langchain/libs/experimental/langchain_experimental/chat_models/__init__.py

"""**Chat Models** are a variation on language models.

While Chat Models use language models under the hood, the interface they expose
is a bit different. Rather than expose a "text in, text out" API, they expose
an interface where "chat messages" are the inputs and outputs.

**Class hierarchy:**

.. code-block::

    BaseLanguageModel --> BaseChatModel --> <name>  # Examples: ChatOpenAI, ChatGooglePalm

**Main helpers:**

.. code-block::

    AIMessage, BaseMessage, HumanMessage
"""  # noqa: E501

from langchain_experimental.chat_models.llm_wrapper import (
    Llama2Chat,
    Mixtral,
    Orca,
    Vicuna,
)

__all__ = ["Llama2Chat", "Orca", "Vicuna", "Mixtral"]
EXPERIMENTAL Generic LLM wrapper to support chat model interface with configurable chat prompt format (#8295) ## Update 2023-09-08 This PR now supports further models in addition to Lllama-2 chat models. See [this comment](#issuecomment-1668988543) for further details. The title of this PR has been updated accordingly. ## Original PR description This PR adds a generic `Llama2Chat` model, a wrapper for LLMs able to serve Llama-2 chat models (like `LlamaCPP`, `HuggingFaceTextGenInference`, ...). It implements `BaseChatModel`, converts a list of chat messages into the [required Llama-2 chat prompt format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2) and forwards the formatted prompt as `str` to the wrapped `LLM`. Usage example: ```python # uses a locally hosted Llama2 chat model llm = HuggingFaceTextGenInference( inference_server_url="http://127.0.0.1:8080/", max_new_tokens=512, top_k=50, temperature=0.1, repetition_penalty=1.03, ) # Wrap llm to support Llama2 chat prompt format. # Resulting model is a chat model model = Llama2Chat(llm=llm) messages = [ SystemMessage(content="You are a helpful assistant."), MessagesPlaceholder(variable_name="chat_history"), HumanMessagePromptTemplate.from_template("{text}"), ] prompt = ChatPromptTemplate.from_messages(messages) memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) chain = LLMChain(llm=model, prompt=prompt, memory=memory) # use chat model in a conversation # ... ``` Also part of this PR are tests and a demo notebook. - Tag maintainer: @hwchase17 - Twitter handle: `@mrt1nz` --------- Co-authored-by: Erick Friis <erick@langchain.dev> 10 months ago			`"""Chat Models are a variation on language models.`

			`While Chat Models use language models under the hood, the interface they expose`
			`is a bit different. Rather than expose a "text in, text out" API, they expose`
			`an interface where "chat messages" are the inputs and outputs.`

			`Class hierarchy:`

			`.. code-block::`

			`BaseLanguageModel --> BaseChatModel --> <name> # Examples: ChatOpenAI, ChatGooglePalm`

			`Main helpers:`

			`.. code-block::`

			`AIMessage, BaseMessage, HumanMessage`
			`""" # noqa: E501`

experimental[minor]: adds mixtral wrapper (#17423) Description: Adds a chat wrapper for Mixtral models using the [prompt template](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1#instruction-format). --------- Co-authored-by: Bagatur <baskaryan@gmail.com> 7 months ago			`from langchain_experimental.chat_models.llm_wrapper import (`
			`Llama2Chat,`
			`Mixtral,`
			`Orca,`
			`Vicuna,`
			`)`

			`__all__ = ["Llama2Chat", "Orca", "Vicuna", "Mixtral"]`