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.
OptionalcacheOptionalcallbacksOptionalmetadataOptionalnameOptionaltagsKeys that the language model accepts as call options.
Convert a runnable to a tool. Return a new instance of RunnableToolLike
which contains the runnable, name, description and schema.
The Zod schema for the input of the tool. Infers the Zod type from the input type of the runnable.
Optionaldescription?: stringThe description of the tool. If not provided, it will default to Takes {schema} where schema is a JSON string representation of the input schema.
Optionalname?: stringThe name of the tool. If not provided, it will default to the name of the runnable.
An instance of RunnableToolLike which is a runnable that can be used as a tool.
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.
Optionaloptions: Partial<BaseLanguageModelCallOptions> | Partial<BaseLanguageModelCallOptions>[]Either a single call options object to apply to each batch call or an array for each call.
OptionalbatchOptions: RunnableBatchOptions & { An array of RunOutputs, or mixed RunOutputs and errors if batchOptions.returnExceptions is set
Optionaloptions: Partial<BaseLanguageModelCallOptions> | Partial<BaseLanguageModelCallOptions>[]OptionalbatchOptions: RunnableBatchOptions & { Optionaloptions: Partial<BaseLanguageModelCallOptions> | Partial<BaseLanguageModelCallOptions>[]OptionalbatchOptions: 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.
Optionaloptions: string[] | BaseLanguageModelCallOptionsThe call options or an array of stop sequences.
Optionalcallbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to a BaseMessage.
The value of the prompt.
Optionaloptions: string[] | BaseLanguageModelCallOptionsThe call options or an array of stop sequences.
Optionalcallbacks: 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.
Optionaloptions: string[] | BaseLanguageModelCallOptionsThe call options or an array of stop sequences.
Optionalcallbacks: 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.
Optionaloptions: string[] | BaseLanguageModelCallOptionsThe call options or an array of stop sequences.
Optionalcallbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to an LLMResult.
Optional_: RunnableConfigGet 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.
Optionaloptions: 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.
Optionaloptions: string[] | BaseLanguageModelCallOptionsThe call options or an array of stop sequences.
Optionalcallbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to a string.
An array of BaseMessage instances.
Optionaloptions: string[] | BaseLanguageModelCallOptionsThe call options or an array of stop sequences.
Optionalcallbacks: CallbacksThe callbacks for the language model.
A Promise that resolves to a BaseMessage.
Stream output in chunks.
Optionaloptions: Partial<BaseLanguageModelCallOptions>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, ...]) | +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
OptionalstreamOptions: Omit<EventStreamCallbackHandlerInput, "autoClose">OptionalstreamOptions: 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.
Optionaloptions: Partial<BaseLanguageModelCallOptions>OptionalstreamOptions: 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.
OptionalonCalled after the runnable finishes running, with the Run object.
Optionalconfig: RunnableConfigOptionalonCalled if the runnable throws an error, with the Run object.
Optionalconfig: RunnableConfigOptionalonCalled before the runnable starts running, with the Run object.
Optionalconfig: RunnableConfigAdd retry logic to an existing runnable.
Optionalfields: { OptionalonOptionalstopA new RunnableRetry that, when invoked, will retry according to the parameters.
Model wrapper that returns outputs formatted to match the given schema.
Optionalconfig: StructuredOutputMethodOptions<false>A new runnable that calls the LLM with structured output.
Model wrapper that returns outputs formatted to match the given schema.
Optionalconfig: StructuredOutputMethodOptions<true>A new runnable that calls the LLM with structured output.
OptionalbindBind tool-like objects to this chat model.
A list of tool definitions to bind to this chat model. Can be a structured tool, an OpenAI formatted tool, or an object matching the provider's specific tool schema.
Optionalkwargs: Partial<BaseLanguageModelCallOptions>Any additional parameters to bind.
StaticdeserializeStaticis
Base class for chat models. It extends the BaseLanguageModel class and provides methods for generating chat based on input messages.