feat!: Rename model_name or model_name_or_path to model in all Embedder classes

e2e/pipelines/test_dense_doc_search.py

@@ -25,8 +25,7 @@ def test_dense_doc_search_pipeline(tmp_path, samples_path):
         instance=DocumentSplitter(split_by="sentence", split_length=250, split_overlap=30), name="splitter"
     )
     indexing_pipeline.add_component(
-        instance=SentenceTransformersDocumentEmbedder(model_name_or_path="sentence-transformers/all-MiniLM-L6-v2"),
-        name="embedder",
+        instance=SentenceTransformersDocumentEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"), name="embedder"
     )
     indexing_pipeline.add_component(instance=DocumentWriter(document_store=InMemoryDocumentStore()), name="writer")
 
@@ -60,8 +59,7 @@ def test_dense_doc_search_pipeline(tmp_path, samples_path):
     # Create the querying pipeline
     query_pipeline = Pipeline()
     query_pipeline.add_component(
-        instance=SentenceTransformersTextEmbedder(model_name_or_path="sentence-transformers/all-MiniLM-L6-v2"),
-        name="text_embedder",
+        instance=SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"), name="text_embedder"
     )
     query_pipeline.add_component(
         instance=InMemoryEmbeddingRetriever(document_store=filled_document_store, top_k=20), name="embedding_retriever"

e2e/pipelines/test_eval_dense_doc_search.py

@@ -25,8 +25,7 @@ def test_dense_doc_search_pipeline(samples_path):
         instance=DocumentSplitter(split_by="sentence", split_length=250, split_overlap=30), name="splitter"
     )
     indexing_pipeline.add_component(
-        instance=SentenceTransformersDocumentEmbedder(model_name_or_path="sentence-transformers/all-MiniLM-L6-v2"),
-        name="embedder",
+        instance=SentenceTransformersDocumentEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"), name="embedder"
     )
     indexing_pipeline.add_component(instance=DocumentWriter(document_store=InMemoryDocumentStore()), name="writer")
 
@@ -45,8 +44,7 @@ def test_dense_doc_search_pipeline(samples_path):
     # Create the querying pipeline
     query_pipeline = Pipeline()
     query_pipeline.add_component(
-        instance=SentenceTransformersTextEmbedder(model_name_or_path="sentence-transformers/all-MiniLM-L6-v2"),
-        name="text_embedder",
+        instance=SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"), name="text_embedder"
     )
     query_pipeline.add_component(
         instance=InMemoryEmbeddingRetriever(document_store=filled_document_store, top_k=20), name="embedding_retriever"

e2e/pipelines/test_eval_hybrid_doc_search_pipeline.py

@@ -13,8 +13,7 @@ def test_hybrid_doc_search_pipeline():
     hybrid_pipeline = Pipeline()
     hybrid_pipeline.add_component(instance=InMemoryBM25Retriever(document_store=document_store), name="bm25_retriever")
     hybrid_pipeline.add_component(
-        instance=SentenceTransformersTextEmbedder(model_name_or_path="sentence-transformers/all-MiniLM-L6-v2"),
-        name="text_embedder",
+        instance=SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"), name="text_embedder"
     )
     hybrid_pipeline.add_component(
         instance=InMemoryEmbeddingRetriever(document_store=document_store), name="embedding_retriever"

e2e/pipelines/test_eval_rag_pipelines.py

@@ -93,8 +93,7 @@ def test_embedding_retrieval_rag_pipeline(tmp_path):
     """
     rag_pipeline = Pipeline()
     rag_pipeline.add_component(
-        instance=SentenceTransformersTextEmbedder(model_name_or_path="sentence-transformers/all-MiniLM-L6-v2"),
-        name="text_embedder",
+        instance=SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"), name="text_embedder"
     )
     rag_pipeline.add_component(
         instance=InMemoryEmbeddingRetriever(document_store=InMemoryDocumentStore()), name="retriever"
@@ -124,7 +123,7 @@ def test_embedding_retrieval_rag_pipeline(tmp_path):
     document_store = rag_pipeline.get_component("retriever").document_store
     indexing_pipeline = Pipeline()
     indexing_pipeline.add_component(
-        instance=SentenceTransformersDocumentEmbedder(model_name_or_path="sentence-transformers/all-MiniLM-L6-v2"),
+        instance=SentenceTransformersDocumentEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"),
         name="document_embedder",
     )
     indexing_pipeline.add_component(instance=DocumentWriter(document_store=document_store), name="document_writer")

