fix: Fix from_dict methods of components using HF models to work with default values (#8003)

* Fix from_dict to work if device isn't provided in init params

* Minor refactoring of from_dict for components that load HF models

* Add tests

* Update tests to test loading with all default parameters

* Add more tests

* Add release notes

* Add unit test for whisper local

* Update reno

* Add fix for ExtractiveReader

* Fix NamedEntityExtractor

haystack/components/audio/whisper_local.py

@@ -104,7 +104,7 @@ class LocalWhisperTranscriber:
             The deserialized component.
         """
         init_params = data["init_parameters"]
-        if init_params["device"] is not None:
+        if init_params.get("device") is not None:
             init_params["device"] = ComponentDevice.from_dict(init_params["device"])
         return default_from_dict(cls, data)
 

haystack/components/embedders/sentence_transformers_document_embedder.py

@@ -126,9 +126,9 @@ class SentenceTransformersDocumentEmbedder:
             Deserialized component.
         """
         init_params = data["init_parameters"]
-        if init_params["device"] is not None:
+        if init_params.get("device") is not None:
             init_params["device"] = ComponentDevice.from_dict(init_params["device"])
-        deserialize_secrets_inplace(data["init_parameters"], keys=["token"])
+        deserialize_secrets_inplace(init_params, keys=["token"])
         return default_from_dict(cls, data)
 
     def warm_up(self):

haystack/components/embedders/sentence_transformers_text_embedder.py

@@ -116,9 +116,9 @@ class SentenceTransformersTextEmbedder:
             Deserialized component.
         """
         init_params = data["init_parameters"]
-        if init_params["device"] is not None:
+        if init_params.get("device") is not None:
             init_params["device"] = ComponentDevice.from_dict(init_params["device"])
-        deserialize_secrets_inplace(data["init_parameters"], keys=["token"])
+        deserialize_secrets_inplace(init_params, keys=["token"])
         return default_from_dict(cls, data)
 
     def warm_up(self):

haystack/components/extractors/named_entity_extractor.py

@@ -221,7 +221,7 @@ class NamedEntityExtractor:
         """
         try:
             init_params = data["init_parameters"]
-            if init_params["device"] is not None:
+            if init_params.get("device") is not None:
                 init_params["device"] = ComponentDevice.from_dict(init_params["device"])
             init_params["backend"] = NamedEntityExtractorBackend[init_params["backend"]]
             return default_from_dict(cls, data)

haystack/components/rankers/sentence_transformers_diversity.py

@@ -142,9 +142,9 @@ class SentenceTransformersDiversityRanker:
             The deserialized component.
         """
         init_params = data["init_parameters"]
-        if init_params["device"] is not None:
+        if init_params.get("device") is not None:
             init_params["device"] = ComponentDevice.from_dict(init_params["device"])
-        deserialize_secrets_inplace(data["init_parameters"], keys=["token"])
+        deserialize_secrets_inplace(init_params, keys=["token"])
         return default_from_dict(cls, data)
 
     def _prepare_texts_to_embed(self, documents: List[Document]) -> List[str]:

haystack/components/rankers/transformers_similarity.py

@@ -176,11 +176,12 @@ class TransformersSimilarityRanker:
         :returns:
             Deserialized component.
         """
-        deserialize_secrets_inplace(data["init_parameters"], keys=["token"])
         init_params = data["init_parameters"]
-        if init_params["device"] is not None:
+        if init_params.get("device") is not None:
             init_params["device"] = ComponentDevice.from_dict(init_params["device"])
-        deserialize_hf_model_kwargs(init_params["model_kwargs"])
+        if init_params.get("model_kwargs") is not None:
+            deserialize_hf_model_kwargs(init_params["model_kwargs"])
+        deserialize_secrets_inplace(init_params, keys=["token"])
 
         return default_from_dict(cls, data)
 

haystack/components/readers/extractive.py

@@ -170,10 +170,11 @@ class ExtractiveReader:
             Deserialized component.
         """
         init_params = data["init_parameters"]
-        deserialize_secrets_inplace(data["init_parameters"], keys=["token"])
-        if init_params["device"] is not None:
+        deserialize_secrets_inplace(init_params, keys=["token"])
+        if init_params.get("device") is not None:
             init_params["device"] = ComponentDevice.from_dict(init_params["device"])
-        deserialize_hf_model_kwargs(init_params["model_kwargs"])
+        if init_params.get("model_kwargs") is not None:
+            deserialize_hf_model_kwargs(init_params["model_kwargs"])
 
         return default_from_dict(cls, data)
 

releasenotes/notes/hf-models-from-dict-default-values-47c2c73136ea6643.yaml

@@ -0,0 +1,3 @@
+fixes:
+  - |
+    This updates the components, TransformersSimilarityRanker, SentenceTransformersDiversityRanker, SentenceTransformersTextEmbedder, SentenceTransformersDocumentEmbedder and LocalWhisperTranscriber from_dict methods to work when loading with init_parameters only containing required parameters.

