OptionalcacheOptionalcallbacksThe async caller should be used by subclasses to make any async calls, which will thus benefit from the concurrency and retry logic.
A path to the module that contains the class, eg. ["langchain", "llms"] Usually should be the same as the entrypoint the class is exported from.
Protectedlc_OptionalmetadataOptionalnameOptionaltagsWhether to print out response text.
Keys that the language model accepts as call options.
Get the identifying parameters
A map of aliases for constructor args. Keys are the attribute names, e.g. "foo". Values are the alias that will replace the key in serialization. This is used to eg. make argument names match Python.
A map of additional attributes to merge with constructor args. Keys are the attribute names, e.g. "foo". Values are the attribute values, which will be serialized. These attributes need to be accepted by the constructor as arguments.
The final serialized identifier for the module.
A map of secrets, which will be omitted from serialization. Keys are paths to the secret in constructor args, e.g. "foo.bar.baz". Values are the secret ids, which will be used when deserializing.
A manual list of keys that should be serialized. If not overridden, all fields passed into the constructor will be serialized.
Get the model default parameters
Internal method that handles batching and configuration for a runnable It takes a function, input values, and optional configuration, and returns a promise that resolves to the output values.
The function to be executed for each input value.
Optionaloptions: OptionalbatchOptions: RunnableBatchOptionsA promise that resolves to the output values.
Make the API call to generate text
Optional_runManager: CallbackManagerForLLMRunProtected_Run the LLM on the given prompts and input.
OptionalrunManager: CallbackManagerForLLMRunProtected_Create a unique cache key for a specific call to a specific language model.
Call options for the model
A unique cache key.
Get the identifying parameters of the LLM.
Get the LLM type identifier
Protected_Optionaloptions: Partial<BaseLLMCallOptions>Protected_Optionaloptions: Partial<BaseLLMCallOptions>Default streaming implementation. Subclasses should override this method if they support streaming output.
Optionaloptions: BaseLLMCallOptionsProtected_Optional_runManager: CallbackManagerForLLMRunProtected_Helper method to transform an Iterator of Input values into an Iterator of
Output values, with callbacks.
Use this to implement stream() or transform() in Runnable subclasses.
Assigns new fields to the dict output of this runnable. Returns a new runnable.
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<BaseLLMCallOptions> | Partial<BaseLLMCallOptions>[]Either a single call options object to apply to each batch call or an array for each call.
OptionalbatchOptions: RunnableBatchOptions & { returnExceptions?: false }Whether to return errors rather than throwing on the first one
OptionalreturnExceptions?: falseWhether to return errors rather than throwing on the first one
An array of RunOutputs, or mixed RunOutputs and errors if batchOptions.returnExceptions is set
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<BaseLLMCallOptions> | Partial<BaseLLMCallOptions>[]Either a single call options object to apply to each batch call or an array for each call.
OptionalbatchOptions: RunnableBatchOptions & { returnExceptions: true }Whether to return errors rather than throwing on the first one
Whether to return errors rather than throwing on the first one
An array of RunOutputs, or mixed RunOutputs and errors if batchOptions.returnExceptions is set
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<BaseLLMCallOptions> | Partial<BaseLLMCallOptions>[]Either a single call options object to apply to each batch call or an array for each call.
OptionalbatchOptions: RunnableBatchOptionsWhether to return errors rather than throwing on the first one
An array of RunOutputs, or mixed RunOutputs and errors if batchOptions.returnExceptions is set
Bind arguments to a Runnable, returning a new Runnable.
A new RunnableBinding that, when invoked, will apply the bound args.
Use withConfig instead. This will be removed in the next breaking release.
Optionaloptions: BaseLLMCallOptions | 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.
Run the LLM on the given prompts and input, handling caching.
Optionaloptions: BaseLLMCallOptions | string[]Optionalcallbacks: CallbacksThis method takes prompt values, options, and callbacks, and generates a result based on the prompts.
Prompt values for the LLM.
Optionaloptions: BaseLLMCallOptions | string[]Options for the LLM call.
Optionalcallbacks: CallbacksCallbacks for the LLM call.
An LLMResult based on the prompts.
Optional_: RunnableConfig<Record<string, any>>Optionalsuffix: stringGet the number of tokens in the content.
The content to get the number of tokens for.
The number of tokens in the content.
Get the parameters used to invoke the model
Optional_options: Omit<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: BaseLLMCallOptionsOptions for the LLM call.
A string result based on the prompt.
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.
Input text for the prediction.
Optionaloptions: BaseLLMCallOptions | 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: BaseLLMCallOptions | string[]Options for the LLM call.
Optionalcallbacks: CallbacksCallbacks for the LLM call.
A predicted message based on the list of messages.
ProtectedpreparePrepare the request payload for the API
Stream output in chunks.
Optionaloptions: Partial<BaseLLMCallOptions>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.
ATTENTION This reference table is for the V2 version of the schema.
