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dbx-tools-model

Python contracts and runtime helpers for Databricks Model Serving. This package mirrors the reusable parts of @dbx-tools/shared-model and @dbx-tools/model without AppKit cache or Mastra dependencies.

Install from PyPI:

pip install dbx-tools-model

To install the current main branch directly from the repository instead:

pip install "dbx-tools-model @ git+https://github.com/reggie-db/dbx-tools.git@main#subdirectory=packages/py/model"

Key features:

  • stable Pydantic endpoint, profile, query, and ranked-result models;
  • live endpoint listing through a structural WorkspaceClient protocol;
  • score-driven model classification with family fallbacks;
  • exact and fuzzy endpoint resolution with deterministic class ordering;
  • Databricks invocation URL and per-request authentication helpers;
  • OpenAI chat request sanitization and content extraction;
  • embedding vector extraction with optional dimension validation.
from databricks.sdk import WorkspaceClient
from dbx_tools.model import ModelClass, list_serving_endpoints, resolve_model

endpoints = list_serving_endpoints(WorkspaceClient())
selection = resolve_model(endpoints, model_class=ModelClass.CHAT_BALANCED)
print(selection.model_id)

The Python port intentionally omits AppKit CacheManager integration, Mastra-specific adapters, and browser-only schemas. Callers can cache the plain Pydantic results with their preferred Python cache.

Relationship to the Databricks SDK

Use the native SDK directly when an endpoint name is already known and its typed query method fits the request. Use this package when endpoint choice, stable cross-runtime models, OpenAI-shaped HTTP invocation, or provider-neutral chat and embedding normalization is the repetitive part.

Module map

  • models — Pydantic wire contracts;
  • classify, classes, fallback — model taxonomy and ordering;
  • resolve — exact/fuzzy ranking and single-model selection;
  • serving — structural WorkspaceClient endpoint listing;
  • invoke — URLs, SDK authentication headers, and JSON POST helpers;
  • chat, embedding — request sanitization and response extraction.

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