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?: ((input) => void) | ((input, config?) => void) | ((input) => Promise<void>) | ((input, config?) => Promise<void>)

Type declaration

    • (input): void
    • Parameters

      Returns void

Type declaration

Type declaration

    • (input): Promise<void>
    • Parameters

      Returns Promise<void>

Type declaration

name?: string

Methods

  • 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.

    event name chunk input output
    on_llm_start [model name] {'input': 'hello'}
    on_llm_stream [model name] 'Hello' OR AIMessageChunk("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_stream some_tool {"x": 1, "y": "2"}
    on_tool_end some_tool {"x": 1, "y": "2"}
    on_retriever_start [retriever name] {"query": "hello"}
    on_retriever_chunk [retriever name] {documents: [...]}
    on_retriever_end [retriever name] {"query": "hello"} {documents: [...]}
    on_prompt_start [template_name] {"question": "hello"}
    on_prompt_end [template_name] {"question": "hello"} ChatPromptValue(messages: [SystemMessage, ...])

    Parameters

    Returns IterableReadableStream<StreamEvent>

  • Parameters

    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, config?) => void | Promise<void>);
          onError?: ((run, config?) => void | Promise<void>);
          onStart?: ((run, config?) => void | Promise<void>);
      }

      The object containing the callback functions.

      • Optional onEnd?: ((run, config?) => void | Promise<void>)

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

          • (run, config?): void | Promise<void>
          • Called after the runnable finishes running, with the Run object.

            Parameters

            Returns void | Promise<void>

      • Optional onError?: ((run, config?) => void | Promise<void>)

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

          • (run, config?): void | Promise<void>
          • Called if the runnable throws an error, with the Run object.

            Parameters

            Returns void | Promise<void>

      • Optional onStart?: ((run, config?) => void | Promise<void>)

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

          • (run, config?): void | Promise<void>
          • Called before the runnable starts running, with the Run object.

            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>

    Example

    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?",
    });

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