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Async callback manager for LLM run.

Callback manager for LLM run.

Simple interface for implementing a custom LLM.

You should subclass this class and implement the following:

• _call method: Run the LLM on the given prompt and input (used by invoke).
• _identifying_params property: Return a dictionary of the identifying parameters This is critical for caching and tracing purposes. Identifying parameters is a dict that identifies the LLM. It should mostly include a model_name.

Optional: Override the following methods to provide more optimizations:

• _acall: Provide a native async version of the _call method. If not provided, will delegate to the synchronous version using run_in_executor. (Used by ainvoke).
• _stream: Stream the LLM on the given prompt and input. stream will use _stream if provided, otherwise it use _call and output will arrive in one chunk.
• _astream: Override to provide a native async version of the _stream method. astream will use _astream if provided, otherwise it will implement a fallback behavior that will use _stream if _stream is implemented, and use _acall if _stream is not implemented.

Fake LLM for testing purposes.

Fake error for testing purposes.

Fake streaming list LLM for testing purposes.

An LLM that will return responses from a list in order.

This model also supports optionally sleeping between successive chunks in a streaming implementation.

Input to a language model.

Configuration for a Runnable.

# Parent sets tags
chain.invoke(input, config={"tags": ["parent"]})
# Child automatically inherits and can add:
# ensure_config({"tags": ["child"]}) -> {"tags": ["parent", "child"]}