language_models

Language Model is a type of model that can generate text or complete text prompts.

LangChain has two main classes to work with language models: Chat Models and “old-fashioned” LLMs.

Chat Models

Language models that use a sequence of messages as inputs and return chat messages as outputs (as opposed to using plain text). These are traditionally newer models ( older models are generally LLMs, see below). Chat models support the assignment of distinct roles to conversation messages, helping to distinguish messages from the AI, users, and instructions such as system messages.

The key abstraction for chat models is BaseChatModel. Implementations should inherit from this class. Please see LangChain how-to guides with more information on how to implement a custom chat model.

To implement a custom Chat Model, inherit from BaseChatModel. See the following guide for more information on how to implement a custom Chat Model:

https://python.langchain.com/docs/how_to/custom_chat_model/

LLMs

Language models that takes a string as input and returns a string. These are traditionally older models (newer models generally are Chat Models, see below).

Although the underlying models are string in, string out, the LangChain wrappers also allow these models to take messages as input. This gives them the same interface as Chat Models. When messages are passed in as input, they will be formatted into a string under the hood before being passed to the underlying model.

To implement a custom LLM, inherit from BaseLLM or LLM. Please see the following guide for more information on how to implement a custom LLM:

https://python.langchain.com/docs/how_to/custom_llm/

Classes

language_models.base.BaseLanguageModel	Abstract base class for interfacing with language models.
language_models.base.BaseLanguageModel[BaseMessage]	Abstract base class for interfacing with language models.
language_models.base.BaseLanguageModel[str]	Abstract base class for interfacing with language models.
language_models.base.LangSmithParams	LangSmith parameters for tracing.
language_models.chat_models.BaseChatModel	Base class for chat models.
language_models.chat_models.SimpleChatModel	Simplified implementation for a chat model to inherit from.
language_models.fake.FakeListLLM	Fake LLM for testing purposes.
language_models.fake.FakeListLLMError	Fake error for testing purposes.
language_models.fake.FakeStreamingListLLM	Fake streaming list LLM for testing purposes.
language_models.fake_chat_models.FakeChatModel	Fake Chat Model wrapper for testing purposes.
language_models.fake_chat_models.FakeListChatModel	Fake ChatModel for testing purposes.
language_models.fake_chat_models.FakeListChatModelError
language_models.fake_chat_models.FakeMessagesListChatModel	Fake ChatModel for testing purposes.
language_models.fake_chat_models.GenericFakeChatModel	Generic fake chat model that can be used to test the chat model interface.
language_models.fake_chat_models.ParrotFakeChatModel	Generic fake chat model that can be used to test the chat model interface.
language_models.llms.BaseLLM	Base LLM abstract interface.
language_models.llms.LLM	Simple interface for implementing a custom LLM.

Functions

language_models.chat_models.agenerate_from_stream(stream)	Async generate from a stream.
language_models.chat_models.generate_from_stream(stream)	Generate from a stream.
language_models.llms.aget_prompts(params, ...)	Get prompts that are already cached.
language_models.llms.aupdate_cache(cache, ...)	Update the cache and get the LLM output.
language_models.llms.create_base_retry_decorator(...)	Create a retry decorator for a given LLM and provided
language_models.llms.get_prompts(params, prompts)	Get prompts that are already cached.
language_models.llms.update_cache(cache, ...)	Update the cache and get the LLM output.