model_name_or_path > model

integrations/astra/examples/example.py

@@ -47,7 +47,7 @@
 p.add_component(instance=DocumentCleaner(), name="cleaner")
 p.add_component(instance=DocumentSplitter(split_by="word", split_length=150, split_overlap=30), name="splitter")
 p.add_component(
-    instance=SentenceTransformersDocumentEmbedder(model_name_or_path="sentence-transformers/all-MiniLM-L6-v2"),
+    instance=SentenceTransformersDocumentEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"),
     name="embedder",
 )
 p.add_component(instance=DocumentWriter(document_store=document_store, policy=DuplicatePolicy.SKIP), name="writer")
@@ -63,7 +63,7 @@
 # Create a querying pipeline on the indexed data
 q = Pipeline()
 q.add_component(
-    instance=SentenceTransformersTextEmbedder(model_name_or_path="sentence-transformers/all-MiniLM-L6-v2"),
+    instance=SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"),
     name="embedder",
 )
 q.add_component("retriever", AstraEmbeddingRetriever(document_store))

integrations/astra/examples/pipeline_example.py

@@ -62,7 +62,7 @@
 ]
 p = Pipeline()
 p.add_component(
-    instance=SentenceTransformersDocumentEmbedder(model_name_or_path="sentence-transformers/all-MiniLM-L6-v2"),
+    instance=SentenceTransformersDocumentEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"),
     name="embedder",
 )
 p.add_component(instance=DocumentWriter(document_store=document_store, policy=DuplicatePolicy.SKIP), name="writer")
@@ -74,7 +74,7 @@
 # Construct rag pipeline
 rag_pipeline = Pipeline()
 rag_pipeline.add_component(
-    instance=SentenceTransformersTextEmbedder(model_name_or_path="sentence-transformers/all-MiniLM-L6-v2"),
+    instance=SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"),
     name="embedder",
 )
 rag_pipeline.add_component(instance=AstraEmbeddingRetriever(document_store=document_store), name="retriever")

integrations/instructor_embedders/src/haystack_integrations/components/embedders/instructor_embedders/embedding_backend/instructor_backend.py

@@ -15,15 +15,13 @@ class _InstructorEmbeddingBackendFactory:
     _instances: ClassVar[Dict[str, "_InstructorEmbeddingBackend"]] = {}
 
     @staticmethod
-    def get_embedding_backend(model_name_or_path: str, device: Optional[str] = None, token: Optional[Secret] = None):
-        embedding_backend_id = f"{model_name_or_path}{device}{token}"
+    def get_embedding_backend(model: str, device: Optional[str] = None, token: Optional[Secret] = None):
+        embedding_backend_id = f"{model}{device}{token}"
 
         if embedding_backend_id in _InstructorEmbeddingBackendFactory._instances:
             return _InstructorEmbeddingBackendFactory._instances[embedding_backend_id]
 
-        embedding_backend = _InstructorEmbeddingBackend(
-            model_name_or_path=model_name_or_path, device=device, token=token
-        )
+        embedding_backend = _InstructorEmbeddingBackend(model=model, device=device, token=token)
         _InstructorEmbeddingBackendFactory._instances[embedding_backend_id] = embedding_backend
         return embedding_backend
 
@@ -33,9 +31,9 @@ class _InstructorEmbeddingBackend:
     Class to manage INSTRUCTOR embeddings.
     """
 
-    def __init__(self, model_name_or_path: str, device: Optional[str] = None, token: Optional[Secret] = None):
+    def __init__(self, model: str, device: Optional[str] = None, token: Optional[Secret] = None):
         self.model = INSTRUCTOR(
-            model_name_or_path=model_name_or_path,
+            model_name_or_path=model,
             device=device,
             use_auth_token=token.resolve_value() if token else None,
         )

integrations/instructor_embedders/src/haystack_integrations/components/embedders/instructor_embedders/instructor_document_embedder.py

@@ -96,7 +96,7 @@ def __init__(
         :param embedding_separator: Separator used to concatenate the meta fields to the Document content.
         """
 
-        self.model_name_or_path = model
+        self.model = model
         # TODO: remove device parameter and use Haystack's device management once migrated
         self.device = device or "cpu"
         self.token = token
@@ -113,7 +113,7 @@ def to_dict(self) -> Dict[str, Any]:
         """
         return default_to_dict(
             self,
-            model=self.model_name_or_path,
+            model=self.model,
             device=self.device,
             token=self.token.to_dict() if self.token else None,
             instruction=self.instruction,
@@ -138,7 +138,7 @@ def warm_up(self):
         """
         if not hasattr(self, "embedding_backend"):
             self.embedding_backend = _InstructorEmbeddingBackendFactory.get_embedding_backend(
-                model_name_or_path=self.model_name_or_path, device=self.device, token=self.token
+                model=self.model, device=self.device, token=self.token
             )
 
     @component.output_types(documents=List[Document])

integrations/instructor_embedders/src/haystack_integrations/components/embedders/instructor_embedders/instructor_text_embedder.py

@@ -66,7 +66,7 @@ def __init__(
         :param normalize_embeddings: If set to true, returned vectors will have the length of 1.
         """
 
