* fix(config): update OpenRouter vision model id
* fix(ci): harden provider health checks
* fix(config): use nano omni for OpenRouter vision
* docs: warn about hosted provider data handling
* fix(config): align OpenRouter vision params
* chore: add __init__.py to engine namespace subpackages
Griffe (used by mkdocstrings) skips directories without __init__.py
when resolving module paths, which prevented the new plugins code
reference from rendering SeedReader, FileSystemSeedReader, and
Processor. Adding empty __init__.py files in engine/resources/,
engine/processing/, and engine/processing/processors/ aligns with
the convention already used in engine/mcp/, engine/models/, etc.
* docs: flesh out docstrings on plugin extension-point classes
Plugin authors now see meaningful descriptions for every field and
method on the bases rendered in the plugins code reference:
- Plugin and PluginType: class docstrings + Attributes tables for
fields and enum members; fix typo in config_qualified_name field
description.
- SingleColumnConfig: document allow_resize.
- ProcessorConfig: document processor_type discriminator.
- SeedSource: document seed_type discriminator.
- FileSystemSeedSource: add class docstring + Attributes table for
path / file_pattern / recursive.
- ColumnGeneratorFullColumn and ColumnGeneratorCellByCell: add
class docstrings explaining when to use each base, plus method
docstrings on the abstract generate() implementations.
* docs: graduate plugins out of experimental mode
Restructures plugin documentation around the now-stable extension
points (column generator, seed reader, processor) and treats plugins
as a first-class story for customizing Data Designer.
- Add code_reference/plugins.md: single-stop reference for the Plugin
object and the config + implementation base classes used by all
three plugin types.
- Add code_reference/generators.md: column generator implementation
base classes, separated from column configs.
- Surface SingleColumnConfig in code_reference/column_configs.md.
- Add plugins/implement.md ("Build Your Own"): per-type implementation
instructions across column generators, seed readers, and processors.
- Add plugins/processor.md: complete processor plugin package example.
- Rewrite plugins/overview.md: open with why plugins exist, drop the
internal-helpers note (PluginRegistry / PluginManager), and focus
the guide on what plugin builders need.
- Refresh plugins/available.md (Catalog) and
plugins/filesystem_seed_reader.md to match the new structure.
- Delete plugins/example.md (replaced by per-type guides).
- Reorder Code Reference nav alphabetically and add the new pages.
- Minor link / wording fixes in concepts/processors.md and
concepts/deployment-options.md.
* docs: simplify plugin docs structure
Replace the overview's how-to walkthrough and the per-type plugin
guides with a single Build Your Own page that covers all three
plugin types side-by-side. Add a dedicated Using Models in Plugins
guide and a seed_readers code reference, and trim the overview down
to what the plugin types are, how to use one, and how discovery
works.
- Rename plugins/implement.md to plugins/build_your_own.md.
- Delete plugins/filesystem_seed_reader.md and plugins/processor.md
(their content is now in build_your_own.md and the per-type code
references).
- Add plugins/models.md for model-backed column generator authoring.
- Add code_reference/seed_readers.md for seed reader implementation
base classes.
- Rewrite plugins/overview.md: shorter intro, type bullets link to
the relevant code reference, drop the multi-step "How do you
create plugins" walkthrough in favor of a single Build a Plugin
pointer, tighten Discovery troubleshooting.
- Refresh plugins/available.md (Available Plugins): point to the
DataDesignerPlugins catalog and explain how to request a community
listing.
- Update cross-page links in concepts/processors.md,
concepts/seed-datasets.md, recipes/plugin_development/markdown_seed_reader.md,
code_reference/plugins.md, and code_reference/generators.md to
match the new structure.
- Update mkdocs.yml nav: rename to Build Your Own, add Using Models,
add seed_readers code reference.
* docs: scroll wide tables horizontally instead of wrapping
Code-heavy reference tables (plugin bases, column generators, etc.)
were wrapping aggressively on narrow viewports, breaking long
identifiers across multiple lines. Switch the table container to
horizontal overflow and prevent code cells from wrapping so
identifiers stay readable.
* docs: address PR #603 review feedback
- Add an Implementation base section to code_reference/processors.md
rendering the engine-side Processor class. This justifies the
engine/processing/__init__.py files added earlier and gives
processor plugin authors an auto-rendered API reference, matching
the pattern used by code_reference/generators.md and seed_readers.md.
