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;
  • reasoning-effort levels inferred from Databricks served-entity identity, with endpoint-family fallback for summaries that omit it;
  • flexible family, version, and model parsing shared by classification, reasoning, sorting, and downstream integrations;
  • 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.

Model-name parsing produces provider-neutral components without maintaining a second family registry:

from dbx_tools.model.models import parse_model_name

parsed = parse_model_name("databricks-qwen35-122b-a10b")
assert parsed is not None
assert parsed.family == "qwen"
assert parsed.version == (3, 5)

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 plus shared family/version/model parsing;
  • 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.

Project links

Data verified by PyPI on Sep 3, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Newer release available (0.6.186)

1 maintainer

PyPI data
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Avatar for reggie-db from gravatar.com reggie-db

GitHub Statistics

Data verified by PyPI on Sep 3, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

Requires

Python <3.14, >=3.10

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