Optionalfields: VertexAIInputOptionalcacheOptionalcallbacksThe async caller should be used by subclasses to make any async calls, which will thus benefit from the concurrency and retry logic.
ProtectedconnectionMaximum number of tokens to generate in the completion.
OptionalmetadataModel to use
Model to use
Alias for model
OptionalnameOptionaloriginalAvailable for gemini-1.5-pro.
The output format of the generated candidate text.
Supported MIME types:
text/plain: Text output.application/json: JSON response in the candidates.ProtectedstreamedOptionaltagsSampling temperature to use
Top-k changes how the model selects tokens for output.
A top-k of 1 means the selected token is the most probable among all tokens in the model’s vocabulary (also called greedy decoding), while a top-k of 3 means that the next token is selected from among the 3 most probable tokens (using temperature).
Top-p changes how the model selects tokens for output.
Tokens are selected from most probable to least until the sum of their probabilities equals the top-p value.
For example, if tokens A, B, and C have a probability of .3, .2, and .1 and the top-p value is .5, then the model will select either A or B as the next token (using temperature).
Whether to print out response text.
Keys that the language model accepts as call options.
Convert a runnable to a tool. Return a new instance of RunnableToolLike
which contains the runnable, name, description and schema.
Optionaldescription?: stringThe description of the tool. Falls back to the description on the Zod schema if not provided, or undefined if neither are provided.
Optionalname?: stringThe name of the tool. If not provided, it will default to the name of the runnable.
The Zod schema for the input of the tool. Infers the Zod type from the input type of the runnable.
An instance of RunnableToolLike which is a runnable that can be used as a tool.
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.
Optionaloptions: Partial<BaseLanguageModelCallOptions> | Partial<BaseLanguageModelCallOptions>[]Either a single call options object to apply to each batch call or an array for each call.
OptionalbatchOptions: RunnableBatchOptions & { An array of RunOutputs, or mixed RunOutputs and errors if batchOptions.returnExceptions is set
Optionaloptions: Partial<BaseLanguageModelCallOptions> | Partial<BaseLanguageModelCallOptions>[]OptionalbatchOptions: RunnableBatchOptions & { Optionaloptions: Partial<BaseLanguageModelCallOptions> | Partial<BaseLanguageModelCallOptions>[]OptionalbatchOptions: RunnableBatchOptionsBind arguments to a Runnable, returning a new Runnable.
A new RunnableBinding that, when invoked, will apply the bound args.
Optionalfields: GoogleAIBaseLLMInput<WebGoogleAuthOptions>Optionalfields: GoogleAIBaseLLMInput<WebGoogleAuthOptions>Optionaloptions: BaseLanguageModelCallOptions | string[]Optionalcallbacks: CallbacksUse .invoke() instead. Will be removed in 0.2.0. Convenience wrapper for generate that takes in a single string prompt and returns a single string output.
ProtectedcreateThis method takes prompt values, options, and callbacks, and generates a result based on the prompts.
Prompt values for the LLM.
Optionaloptions: BaseLanguageModelCallOptions | string[]Options for the LLM call.
Optionalcallbacks: CallbacksCallbacks for the LLM call.
An LLMResult based on the prompts.
This method takes an input and options, and returns a string. It converts the input to a prompt value and generates a result based on the prompt.
Input for the LLM.
Optionaloptions: BaseLanguageModelCallOptionsOptions for the LLM call.
A string result based on the prompt.
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.
Input text for the prediction.
Optionaloptions: BaseLanguageModelCallOptions | string[]Options for the LLM call.
Optionalcallbacks: CallbacksCallbacks for the LLM call.
A prediction based on the input text.
A list of messages for the prediction.
Optionaloptions: BaseLanguageModelCallOptions | string[]Options for the LLM call.
Optional_callbacks: CallbacksA predicted message based on the list of messages.
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.
ATTENTION This reference table is for the V2 version of the schema.
