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pydantic_ai.settings

模型设置

基类：TypedDict

用于配置大语言模型（LLM）的设置。

这里我们仅包含适用于多个模型/模型提供商的设置，但并非所有模型都支持所有这些设置。

源代码位于 pydantic_ai_slim/pydantic_ai/settings.py 
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class ModelSettings(TypedDict, total=False):
    """Settings to configure an LLM.

    Here we include only settings which apply to multiple models / model providers,
    though not all of these settings are supported by all models.
    """

    max_tokens: int
    """The maximum number of tokens to generate before stopping.

    Supported by:

    * Gemini
    * Anthropic
    * OpenAI
    * Groq
    * Cohere
    * Mistral
    * Bedrock
    * MCP Sampling
    """

    temperature: float
    """Amount of randomness injected into the response.

    Use `temperature` closer to `0.0` for analytical / multiple choice, and closer to a model's
    maximum `temperature` for creative and generative tasks.

    Note that even with `temperature` of `0.0`, the results will not be fully deterministic.

    Supported by:

    * Gemini
    * Anthropic
    * OpenAI
    * Groq
    * Cohere
    * Mistral
    * Bedrock
    """

    top_p: float
    """An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass.

    So 0.1 means only the tokens comprising the top 10% probability mass are considered.

    You should either alter `temperature` or `top_p`, but not both.

    Supported by:

    * Gemini
    * Anthropic
    * OpenAI
    * Groq
    * Cohere
    * Mistral
    * Bedrock
    """

    timeout: float | Timeout
    """Override the client-level default timeout for a request, in seconds.

    Supported by:

    * Gemini
    * Anthropic
    * OpenAI
    * Groq
    * Mistral
    """

    parallel_tool_calls: bool
    """Whether to allow parallel tool calls.

    Supported by:

    * OpenAI (some models, not o1)
    * Groq
    * Anthropic
    """

    seed: int
    """The random seed to use for the model, theoretically allowing for deterministic results.

    Supported by:

    * OpenAI
    * Groq
    * Cohere
    * Mistral
    """

    presence_penalty: float
    """Penalize new tokens based on whether they have appeared in the text so far.

    Supported by:

    * OpenAI
    * Groq
    * Cohere
    * Gemini
    * Mistral
    """

    frequency_penalty: float
    """Penalize new tokens based on their existing frequency in the text so far.

    Supported by:

    * OpenAI
    * Groq
    * Cohere
    * Gemini
    * Mistral
    """

    logit_bias: dict[str, int]
    """Modify the likelihood of specified tokens appearing in the completion.

    Supported by:

    * OpenAI
    * Groq
    """

    stop_sequences: list[str]
    """Sequences that will cause the model to stop generating.

    Supported by:

    * OpenAI
    * Anthropic
    * Bedrock
    * Mistral
    * Groq
    * Cohere
    * Google
    """

    extra_headers: dict[str, str]
    """Extra headers to send to the model.

    Supported by:

    * OpenAI
    * Anthropic
    * Groq
    """

    extra_body: object
    """Extra body to send to the model.

    Supported by:

    * OpenAI
    * Anthropic
    * Groq
    """

max_tokens 实例属性 

max_tokens: int

在停止生成前所生成的最大令牌（token）数量。

支持的模型

  • Gemini
  • Anthropic
  • OpenAI
  • Groq
  • Cohere
  • Mistral
  • Bedrock
  • MCP 采样

temperature 实例属性 

temperature: float

注入到响应中的随机性大小。

对于分析性/多项选择任务，请使用接近 0.0 的 temperature 值；对于创造性和生成性任务，请使用接近模型所允许的最大 temperature 值。

请注意，即使 temperature 值为 0.0，结果也不会完全确定。

支持的模型

  • Gemini
  • Anthropic
  • OpenAI
  • Groq
  • Cohere
  • Mistral
  • Bedrock

top_p 实例属性 

top_p: float

一种替代温度采样的方法，称为核采样（nucleus sampling），模型会考虑概率总和为 top_p 的令牌结果。

因此，0.1 意味着只考虑构成最高 10% 概率质量的令牌。

您应该只更改 temperature 或 top_p 中的一个，而不是两者都更改。

支持的模型

  • Gemini
  • Anthropic
  • OpenAI
  • Groq
  • Cohere
  • Mistral
  • Bedrock

timeout 实例属性 

timeout: float | Timeout

覆盖客户端级别的默认请求超时时间，单位为秒。

支持的模型

  • Gemini
  • Anthropic
  • OpenAI
  • Groq
  • Mistral

parallel_tool_calls 实例属性 

parallel_tool_calls: bool

是否允许并行工具调用。

支持的模型

  • OpenAI（某些模型，非 o1）
  • Groq
  • Anthropic

seed 实例属性 

seed: int

用于模型的随机种子，理论上可以实现确定性结果。

支持的模型

  • OpenAI
  • Groq
  • Cohere
  • Mistral

presence_penalty 实例属性 

presence_penalty: float

根据新令牌是否已在文本中出现过来对其进行惩罚。

支持的模型

  • OpenAI
  • Groq
  • Cohere
  • Gemini
  • Mistral

frequency_penalty 实例属性 

frequency_penalty: float

根据新令牌在文本中现有的频率来对其进行惩罚。

支持的模型

  • OpenAI
  • Groq
  • Cohere
  • Gemini
  • Mistral

logit_bias 实例属性 

logit_bias: dict[str, int]

修改指定令牌在补全结果中出现的可能性。

支持的模型

  • OpenAI
  • Groq

stop_sequences 实例属性 

stop_sequences: list[str]

会导致模型停止生成的序列。

支持的模型

  • OpenAI
  • Anthropic
  • Bedrock
  • Mistral
  • Groq
  • Cohere
  • Google

extra_headers 实例属性 

extra_headers: dict[str, str]

发送给模型的额外请求头。

支持的模型

  • OpenAI
  • Anthropic
  • Groq

extra_body 实例属性 

extra_body: object

发送给模型的额外请求体。

支持的模型

  • OpenAI
  • Anthropic
  • Groq