U ™ÀÂhã@s>ddlZddlmZddlmZe e¡ZGdd„deƒZdS)éN)Úurlparse)Ú ChatMlflowc@sPeZdZUdZdZeed<eedœdd„ƒZeedœdd„ƒZ d dœd d„Z d S)ÚChatDatabricksaù`Databricks` chat models API. To use, you should have the ``mlflow`` python package installed. For more information, see https://mlflow.org/docs/latest/llms/deployments. Example: .. code-block:: python from langchain_community.chat_models import ChatDatabricks chat_model = ChatDatabricks( target_uri="databricks", endpoint="databricks-llama-2-70b-chat", temperature=0.1, ) # single input invocation print(chat_model.invoke("What is MLflow?").content) # single input invocation with streaming response for chunk in chat_model.stream("What is MLflow?"): print(chunk.content, end="|") Ú databricksÚ target_uri)ÚreturncCsdS)zReturn type of chat model.zdatabricks-chat©©ÚselfrrúN/tmp/pip-unpacked-wheel-9gdii04g/langchain_community/chat_models/databricks.pyÚ _llm_type%szChatDatabricks._llm_typecCsdS)NÚrr rrrÚ_mlflow_extras*szChatDatabricks._mlflow_extrasNcCs*|jdkrdSt|jƒjdkr&tdƒ‚dS)NrzBInvalid target URI. The target URI must be a valid databricks URI.)rrÚschemeÚ ValueErrorr rrrÚ _validate_uri.s ÿzChatDatabricks._validate_uri)Ú__name__Ú __module__Ú__qualname__Ú__doc__rÚstrÚ__annotations__Úpropertyrrrrrrrr s r) ÚloggingÚurllib.parserZ&langchain_community.chat_models.mlflowrÚ getLoggerrÚloggerrrrrrÚs