Do not emit warnings for MLflow and Databricks llms

[harupy]

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pip install git+https://github.com/mlflow/mlflow.git@refs/pull/10725/merge

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gh pr checkout 10725

Related Issues/PRs

#xxx

What changes are proposed in this pull request?

Do not emit warnings for MLflow and Databricks llms.

How is this PR tested?

• Existing unit/integration tests
• New unit/integration tests
• Manual tests

from langchain.chains import LLMChain
from langchain.chat_models import ChatDatabricks, ChatMlflow
from langchain.llms import Databricks, Mlflow
from langchain.prompts import PromptTemplate

import mlflow

prompt = PromptTemplate(
    input_variables=["product"],
    template="Tell me a {adjective} joke",
)
for llm in [
    Mlflow(
        target_uri="http://localhost:5000",
        endpoint="test",
    ),
    Databricks(endpoint_name="test"),
    ChatDatabricks(endpoint="test"),
    ChatMlflow(
        target_uri="http://localhost:5000",
        endpoint="test",
    ),
]:
    chain = LLMChain(llm=llm, prompt=prompt)
    with mlflow.start_run():
        mlflow.langchain.log_model(chain, "model")

Does this PR require documentation update?

• No. You can skip the rest of this section.
• Yes. I've updated:
  • Examples
  • API references
  • Instructions

Release Notes

Is this a user-facing change?

• No. You can skip the rest of this section.
• Yes. Give a description of this change to be included in the release notes for MLflow users.

What component(s), interfaces, languages, and integrations does this PR affect?

Components

• area/artifacts: Artifact stores and artifact logging
• area/build: Build and test infrastructure for MLflow
• area/deployments: MLflow Deployments client APIs, server, and third-party Deployments integrations
• area/docs: MLflow documentation pages
• area/examples: Example code
• area/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registry
• area/models: MLmodel format, model serialization/deserialization, flavors
• area/recipes: Recipes, Recipe APIs, Recipe configs, Recipe Templates
• area/projects: MLproject format, project running backends
• area/scoring: MLflow Model server, model deployment tools, Spark UDFs
• area/server-infra: MLflow Tracking server backend
• area/tracking: Tracking Service, tracking client APIs, autologging

Interface

• area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev server
• area/docker: Docker use across MLflow's components, such as MLflow Projects and MLflow Models
• area/sqlalchemy: Use of SQLAlchemy in the Tracking Service or Model Registry
• area/windows: Windows support

Language

• language/r: R APIs and clients
• language/java: Java APIs and clients
• language/new: Proposals for new client languages

Integrations

• integrations/azure: Azure and Azure ML integrations
• integrations/sagemaker: SageMaker integrations
• integrations/databricks: Databricks integrations

How should the PR be classified in the release notes? Choose one:

• rn/none - No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" section
• rn/breaking-change - The PR will be mentioned in the "Breaking Changes" section
• rn/feature - A new user-facing feature worth mentioning in the release notes
• rn/bug-fix - A user-facing bug fix worth mentioning in the release notes
• rn/documentation - A user-facing documentation change worth mentioning in the release notes

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[serena-ruan]

LGTM!