- build_your_own.md: replace the placeholder "x" emoji on the
IndexMultiplier example with the actual multiplication sign.
- build_your_own.md: drop the manual `re.compile + apply(lambda)`
pattern in the regex-filter processor in favor of the idiomatic
`Series.str.contains(..., regex=True)`.
- build_your_own.md: add a kernel-restart caveat after the editable
install instructions — PluginRegistry caches discovery on first
import, so notebooks need a fresh kernel to pick up freshly
installed plugins.
- build_your_own.md: state explicitly what `assert_valid_plugin`
checks (config base + plugin-type-appropriate impl base).
- code_reference/plugins.md: link out to the processors code
reference alongside generators and seed_readers.
* docs: split code reference by package
* docs: add interface code reference
* docs: add code reference overviews
* docs: refine code reference pages
* docs: improve code reference tables
* docs: correct reference docstrings
* docs: embed plugin catalog table
* docs: note plugin discovery restart caveat
* docs: explain generator base class choice
* docs: mention async cell generator examples
* docs: clarify plugin model usage
* docs: clarify plugin model aliases
* docs: address plugin review feedback
* docs: update available plugins page
* feat(models): deprecate implicit default provider routing
Emit DeprecationWarning whenever the legacy "implicit default
provider" path is exercised: `ModelConfig.provider=None`, the
registry-level `ModelProviderRegistry.default`, the YAML
`default:` key in `~/.data-designer/model_providers.yaml`, and
the CLI's "Change default provider" workflow.
`resolve_model_provider_registry` skips passing `default=` in the
single-provider case so the common construction path stays quiet.
Multi-provider registries still pass `default` (per
`check_implicit_default`) and warn accordingly.
Update docs, the package README, and test fixtures to specify
`provider=` explicitly on every `ModelConfig`. New tests cover
each warning entry point and pin the post-deprecation happy paths.
Refs #589
Made-with: Cursor
* fix(models): address PR #594 review feedback
Greptile P1: ProviderRepository.load emitted its DeprecationWarning
inside a `try/except Exception` block. Under
`filterwarnings("error", DeprecationWarning)` the warn would raise,
the except would swallow it, and `load()` would silently return None
(losing the registry). Move the warn outside the catch-all so the
strict-warning path no longer drops valid configs.
Greptile P2 / johnnygreco: `_warn_on_implicit_provider` and
`_warn_on_explicit_default` use `stacklevel=2`, which lands inside
pydantic v2's validator dispatch rather than at the user's
`ModelConfig(...)` / `ModelProviderRegistry(...)` call. That broke
both attribution (the source line was unhelpful) and Python's
once-per-location dedup (every call collapsed to the same
pydantic-internal key, suppressing all but the first warning).
Introduce `data_designer.config.utils.warning_helpers.warn_at_caller`,
which walks past the helper, validator, and any pydantic frames to
find the user's call site and emits via `warnings.warn_explicit` with
the user frame's `__warningregistry__`. Keeps attribution accurate
and dedup keyed on the user's (filename, lineno).
johnnygreco: align the `provider_repository.py` warning copy with the
sibling site in `default_model_settings.py` ("specify provider=
explicitly on each ModelConfig instead") so both YAML-default warning
sites give the same migration instruction. The previous wording
pointed users at "ModelConfig entries" inside `model_providers.yaml`,
where ModelConfig entries don't actually live.
johnnygreco: dedup the cascade in `DataDesigner.__init__`. With
`model_providers=None` and a YAML `default:`, the user previously saw
two DeprecationWarnings for the same root cause —
`get_default_provider_name()` warns about the YAML key, then
`resolve_model_provider_registry(...)` re-warns from
`_warn_on_explicit_default`. Suppress the registry-level duplicate in
the YAML-fallback branch via `warnings.catch_warnings()` so users see
exactly one warning per user action.
johnnygreco: tighten `_warn_on_explicit_default` to fire only when
`default is not None`. Passing `default=None` explicitly is
semantically equivalent to omitting it (caller is opting *out* of a
registry-level default), and shouldn't trigger the deprecation
nudge.
johnnygreco: add a `model_validate({...})` regression test for
`ModelConfig` so the deserialization path (legacy on-disk configs)
is pinned alongside the construction path.
