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dbx tools

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:

Terminal window
pip install dbx-tools-model

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

Terminal window
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;
  • reasoning-effort levels inferred from Databricks served-entity identity, with endpoint-family fallback for summaries that omit it;
  • exact and fuzzy endpoint resolution with deterministic class ordering;
  • Databricks invocation URL and process-serialized per-request authentication helpers, so concurrent SDK refreshes converge;
  • shared Responses-only endpoint policy, including Codex and GPT 5.4+ while excluding GPT-OSS;
  • OpenAI chat request sanitization, assistant-prefill repair, 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)
print(
    next(
        endpoint.reasoning_efforts for endpoint in endpoints if endpoint.name == 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

Section titled “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

Section titled “Module map”
  • models — Pydantic wire contracts;
  • reasoning — model-family and served-entity reasoning-level inference;
  • classify, classes, fallback — model taxonomy and ordering;
  • resolve — exact/fuzzy ranking and single-model selection;
  • serving — structural WorkspaceClient endpoint listing;
  • invoke - URLs, Responses-only policy, process-serialized SDK authentication headers, and JSON POST helpers;
  • chat, embedding — request repair/sanitization and response extraction.