LLM Wrapper to use
Key to use for output, defaults to text
Prompt object to use
Whether to print out response text.
Optional callbacksOptional llmKwargs to pass to LLM
Optional memoryOptional metadataOptional outputOutputParser to use
Optional tagsCall the chain on all inputs in the list
Optional config: (RunnableConfig | Callbacks)[]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.
Run the core logic of this chain and add to output if desired.
Wraps _call and handles memory.
Optional config: BaseCallbackConfig | CallbacksInvoke the chain with the provided input and returns the output.
Input values for the chain run.
Optional config: RunnableConfigOptional configuration for the Runnable.
Promise that resolves with the output of the chain run.
Return a new Runnable that maps a list of inputs to a list of outputs, by calling invoke() with each input.
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.
Format prompt with values and pass to LLM
keys to pass to prompt template
Optional callbackManager: CallbackManagerCallbackManager to use
Completion from LLM.
llm.predict({ adjective: "funny" })
Optional config: RunnableConfig | CallbacksStream 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 chain from a json-like object describing it.
Static fromLLMStatic method to create a new TaskPrioritizationChain from a BaseLanguageModel. It generates a prompt using the PromptTemplate class and the task prioritization template, and returns a new instance of TaskPrioritizationChain.
Object with fields used to initialize the chain, excluding the prompt.
A new instance of TaskPrioritizationChain.
Static isGenerated using TypeDoc
Chain to prioritize tasks.