generative-ai-python

google.generativeai.protos.GenerationConfig

Configuration options for model generation and outputs.

Not all parameters are configurable for every model.

Attributes

`candidate_count` `int` Optional. Number of generated responses to return. Currently, this value can only be set to 1. If unset, this will default to 1.
`stop_sequences` `MutableSequence[str]` Optional. 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` `int` Optional. The maximum number of tokens to include in a response candidate. Note: The default value varies by model, see the Model.output_token_limit attribute of the ``Model`` returned from the ``getModel`` function.
`temperature` `float` Optional. Controls the randomness of the output. Note: The default value varies by model, see the Model.temperature attribute of the ``Model`` returned from the ``getModel`` function. Values can range from [0.0, 2.0].
`top_p` `float` Optional. The maximum cumulative probability of tokens to consider when sampling. The model uses combined Top-k and Top-p (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 the number of tokens based on the cumulative probability. Note: The default value varies by ``Model`` and is specified by the\ Model.top_p attribute returned from the ``getModel`` function. An empty ``top_k`` attribute indicates that the model doesn't apply top-k sampling and doesn't allow setting ``top_k`` on requests.
`top_k` `int` Optional. The maximum number of tokens to consider when sampling. Gemini models use Top-p (nucleus) sampling or a combination of Top-k and nucleus sampling. Top-k sampling considers the set of ``top_k`` most probable tokens. Models running with nucleus sampling don't allow top_k setting. Note: The default value varies by ``Model`` and is specified by the\ Model.top_p attribute returned from the ``getModel`` function. An empty ``top_k`` attribute indicates that the model doesn't apply top-k sampling and doesn't allow setting ``top_k`` on requests.
`response_mime_type` `str` Optional. MIME type of the generated candidate text. Supported MIME types are: ``text/plain``: (default) Text output. ``application/json``: JSON response in the response candidates. ``text/x.enum``: ENUM as a string response in the response candidates. Refer to the `docs <https://ai.google.dev/gemini-api/docs/prompting_with_media#plain_text_formats>`__ for a list of all supported text MIME types.
`response_schema` `google.ai.generativelanguage.Schema` Optional. Output schema of the generated candidate text. Schemas must be a subset of the `OpenAPI schema <https://spec.openapis.org/oas/v3.0.3#schema>`__ and can be objects, primitives or arrays. If set, a compatible ``response_mime_type`` must also be set. Compatible MIME types: ``application/json``: Schema for JSON response. Refer to the `JSON text generation guide <https://ai.google.dev/gemini-api/docs/json-mode>`__ for more details.
`presence_penalty` `float` Optional. Presence penalty applied to the next token's logprobs if the token has already been seen in the response. This penalty is binary on/off and not dependant on the number of times the token is used (after the first). Use [frequency_penalty][google.ai.generativelanguage.v1beta.GenerationConfig.frequency_penalty] for a penalty that increases with each use. A positive penalty will discourage the use of tokens that have already been used in the response, increasing the vocabulary. A negative penalty will encourage the use of tokens that have already been used in the response, decreasing the vocabulary.
`frequency_penalty` `float` Optional. Frequency penalty applied to the next token's logprobs, multiplied by the number of times each token has been seen in the respponse so far. A positive penalty will discourage the use of tokens that have already been used, proportional to the number of times the token has been used: The more a token is used, the more dificult it is for the model to use that token again increasing the vocabulary of responses. Caution: A *negative* penalty will encourage the model to reuse tokens proportional to the number of times the token has been used. Small negative values will reduce the vocabulary of a response. Larger negative values will cause the model to start repeating a common token until it hits the [max_output_tokens][google.ai.generativelanguage.v1beta.GenerationConfig.max_output_tokens] limit: "...the the the the the...".
`response_logprobs` `bool` Optional. If true, export the logprobs results in response.
`logprobs` `int` Optional. Only valid if [response_logprobs=True][google.ai.generativelanguage.v1beta.GenerationConfig.response_logprobs]. This sets the number of top logprobs to return at each decoding step in the [Candidate.logprobs_result][google.ai.generativelanguage.v1beta.Candidate.logprobs_result]. This field is a member of `oneof`_ ``_logprobs``.
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