LLM

Pass a sequence of prompts to a model and return generations.

Asynchronously pass a sequence of prompts to a model and return generations.

Return a dictionary of the LLM.

Save the LLM.

Get the number of tokens present in the text.

Get the number of tokens in the messages.

The type of config this Runnable accepts specified as a Pydantic model.

Get a JSON schema that represents the config of the Runnable.

Return a graph representation of this Runnable.

Return a list of prompts used by this Runnable.

Pipe Runnable objects.

Pick keys from the output dict of this Runnable.

Assigns new fields to the dict output of this Runnable.

Transform a single input into an output.

Transform a single input into an output.

Default implementation runs invoke in parallel using a thread pool executor.

Run invoke in parallel on a list of inputs.

Default implementation runs ainvoke in parallel using asyncio.gather.

Run ainvoke in parallel on a list of inputs.

Default implementation of stream, which calls invoke.

Default implementation of astream, which calls ainvoke.

Stream all output from a Runnable, as reported to the callback system.

Generate a stream of events.

Generate a stream of events synchronously.

Transform inputs to outputs.

Transform inputs to outputs.

Bind arguments to a Runnable, returning a new Runnable.

Bind config to a Runnable, returning a new Runnable.

Bind lifecycle listeners to a Runnable, returning a new Runnable.

Bind async lifecycle listeners to a Runnable.

Bind input and output types to a Runnable, returning a new Runnable.

Create a new Runnable that retries the original Runnable on exceptions.

Return a new Runnable that maps a list of inputs to a list of outputs.

Add fallbacks to a Runnable, returning a new Runnable.

Create a BaseTool from a Runnable.