test/components/audio/test_whisper_local.py

@@ -74,6 +74,13 @@ class TestLocalWhisperTranscriber:
         assert transcriber.whisper_params == {}
         assert transcriber._model is None
 
+    def test_from_dict_no_default_parameters(self):
+        data = {"type": "haystack.components.audio.whisper_local.LocalWhisperTranscriber", "init_parameters": {}}
+        transcriber = LocalWhisperTranscriber.from_dict(data)
+        assert transcriber.model == "large"
+        assert transcriber.device == ComponentDevice.resolve_device(None)
+        assert transcriber.whisper_params == {}
+
     def test_from_dict_none_device(self):
         data = {
             "type": "haystack.components.audio.whisper_local.LocalWhisperTranscriber",

test/components/embedders/test_sentence_transformers_document_embedder.py

@@ -137,6 +137,25 @@ class TestSentenceTransformersDocumentEmbedder:
         assert component.trust_remote_code
         assert component.meta_fields_to_embed == ["meta_field"]
 
+    def test_from_dict_no_default_parameters(self):
+        component = SentenceTransformersDocumentEmbedder.from_dict(
+            {
+                "type": "haystack.components.embedders.sentence_transformers_document_embedder.SentenceTransformersDocumentEmbedder",
+                "init_parameters": {},
+            }
+        )
+        assert component.model == "sentence-transformers/all-mpnet-base-v2"
+        assert component.device == ComponentDevice.resolve_device(None)
+        assert component.token == Secret.from_env_var(["HF_API_TOKEN", "HF_TOKEN"], strict=False)
+        assert component.prefix == ""
+        assert component.suffix == ""
+        assert component.batch_size == 32
+        assert component.progress_bar is True
+        assert component.normalize_embeddings is False
+        assert component.embedding_separator == "\n"
+        assert component.trust_remote_code is False
+        assert component.meta_fields_to_embed == []
+
     def test_from_dict_none_device(self):
         init_parameters = {
             "model": "model",

test/components/embedders/test_sentence_transformers_text_embedder.py

@@ -122,6 +122,22 @@ class TestSentenceTransformersTextEmbedder:
         assert component.normalize_embeddings is False
         assert component.trust_remote_code is False
 
+    def test_from_dict_no_default_parameters(self):
+        data = {
+            "type": "haystack.components.embedders.sentence_transformers_text_embedder.SentenceTransformersTextEmbedder",
+            "init_parameters": {},
+        }
+        component = SentenceTransformersTextEmbedder.from_dict(data)
+        assert component.model == "sentence-transformers/all-mpnet-base-v2"
+        assert component.device == ComponentDevice.resolve_device(None)
+        assert component.token == Secret.from_env_var(["HF_API_TOKEN", "HF_TOKEN"], strict=False)
+        assert component.prefix == ""
+        assert component.suffix == ""
+        assert component.batch_size == 32
+        assert component.progress_bar is True
+        assert component.normalize_embeddings is False
+        assert component.trust_remote_code is False
+
     def test_from_dict_none_device(self):
         data = {
             "type": "haystack.components.embedders.sentence_transformers_text_embedder.SentenceTransformersTextEmbedder",

test/components/extractors/test_named_entity_extractor.py

@@ -40,6 +40,17 @@ def test_named_entity_extractor_serde():
         _ = NamedEntityExtractor.from_dict(serde_data)
 