Tests:
- Update `test_load_exists` and `test_save` to omit `default=` so the
roundtrip stops exercising the deprecated YAML-default path
unguarded (Greptile note).
- Wrap `test_resolve_model_provider_registry_with_explicit_default`,
`test_get_provider`, and
`test_init_user_supplied_providers_preserve_first_wins_over_yaml_default`
in `pytest.warns` so the suite stays green under
`-W error::DeprecationWarning` (Greptile note).
- Add `test_explicit_default_none_does_not_emit_deprecation_warning`
to pin the tightened predicate.
- Add `test_init_yaml_default_emits_single_deprecation_warning` to
pin the cascade-dedup behavior.
Refs #589
Made-with: Cursor
* fix(models): make deprecation warnings visible under default filters
andreatgretel (PR #594): the YAML-default warning in
`get_default_provider_name` and the registry-default warning emitted
from inside DataDesigner helpers were attributing to data_designer
library frames, not user code. Python's default filter chain includes
`ignore::DeprecationWarning`, so library-attributed entries are
silenced — meaning a normal `DataDesigner()` call with a YAML
`default:` set showed nothing, and `resolve_model_provider_registry`
warnings were similarly invisible. Two related changes:
1. `warn_at_caller`: extend the default skip-list from `("pydantic",)`
to `("pydantic", "pydantic_core", "data_designer")` so the walk
escapes both pydantic's validator-dispatch frames and data_designer
helper frames before attributing. Also tighten the prefix predicate
to exact-or-dotted-prefix matching (`name == p or
name.startswith(p + ".")`) so e.g. `pydantic_helpers` is not
falsely matched as part of `pydantic` (johnnygreco nit). Allow
callers to pass a custom `skip_prefixes` for flexibility. Drop the
"skip frame 0+1 unconditionally" guard now that prefix matching
covers it.
2. `get_default_provider_name`: switch from
`warnings.warn(stacklevel=2)` to `warn_at_caller`. The previous
stacklevel pointed into `default_model_settings.py`, which is a
library file → silenced under default filters. Verified the fix
empirically with `python -W default`: warning is now attributed to
the user's call site and rendered.
johnnygreco (PR #594): add the missing
`test_explicit_default_none_does_not_emit_deprecation_warning`
regression for the `self.default is not None` predicate landed in
the prior round.
Tests:
- New `test_warning_helpers.py` pins prefix-matching precision
(rejects `pydantic_helpers` / `data_designer_other`), default
skip-list contents, attribution past skip-prefix frames, and
per-call-site dedup behavior.
- `test_get_default_provider_name_warning_attributes_to_user_frame`
pins andreatgretel's repro for the YAML-default site.
- `test_explicit_default_warning_attributes_to_user_frame` pins the
multi-frame case: construction goes through
`resolve_model_provider_registry`, so the walk has to escape both
pydantic and data_designer before landing on the test file.
- `test_explicit_default_none_does_not_emit_deprecation_warning`
pins johnnygreco's predicate-tightening regression.
3,124 tests pass (540 config + 1,923 engine + 653 interface; +10 net
from this round).
Refs #589
Made-with: Cursor
* fix(models): apply warn_at_caller to remaining deprecation sites
greptile-apps (PR #594, r3189904028): `ProviderRepository.load`'s
YAML-default `DeprecationWarning` was using `warnings.warn(stacklevel=2)`,
which attributes to whichever data_designer frame called `load()` —
controllers, services, list/reset commands, agent introspection. Every
real call path lands on `data_designer.cli.*`, which falls under
Python's default `ignore::DeprecationWarning` filter and is silenced.
Audit found two more sites with the same problem:
- `DatasetBuilder._resolve_async_compatibility` (`allow_resize` /
issue #552) — was using `stacklevel=4` to walk past
`_resolve_async_compatibility -> build/build_preview -> interface ->
user`. Brittle: any added frame (decorator, async wrapping, the
`try/except DeprecationWarning: raise` boundary) shifts attribution
silently. The existing test passed only because it used
`simplefilter("always") + record=True`, which records warnings
regardless of attribution.