e2e/pipelines/test_hybrid_doc_search_pipeline.py

@@ -14,8 +14,7 @@ def test_hybrid_doc_search_pipeline(tmp_path):
     hybrid_pipeline = Pipeline()
     hybrid_pipeline.add_component(instance=InMemoryBM25Retriever(document_store=document_store), name="bm25_retriever")
     hybrid_pipeline.add_component(
-        instance=SentenceTransformersTextEmbedder(model_name_or_path="sentence-transformers/all-MiniLM-L6-v2"),
-        name="text_embedder",
+        instance=SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"), name="text_embedder"
     )
     hybrid_pipeline.add_component(
         instance=InMemoryEmbeddingRetriever(document_store=document_store), name="embedding_retriever"

e2e/pipelines/test_preprocessing_pipeline.py

@@ -25,8 +25,7 @@ def test_preprocessing_pipeline(tmp_path):
         instance=DocumentSplitter(split_by="sentence", split_length=1), name="splitter"
     )
     preprocessing_pipeline.add_component(
-        instance=SentenceTransformersDocumentEmbedder(model_name_or_path="sentence-transformers/all-MiniLM-L6-v2"),
-        name="embedder",
+        instance=SentenceTransformersDocumentEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"), name="embedder"
     )
     preprocessing_pipeline.add_component(instance=DocumentWriter(document_store=document_store), name="writer")
     preprocessing_pipeline.connect("file_type_router.text/plain", "text_file_converter.sources")

e2e/pipelines/test_rag_pipelines.py

@@ -95,8 +95,7 @@ def test_embedding_retrieval_rag_pipeline(tmp_path):
     """
     rag_pipeline = Pipeline()
     rag_pipeline.add_component(
-        instance=SentenceTransformersTextEmbedder(model_name_or_path="sentence-transformers/all-MiniLM-L6-v2"),
-        name="text_embedder",
+        instance=SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"), name="text_embedder"
     )
     rag_pipeline.add_component(
         instance=InMemoryEmbeddingRetriever(document_store=InMemoryDocumentStore()), name="retriever"
@@ -131,7 +130,7 @@ def test_embedding_retrieval_rag_pipeline(tmp_path):
     document_store = rag_pipeline.get_component("retriever").document_store
     indexing_pipeline = Pipeline()
     indexing_pipeline.add_component(
-        instance=SentenceTransformersDocumentEmbedder(model_name_or_path="sentence-transformers/all-MiniLM-L6-v2"),
+        instance=SentenceTransformersDocumentEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"),
         name="document_embedder",
     )
     indexing_pipeline.add_component(instance=DocumentWriter(document_store=document_store), name="document_writer")

examples/pipelines/indexing_pipeline.py

@@ -20,8 +20,7 @@
 p.add_component(instance=DocumentCleaner(), name="cleaner")
 p.add_component(instance=DocumentSplitter(split_by="sentence", split_length=250, split_overlap=30), name="splitter")
 p.add_component(
-    instance=SentenceTransformersDocumentEmbedder(model_name_or_path="sentence-transformers/all-MiniLM-L6-v2"),
-    name="embedder",
+    instance=SentenceTransformersDocumentEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"), name="embedder"
 )
 p.add_component(instance=DocumentWriter(document_store=InMemoryDocumentStore()), name="writer")
 

haystack/components/embedders/backends/sentence_transformers_backend.py

@@ -14,15 +14,13 @@ class _SentenceTransformersEmbeddingBackendFactory:
     _instances: Dict[str, "_SentenceTransformersEmbeddingBackend"] = {}
 
