BaseChatModel

An optional rate limiter to use for limiting the number of requests.

Whether to disable streaming for this model.

If streaming is bypassed, then stream/astream/astream_events will defer to invoke/ainvoke.

• If True, will always bypass streaming case.
• If 'tool_calling', will bypass streaming case only when the model is called with a tools keyword argument. In other words, LangChain will automatically switch to non-streaming behavior (invoke) only when the tools argument is provided. This offers the best of both worlds.
• If False (Default), will always use streaming case if available.

The main reason for this flag is that code might be written using stream and a user may want to swap out a given model for another model whose implementation does not properly support streaming.

Profile detailing model capabilities.

If not specified, automatically loaded from the provider package on initialization if data is available.

Example profile data includes context window sizes, supported modalities, or support for tool calling, structured output, and other features.

Get the output type for this Runnable.

Stream events from this chat model.

For version="v1" / "v2", yields StreamEvent dicts (see Runnable.stream_events). For version="v3", returns a ChatModelStream exposing typed projections (.text, .reasoning, .tool_calls, .output).

Async variant of stream_events. See stream_events for full docs.

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

This method should make use of batched calls for models that expose a batched API.

Use this method when you want to:

1. Take advantage of batched calls,
2. Need more output from the model than just the top generated value,
3. Are building chains that are agnostic to the underlying language model type (e.g., pure text completion models vs chat models).

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

This method should make use of batched calls for models that expose a batched API.

Use this method when you want to:

1. Take advantage of batched calls,
2. Need more output from the model than just the top generated value,
3. Are building chains that are agnostic to the underlying language model type (e.g., pure text completion models vs chat models).

Return a dictionary representation of the chat model.

Bind kwargs to this chat model, returning a typed _ChatModelBinding.

Overrides Runnable.bind so the result preserves chat-model-specific stream_events / astream_events overloads. Without this override, model.bind(...).stream_events(version="v3") would type as Iterator[Any] and await model.bind(...).astream_events(version="v3") as Any, forcing callers to cast.

Bind tools to the model.

Model wrapper that returns outputs formatted to match the given schema.