- `ProviderController._handle_change_default` — was using
`stacklevel=2`, which lands on the menu dispatcher in the same
controller module. `print_warning` already shows the message
visually, but programmatic observers (`pytest.warns`,
`filterwarnings("error", ...)`) saw a library-attributed entry that
default filters silenced.
All three migrated to `warn_at_caller` (the helper from 247fa30) so
attribution lands on the user's call site regardless of internal
chain shape. `data_designer` is already in
`DEFAULT_INTERNAL_PREFIXES`, so the walk escapes the entire library
in one pass.
Added attribution regression tests at each site asserting
`warning.filename == __file__`. A future regression to
`warnings.warn(stacklevel=N)` now fails CI instead of silently
silencing the user-facing nudge:
- `test_load_with_yaml_default_attributes_warning_to_caller`
(test_provider_repository.py)
- `test_resolve_async_compatibility` extended with the same assertion
- `test_handle_change_default_emits_deprecation_warning` rewritten
from `pytest.warns(...)` to a `catch_warnings(record=True)` block
that filters for the message and asserts `filename == __file__`
(`pytest.warns` does not check attribution, so the rewrite is
required to actually catch the regression).
3,125 tests pass (548 config + 1,923 engine + 654 interface).
Refs #589
* Updated default reasoning model for nvidia
* Updated inference params for super
* Add reasoning_effort to Nemotron Super params, update stale docs
- Add extra_body.reasoning_effort=medium to
NEMOTRON_3_SUPER_120B_A12B_INFERENCE_PARAMS (mirrors GPT-5 config)
- Update README telemetry example and model-configs.md to use
nvidia/nemotron-3-super-120b-a12b instead of openai/gpt-oss-20b
- Broaden inference-parameters.md reasoning effort tip to cover
Nemotron Super
* Remove build-time README accidentally tracked
---------
Co-authored-by: Andre Manoel <amanoel@nvidia.com>
Co-authored-by: Andre Manoel <165937436+andreatgretel@users.noreply.github.com>
* adjust plan
* feat: remove litellm dependency and bridge path (PR-7)
- Delete litellm_bridge.py adapter, litellm_overrides.py, and their tests
- Remove LiteLLM fallback branch and DATA_DESIGNER_MODEL_BACKEND env var
from clients/factory.py; unknown provider_type now raises ValueError
- Remove apply_litellm_patches() call from models/factory.py
- Remove LiteLLM exception match arms and DownstreamLLMExceptionMessageParser
from models/errors.py; port context window detail extraction to
_extract_context_window_detail for native ProviderError path
- Remove litellm from lazy_heavy_imports.py and pyproject.toml runtime deps
- Remove flatten_extra_body parameter from TransportKwargs.from_request
- Clean up LiteLLM references in docstrings, comments, and AGENTS.md
- Add full ProviderErrorKind test coverage to test_model_errors.py
- Update benchmark script to patch OpenAICompatibleClient instead of
CustomRouter
Made-with: Cursor
* fix: forward tools to _fake_response in benchmark patch
The old CustomRouter patch forwarded **kwargs (including tools) to
_fake_response, but the new OpenAICompatibleClient patch only passed
model and messages — silently disabling tool-call simulation in
benchmark scenarios that exercise allow_tools.
