generative-ai-python

google.generativeai.types.GenerationConfig

A simple dataclass used to configure the generation parameters of GenerativeModel.generate_content.

google.generativeai.types.GenerationConfig(
    candidate_count: (int | None) = None,
    stop_sequences: (Iterable[str] | None) = None,
    max_output_tokens: (int | None) = None,
    temperature: (float | None) = None,
    top_p: (float | None) = None,
    top_k: (int | None) = None,
    seed: (int | None) = None,
    response_mime_type: (str | None) = None,
    response_schema: (protos.Schema | Mapping[str, Any] | type | None) = None,
    presence_penalty: (float | None) = None,
    frequency_penalty: (float | None) = None,
    response_logprobs: (bool | None) = None,
    logprobs: (int | None) = None
)

Attributes

`candidate_count` Number of generated responses to return.
`stop_sequences` The set of character sequences (up to 5) that will stop output generation. If specified, the API will stop at the first appearance of a stop sequence. The stop sequence will not be included as part of the response.
`max_output_tokens` The maximum number of tokens to include in a candidate. If unset, this will default to output_token_limit specified in the model's specification.
`temperature` Controls the randomness of the output. Note: The default value varies by model, see the Model.temperature attribute of the `Model` returned the `genai.get_model` function. Values can range from [0.0,1.0], inclusive. A value closer to 1.0 will produce responses that are more varied and creative, while a value closer to 0.0 will typically result in more straightforward responses from the model.
`top_p` Optional. The maximum cumulative probability of tokens to consider when sampling. The model uses combined Top-k and nucleus sampling. Tokens are sorted based on their assigned probabilities so that only the most likely tokens are considered. Top-k sampling directly limits the maximum number of tokens to consider, while Nucleus sampling limits number of tokens based on the cumulative probability. Note: The default value varies by model, see the Model.top_p attribute of the `Model` returned the `genai.get_model` function.
`top_k` `int` Optional. The maximum number of tokens to consider when sampling. The model uses combined Top-k and nucleus sampling. Top-k sampling considers the set of `top_k` most probable tokens. Defaults to 40. Note: The default value varies by model, see the Model.top_k attribute of the `Model` returned the `genai.get_model` function.
`seed` Optional. Seed used in decoding. If not set, the request uses a randomly generated seed.
`response_mime_type` Optional. Output response mimetype of the generated candidate text. Supported mimetype: `text/plain`: (default) Text output. `text/x-enum`: for use with a string-enum in `response_schema` `application/json`: JSON response in the candidates.
`response_schema` Optional. Specifies the format of the JSON requested if response_mime_type is `application/json`.
`presence_penalty` Optional.
`frequency_penalty` Optional.
`response_logprobs` Optional. If true, export the `logprobs` results in response.
`logprobs` Optional. Number of candidates of log probabilities to return at each step of decoding.

Methods

__eq__

__eq__(
    other
)

Return self==value.

Class Variables

candidate_count `None`
frequency_penalty `None`
logprobs `None`
max_output_tokens `None`
presence_penalty `None`
response_logprobs `None`
response_mime_type `None`
response_schema `None`
seed `None`
stop_sequences `None`
temperature `None`
top_k `None`
top_p `None`
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