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import logging
from urllib.parse import urlparse
from langchain_community.chat_models.mlflow import ChatMlflow
logger = logging.getLogger(__name__)
class ChatDatabricks(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.
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="|")
"""
target_uri: str = "databricks"
"""The target URI to use. Defaults to ``databricks``."""
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
return "databricks-chat"
@property
def _mlflow_extras(self) -> str:
return ""
def _validate_uri(self) -> None:
if self.target_uri == "databricks":
return
if urlparse(self.target_uri).scheme != "databricks":
raise ValueError(
"Invalid target URI. The target URI must be a valid databricks URI."
)
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