Optional fields: Partial<BaseBedrockInput> & BaseLanguageModelParamsThe async caller should be used by subclasses to make any async calls, which will thus benefit from the concurrency and retry logic.
Optional init: RequestInitWhether to print out response text.
Optional cacheOptional callbacksOptional endpointOptional maxOptional metadataOptional modelOptional stopOptional tagsOptional temperatureKeys that the language model accepts as call options.
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.
Makes a single call to the chat model.
An array of BaseMessage instances.
Optional options: string[] | BaseLanguageModelCallOptionsThe call options or an array of stop sequences.
Optional callbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to a BaseMessage.
Makes a single call to the chat model with a prompt value.
The value of the prompt.
Optional options: string[] | BaseLanguageModelCallOptionsThe call options or an array of stop sequences.
Optional callbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to a BaseMessage.
Generates chat based on the input messages.
An array of arrays of BaseMessage instances.
Optional options: string[] | BaseLanguageModelCallOptionsThe 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: string[] | BaseLanguageModelCallOptionsThe 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.
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.
Predicts the next message based on a text input.
The text input.
Optional options: string[] | BaseLanguageModelCallOptionsThe call options or an array of stop sequences.
Optional callbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to a string.
Predicts the next message based on the input messages.
An array of BaseMessage instances.
Optional options: string[] | BaseLanguageModelCallOptionsThe call options or an array of stop sequences.
Optional callbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to a BaseMessage.
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: BaseCallbackConfigOptional onCalled if the runnable throws an error, with the Run object.
Optional config: BaseCallbackConfigOptional onCalled before the runnable starts running, with the Run object.
Optional config: BaseCallbackConfigAdd 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 type of Large Language Model (LLM) that interacts with the Bedrock service. It extends the base
LLMclass and implements theBaseBedrockInputinterface. The class is designed to authenticate and interact with the Bedrock service, which is a part of Amazon Web Services (AWS). It uses AWS credentials for authentication and can be configured with various parameters such as the model to use, the AWS region, and the maximum number of tokens to generate.Example