Prompt template that contains few-shot examples.

Example

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

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.

Default Value

""

suffix: string = ""

A prompt template string to put after the examples.

templateFormat: "f-string" = "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

  • 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 AsyncGenerator<StreamEvent, any, unknown>

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

            Parameters

            Returns void | Promise<void>

    Returns Runnable<any, StringPromptValueInterface, RunnableConfig>

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