Made-with: Cursor
* fix: address PR-7 review feedback
- Return ChatCompletionResponse from benchmark fakes instead of
FakeResponse to match the native client contract (facade expects
.message, not .choices[0].message)
- Add ids= to parametrize block in test_model_errors.py for readability
- Remove unnecessary try/except from _extract_context_window_detail;
the `if marker in` guard is sufficient
- Make context window marker match case-insensitive
- Replace stale httpx.AsyncClient callout in async_concurrency.py
docstring with generic "async-stateful resources"
Made-with: Cursor
* docs: add deployment, performance tuning guides and streamline getting started
- Add deployment-options.md: Library vs. Microservice decision guide
- Add inference-architecture.md: Separation of concerns with LLM servers
- Add performance-tuning.md: Concurrency and batching optimization guide
- Streamline index.md: Merge installation, add quick example, simplify
- Remove quick-start.md: Content merged into welcome page
- Remove installation.md: Content merged into welcome page
- Update model docs: Add concurrency control sections and cross-references
- Update mkdocs.yml: Add new Architecture section to navigation
* docs: add tasteful emojis to new documentation pages
* docs: consolidate redundant concurrency and troubleshooting content
- Remove duplicate max_parallel_requests tables from model-configs.md and inference-parameters.md
- Remove duplicate Concurrency Control section from model-configs.md
- Simplify Concurrency Control in inference-parameters.md to link to performance-tuning.md
- Remove Troubleshooting section from inference-architecture.md (covered in performance-tuning.md)
- performance-tuning.md is now the authoritative source for tuning guidance
* Simplified doc additions
* Switched default model to nemotron 3 nano
* Addressed feedback
* Added first blog draft
* Add generation type to ModelConfig
* pass tests
* added generate_text_embeddings
* tests
* remove sensitive=True old artifact no longer needed
* Slight refactor
* slight refactor
* Added embedding generator
* chunk_separator -> chunk_pattern
* update tests
* rename for consistency
* Restructure InferenceParameters -> CompletionInferenceParameters, BaseInferenceParameters, EmbeddingInferenceParameters
* Remove purpose from consolidated kwargs
* WithModelConfiguration.inference_parameters should should be typed with BaseInferenceParameters
* Type as WithModelGeneration
* Add image generation modality
* update return type for generate_kwargs
* make generation_type a field of ModelConfig as opposed to a prop resolved based on the type of InferenceParameters
* remove regex based chunking from embedding generator
* Remove image generation for now
* more tests and updates
* column_type_is_llm_generated -> column_type_is_model_generated
* change set to list: fix flaky tests
* CompletionInferenceParameters -> ChatCompletionInferenceParameters for consistency with generation_type
* Update docs
* fix deprecation warning originating from cli model settings
* update display of inference parameters in cli list
* save prog on inference parameter
* updates for the ocnfig builder
* update cli readme
* update cli for inference parmeters
* update inference parameter names
* flip order of vars
* WithCompletion -> WithChatCompletion
* specify InferenceParamsT
* Update columns.md with EmbeddingColumnConfig info
* make generation_type a descriminator field in inference params. add configuration support for max_parallel_requests and timeout
* DRY out some stuff in field.py
* docs for custom model settings
* Update nomenclature. prompt tokens -> input tokens, completion tokens -> output tokens in column statistics for consistency
* Add nvidia-embedding and openai-embedding to default model configs
* Fix typo in docs
* Make generate collab notebooks
* Address PR comments
* Add generation type to ModelConfig
* pass tests
* added generate_text_embeddings
* tests
* remove sensitive=True old artifact no longer needed
* Slight refactor
* slight refactor
* Added embedding generator
* chunk_separator -> chunk_pattern
* update tests
* rename for consistency
* Restructure InferenceParameters -> CompletionInferenceParameters, BaseInferenceParameters, EmbeddingInferenceParameters
* Remove purpose from consolidated kwargs
* WithModelConfiguration.inference_parameters should should be typed with BaseInferenceParameters
* Type as WithModelGeneration
* Add image generation modality
* update return type for generate_kwargs
* make generation_type a field of ModelConfig as opposed to a prop resolved based on the type of InferenceParameters
* remove regex based chunking from embedding generator
* Remove image generation for now
* more tests and updates
* column_type_is_llm_generated -> column_type_is_model_generated
* change set to list: fix flaky tests
* CompletionInferenceParameters -> ChatCompletionInferenceParameters for consistency with generation_type
* Update docs
* fix deprecation warning originating from cli model settings
* update display of inference parameters in cli list
* save prog on inference parameter
* updates for the ocnfig builder
* update cli readme
* update cli for inference parmeters
* update inference parameter names
* flip order of vars
* WithCompletion -> WithChatCompletion
* specify InferenceParamsT
* Update columns.md with EmbeddingColumnConfig info
* make generation_type a descriminator field in inference params. add configuration support for max_parallel_requests and timeout
* DRY out some stuff in field.py
* Update nomenclature. prompt tokens -> input tokens, completion tokens -> output tokens in column statistics for consistency
* Add nvidia-embedding and openai-embedding to default model configs
* Fix typo in docs
* Make generate collab notebooks
* fine-tune -> adjust
