Class that represents a runnable branch. The RunnableBranch is initialized with an array of branches and a default branch. When invoked, it evaluates the condition of each branch in order and executes the corresponding branch if the condition is true. If none of the conditions are true, it executes the default branch.
class RunnableBranchRunnable<RunInput, RunOutput>const branch = RunnableBranch.from([
[
(x: { topic: string; question: string }) =>
x.topic.toLowerCase().includes("anthropic"),
anthropicChain,
],
[
(x: { topic: string; question: string }) =>
x.topic.toLowerCase().includes("langchain"),
langChainChain,
],
generalChain,
]);
const fullChain = RunnableSequence.from([
{
topic: classificationChain,
question: (input: { question: string }) => input.question,
},
branch,
]);
const result = await fullChain.invoke({
question: "how do I use LangChain?",
});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.
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.
Default streaming implementation. Subclasses should override this method if they support streaming output.
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.
Default implementation of batch, which calls invoke N times. Subclasses should override this method if they can batch more efficiently.
Method to invoke the document transformation. This method calls the transformDocuments method with the provided input.
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.
Stream output in chunks.
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);
}
}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.
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.
Create a new runnable from the current one that will try invoking other passed fallback runnables if the initial invocation fails.
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.
Add retry logic to an existing runnable.
Convenience method for instantiating a RunnableBranch from RunnableLikes (objects, functions, or Runnables).
Each item in the input except for the last one should be a tuple with two items. The first is a "condition" RunnableLike that returns "true" if the second RunnableLike in the tuple should run.
The final item in the input should be a RunnableLike that acts as a default branch if no other branches match.
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.
A path to the module that contains the class, eg. ["langchain", "llms"]
Internal method that handles batching and configuration for a runnable
Default streaming implementation.
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
Default implementation of batch, which calls invoke N times.
Method to invoke the document transformation. This method calls the
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,
Stream output in chunks.
Generate a stream of events emitted by the internal steps of the runnable.
Stream all output from a runnable, as reported to the callback system.
Default implementation of transform, which buffers input and then calls stream.
Bind config to a Runnable, returning a new Runnable.
Create a new runnable from the current one that will try invoking
Bind lifecycle listeners to a Runnable, returning a new Runnable.
Add retry logic to an existing runnable.
The name of the serializable. Override to provide an alias or
A path to the module that contains the class, eg. ["langchain", "llms"]