Prompt template that contains few-shot examples.

const examplePrompt = PromptTemplate.fromTemplate(
"Input: {input}\nOutput: {output}",
);

const exampleSelector = await SemanticSimilarityExampleSelector.fromExamples(
[
{ input: "happy", output: "sad" },
{ input: "tall", output: "short" },
{ input: "energetic", output: "lethargic" },
{ input: "sunny", output: "gloomy" },
{ input: "windy", output: "calm" },
],
new OpenAIEmbeddings(),
HNSWLib,
{ k: 1 },
);

const dynamicPrompt = new FewShotPromptTemplate({
exampleSelector,
examplePrompt,
prefix: "Give the antonym of every input",
suffix: "Input: {adjective}\nOutput:",
inputVariables: ["adjective"],
});

// Format the dynamic prompt with the input 'rainy'
console.log(await dynamicPrompt.format({ adjective: "rainy" }));

Hierarchy (view full)

Implements

Constructors

Properties

PromptValueReturnType: StringPromptValueInterface
examplePrompt: PromptTemplate<any, any>

An PromptTemplate used to format a single example.

exampleSeparator: string = "\n\n"

String separator used to join the prefix, the examples, and suffix.

inputVariables: string[]

A list of variable names the prompt template expects

partialVariables: PartialValues<any>

Partial variables

prefix: string = ""

A prompt template string to put before the examples.

""

suffix: string = ""

A prompt template string to put after the examples.

templateFormat: TemplateFormat = "f-string"

The format of the prompt template. Options are: 'f-string'

validateTemplate: boolean = true

Whether or not to try validating the template on initialization.

exampleSelector?: BaseExampleSelector

An BaseExampleSelector Examples to format into the prompt. Exactly one of this or examples must be provided.

examples?: InputValues[]

Examples to format into the prompt. Exactly one of this or exampleSelector must be provided.

name?: string
outputParser?: BaseOutputParser<unknown>

How to parse the output of calling an LLM on this formatted prompt

Methods

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

    Type Parameters

    • T extends any = any

    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<ToolCall | T, ZodTypeDef, ToolCall | T>, StringPromptValueInterface>

    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 | some_runnable | | | | +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ | on_chain_stream | some_runnable | "hello world!, goodbye world!" | | | +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ | on_chain_end | some_runnable | | [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, ...]) | +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+

    The "on_chain_*" events are the default for Runnables that don't fit one of the above categories.

    In addition to the standard events above, users can also dispatch custom events.

    Custom events will be only be surfaced with in the v2 version of the API!

    A custom event has following format:

    +-----------+------+-----------------------------------------------------------------------------------------------------------+ | Attribute | Type | Description | +===========+======+===========================================================================================================+ | name | str | A user defined name for the event. | +-----------+------+-----------------------------------------------------------------------------------------------------------+ | data | Any | The data associated with the event. This can be anything, though we suggest making it JSON serializable. | +-----------+------+-----------------------------------------------------------------------------------------------------------+

    Here's an example:

    Parameters

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

    Returns IterableReadableStream<StreamEvent>

    import { RunnableLambda } from "@langchain/core/runnables";
    import { dispatchCustomEvent } from "@langchain/core/callbacks/dispatch";
    // Use this import for web environments that don't support "async_hooks"
    // and manually pass config to child runs.
    // import { dispatchCustomEvent } from "@langchain/core/callbacks/dispatch/web";

    const slowThing = RunnableLambda.from(async (someInput: string) => {
    // Placeholder for some slow operation
    await new Promise((resolve) => setTimeout(resolve, 100));
    await dispatchCustomEvent("progress_event", {
    message: "Finished step 1 of 2",
    });
    await new Promise((resolve) => setTimeout(resolve, 100));
    return "Done";
    });

    const eventStream = await slowThing.streamEvents("hello world", {
    version: "v2",
    });

    for await (const event of eventStream) {
    if (event.event === "on_custom_event") {
    console.log(event);
    }
    }
  • Parameters

    • input: any
    • 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>

""