Optional fields: Partial<AnthropicInput> & BaseLanguageModelParamsThe async caller should be used by subclasses to make any async calls, which will thus benefit from the concurrency and retry logic.
Overridable Anthropic ClientOptions
A path to the module that contains the class, eg. ["langchain", "llms"] Usually should be the same as the entrypoint the class is exported from.
A maximum number of tokens to generate before stopping.
Model name to use
Whether to stream the results or not
Amount of randomness injected into the response. Ranges from 0 to 1. Use temp closer to 0 for analytical / multiple choice, and temp closer to 1 for creative and generative tasks.
Only sample from the top K options for each subsequent token. Used to remove "long tail" low probability responses. Defaults to -1, which disables it.
Does nucleus sampling, in which we compute the cumulative distribution over all the options for each subsequent token in decreasing probability order and cut it off once it reaches a particular probability specified by top_p. Defaults to -1, which disables it. Note that you should either alter temperature or top_p, but not both.
Whether to print out response text.
Optional anthropicAnthropic API key
Optional apiOptional cacheOptional callbacksOptional invocationHolds any additional parameters that are valid to pass to anthropic.complete that are not explicitly specified on this class.
Optional metadataOptional stopA list of strings upon which to stop generating.
You probably want ["\n\nHuman:"], as that's the cue for
the next turn in the dialog agent.
Optional tagsProtected batchProtected lc_Protected streamingKeys that the language model accepts as call options.
A map of aliases for constructor args. Keys are the attribute names, e.g. "foo". Values are the alias that will replace the key in serialization. This is used to eg. make argument names match Python.
A map of additional attributes to merge with constructor args. Keys are the attribute names, e.g. "foo". Values are the attribute values, which will be serialized. These attributes need to be accepted by the constructor as arguments.
The final serialized identifier for the module.
A map of secrets, which will be omitted from serialization. Keys are paths to the secret in constructor args, e.g. "foo.bar.baz". Values are the secret ids, which will be used when deserializing.
Internal method that handles batching and configuration for a runnable It takes a function, input values, and optional configuration, and returns a promise that resolves to the output values.
The function to be executed for each input value.
Optional options: Partial<CallOptions>[]Optional runManagers: (undefined | CallbackManagerForChainRun)[]Optional batchOptions: RunnableBatchOptionsOptional options: Partial<CallOptions & { Optional batchOptions: RunnableBatchOptionsA promise that resolves to the output values.
Default streaming implementation. Subclasses should override this method if they support streaming output.
Optional options: CallOptionsOptional runManager: CallbackManagerForLLMRunDefault 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<CallOptions> | Partial<CallOptions>[]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<CallOptions> | Partial<CallOptions>[]Optional batchOptions: RunnableBatchOptions & { Optional options: Partial<CallOptions> | Partial<CallOptions>[]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[] | CallOptionsThe 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[] | CallOptionsThe 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[] | CallOptionsThe 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[] | CallOptionsThe call options or an array of stop sequences.
Optional callbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to an LLMResult.
Invokes the chat model with a single input.
The input for the language model.
Optional options: CallOptionsThe 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[] | CallOptionsThe 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[] | CallOptionsThe 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<CallOptions>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<CallOptions>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.
Add 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 isStatic lc_Protected _callOptional options: Partial<CallOptions> & { Protected _getProtected _getProtected _separateOptional options: Partial<CallOptions>Protected _transformHelper method to transform an Iterator of Input values into an Iterator of
Output values, with callbacks.
Use this to implement stream() or transform() in Runnable subclasses.
Optional runManager: CallbackManagerForChainRunOptional options: Partial<CallOptions>Optional options: CallOptions & { Protected createProtected formatFormats messages as a prompt for the model.
The base messages to format as a prompt.
The formatted prompt.
Static Protected _convertGenerated using TypeDoc
Wrapper around Anthropic large language models.
To use you should have the
@anthropic-ai/sdkpackage installed, with theANTHROPIC_API_KEYenvironment variable set.Remarks
Any parameters that are valid to be passed to
anthropic.completecan be passed through invocationKwargs, even if not explicitly available on this class.