+----------------------+-----------------------------+------------------------------------------+
| event | input | output/chunk |
+======================+=============================+==========================================+
| on_chat_model_start | {"messages": BaseMessage[]} | |
+----------------------+-----------------------------+------------------------------------------+
| on_chat_model_stream | | AIMessageChunk("hello") |
+----------------------+-----------------------------+------------------------------------------+
| on_chat_model_end | {"messages": BaseMessage[]} | AIMessageChunk("hello world") |
+----------------------+-----------------------------+------------------------------------------+
| on_llm_start | {'input': 'hello'} | |
+----------------------+-----------------------------+------------------------------------------+
| on_llm_stream | | 'Hello' |
+----------------------+-----------------------------+------------------------------------------+
| on_llm_end | 'Hello human!' | |
+----------------------+-----------------------------+------------------------------------------+
| on_chain_start | | |
+----------------------+-----------------------------+------------------------------------------+
| on_chain_stream | | "hello world!" |
+----------------------+-----------------------------+------------------------------------------+
| on_chain_end | [Document(...)] | "hello world!, goodbye world!" |
+----------------------+-----------------------------+------------------------------------------+
| on_tool_start | {"x": 1, "y": "2"} | |
+----------------------+-----------------------------+------------------------------------------+
| on_tool_end | | {"x": 1, "y": "2"} |
+----------------------+-----------------------------+------------------------------------------+
| on_retriever_start | {"query": "hello"} | |
+----------------------+-----------------------------+------------------------------------------+
| on_retriever_end | {"query": "hello"} | [Document(...), ..] |
+----------------------+-----------------------------+------------------------------------------+
| on_prompt_start | {"question": "hello"} | |
+----------------------+-----------------------------+------------------------------------------+
| on_prompt_end | {"question": "hello"} | ChatPromptValue(messages: BaseMessage[]) |
+----------------------+-----------------------------+------------------------------------------+
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. |
+-----------+------+------------------------------------------------------------+
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">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 | input | output/chunk |
+======================+=============================+==========================================+
| on_chat_model_start | {"messages": BaseMessage[]} | |
+----------------------+-----------------------------+------------------------------------------+
| on_chat_model_stream | | AIMessageChunk("hello") |
+----------------------+-----------------------------+------------------------------------------+
| on_chat_model_end | {"messages": BaseMessage[]} | AIMessageChunk("hello world") |
+----------------------+-----------------------------+------------------------------------------+
| on_llm_start | {'input': 'hello'} | |
+----------------------+-----------------------------+------------------------------------------+
| on_llm_stream | | 'Hello' |
+----------------------+-----------------------------+------------------------------------------+
| on_llm_end | 'Hello human!' | |
+----------------------+-----------------------------+------------------------------------------+
| on_chain_start | | |
+----------------------+-----------------------------+------------------------------------------+
| on_chain_stream | | "hello world!" |
+----------------------+-----------------------------+------------------------------------------+
| on_chain_end | [Document(...)] | "hello world!, goodbye world!" |
+----------------------+-----------------------------+------------------------------------------+
| on_tool_start | {"x": 1, "y": "2"} | |
+----------------------+-----------------------------+------------------------------------------+
| on_tool_end | | {"x": 1, "y": "2"} |
+----------------------+-----------------------------+------------------------------------------+
| on_retriever_start | {"query": "hello"} | |
+----------------------+-----------------------------+------------------------------------------+
| on_retriever_end | {"query": "hello"} | [Document(...), ..] |
+----------------------+-----------------------------+------------------------------------------+
| on_prompt_start | {"question": "hello"} | |
+----------------------+-----------------------------+------------------------------------------+
| on_prompt_end | {"question": "hello"} | ChatPromptValue(messages: BaseMessage[]) |
+----------------------+-----------------------------+------------------------------------------+
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. |
+-----------+------+------------------------------------------------------------+
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">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<BaseLLMCallOptions>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.
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.
OptionalonEnd?: (run: Run, config?: RunnableConfig<Record<string, any>>) => void | Promise<void>Called after the runnable finishes running, with the Run object.
OptionalonError?: (run: Run, config?: RunnableConfig<Record<string, any>>) => void | Promise<void>Called if the runnable throws an error, with the Run object.
OptionalonStart?: (run: Run, config?: RunnableConfig<Record<string, any>>) => void | Promise<void>Called before the runnable starts running, with the Run object.
Add retry logic to an existing runnable.
Optionalfields: {OptionalonFailedAttempt?: RunnableRetryFailedAttemptHandlerA function that is called when a retry fails.
OptionalstopAfterAttempt?: numberThe number of attempts to retry.
A new RunnableRetry that, when invoked, will retry according to the parameters.
OptionalwithModel wrapper that returns outputs formatted to match the given schema.
The output type for the Runnable, expected to be a Zod schema object for structured output validation.
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.
Protected Static_StaticdeserializeStaticisStaticlc_The name of the serializable. Override to provide an alias or to preserve the serialized module name in minified environments.
Implemented as a static method to support loading logic.
BaseLLM is a base class for LLMs that communicate with a remote API. It extends the LangChain LLM class and provides configurable parameters for model inference.