The async caller should be used by subclasses to make any async calls, which will thus benefit from the concurrency and retry logic.
Whether to print out response text.
Optional cacheOptional callbacksOptional metadataOptional nameOptional responsesOptional sleepOptional tagsOptional thrownKeys 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<BaseLLMCallOptions> | Partial<BaseLLMCallOptions>[]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<BaseLLMCallOptions> | Partial<BaseLLMCallOptions>[]Optional batchOptions: RunnableBatchOptions & { Optional options: Partial<BaseLLMCallOptions> | Partial<BaseLLMCallOptions>[]Optional batchOptions: RunnableBatchOptionsBind arguments to a Runnable, returning a new Runnable.
A new RunnableBinding that, when invoked, will apply the bound args.
Optional options: string[] | BaseLLMCallOptionsOptional callbacks: CallbackManager | (BaseCallbackHandlerMethodsClass | BaseCallbackHandler)[]Use .invoke() instead. Will be removed in 0.2.0. Convenience wrapper for generate that takes in a single string prompt and returns a single string output.
Run the LLM on the given prompts and input, handling caching.
Optional options: string[] | BaseLLMCallOptionsOptional callbacks: CallbackManager | (BaseCallbackHandlerMethodsClass | BaseCallbackHandler)[]This method takes prompt values, options, and callbacks, and generates a result based on the prompts.
Prompt values for the LLM.
Optional options: string[] | BaseLLMCallOptionsOptions for the LLM call.
Optional callbacks: CallbackManager | (BaseCallbackHandlerMethodsClass | BaseCallbackHandler)[]Callbacks for the LLM call.
An LLMResult based on the prompts.
Optional _: RunnableConfigGet the parameters used to invoke the model
Optional _options: Omit<BaseLLMCallOptions, never>This method takes an input and options, and returns a string. It converts the input to a prompt value and generates a result based on the prompt.
Input for the LLM.
Optional options: BaseLLMCallOptionsOptions for the LLM call.
A string result based on the prompt.
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.
Input text for the prediction.
Optional options: string[] | BaseLLMCallOptionsOptions for the LLM call.
Optional callbacks: CallbackManager | (BaseCallbackHandlerMethodsClass | BaseCallbackHandler)[]Callbacks for the LLM call.
A prediction based on the input text.
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.
This method is similar to call, but it's used for making predictions
based on the input text.
A list of messages for the prediction.
Optional options: string[] | BaseLLMCallOptionsOptions for the LLM call.
Optional callbacks: CallbackManager | (BaseCallbackHandlerMethodsClass | BaseCallbackHandler)[]Callbacks for the LLM call.
A predicted message based on the list of messages.
Use .invoke() instead. Will be removed in 0.2.0.
This method takes a list of messages, options, and callbacks, and returns a predicted message.
Return a json-like object representing this LLM.
Stream output in chunks.
Optional options: Partial<BaseLLMCallOptions>A readable stream that is also an iterable.
Generate a stream of events emitted by the internal steps of the runnable.
Use to create an iterator over StreamEvents that provide real-time information about the progress of the runnable, including StreamEvents from intermediate results.
A StreamEvent is a dictionary with the following schema:
event: string - Event names are of the format: on_[runnable_type]_(start|stream|end).name: string - The name of the runnable that generated the event.run_id: string - Randomly generated ID associated with the given execution of
the runnable that emitted the event. A child runnable that gets invoked as part of the execution of a
parent runnable is assigned its own unique ID.tags: string[] - The tags of the runnable that generated the event.metadata: Record<string, any> - The metadata of the runnable that generated the event.data: Record<string, any>Below is a table that illustrates some events that might be emitted by various chains. Metadata fields have been omitted from the table for brevity. Chain definitions have been included after the table.
ATTENTION This reference table is for the V2 version of the schema.
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ | event | name | chunk | input | output | +======================+==================+=================================+===============================================+=================================================+ | on_chat_model_start | [model name] | | {"messages": [[SystemMessage, HumanMessage]]} | | +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ | on_chat_model_stream | [model name] | AIMessageChunk(content="hello") | | | +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ | on_chat_model_end | [model name] | | {"messages": [[SystemMessage, HumanMessage]]} | AIMessageChunk(content="hello world") | +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ | on_llm_start | [model name] | | {'input': 'hello'} | | +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ | on_llm_stream | [model name] | 'Hello' | | | +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ | on_llm_end | [model name] | | 'Hello human!' | | +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ | on_chain_start | format_docs | | | | +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ | on_chain_stream | format_docs | "hello world!, goodbye world!" | | | +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ | on_chain_end | format_docs | | [Document(...)] | "hello world!, goodbye world!" | +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ | on_tool_start | some_tool | | {"x": 1, "y": "2"} | | +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ | on_tool_end | some_tool | | | {"x": 1, "y": "2"} | +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ | on_retriever_start | [retriever name] | | {"query": "hello"} | | +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ | on_retriever_end | [retriever name] | | {"query": "hello"} | [Document(...), ..] | +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ | on_prompt_start | [template_name] | | {"question": "hello"} | | +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ | on_prompt_end | [template_name] | | {"question": "hello"} | ChatPromptValue(messages: [SystemMessage, ...]) | +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
Optional streamOptions: Omit<EventStreamCallbackHandlerInput, "autoClose">Optional streamOptions: Omit<EventStreamCallbackHandlerInput, "autoClose">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<BaseLLMCallOptions>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.
Called after the runnable finishes running, with the Run object.
Optional config: RunnableConfigOptional onCalled if the runnable throws an error, with the Run object.
Called if the runnable throws an error, with the Run object.
Optional config: RunnableConfigOptional onCalled before the runnable starts running, with the Run object.
Called 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.
Optional withOptional config: StructuredOutputMethodOptions<false>Optional config: StructuredOutputMethodOptions<true>Model wrapper that returns outputs formatted to match the given schema.
The schema for the structured output. Either as a Zod schema or a valid JSON schema object. If a Zod schema is passed, the returned attributes will be validated, whereas with JSON schema they will not be.
Optional config: StructuredOutputMethodOptions<boolean>A new runnable that calls the LLM with structured output.
Static deserializeLoad an LLM from a json-like object describing it.
Static isGenerated using TypeDoc
LLM class that provides a simpler interface to subclass than BaseLLM.
Requires only implementing a simpler _call method instead of _generate.