Fix promptlab pyfunc models not working for chat routes #10346
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Signed-off-by: Daniel Lok <daniel.lok@databricks.com>
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harupy
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Nov 10, 2023
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| def construct_model(route): | ||
| prompt_parameters = [Param(key="thing", value="books")] | ||
| model_parameters = [Param(key="temperature", value=0.5), Param(key="max_tokens", value=10)] | ||
| prompt_template = "Write me a story about {{ thing }}." | ||
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| return _PromptlabModel(prompt_template, prompt_parameters, model_parameters, route) |
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/ai Remove variables. Immediately return _PromptlabModel.
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You can simplify the function like this:
def construct_model(route):
return _PromptlabModel(
"Write me a story about {{ thing }}.",
[Param(key="thing", value="books")],
[Param(key="temperature", value=0.5), Param(key="max_tokens", value=10)],
route)This code does exactly the same thing as before but without creating intermediary variables.
Click here to see the usage data
{
"prompt_tokens": 123,
"completion_tokens": 92,
"total_tokens": 215,
"estimated_cost_in_usd": 0.00921
}There was a problem hiding this comment.
Not a blocker, but to remove useless variable assignment.
harupy
approved these changes
Nov 11, 2023
Signed-off-by: Daniel Lok <daniel.lok@databricks.com>
KonakanchiSwathi
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to KonakanchiSwathi/mlflow
that referenced
this pull request
Nov 29, 2023
Signed-off-by: Daniel Lok <daniel.lok@databricks.com> Signed-off-by: swathi <konakanchi.swathi@gmail.com>
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Related Issues/PRs
#xxxWhat changes are proposed in this pull request?
Currently, the promptlab pyfunc model is hardcoded to query the completions route. Same thing for response parsing.
In the JS, we have a switch for both the query and the response parsing. This PR just copies the same logic to the pyfunc model.
How is this PR tested?
Does this PR require documentation update?
Release Notes
Is this a user-facing change?
What component(s), interfaces, languages, and integrations does this PR affect?
Components
area/artifacts: Artifact stores and artifact loggingarea/build: Build and test infrastructure for MLflowarea/docs: MLflow documentation pagesarea/examples: Example codearea/gateway: AI Gateway service, Gateway client APIs, third-party Gateway integrationsarea/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registryarea/models: MLmodel format, model serialization/deserialization, flavorsarea/recipes: Recipes, Recipe APIs, Recipe configs, Recipe Templatesarea/projects: MLproject format, project running backendsarea/scoring: MLflow Model server, model deployment tools, Spark UDFsarea/server-infra: MLflow Tracking server backendarea/tracking: Tracking Service, tracking client APIs, autologgingInterface
area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev serverarea/docker: Docker use across MLflow's components, such as MLflow Projects and MLflow Modelsarea/sqlalchemy: Use of SQLAlchemy in the Tracking Service or Model Registryarea/windows: Windows supportLanguage
language/r: R APIs and clientslanguage/java: Java APIs and clientslanguage/new: Proposals for new client languagesIntegrations
integrations/azure: Azure and Azure ML integrationsintegrations/sagemaker: SageMaker integrationsintegrations/databricks: Databricks integrationsHow 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" sectionrn/breaking-change- The PR will be mentioned in the "Breaking Changes" sectionrn/feature- A new user-facing feature worth mentioning in the release notesrn/bug-fix- A user-facing bug fix worth mentioning in the release notesrn/documentation- A user-facing documentation change worth mentioning in the release notes