chore: fix linting/isort (#1215)

* fix linting

* vertex fix

integrations/amazon_bedrock/src/haystack_integrations/components/embedders/amazon_bedrock/__init__.py

@@ -4,4 +4,4 @@
 from .document_embedder import AmazonBedrockDocumentEmbedder
 from .text_embedder import AmazonBedrockTextEmbedder
 
-__all__ = ["AmazonBedrockTextEmbedder", "AmazonBedrockDocumentEmbedder"]
+__all__ = ["AmazonBedrockDocumentEmbedder", "AmazonBedrockTextEmbedder"]

integrations/amazon_bedrock/src/haystack_integrations/components/embedders/amazon_bedrock/document_embedder.py

@@ -236,7 +236,7 @@ def run(self, documents: List[Document]):
             - `documents`: The `Document`s with the `embedding` field populated.
         :raises AmazonBedrockInferenceError: If the inference fails.
         """
-        if not isinstance(documents, list) or documents and not isinstance(documents[0], Document):
+        if not isinstance(documents, list) or (documents and not isinstance(documents[0], Document)):
             msg = (
                 "AmazonBedrockDocumentEmbedder expects a list of Documents as input."
                 "In case you want to embed a string, please use the AmazonBedrockTextEmbedder."

integrations/amazon_bedrock/src/haystack_integrations/components/generators/amazon_bedrock/__init__.py

@@ -4,4 +4,4 @@
 from .chat.chat_generator import AmazonBedrockChatGenerator
 from .generator import AmazonBedrockGenerator
 
-__all__ = ["AmazonBedrockGenerator", "AmazonBedrockChatGenerator"]
+__all__ = ["AmazonBedrockChatGenerator", "AmazonBedrockGenerator"]

integrations/anthropic/src/haystack_integrations/components/generators/anthropic/__init__.py

@@ -5,4 +5,4 @@
 from .chat.vertex_chat_generator import AnthropicVertexChatGenerator
 from .generator import AnthropicGenerator
 
-__all__ = ["AnthropicGenerator", "AnthropicChatGenerator", "AnthropicVertexChatGenerator"]
+__all__ = ["AnthropicChatGenerator", "AnthropicGenerator", "AnthropicVertexChatGenerator"]

integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/__init__.py

@@ -4,4 +4,4 @@
 from .document_store import DEFAULT_VECTOR_SEARCH, AzureAISearchDocumentStore
 from .filters import _normalize_filters
 
-__all__ = ["AzureAISearchDocumentStore", "DEFAULT_VECTOR_SEARCH", "_normalize_filters"]
+__all__ = ["DEFAULT_VECTOR_SEARCH", "AzureAISearchDocumentStore", "_normalize_filters"]

integrations/chroma/src/haystack_integrations/components/retrievers/chroma/__init__.py

@@ -1,3 +1,3 @@
 from .retriever import ChromaEmbeddingRetriever, ChromaQueryTextRetriever
 
-__all__ = ["ChromaQueryTextRetriever", "ChromaEmbeddingRetriever"]
+__all__ = ["ChromaEmbeddingRetriever", "ChromaQueryTextRetriever"]

integrations/cohere/src/haystack_integrations/components/embedders/cohere/document_embedder.py

@@ -146,7 +146,7 @@ def run(self, documents: List[Document]):
             - `meta`: metadata about the embedding process.
         :raises TypeError: if the input is not a list of `Documents`.
         """
-        if not isinstance(documents, list) or documents and not isinstance(documents[0], Document):
+        if not isinstance(documents, list) or (documents and not isinstance(documents[0], Document)):
             msg = (
                 "CohereDocumentEmbedder expects a list of Documents as input."
                 "In case you want to embed a string, please use the CohereTextEmbedder."

integrations/cohere/src/haystack_integrations/components/generators/cohere/__init__.py

@@ -4,4 +4,4 @@
 from .chat.chat_generator import CohereChatGenerator
 from .generator import CohereGenerator
 
-__all__ = ["CohereGenerator", "CohereChatGenerator"]
+__all__ = ["CohereChatGenerator", "CohereGenerator"]

integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/__init__.py

@@ -8,7 +8,7 @@
 
 __all__ = [
     "FastembedDocumentEmbedder",
-    "FastembedTextEmbedder",
     "FastembedSparseDocumentEmbedder",
     "FastembedSparseTextEmbedder",
+    "FastembedTextEmbedder",
 ]

integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_document_embedder.py

@@ -158,7 +158,7 @@ def run(self, documents: List[Document]):
         :returns: A dictionary with the following keys:
             - `documents`: List of Documents with each Document's `embedding` field set to the computed embeddings.
         """
-        if not isinstance(documents, list) or documents and not isinstance(documents[0], Document):
+        if not isinstance(documents, list) or (documents and not isinstance(documents[0], Document)):
             msg = (
                 "FastembedDocumentEmbedder expects a list of Documents as input. "
                 "In case you want to embed a list of strings, please use the FastembedTextEmbedder."

integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_sparse_document_embedder.py

@@ -150,7 +150,7 @@ def run(self, documents: List[Document]):
             - `documents`: List of Documents with each Document's `sparse_embedding`
                             field set to the computed embeddings.
         """
-        if not isinstance(documents, list) or documents and not isinstance(documents[0], Document):
+        if not isinstance(documents, list) or (documents and not isinstance(documents[0], Document)):
             msg = (
                 "FastembedSparseDocumentEmbedder expects a list of Documents as input. "
                 "In case you want to embed a list of strings, please use the FastembedTextEmbedder."