-        self.model_name_or_path = model
+        self.model = model
         # TODO: remove device parameter and use Haystack's device management once migrated
         self.device = device or "cpu"
         self.token = token
@@ -81,7 +81,7 @@ def to_dict(self) -> Dict[str, Any]:
         """
         return default_to_dict(
             self,
-            model=self.model_name_or_path,
+            model=self.model,
             device=self.device,
             token=self.token.to_dict() if self.token else None,
             instruction=self.instruction,
@@ -104,7 +104,7 @@ def warm_up(self):
         """
         if not hasattr(self, "embedding_backend"):
             self.embedding_backend = _InstructorEmbeddingBackendFactory.get_embedding_backend(
-                model_name_or_path=self.model_name_or_path, device=self.device, token=self.token
+                model=self.model, device=self.device, token=self.token
             )
 
     @component.output_types(embedding=List[float])

integrations/instructor_embedders/tests/test_instructor_backend.py

@@ -11,11 +11,11 @@
 )
 def test_factory_behavior(mock_instructor):  # noqa: ARG001
     embedding_backend = _InstructorEmbeddingBackendFactory.get_embedding_backend(
-        model_name_or_path="hkunlp/instructor-large", device="cpu"
+        model="hkunlp/instructor-large", device="cpu"
     )
     same_embedding_backend = _InstructorEmbeddingBackendFactory.get_embedding_backend("hkunlp/instructor-large", "cpu")
     another_embedding_backend = _InstructorEmbeddingBackendFactory.get_embedding_backend(
-        model_name_or_path="hkunlp/instructor-base", device="cpu"
+        model="hkunlp/instructor-base", device="cpu"
     )
 
     assert same_embedding_backend is embedding_backend
@@ -30,7 +30,7 @@ def test_factory_behavior(mock_instructor):  # noqa: ARG001
 )
 def test_model_initialization(mock_instructor):
     _InstructorEmbeddingBackendFactory.get_embedding_backend(
-        model_name_or_path="hkunlp/instructor-base", device="cpu", token=Secret.from_token("fake-api-token")
+        model="hkunlp/instructor-base", device="cpu", token=Secret.from_token("fake-api-token")
     )
     mock_instructor.assert_called_once_with(
         model_name_or_path="hkunlp/instructor-base", device="cpu", use_auth_token="fake-api-token"
@@ -43,9 +43,7 @@ def test_model_initialization(mock_instructor):
     "haystack_integrations.components.embedders.instructor_embedders.embedding_backend.instructor_backend.INSTRUCTOR"
 )
 def test_embedding_function_with_kwargs(mock_instructor):  # noqa: ARG001
-    embedding_backend = _InstructorEmbeddingBackendFactory.get_embedding_backend(
-        model_name_or_path="hkunlp/instructor-base"
-    )
+    embedding_backend = _InstructorEmbeddingBackendFactory.get_embedding_backend(model="hkunlp/instructor-base")
 
     data = [["instruction", "sentence1"], ["instruction", "sentence2"]]
     embedding_backend.embed(data=data, normalize_embeddings=True)

integrations/instructor_embedders/tests/test_instructor_document_embedder.py

@@ -13,7 +13,7 @@ def test_init_default(self):
         Test default initialization parameters for InstructorDocumentEmbedder.
         """
         embedder = InstructorDocumentEmbedder(model="hkunlp/instructor-base")
-        assert embedder.model_name_or_path == "hkunlp/instructor-base"
+        assert embedder.model == "hkunlp/instructor-base"
         assert embedder.device == "cpu"
         assert embedder.token == Secret.from_env_var("HF_API_TOKEN", strict=False)
         assert embedder.instruction == "Represent the document"
@@ -38,7 +38,7 @@ def test_init_with_parameters(self):
             meta_fields_to_embed=["test_field"],
             embedding_separator=" | ",
         )
-        assert embedder.model_name_or_path == "hkunlp/instructor-base"
+        assert embedder.model == "hkunlp/instructor-base"
         assert embedder.device == "cuda"
         assert embedder.token == Secret.from_token("fake-api-token")
         assert embedder.instruction == "Represent the 'domain' 'text_type' for 'task_objective'"
@@ -118,7 +118,7 @@ def test_from_dict(self):
             },
         }
         embedder = InstructorDocumentEmbedder.from_dict(embedder_dict)
-        assert embedder.model_name_or_path == "hkunlp/instructor-base"
+        assert embedder.model == "hkunlp/instructor-base"
         assert embedder.device == "cpu"
         assert embedder.token == Secret.from_env_var("HF_API_TOKEN", strict=False)
         assert embedder.instruction == "Represent the 'domain' 'text_type' for 'task_objective'"
@@ -147,7 +147,7 @@ def test_from_dict_with_custom_init_parameters(self):
             },
         }
         embedder = InstructorDocumentEmbedder.from_dict(embedder_dict)
-        assert embedder.model_name_or_path == "hkunlp/instructor-base"
+        assert embedder.model == "hkunlp/instructor-base"
         assert embedder.device == "cuda"
         assert embedder.token == Secret.from_env_var("HF_API_TOKEN", strict=False)
         assert embedder.instruction == "Represent the financial document for retrieval"
@@ -168,7 +168,7 @@ def test_warmup(self, mocked_factory):
         mocked_factory.get_embedding_backend.assert_not_called()
         embedder.warm_up()
         mocked_factory.get_embedding_backend.assert_called_once_with(
-            model_name_or_path="hkunlp/instructor-base",
+            model="hkunlp/instructor-base",
             device="cpu",
             token=Secret.from_env_var("HF_API_TOKEN", strict=False),
         )

