interface BaseChatGoogleFieldsCustom / additional headers to include with every request to the API. When set on both constructor params and per-invocation call options, per-invocation headers take precidence for matching keys.
OpenAI-compatible alias for media input detail level.
This is mapped to generationConfig.mediaResolution:
"auto" => default Gemini behavior (mediaResolution omitted)"low" => "MEDIA_RESOLUTION_LOW""high" => "MEDIA_RESOLUTION_HIGH"If both mediaResolution and detail are provided, mediaResolution wins.
Positive values penalize tokens that repeatedly appear in the generated text, decreasing the probability of repeating content.
Maximum number of tokens that can be generated in the response. A token is approximately four characters. 100 tokens correspond to roughly 60-80 words. Specify a lower value for shorter responses and a higher value for potentially longer responses.
The number of reasoning tokens that the model should generate. If explicitly set, then the reasoning blocks will be returned.
Media resolution for input media processing.
This maps to generationConfig.mediaResolution, which is a scalar enum-like
string value in the Gemini API.
Positive values penalize tokens that already appear in the generated text, increasing the probability of generating more diverse content.
An alias for maxReasoningTokens under Gemini 2.5 or
the primary thinking/reasoning setting for Gemini 3.
If explicitly set, then the reasoning blocks will be returned.
Per request settings for blocking unsafe content
When seed is fixed to a specific value, the model makes a best effort to provide the same response for repeated requests. Deterministic output isn't guaranteed. Also, changing the model or parameter settings, such as the temperature, can cause variations in the response even when you use the same seed value. By default, a random seed value is used.
Specifies a list of strings that tells the model to stop generating text if one of the strings is encountered in the response. If a string appears multiple times in the response, then the response truncates where it's first encountered. The strings are case-sensitive.
The temperature is used for sampling during response generation, which occurs when topP and topK are applied. Temperature 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.
Specify a lower value for less random responses and a higher value for more random responses.
An alias for maxReasoningTokens for compatibility.
An alias for reasoningEffort for compatibility.
A list of tools the model may use to generate the next response. Can be LangChain tools, OpenAI tools, or Gemini function declarations.
Top-K changes how the model selects tokens for output. A top-K of 1 means the selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a top-K of 3 means that the next token is selected from among the 3 most probable tokens (using temperature).
Top-P changes how the model selects tokens for output. Tokens are selected from the most probable to least probable until the sum of their probabilities equals the top-P value.
Specify a lower value for less random responses and a higher value for more random responses.