The params which can be passed to the API at request time.

interface GoogleAIBaseLanguageModelCallOptions {
    allowed_function_names?: string[];
    callbacks?: Callbacks;
    configurable?: Record<string, any>;
    convertSystemMessageToHumanContent?: boolean;
    maxConcurrency?: number;
    maxOutputTokens?: number;
    metadata?: Record<string, unknown>;
    model?: string;
    modelName?: string;
    recursionLimit?: number;
    responseMimeType?: GoogleAIResponseMimeType;
    runId?: string;
    runName?: string;
    safetyHandler?: GoogleAISafetyHandler;
    safetySettings?: GoogleAISafetySetting[];
    signal?: AbortSignal;
    stop?: string[];
    stopSequences?: string[];
    streamUsage?: boolean;
    streaming?: boolean;
    tags?: string[];
    temperature?: number;
    timeout?: number;
    tool_choice?: ToolChoice;
    tools?: GoogleAIToolType[];
    topK?: number;
    topP?: number;
}

Hierarchy (view full)

Properties

allowed_function_names?: string[]

Allowed functions to call when the mode is "any". If empty, any one of the provided functions are called.

callbacks?: Callbacks

Callbacks for this call and any sub-calls (eg. a Chain calling an LLM). Tags are passed to all callbacks, metadata is passed to handle*Start callbacks.

configurable?: Record<string, any>

Runtime values for attributes previously made configurable on this Runnable, or sub-Runnables.

convertSystemMessageToHumanContent?: boolean
maxConcurrency?: number

Maximum number of parallel calls to make.

maxOutputTokens?: number

Maximum number of tokens to generate in the completion.

metadata?: Record<string, unknown>

Metadata for this call and any sub-calls (eg. a Chain calling an LLM). Keys should be strings, values should be JSON-serializable.

model?: string

Model to use

modelName?: string

Model to use Alias for model

recursionLimit?: number

Maximum number of times a call can recurse. If not provided, defaults to 25.

responseMimeType?: GoogleAIResponseMimeType

Available for gemini-1.5-pro. The output format of the generated candidate text. Supported MIME types:

  • text/plain: Text output.
  • application/json: JSON response in the candidates.
"text/plain"
runId?: string

Unique identifier for the tracer run for this call. If not provided, a new UUID will be generated.

runName?: string

Name for the tracer run for this call. Defaults to the name of the class.

safetyHandler?: GoogleAISafetyHandler
safetySettings?: GoogleAISafetySetting[]
signal?: AbortSignal

Abort signal for this call. If provided, the call will be aborted when the signal is aborted.

stop?: string[]

Stop tokens to use for this call. If not provided, the default stop tokens for the model will be used.

stopSequences?: string[]
streamUsage?: boolean

Whether or not to include usage data, like token counts in the streamed response chunks.

true
streaming?: boolean

Whether or not to stream.

false
tags?: string[]

Tags for this call and any sub-calls (eg. a Chain calling an LLM). You can use these to filter calls.

temperature?: number

Sampling temperature to use

timeout?: number

Timeout for this call in milliseconds.

tool_choice?: ToolChoice

Specifies how the chat model should use tools.

undefined

Possible values:
- "auto": The model may choose to use any of the provided tools, or none.
- "any": The model must use one of the provided tools.
- "none": The model must not use any tools.
- A string (not "auto", "any", or "none"): The name of a specific tool the model must use.
- An object: A custom schema specifying tool choice parameters. Specific to the provider.

Note: Not all providers support tool_choice. An error will be thrown
if used with an unsupported model.
topK?: number

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).

topP?: number

Top-p changes how the model selects tokens for output.

Tokens are selected from most probable to least until the sum of their probabilities equals the top-p value.

For example, if tokens A, B, and C have a probability of .3, .2, and .1 and the top-p value is .5, then the model will select either A or B as the next token (using temperature).