A list of variable names the prompt template expects
Partial variables
The prompt template
The format of the prompt template. Options are "f-string" and "mustache"
Whether or not to try validating the template on initialization
true
Optional nameOptional outputHow to parse the output of calling an LLM on this formatted prompt
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.
Optional options: Partial<RunnableConfig> | Partial<RunnableConfig>[]Either a single call options object to apply to each batch call or an array for each call.
Optional batchOptions: RunnableBatchOptions & { An array of RunOutputs, or mixed RunOutputs and errors if batchOptions.returnExceptions is set
Optional options: Partial<RunnableConfig> | Partial<RunnableConfig>[]Optional batchOptions: RunnableBatchOptions & { Optional options: Partial<RunnableConfig> | Partial<RunnableConfig>[]Optional batchOptions: RunnableBatchOptionsBind arguments to a Runnable, returning a new Runnable.
A new RunnableBinding that, when invoked, will apply the bound args.
Formats the prompt template with the provided values.
The values to be used to format the prompt template.
A promise that resolves to a string which is the formatted prompt.
Formats the prompt given the input values and returns a formatted prompt value.
The input values to format the prompt.
A Promise that resolves to a formatted prompt value.
Optional _: RunnableConfigInvokes the prompt template with the given input and options.
The input to invoke the prompt template with.
Optional options: BaseCallbackConfigOptional configuration for the callback.
A Promise that resolves to the output of the prompt template.
Return a new Runnable that maps a list of inputs to a list of outputs, by calling invoke() with each input.
Merges partial variables and user variables.
The user variables to merge with the partial variables.
A Promise that resolves to an object containing the merged variables.
Partially applies values to the prompt template.
The values to be partially applied to the prompt template.
A new instance of PromptTemplate with the partially applied values.
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.
Return a json-like object representing this prompt template.
Stream output in chunks.
Optional options: Partial<RunnableConfig>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.
| 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, ...]) |
Optional streamOptions: Omit<LogStreamCallbackHandlerInput, "autoClose">Optional streamOptions: Omit<LogStreamCallbackHandlerInput, "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.
Optional options: Partial<RunnableConfig>Optional streamOptions: 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.
Optional onCalled after the runnable finishes running, with the Run object.
Called after the runnable finishes running, with the Run object.
Optional config: RunnableConfigOptional onCalled if the runnable throws an error, with the Run object.
Called if the runnable throws an error, with the Run object.
Optional config: RunnableConfigOptional onCalled before the runnable starts running, with the Run object.
Called before the runnable starts running, with the Run object.
Optional config: RunnableConfigAdd retry logic to an existing runnable.
Optional fields: { Optional onOptional stopA new RunnableRetry that, when invoked, will retry according to the parameters.
Static deserializeLoad a prompt template from a json-like object describing it.
This feature is deprecated and will be removed in the future.
It is not recommended for use.
Deserializing needs to be async because templates (e.g. FewShotPromptTemplate) can reference remote resources that we read asynchronously with a web request.
Static fromTake examples in list format with prefix and suffix to create a prompt.
Intended to be used a a way to dynamically create a prompt from examples.
List of examples to use in the prompt.
String to go after the list of examples. Should generally set up the user's input.
A list of variable names the final prompt template will expect
The separator to use in between examples
String that should go before any examples. Generally includes examples.
The final prompt template generated.
Static fromLoad prompt template from a template f-string
Optional options: Omit<PromptTemplateInput<RunInput, string, "f-string">, "template" | "inputVariables">Optional options: Omit<PromptTemplateInput<RunInput, string, TemplateFormat>, "template" | "inputVariables">Optional options: Omit<PromptTemplateInput<RunInput, string, "mustache">, "template" | "inputVariables">Static isGenerated using TypeDoc
Schema to represent a basic prompt for an LLM.
Example