     @staticmethod
-    def get_embedding_backend(
-        model_name_or_path: str, device: Optional[str] = None, use_auth_token: Union[bool, str, None] = None
-    ):
-        embedding_backend_id = f"{model_name_or_path}{device}{use_auth_token}"
+    def get_embedding_backend(model: str, device: Optional[str] = None, use_auth_token: Union[bool, str, None] = None):
+        embedding_backend_id = f"{model}{device}{use_auth_token}"
 
         if embedding_backend_id in _SentenceTransformersEmbeddingBackendFactory._instances:
             return _SentenceTransformersEmbeddingBackendFactory._instances[embedding_backend_id]
         embedding_backend = _SentenceTransformersEmbeddingBackend(
-            model_name_or_path=model_name_or_path, device=device, use_auth_token=use_auth_token
+            model=model, device=device, use_auth_token=use_auth_token
         )
         _SentenceTransformersEmbeddingBackendFactory._instances[embedding_backend_id] = embedding_backend
         return embedding_backend
@@ -33,13 +31,9 @@ class _SentenceTransformersEmbeddingBackend:
     Class to manage Sentence Transformers embeddings.
     """
 
-    def __init__(
-        self, model_name_or_path: str, device: Optional[str] = None, use_auth_token: Union[bool, str, None] = None
-    ):
+    def __init__(self, model: str, device: Optional[str] = None, use_auth_token: Union[bool, str, None] = None):
         sentence_transformers_import.check()
-        self.model = SentenceTransformer(
-            model_name_or_path=model_name_or_path, device=device, use_auth_token=use_auth_token
-        )
+        self.model = SentenceTransformer(model_name_or_path=model, device=device, use_auth_token=use_auth_token)
 
     def embed(self, data: List[str], **kwargs) -> List[List[float]]:
         embeddings = self.model.encode(data, **kwargs).tolist()

haystack/components/embedders/openai_document_embedder.py

@@ -31,7 +31,7 @@ class OpenAIDocumentEmbedder:
     def __init__(
         self,
         api_key: Optional[str] = None,
-        model_name: str = "text-embedding-ada-002",
+        model: str = "text-embedding-ada-002",
         api_base_url: Optional[str] = None,
         organization: Optional[str] = None,
         prefix: str = "",
@@ -45,7 +45,7 @@ def __init__(
         Create a OpenAIDocumentEmbedder component.
         :param api_key: The OpenAI API key. It can be explicitly provided or automatically read from the
                         environment variable OPENAI_API_KEY (recommended).
-        :param model_name: The name of the model to use.
+        :param model: The name of the model to use.
         :param api_base_url: The OpenAI API Base url, defaults to None. For more details, see OpenAI [docs](https://platform.openai.com/docs/api-reference/audio).
         :param organization: The Organization ID, defaults to `None`. See
         [production best practices](https://platform.openai.com/docs/guides/production-best-practices/setting-up-your-organization).
@@ -57,7 +57,7 @@ def __init__(
         :param meta_fields_to_embed: List of meta fields that should be embedded along with the Document text.
         :param embedding_separator: Separator used to concatenate the meta fields to the Document text.
         """
-        self.model_name = model_name
+        self.model = model
         self.api_base_url = api_base_url
         self.organization = organization
         self.prefix = prefix
@@ -73,7 +73,7 @@ def _get_telemetry_data(self) -> Dict[str, Any]:
         """
         Data that is sent to Posthog for usage analytics.
         """
-        return {"model": self.model_name}
+        return {"model": self.model}
 
     def to_dict(self) -> Dict[str, Any]:
         """
@@ -82,7 +82,7 @@ def to_dict(self) -> Dict[str, Any]:
         """
         return default_to_dict(
             self,
-            model_name=self.model_name,
+            model=self.model,
             organization=self.organization,
             api_base_url=self.api_base_url,
             prefix=self.prefix,
@@ -124,7 +124,7 @@ def _embed_batch(self, texts_to_embed: List[str], batch_size: int) -> Tuple[List
             range(0, len(texts_to_embed), batch_size), disable=not self.progress_bar, desc="Calculating embeddings"
         ):
             batch = texts_to_embed[i : i + batch_size]
-            response = self.client.embeddings.create(model=self.model_name, input=batch)
+            response = self.client.embeddings.create(model=self.model, input=batch)
             embeddings = [el.embedding for el in response.data]
             all_embeddings.extend(embeddings)
 

