A runnable to passthrough inputs unchanged or with additional keys.

This runnable behaves almost like the identity function, except that it can be configured to add additional keys to the output, if the input is an object.

The example below demonstrates how to use RunnablePassthrough to passthrough the input from the .invoke()`

@example

const chain = RunnableSequence.from([
{
question: new RunnablePassthrough(),
context: async () => loadContextFromStore(),
},
prompt,
llm,
outputParser,
]);
const response = await chain.invoke(
"I can pass a single string instead of an object since I'm using `RunnablePassthrough`."
);

Type Parameters

  • RunInput = any

Hierarchy (view full)

Constructors

Properties

func?: RunnablePassthroughFunc<RunInput>
name?: string

Methods

  • Convert a runnable to a tool. Return a new instance of RunnableToolLike which contains the runnable, name, description and schema.

    Type Parameters

    Parameters

    • fields: {
          schema: ZodType<T, ZodTypeDef, T>;
          description?: string;
          name?: string;
      }
      • schema: ZodType<T, ZodTypeDef, T>

        The Zod schema for the input of the tool. Infers the Zod type from the input type of the runnable.

      • Optionaldescription?: string

        The description of the tool. Falls back to the description on the Zod schema if not provided, or undefined if neither are provided.

      • Optionalname?: string

        The name of the tool. If not provided, it will default to the name of the runnable.

    Returns RunnableToolLike<ZodType<T, ZodTypeDef, T>, RunInput>

    An instance of RunnableToolLike which is a runnable that can be used as a tool.

  • 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, ...]) | +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+

    Parameters

    • input: RunInput
    • options: Partial<RunnableConfig> & {
          version: "v1" | "v2";
      }
    • OptionalstreamOptions: Omit<EventStreamCallbackHandlerInput, "autoClose">

    Returns IterableReadableStream<StreamEvent>

  • Parameters

    • input: RunInput
    • options: Partial<RunnableConfig> & {
          encoding: "text/event-stream";
          version: "v1" | "v2";
      }
    • OptionalstreamOptions: Omit<EventStreamCallbackHandlerInput, "autoClose">

    Returns IterableReadableStream<Uint8Array>

  • 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.

    Parameters

    Returns AsyncGenerator<RunLogPatch, any, unknown>

  • 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.

    Parameters

    • params: {
          onEnd?: ((run: Run, config?: RunnableConfig) => void | Promise<void>);
          onError?: ((run: Run, config?: RunnableConfig) => void | Promise<void>);
          onStart?: ((run: Run, config?: RunnableConfig) => void | Promise<void>);
      }

      The object containing the callback functions.

      • OptionalonEnd?: ((run: Run, config?: RunnableConfig) => void | Promise<void>)

        Called after the runnable finishes running, with the Run object.

          • (run, config?): void | Promise<void>
          • Parameters

            Returns void | Promise<void>

      • OptionalonError?: ((run: Run, config?: RunnableConfig) => void | Promise<void>)

        Called if the runnable throws an error, with the Run object.

          • (run, config?): void | Promise<void>
          • Parameters

            Returns void | Promise<void>

      • OptionalonStart?: ((run: Run, config?: RunnableConfig) => void | Promise<void>)

        Called before the runnable starts running, with the Run object.

          • (run, config?): void | Promise<void>
          • Parameters

            Returns void | Promise<void>

    Returns Runnable<RunInput, RunInput, RunnableConfig>

  • A runnable that assigns key-value pairs to the input.

    The example below shows how you could use it with an inline function.

    Type Parameters

    • RunInput extends Record<string, unknown> = Record<string, unknown>
    • RunOutput extends Record<string, unknown> = Record<string, unknown>

    Parameters

    Returns RunnableAssign<RunInput, RunInput & RunOutput, RunnableConfig>

    const prompt =
    PromptTemplate.fromTemplate(`Write a SQL query to answer the question using the following schema: {schema}
    Question: {question}
    SQL Query:`);

    // The `RunnablePassthrough.assign()` is used here to passthrough the input from the `.invoke()`
    // call (in this example it's the question), along with any inputs passed to the `.assign()` method.
    // In this case, we're passing the schema.
    const sqlQueryGeneratorChain = RunnableSequence.from([
    RunnablePassthrough.assign({
    schema: async () => db.getTableInfo(),
    }),
    prompt,
    new ChatOpenAI({}).bind({ stop: ["\nSQLResult:"] }),
    new StringOutputParser(),
    ]);
    const result = await sqlQueryGeneratorChain.invoke({
    question: "How many employees are there?",
    });