerationConfig.

    • toBuilder

      public abstract GenerationConfig.Builder toBuilder()
      Creates a builder with the same values as this instance.

    • fromJson

      public static GenerationConfig fromJson(String jsonString)
      Deserializes a JSON string to a GenerationConfig object.