haystack/components/embedders/openai_text_embedder.py

@@ -29,7 +29,7 @@ class OpenAITextEmbedder:
     def __init__(
         self,
         api_key: Optional[str] = None,
-        model_name: str = "text-embedding-ada-002",
+        model: str = "text-embedding-ada-002",
         api_base_url: Optional[str] = None,
         organization: Optional[str] = None,
         prefix: str = "",
@@ -40,15 +40,15 @@ def __init__(
 
         :param api_key: The OpenAI API key. It can be explicitly provided or automatically read from the
             environment variable OPENAI_API_KEY (recommended).
-        :param model_name: The name of the OpenAI model to use. For more details on the available models,
+        :param model: The name of the OpenAI model to use. For more details on the available models,
             see [OpenAI documentation](https://platform.openai.com/docs/guides/embeddings/embedding-models).
         :param organization: The Organization ID, defaults to `None`. See
         [production best practices](https://platform.openai.com/docs/guides/production-best-practices/setting-up-your-organization).
         :param api_base_url: The OpenAI API Base url, defaults to None. For more details, see OpenAI [docs](https://platform.openai.com/docs/api-reference/audio).
         :param prefix: A string to add to the beginning of each text.
         :param suffix: A string to add to the end of each text.
         """
-        self.model_name = model_name
+        self.model = model
         self.organization = organization
         self.prefix = prefix
         self.suffix = suffix
@@ -59,7 +59,7 @@ def _get_telemetry_data(self) -> Dict[str, Any]:
         """
         Data that is sent to Posthog for usage analytics.
         """
-        return {"model": self.model_name}
+        return {"model": self.model}
 
     def to_dict(self) -> Dict[str, Any]:
         """
@@ -68,7 +68,7 @@ def to_dict(self) -> Dict[str, Any]:
         """
 
         return default_to_dict(
-            self, model_name=self.model_name, organization=self.organization, prefix=self.prefix, suffix=self.suffix
+            self, model=self.model, organization=self.organization, prefix=self.prefix, suffix=self.suffix
         )
 
     @component.output_types(embedding=List[float], meta=Dict[str, Any])
@@ -86,7 +86,7 @@ def run(self, text: str):
         # replace newlines, which can negatively affect performance.
         text_to_embed = text_to_embed.replace("\n", " ")
 
-        response = self.client.embeddings.create(model=self.model_name, input=text_to_embed)
+        response = self.client.embeddings.create(model=self.model, input=text_to_embed)
         meta = {"model": response.model, "usage": dict(response.usage)}
 
         return {"embedding": response.data[0].embedding, "meta": meta}

haystack/components/embedders/sentence_transformers_document_embedder.py

@@ -29,7 +29,7 @@ class SentenceTransformersDocumentEmbedder:
 
     def __init__(
         self,
-        model_name_or_path: str = "sentence-transformers/all-mpnet-base-v2",
+        model: str = "sentence-transformers/all-mpnet-base-v2",
         device: Optional[str] = None,
         token: Union[bool, str, None] = None,
         prefix: str = "",
@@ -43,7 +43,7 @@ def __init__(
         """
         Create a SentenceTransformersDocumentEmbedder component.
 
