Whether to print out response text.
Optional callbacksOptional criterionOptional evaluationOptional llmOptional memoryOptional metadataOptional skipOptional skipOptional tagsCall the chain on all inputs in the list
Optional config: (BaseCallbackConfig | 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<BaseCallbackConfig> | Partial<BaseCallbackConfig>[]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<BaseCallbackConfig> | Partial<BaseCallbackConfig>[]Optional batchOptions: RunnableBatchOptions & { Optional options: Partial<BaseCallbackConfig> | Partial<BaseCallbackConfig>[]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 | CallbacksCheck if the evaluation arguments are valid.
Optional reference: stringThe reference label.
Optional input: stringThe input string.
If the evaluator requires an input string but none is provided, or if the evaluator requires a reference label but none is provided.
Evaluate a trajectory.
Optional callOptions: BaseLanguageModelCallOptionsOptional config: BaseCallbackConfig | CallbacksThe evaluation result.
Get the agent trajectory as a formatted string.
The agent trajectory.
The formatted agent trajectory.
Invoke the chain with the provided input and returns the output.
Input values for the chain run.
Optional config: BaseCallbackConfigOptional 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: BaseCallbackConfig | CallbacksStream output in chunks.
Optional options: Partial<BaseCallbackConfig>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<BaseCallbackConfig>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.
Add retry logic to an existing runnable.
Optional fields: { Optional onOptional stopA new RunnableRetry that, when invoked, will retry according to the parameters.
Static deserializeStatic fromLLMCreate a new TrajectoryEvalChain.
Optional agentTools: StructuredTool<ZodObject<any, any, any, any, {}>>[]The tools used by the agent.
Optional chainOptions: Partial<Omit<LLMEvalChainInput<EvalOutputType, BaseLanguageModel<any, BaseLanguageModelCallOptions>>, "llm">>The options for the chain.
Static isStatic resolveOptional prompt: BasePromptTemplate<any, BasePromptValue, any>Optional agentTools: StructuredTool<ZodObject<any, any, any, any, {}>>[]Static toolsGet the description of the agent tools.
The description of the agent tools.
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A chain for evaluating ReAct style agents.
This chain is used to evaluate ReAct style agents by reasoning about the sequence of actions taken and their outcomes.