integrations/instructor_embedders/tests/test_instructor_text_embedder.py

@@ -12,7 +12,7 @@ def test_init_default(self):
         Test default initialization parameters for InstructorTextEmbedder.
         """
         embedder = InstructorTextEmbedder(model="hkunlp/instructor-base")
-        assert embedder.model_name_or_path == "hkunlp/instructor-base"
+        assert embedder.model == "hkunlp/instructor-base"
         assert embedder.device == "cpu"
         assert embedder.token == Secret.from_env_var("HF_API_TOKEN", strict=False)
         assert embedder.instruction == "Represent the sentence"
@@ -33,7 +33,7 @@ def test_init_with_parameters(self):
             progress_bar=False,
             normalize_embeddings=True,
         )
-        assert embedder.model_name_or_path == "hkunlp/instructor-base"
+        assert embedder.model == "hkunlp/instructor-base"
         assert embedder.device == "cuda"
         assert embedder.token == Secret.from_token("fake-api-token")
         assert embedder.instruction == "Represent the 'domain' 'text_type' for 'task_objective'"
@@ -103,7 +103,7 @@ def test_from_dict(self):
             },
         }
         embedder = InstructorTextEmbedder.from_dict(embedder_dict)
-        assert embedder.model_name_or_path == "hkunlp/instructor-base"
+        assert embedder.model == "hkunlp/instructor-base"
         assert embedder.device == "cpu"
         assert embedder.token == Secret.from_env_var("HF_API_TOKEN", strict=False)
         assert embedder.instruction == "Represent the 'domain' 'text_type' for 'task_objective'"
@@ -128,7 +128,7 @@ def test_from_dict_with_custom_init_parameters(self):
             },
         }
         embedder = InstructorTextEmbedder.from_dict(embedder_dict)
-        assert embedder.model_name_or_path == "hkunlp/instructor-base"
+        assert embedder.model == "hkunlp/instructor-base"
         assert embedder.device == "cuda"
         assert embedder.token == Secret.from_env_var("HF_API_TOKEN", strict=False)
         assert embedder.instruction == "Represent the financial document for retrieval"
@@ -147,7 +147,7 @@ def test_warmup(self, mocked_factory):
         mocked_factory.get_embedding_backend.assert_not_called()
         embedder.warm_up()
         mocked_factory.get_embedding_backend.assert_called_once_with(
-            model_name_or_path="hkunlp/instructor-base",
+            model="hkunlp/instructor-base",
             device="cpu",
             token=Secret.from_env_var("HF_API_TOKEN", strict=False),
         )

integrations/llama_cpp/README.md

@@ -161,7 +161,7 @@ Index the documents to the `InMemoryDocumentStore` using the `SentenceTransforme
 
 ```python
 doc_store = InMemoryDocumentStore(embedding_similarity_function="cosine")
-doc_embedder = SentenceTransformersDocumentEmbedder(model_name_or_path="sentence-transformers/all-MiniLM-L6-v2")
+doc_embedder = SentenceTransformersDocumentEmbedder(model="sentence-transformers/all-MiniLM-L6-v2")
 
 # Indexing Pipeline
 indexing_pipeline = Pipeline()
@@ -188,7 +188,7 @@ GPT4 Correct Assistant:
 
 rag_pipeline = Pipeline()
 
-text_embedder = SentenceTransformersTextEmbedder(model_name_or_path="sentence-transformers/all-MiniLM-L6-v2")
+text_embedder = SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2")
 
 # Load the LLM using LlamaCppGenerator
 model_path = "openchat-3.5-1210.Q3_K_S.gguf"

integrations/llama_cpp/examples/rag_pipeline_example.py

@@ -23,7 +23,7 @@
 ]
 
 doc_store = InMemoryDocumentStore(embedding_similarity_function="cosine")
-doc_embedder = SentenceTransformersDocumentEmbedder(model_name_or_path="sentence-transformers/all-MiniLM-L6-v2")
+doc_embedder = SentenceTransformersDocumentEmbedder(model="sentence-transformers/all-MiniLM-L6-v2")
 
 
 # Indexing Pipeline
@@ -47,7 +47,7 @@
 """
 rag_pipeline = Pipeline()
 
-text_embedder = SentenceTransformersTextEmbedder(model_name_or_path="sentence-transformers/all-MiniLM-L6-v2")
+text_embedder = SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2")
 
 model_path = "openchat-3.5-1210.Q3_K_S.gguf"
 generator = LlamaCppGenerator(model_path=model_path, n_ctx=4096, n_batch=128)