-        :param model_name_or_path: Local path or name of the model in Hugging Face's model hub,
+        :param model: Local path or name of the model in Hugging Face's model hub,
             such as ``'sentence-transformers/all-mpnet-base-v2'``.
         :param device: Device (like 'cuda' / 'cpu') that should be used for computation.
             Defaults to CPU.
@@ -61,7 +61,7 @@ def __init__(
         :param embedding_separator: Separator used to concatenate the meta fields to the Document content.
         """
 
-        self.model_name_or_path = model_name_or_path
+        self.model = model
         # TODO: remove device parameter and use Haystack's device management once migrated
         self.device = device or "cpu"
         self.token = token
@@ -77,15 +77,15 @@ def _get_telemetry_data(self) -> Dict[str, Any]:
         """
         Data that is sent to Posthog for usage analytics.
         """
-        return {"model": self.model_name_or_path}
+        return {"model": self.model}
 
     def to_dict(self) -> Dict[str, Any]:
         """
         Serialize this component to a dictionary.
         """
         return default_to_dict(
             self,
-            model_name_or_path=self.model_name_or_path,
+            model=self.model,
             device=self.device,
             token=self.token if not isinstance(self.token, str) else None,  # don't serialize valid tokens
             prefix=self.prefix,
@@ -103,7 +103,7 @@ def warm_up(self):
         """
         if not hasattr(self, "embedding_backend"):
             self.embedding_backend = _SentenceTransformersEmbeddingBackendFactory.get_embedding_backend(
-                model_name_or_path=self.model_name_or_path, device=self.device, use_auth_token=self.token
+                model=self.model, device=self.device, use_auth_token=self.token
             )
 
     @component.output_types(documents=List[Document])

haystack/components/embedders/sentence_transformers_text_embedder.py

@@ -28,7 +28,7 @@ class SentenceTransformersTextEmbedder:
 
     def __init__(
         self,
-        model_name_or_path: str = "sentence-transformers/all-mpnet-base-v2",
+        model: str = "sentence-transformers/all-mpnet-base-v2",
         device: Optional[str] = None,
         token: Union[bool, str, None] = None,
         prefix: str = "",
@@ -40,7 +40,7 @@ def __init__(
         """
         Create a SentenceTransformersTextEmbedder component.
 
-        :param model_name_or_path: Local path or name of the model in Hugging Face's model hub,
+        :param model: Local path or name of the model in Hugging Face's model hub,
             such as ``'sentence-transformers/all-mpnet-base-v2'``.
         :param device: Device (like 'cuda' / 'cpu') that should be used for computation.
             Defaults to CPU.
@@ -56,7 +56,7 @@ def __init__(
         :param normalize_embeddings: If set to true, returned vectors will have length 1.
         """
 
-        self.model_name_or_path = model_name_or_path
+        self.model = model
         # TODO: remove device parameter and use Haystack's device management once migrated
         self.device = device or "cpu"
         self.token = token
@@ -70,15 +70,15 @@ def _get_telemetry_data(self) -> Dict[str, Any]:
         """
         Data that is sent to Posthog for usage analytics.
         """
-        return {"model": self.model_name_or_path}
+        return {"model": self.model}
 
     def to_dict(self) -> Dict[str, Any]:
         """
         Serialize this component to a dictionary.
         """
         return default_to_dict(
             self,
-            model_name_or_path=self.model_name_or_path,
+            model=self.model,
             device=self.device,
             token=self.token if not isinstance(self.token, str) else None,  # don't serialize valid tokens
             prefix=self.prefix,
@@ -94,7 +94,7 @@ def warm_up(self):
         """
         if not hasattr(self, "embedding_backend"):
             self.embedding_backend = _SentenceTransformersEmbeddingBackendFactory.get_embedding_backend(
-                model_name_or_path=self.model_name_or_path, device=self.device, use_auth_token=self.token
+                model=self.model, device=self.device, use_auth_token=self.token
             )
 
     @component.output_types(embedding=List[float])

haystack/components/joiners/document_joiner.py

@@ -31,7 +31,7 @@ class DocumentJoiner:
     p = Pipeline()
     p.add_component(instance=InMemoryBM25Retriever(document_store=document_store), name="bm25_retriever")
     p.add_component(
-            instance=SentenceTransformersTextEmbedder(model_name_or_path="sentence-transformers/all-MiniLM-L6-v2"),
+            instance=SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"),
             name="text_embedder",
         )
     p.add_component(instance=InMemoryEmbeddingRetriever(document_store=document_store), name="embedding_retriever")