fix(analyzer): honour config_path for LangExtract recognizers in YAML registry#2153
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… 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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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
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