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| event | name | chunk | input | output |
+======================+==================+=================================+===============================================+=================================================+
| on_chat_model_start | [model name] | | {"messages": [[SystemMessage, HumanMessage]]} | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_chat_model_stream | [model name] | AIMessageChunk(content="hello") | | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_chat_model_end | [model name] | | {"messages": [[SystemMessage, HumanMessage]]} | AIMessageChunk(content="hello world") |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_llm_start | [model name] | | {'input': 'hello'} | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_llm_stream | [model name] | 'Hello' | | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_llm_end | [model name] | | 'Hello human!' | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_chain_start | some_runnable | | | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_chain_stream | some_runnable | "hello world!, goodbye world!" | | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_chain_end | some_runnable | | [Document(...)] | "hello world!, goodbye world!" |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_tool_start | some_tool | | {"x": 1, "y": "2"} | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_tool_end | some_tool | | | {"x": 1, "y": "2"} |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_retriever_start | [retriever name] | | {"query": "hello"} | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_retriever_end | [retriever name] | | {"query": "hello"} | [Document(...), ..] |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_prompt_start | [template_name] | | {"question": "hello"} | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_prompt_end | [template_name] | | {"question": "hello"} | ChatPromptValue(messages: [SystemMessage, ...]) |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
The "on_chain_*" events are the default for Runnables that don't fit one of the above categories.
In addition to the standard events above, users can also dispatch custom events.
Custom events will be only be surfaced with in the v2 version of the API!
A custom event has following format:
+-----------+------+-----------------------------------------------------------------------------------------------------------+
| Attribute | Type | Description |
+===========+======+===========================================================================================================+
| name | str | A user defined name for the event. |
+-----------+------+-----------------------------------------------------------------------------------------------------------+
| data | Any | The data associated with the event. This can be anything, though we suggest making it JSON serializable. |
+-----------+------+-----------------------------------------------------------------------------------------------------------+
Here's an example:
import { RunnableLambda } from "@langchain/core/runnables";
import { dispatchCustomEvent } from "@langchain/core/callbacks/dispatch";
// Use this import for web environments that don't support "async_hooks"
// and manually pass config to child runs.
// import { dispatchCustomEvent } from "@langchain/core/callbacks/dispatch/web";
const slowThing = RunnableLambda.from(async (someInput: string) => {
// Placeholder for some slow operation
await new Promise((resolve) => setTimeout(resolve, 100));
await dispatchCustomEvent("progress_event", {
message: "Finished step 1 of 2",
});
await new Promise((resolve) => setTimeout(resolve, 100));
return "Done";
});
const eventStream = await slowThing.streamEvents("hello world", {
version: "v2",
});
for await (const event of eventStream) {
if (event.event === "on_custom_event") {
console.log(event);
}
}
OptionalstreamOptions: Omit<EventStreamCallbackHandlerInput, "autoClose">OptionalstreamOptions: Omit<EventStreamCallbackHandlerInput, "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.
Optionaloptions: Partial<BaseLanguageModelCallOptions>OptionalstreamOptions: 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.
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.
OptionalonCalled after the runnable finishes running, with the Run object.
Optionalconfig: RunnableConfig<Record<string, any>>OptionalonCalled if the runnable throws an error, with the Run object.
Optionalconfig: RunnableConfig<Record<string, any>>OptionalonCalled before the runnable starts running, with the Run object.
Optionalconfig: RunnableConfig<Record<string, any>>Add retry logic to an existing runnable.
Optionalfields: { OptionalonOptionalstopA new RunnableRetry that, when invoked, will retry according to the parameters.
OptionalwithModel wrapper that returns outputs formatted to match the given schema.
The schema for the structured output. Either as a Zod schema or a valid JSON schema object. If a Zod schema is passed, the returned attributes will be validated, whereas with JSON schema they will not be.
Optionalconfig: StructuredOutputMethodOptions<boolean>A new runnable that calls the LLM with structured output.
StaticdeserializeStaticis
Integration with a Google Vertex AI LLM using the "@langchain/google-webauth" package for auth.