llms

Get the value of the llm_cache global setting.

Return a dict representation of an object.

Convert a sequence of messages to a list of messages.

Ensure that a config is a dict with all keys present.

Get a list of configs from a single config or a list of configs.

It is useful for subclasses overriding batch() or abatch().

Run a function in an executor.

Create a retry decorator for a given LLM and provided a list of error types.

Get prompts that are already cached.

Get prompts that are already cached. Async version.

Update the cache and get the LLM output.

Update the cache and get the LLM output. Async version.

Interface for a caching layer for LLMs and Chat models.

The cache interface consists of the following methods:

• lookup: Look up a value based on a prompt and llm_string.
• update: Update the cache based on a prompt and llm_string.
• clear: Clear the cache.

In addition, the cache interface provides an async version of each method.

The default implementation of the async methods is to run the synchronous method in an executor. It's recommended to override the async methods and provide async implementations to avoid unnecessary overhead.

Async callback manager that handles callbacks from LangChain.

Async callback manager for LLM run.

Base callback manager.

Callback manager for LangChain.

Callback manager for LLM run.

Abstract base class for interfacing with language models.

All language model wrappers inherited from BaseLanguageModel.

LangSmith parameters for tracing.

A single text generation output.

Generation represents the response from an "old-fashioned" LLM (string-in, string-out) that generates regular text (not chat messages).

This model is used internally by chat model and will eventually be mapped to a more general LLMResult object, and then projected into an AIMessage object.

LangChain users working with chat models will usually access information via AIMessage (returned from runnable interfaces) or LLMResult (available via callbacks). Please refer to AIMessage and LLMResult for more information.

GenerationChunk, which can be concatenated with other Generation chunks.

A container for results of an LLM call.

Both chat models and LLMs generate an LLMResult object. This object contains the generated outputs and any additional information that the model provider wants to return.

Class that contains metadata for a single execution of a chain or model.

Defined for backwards compatibility with older versions of langchain_core.

Users can acquire the run_id information from callbacks or via run_id information present in the astream_event API (depending on the use case).

Chat prompt value.

A type of a prompt value that is built from messages.

Base abstract class for inputs to any language model.

PromptValues can be converted to both LLM (pure text-generation) inputs and chat model inputs.

String prompt value.

Base LLM abstract interface.

It should take in a prompt and return a string.

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.

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"]}