Optional fields: GoogleGenerativeAIChatInputThe async caller should be used by subclasses to make any async calls, which will thus benefit from the concurrency and retry logic.
Model Name to use
Note: The format must follow the pattern - {model}
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.
Note: The stop sequence will not be included as part of the response. Note: stopSequences is only supported for Gemini models
Whether to stream the results or not
Whether to print out response text.
Optional apiGoogle API key to use
Optional cacheOptional callbacksOptional maxMaximum number of tokens to generate in the completion.
Optional metadataOptional nameOptional safetyA list of unique SafetySetting instances for blocking unsafe content. The API will block
any prompts and responses that fail to meet the thresholds set by these settings. If there
is no SafetySetting for a given SafetyCategory provided in the list, the API will use
the default safety setting for that category.
Optional tagsOptional temperatureControls the randomness of the output.
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 less surprising responses from the model.
Note: The default value varies by model
Optional topKTop-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).
Note: The default value varies by model
Optional topPTop-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).
Note: The default value varies by model
Keys that the language model accepts as call options.
Assigns new fields to the dict output of this runnable. Returns a new runnable.
Default implementation of batch, which calls invoke N times. Subclasses should override this method if they can batch more efficiently.
Array of inputs to each batch call.
Optional options: Partial<BaseLanguageModelCallOptions> | Partial<BaseLanguageModelCallOptions>[]Either a single call options object to apply to each batch call or an array for each call.
Optional batchOptions: RunnableBatchOptions & { An array of RunOutputs, or mixed RunOutputs and errors if batchOptions.returnExceptions is set
Optional options: Partial<BaseLanguageModelCallOptions> | Partial<BaseLanguageModelCallOptions>[]Optional batchOptions: RunnableBatchOptions & { Optional options: Partial<BaseLanguageModelCallOptions> | Partial<BaseLanguageModelCallOptions>[]Optional batchOptions: RunnableBatchOptionsBind arguments to a Runnable, returning a new Runnable.
A new RunnableBinding that, when invoked, will apply the bound args.
An array of BaseMessage instances.
Optional options: BaseLanguageModelCallOptions | string[]The call options or an array of stop sequences.
Optional callbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to a BaseMessage.
Use .invoke() instead. Will be removed in 0.2.0.
This feature is deprecated and will be removed in the future.
It is not recommended for use.
Makes a single call to the chat model.
The value of the prompt.
Optional options: BaseLanguageModelCallOptions | string[]The call options or an array of stop sequences.
Optional callbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to a BaseMessage.
Use .invoke() instead. Will be removed in 0.2.0.
Makes a single call to the chat model with a prompt value.
Generates chat based on the input messages.
An array of arrays of BaseMessage instances.
Optional options: BaseLanguageModelCallOptions | string[]The call options or an array of stop sequences.
Optional callbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to an LLMResult.
Generates a prompt based on the input prompt values.
An array of BasePromptValue instances.
Optional options: BaseLanguageModelCallOptions | string[]The call options or an array of stop sequences.
Optional callbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to an LLMResult.
Get the parameters used to invoke the model
Optional _options: Omit<BaseLanguageModelCallOptions, never>Invokes the chat model with a single input.
The input for the language model.
Optional options: BaseLanguageModelCallOptionsThe call options.
A Promise that resolves to a BaseMessageChunk.
Return a new Runnable that maps a list of inputs to a list of outputs, by calling invoke() with each input.
Pick keys from the dict output of this runnable. Returns a new runnable.
Create a new runnable sequence that runs each individual runnable in series, piping the output of one runnable into another runnable or runnable-like.
A runnable, function, or object whose values are functions or runnables.
A new runnable sequence.
The text input.
Optional options: BaseLanguageModelCallOptions | string[]The call options or an array of stop sequences.
Optional callbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to a string.
Use .invoke() instead. Will be removed in 0.2.0.
Predicts the next message based on a text input.
An array of BaseMessage instances.
Optional options: BaseLanguageModelCallOptions | string[]The call options or an array of stop sequences.
Optional callbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to a BaseMessage.
Use .invoke() instead. Will be removed in 0.2.0.
Predicts the next message based on the input messages.
Return a json-like object representing this LLM.
Stream output in chunks.
Optional options: Partial<BaseLanguageModelCallOptions>A readable stream that is also an iterable.
Stream all output from a runnable, as reported to the callback system. This includes all inner runs of LLMs, Retrievers, Tools, etc. Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in each step, and the final state of the run. The jsonpatch ops can be applied in order to construct state.
Optional options: Partial<BaseLanguageModelCallOptions>Optional streamOptions: Omit<LogStreamCallbackHandlerInput, "autoClose">Default implementation of transform, which buffers input and then calls stream. Subclasses should override this method if they can start producing output while input is still being generated.
Bind config to a Runnable, returning a new Runnable.
New configuration parameters to attach to the new runnable.
A new RunnableBinding with a config matching what's passed.
Create a new runnable from the current one that will try invoking other passed fallback runnables if the initial invocation fails.
Other runnables to call if the runnable errors.
A new RunnableWithFallbacks.
Bind lifecycle listeners to a Runnable, returning a new Runnable. The Run object contains information about the run, including its id, type, input, output, error, startTime, endTime, and any tags or metadata added to the run.
The object containing the callback functions.
Optional onCalled after the runnable finishes running, with the Run object.
Optional config: RunnableConfigOptional onCalled if the runnable throws an error, with the Run object.
Optional config: RunnableConfigOptional onCalled before the runnable starts running, with the Run object.
Optional config: RunnableConfigAdd retry logic to an existing runnable.
Optional fields: { Optional onOptional stopA new RunnableRetry that, when invoked, will retry according to the parameters.
Static deserializeLoad an LLM from a json-like object describing it.
Static isGenerated using TypeDoc
A class that wraps the Google Palm chat model.
Example