Chat Models#

pydantic model langchain.chat_models.AzureChatOpenAI[source]#

Wrapper around Azure OpenAI Chat Completion API. To use this class you must have a deployed model on Azure OpenAI. Use deployment_name in the constructor to refer to the “Model deployment name” in the Azure portal.

In addition, you should have the openai python package installed, and the following environment variables set or passed in constructor in lower case: - OPENAI_API_TYPE (default: azure) - OPENAI_API_KEY - OPENAI_API_BASE - OPENAI_API_VERSION

For exmaple, if you have gpt-35-turbo deployed, with the deployment name 35-turbo-dev, the constructor should look like:

Be aware the API version may change.

Any parameters that are valid to be passed to the openai.create call can be passed in, even if not explicitly saved on this class.

Validators

  • build_extra » all fields

  • set_callback_manager » callback_manager

  • validate_environment » all fields

field deployment_name: str = ''#
field openai_api_base: str = ''#
field openai_api_key: str = ''#
field openai_api_type: str = 'azure'#
field openai_api_version: str = ''#
field openai_organization: str = ''#
pydantic model langchain.chat_models.ChatAnthropic[source]#

Wrapper around Anthropic’s large language model.

To use, you should have the anthropic python package installed, and the environment variable ANTHROPIC_API_KEY set with your API key, or pass it as a named parameter to the constructor.

Example

Validators

  • set_callback_manager » callback_manager

  • validate_environment » all fields

field callback_manager: langchain.callbacks.base.BaseCallbackManager [Optional]#
field verbose: bool [Optional]#

Whether to print out response text.

pydantic model langchain.chat_models.ChatOpenAI[source]#

Wrapper around OpenAI Chat large language models.

To use, you should have the openai python package installed, and the environment variable OPENAI_API_KEY set with your API key.

Any parameters that are valid to be passed to the openai.create call can be passed in, even if not explicitly saved on this class.

Example

from langchain.chat_models import ChatOpenAI
openai = ChatOpenAI(model_name="gpt-3.5-turbo")
Validators

  • build_extra » all fields

  • set_callback_manager » callback_manager

  • validate_environment » all fields

field max_retries: int = 6#

Maximum number of retries to make when generating.

field max_tokens: Optional[int] = None#

Maximum number of tokens to generate.

field model_kwargs: Dict[str, Any] [Optional]#

Holds any model parameters valid for create call not explicitly specified.

field model_name: str = 'gpt-3.5-turbo'#

Model name to use.

field n: int = 1#

Number of chat completions to generate for each prompt.

field openai_api_key: Optional[str] = None#
field openai_organization: Optional[str] = None#
field request_timeout: int = 60#

Timeout in seconds for the OpenAPI request.

field streaming: bool = False#

Whether to stream the results or not.

field temperature: float = 0.7#

What sampling temperature to use.

completion_with_retry(**kwargs: Any) → Any[source]#

Use tenacity to retry the completion call.

get_num_tokens(text: str) → int[source]#

Calculate num tokens with tiktoken package.

get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int[source]#

Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.

Official documentation: openai/openai-cookbook main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb

pydantic model langchain.chat_models.PromptLayerChatOpenAI[source]#

Wrapper around OpenAI Chat large language models and PromptLayer.

To use, you should have the openai and promptlayer python package installed, and the environment variable OPENAI_API_KEY and PROMPTLAYER_API_KEY set with your openAI API key and promptlayer key respectively.

All parameters that can be passed to the OpenAI LLM can also be passed here. The PromptLayerChatOpenAI adds to optional :param pl_tags: List of strings to tag the request with. :param return_pl_id: If True, the PromptLayer request ID will be

returned in the generation_info field of the Generation object.

Example

from langchain.chat_models import PromptLayerChatOpenAI
openai = PromptLayerChatOpenAI(model_name="gpt-3.5-turbo")
Validators

  • build_extra » all fields

  • set_callback_manager » callback_manager

  • validate_environment » all fields

field pl_tags: Optional[List[str]] = None#
field return_pl_id: Optional[bool] = False#