 
+def test_named_entity_extractor_from_dict_no_default_parameters_hf():
+    data = {
+        "type": "haystack.components.extractors.named_entity_extractor.NamedEntityExtractor",
+        "init_parameters": {"backend": "HUGGING_FACE", "model": "dslim/bert-base-NER"},
+    }
+    extractor = NamedEntityExtractor.from_dict(data)
+
+    assert extractor._backend.model_name == "dslim/bert-base-NER"
+    assert extractor._backend.device == ComponentDevice.resolve_device(None)
+
+
 # tests for NamedEntityExtractor serialization/deserialization in a pipeline
 def test_named_entity_extractor_pipeline_serde(tmp_path):
     extractor = NamedEntityExtractor(backend=NamedEntityExtractorBackend.HUGGING_FACE, model="dslim/bert-base-NER")

test/components/rankers/test_sentence_transformers_diversity.py

@@ -144,6 +144,25 @@ class TestSentenceTransformersDiversityRanker:
         assert ranker.meta_fields_to_embed == []
         assert ranker.embedding_separator == "\n"
 
+    def test_from_dict_no_default_parameters(self):
+        data = {
+            "type": "haystack.components.rankers.sentence_transformers_diversity.SentenceTransformersDiversityRanker",
+            "init_parameters": {},
+        }
+        ranker = SentenceTransformersDiversityRanker.from_dict(data)
+
+        assert ranker.model_name_or_path == "sentence-transformers/all-MiniLM-L6-v2"
+        assert ranker.top_k == 10
+        assert ranker.device == ComponentDevice.resolve_device(None)
+        assert ranker.similarity == "cosine"
+        assert ranker.token == Secret.from_env_var(["HF_API_TOKEN", "HF_TOKEN"], strict=False)
+        assert ranker.query_prefix == ""
+        assert ranker.document_prefix == ""
+        assert ranker.query_suffix == ""
+        assert ranker.document_suffix == ""
+        assert ranker.meta_fields_to_embed == []
+        assert ranker.embedding_separator == "\n"
+
     def test_to_dict_with_custom_init_parameters(self):
         component = SentenceTransformersDiversityRanker(
             model="sentence-transformers/msmarco-distilbert-base-v4",

test/components/rankers/test_transformers_similarity.py

@@ -172,6 +172,27 @@ class TestSimilarityRanker:
             "device_map": ComponentDevice.resolve_device(None).to_hf(),
         }
 
+    def test_from_dict_no_default_parameters(self):
+        data = {
+            "type": "haystack.components.rankers.transformers_similarity.TransformersSimilarityRanker",
+            "init_parameters": {},
+        }
+
+        component = TransformersSimilarityRanker.from_dict(data)
+        assert component.device is None
+        assert component.model_name_or_path == "cross-encoder/ms-marco-MiniLM-L-6-v2"
+        assert component.token == Secret.from_env_var(["HF_API_TOKEN", "HF_TOKEN"], strict=False)
+        assert component.top_k == 10
+        assert component.query_prefix == ""
+        assert component.document_prefix == ""
+        assert component.meta_fields_to_embed == []
+        assert component.embedding_separator == "\n"
+        assert component.scale_score
+        assert component.calibration_factor == 1.0
+        assert component.score_threshold is None
+        # torch_dtype is correctly deserialized
+        assert component.model_kwargs == {"device_map": ComponentDevice.resolve_device(None).to_hf()}
+
     @patch("torch.sigmoid")
     @patch("torch.sort")
     def test_embed_meta(self, mocked_sort, mocked_sigmoid):

test/components/readers/test_extractive.py

@@ -243,6 +243,25 @@ def test_from_dict():
     }
 
 
+def test_from_dict_no_default_parameters():
+    data = {"type": "haystack.components.readers.extractive.ExtractiveReader", "init_parameters": {}}
+
+    component = ExtractiveReader.from_dict(data)
+    assert component.model_name_or_path == "deepset/roberta-base-squad2-distilled"
+    assert component.device is None
+    assert component.token == Secret.from_env_var(["HF_API_TOKEN", "HF_TOKEN"], strict=False)
+    assert component.top_k == 20
+    assert component.score_threshold is None
+    assert component.max_seq_length == 384
+    assert component.stride == 128
+    assert component.max_batch_size is None
+    assert component.answers_per_seq is None
+    assert component.no_answer
+    assert component.calibration_factor == 0.1
+    assert component.overlap_threshold == 0.01
+    assert component.model_kwargs == {"device_map": ComponentDevice.resolve_device(None).to_hf()}
+
+
 def test_from_dict_no_token():
     data = {
         "type": "haystack.components.readers.extractive.ExtractiveReader",