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**Description:** Make ChatDatabricks model supports stream **Issue:** N/A **Dependencies:** MLflow nightly build version (we will release next MLflow version soon) **Twitter handle:** N/A Manually test: (Before testing, please install `pip install git+https://github.com/mlflow/mlflow.git`) ```python # Test Databricks Foundation LLM model from langchain.chat_models import ChatDatabricks chat_model = ChatDatabricks( endpoint="databricks-llama-2-70b-chat", max_tokens=500 ) from langchain_core.messages import AIMessageChunk for chunk in chat_model.stream("What is mlflow?"): print(chunk.content, end="|") ``` - [x] **Add tests and docs**: If you're adding a new integration, please include 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory. - [x] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. See contribution guidelines for more: https://python.langchain.com/docs/contributing/ Additional guidelines: - Make sure optional dependencies are imported within a function. - Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests. - Most PRs should not touch more than one package. - Changes should be backwards compatible. - If you are adding something to community, do not re-import it in langchain. If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, hwchase17. --------- Signed-off-by: Weichen Xu <weichen.xu@databricks.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
import logging
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from urllib.parse import urlparse
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from langchain_community.chat_models.mlflow import ChatMlflow
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logger = logging.getLogger(__name__)
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class ChatDatabricks(ChatMlflow):
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"""`Databricks` chat models API.
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To use, you should have the ``mlflow`` python package installed.
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For more information, see https://mlflow.org/docs/latest/llms/deployments.
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Example:
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.. code-block:: python
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from langchain_community.chat_models import ChatDatabricks
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chat = ChatDatabricks(
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target_uri="databricks",
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endpoint="databricks-llama-2-70b-chat",
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temperature-0.1,
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)
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# single input invocation
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print(chat_model.invoke("What is MLflow?").content)
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# single input invocation with streaming response
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for chunk in chat_model.stream("What is MLflow?"):
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print(chunk.content, end="|")
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"""
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target_uri: str = "databricks"
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"""The target URI to use. Defaults to ``databricks``."""
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@property
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def _llm_type(self) -> str:
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"""Return type of chat model."""
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return "databricks-chat"
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@property
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def _mlflow_extras(self) -> str:
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return ""
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def _validate_uri(self) -> None:
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if self.target_uri == "databricks":
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return
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if urlparse(self.target_uri).scheme != "databricks":
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raise ValueError(
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"Invalid target URI. The target URI must be a valid databricks URI."
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)
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