fix(analyzer): honour config_path for LangExtract recognizers in YAML registry#2157
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RonShakutai merged 6 commits intoJul 13, 2026
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…izer Fixes #1942. Merge language_model_params into provider_kwargs so timeout, num_ctx and other LLM provider params configured in YAML actually reach the provider (e.g. Ollama) instead of being silently dropped. Also fixes TypeError when kwargs: or language_model_params: is null in YAML. - Merge language_model_params into provider_kwargs via setdefault (so explicit kwargs: entries still win for backwards compatibility) - Guard against null YAML values with 'or {}' - Strengthen regression tests to assert params reach ModelConfig.provider_kwargs - Add test for kwargs: null edge case - Document fix in CHANGELOG Related: #1943 (lsternlicht/fix-basic-langextract-language-model-params-dropped)
… registry LM recognizers (BasicLangExtractRecognizer, AzureOpenAILangExtractRecognizer) configured via a recognizer registry YAML silently ignored config_path: the strict PredefinedRecognizerConfig schema has no config_path field and forbids extras, so Pydantic dropped it and the recognizer fell back to its bundled default model configuration. Add a LangExtractRecognizerConfig model (extra=allow, explicit config_path field) mirroring the existing HuggingFace/GLiNER configs, and register both LM recognizer class names in CONFIG_MODEL_MAP so config_path (and other recognizer-specific kwargs) survive validation and reach the constructor. Adds regression tests covering config_path preservation via both the model and the full ConfigurationValidator registry path. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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@SharonHart dont merge this PR please |
…ecognizer-custom-yaml-config # Conflicts: # CHANGELOG.md
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Pull request overview
This PR fixes YAML-based recognizer registry validation so LangExtract-based language model recognizers preserve config_path (and other relevant constructor kwargs) instead of silently dropping them during Pydantic validation, ensuring the configured LangExtract model YAML is actually used at runtime.
Changes:
- Added
LangExtractRecognizerConfigand mappedBasicLangExtractRecognizer/AzureOpenAILangExtractRecognizerinCONFIG_MODEL_MAPsoconfig_pathsurvives validation. - Added regression tests covering config-model selection and
config_pathpreservation through full registry validation. - Documented the fix in the Analyzer “Fixed” section of the changelog.
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Copilot reviewed 3 out of 3 changed files in this pull request and generated 1 comment.
| File | Description |
|---|---|
| presidio-analyzer/presidio_analyzer/input_validation/yaml_recognizer_models.py | Introduces LangExtractRecognizerConfig and registers LangExtract recognizer class names in CONFIG_MODEL_MAP. |
| presidio-analyzer/tests/test_yaml_recognizer_models.py | Adds regression tests ensuring config_path is preserved and the correct config model is selected. |
| CHANGELOG.md | Records the behavior fix for LangExtract recognizers configured via YAML registry. |
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Change Description
LM recognizers (
BasicLangExtractRecognizer,AzureOpenAILangExtractRecognizer) configured via a recognizer-registry YAML now honourconfig_path.Bug: these class names were missing from
CONFIG_MODEL_MAP, so their YAML entry was validated by the strictPredefinedRecognizerConfig(noconfig_pathfield, noextra="allow"). Pydantic silently droppedconfig_path, and the recognizer fell back to its bundled default model (qwen2.5:1.5b) instead of the configured one.Fix: add a
LangExtractRecognizerConfig(extra="allow"+ explicitconfig_path) mirroring the existing HuggingFace/GLiNER configs, and register both class names inCONFIG_MODEL_MAP. Nowconfig_pathsurvives validation and reaches the constructor.Adds 3 regression tests. Verified end-to-end: an Ollama recognizer now builds with the configured model (e.g.
llama3.2:3b).Issue reference
N/A
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