langchain_community.chat_models.databricks.ChatDatabricks¶
class langchain_community.chat_models.databricks.ChatDatabricks[source]¶
Bases: ChatMlflow
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/databricks.html.
Example
from langchain_community.chat_models import ChatDatabricks
chat = ChatDatabricks(
    target_uri="databricks",
    endpoint="chat",
    temperature-0.1,
)
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param cache: Optional[bool] = None¶
Whether to cache the response.
param callback_manager: Optional[BaseCallbackManager] = None¶
Callback manager to add to the run trace.
param callbacks: Callbacks = None¶
Callbacks to add to the run trace.
param endpoint: str [Required]¶
The endpoint to use.
param extra_params: dict [Optional]¶
Any extra parameters to pass to the endpoint.
param max_tokens: Optional[int] = None¶
The maximum number of tokens to generate.
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param n: int = 1¶
The number of completion choices to generate.
param stop: Optional[List[str]] = None¶
The stop sequence.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param target_uri: str = 'databricks'¶
The target URI to use. Defaults to databricks.
param temperature: float = 0.0¶
The sampling temperature.