A list of variable names the prompt template expects
Partial variables
Optional outputHow to parse the output of calling an LLM on this formatted prompt
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 messages based on the provided parameters.
An array of formatted messages.
Invokes 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.
This method is not implemented in the AutoGPTPrompt class and will throw an error if called.
Partial values.
Throws an error.
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.
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.
Optional config: RunnableConfigOptional onCalled if the runnable throws an error, with the Run object.
Optional config: RunnableConfigOptional onCalled 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.
Deserializing needs to be async because templates (e.g. FewShotPromptTemplate) can reference remote resources that we read asynchronously with a web request.
Static isGenerated using TypeDoc
Class used to generate prompts for the AutoGPT model. It takes into account the AI's name, role, tools, token counter, and send token limit. The class also handles the formatting of messages and the construction of the full prompt.