integrations/fastembed/src/haystack_integrations/components/rankers/fastembed/ranker.py

@@ -157,7 +157,7 @@ def run(self, query: str, documents: List[Document], top_k: Optional[int] = None
 
         :raises ValueError: If `top_k` is not > 0.
         """
-        if not isinstance(documents, list) or documents and not isinstance(documents[0], Document):
+        if not isinstance(documents, list) or (documents and not isinstance(documents[0], Document)):
             msg = "FastembedRanker expects a list of Documents as input. "
             raise TypeError(msg)
         if query == "":

integrations/google_ai/src/haystack_integrations/components/generators/google_ai/__init__.py

@@ -4,4 +4,4 @@
 from .chat.gemini import GoogleAIGeminiChatGenerator
 from .gemini import GoogleAIGeminiGenerator
 
-__all__ = ["GoogleAIGeminiGenerator", "GoogleAIGeminiChatGenerator"]
+__all__ = ["GoogleAIGeminiChatGenerator", "GoogleAIGeminiGenerator"]

integrations/google_vertex/src/haystack_integrations/components/generators/google_vertex/__init__.py

@@ -11,8 +11,8 @@
 
 __all__ = [
     "VertexAICodeGenerator",
-    "VertexAIGeminiGenerator",
     "VertexAIGeminiChatGenerator",
+    "VertexAIGeminiGenerator",
     "VertexAIImageCaptioner",
     "VertexAIImageGenerator",
     "VertexAIImageQA",

integrations/google_vertex/tests/chat/test_gemini.py

@@ -161,13 +161,13 @@ def test_to_dict_with_params(_mock_vertexai_init, _mock_generative_model):
                             "name": "get_current_weather",
                             "description": "Get the current weather in a given location",
                             "parameters": {
-                                "type_": "OBJECT",
+                                "type": "OBJECT",
                                 "properties": {
                                     "location": {
-                                        "type_": "STRING",
+                                        "type": "STRING",
                                         "description": "The city and state, e.g. San Francisco, CA",
                                     },
-                                    "unit": {"type_": "STRING", "enum": ["celsius", "fahrenheit"]},
+                                    "unit": {"type": "STRING", "enum": ["celsius", "fahrenheit"]},
                                 },
                                 "required": ["location"],
                                 "property_ordering": ["location", "unit"],

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

@@ -158,7 +158,7 @@ def run(self, documents: List[Document]):
 
         param documents: A list of Documents to embed.
         """
-        if not isinstance(documents, list) or documents and not isinstance(documents[0], Document):
+        if not isinstance(documents, list) or (documents and not isinstance(documents[0], Document)):
             msg = (
                 "InstructorDocumentEmbedder expects a list of Documents as input. "
                 "In case you want to embed a list of strings, please use the InstructorTextEmbedder."

integrations/jina/src/haystack_integrations/components/embedders/jina/document_embedder.py

@@ -200,7 +200,7 @@ def run(self, documents: List[Document]):
             - `meta`: A dictionary with metadata including the model name and usage statistics.
         :raises TypeError: If the input is not a list of Documents.
         """
-        if not isinstance(documents, list) or documents and not isinstance(documents[0], Document):
+        if not isinstance(documents, list) or (documents and not isinstance(documents[0], Document)):
             msg = (
                 "JinaDocumentEmbedder expects a list of Documents as input."
                 "In case you want to embed a string, please use the JinaTextEmbedder."

integrations/llama_cpp/src/haystack_integrations/components/generators/llama_cpp/__init__.py

@@ -5,4 +5,4 @@
 from .chat.chat_generator import LlamaCppChatGenerator
 from .generator import LlamaCppGenerator
 
-__all__ = ["LlamaCppGenerator", "LlamaCppChatGenerator"]
+__all__ = ["LlamaCppChatGenerator", "LlamaCppGenerator"]

integrations/nvidia/src/haystack_integrations/components/embedders/nvidia/__init__.py

@@ -6,4 +6,4 @@
 from .text_embedder import NvidiaTextEmbedder
 from .truncate import EmbeddingTruncateMode
 
-__all__ = ["NvidiaDocumentEmbedder", "NvidiaTextEmbedder", "EmbeddingTruncateMode"]
+__all__ = ["EmbeddingTruncateMode", "NvidiaDocumentEmbedder", "NvidiaTextEmbedder"]

integrations/nvidia/src/haystack_integrations/components/embedders/nvidia/document_embedder.py

@@ -242,7 +242,7 @@ def run(self, documents: List[Document]):
         if not self._initialized:
             msg = "The embedding model has not been loaded. Please call warm_up() before running."
             raise RuntimeError(msg)
-        elif not isinstance(documents, list) or documents and not isinstance(documents[0], Document):
+        elif not isinstance(documents, list) or (documents and not isinstance(documents[0], Document)):
             msg = (
                 "NvidiaDocumentEmbedder expects a list of Documents as input."
                 "In case you want to embed a string, please use the NvidiaTextEmbedder."

integrations/nvidia/src/haystack_integrations/utils/nvidia/__init__.py

@@ -5,4 +5,4 @@
 from .nim_backend import Model, NimBackend
 from .utils import is_hosted, url_validation
 
-__all__ = ["NimBackend", "Model", "is_hosted", "url_validation"]
+__all__ = ["Model", "NimBackend", "is_hosted", "url_validation"]

integrations/optimum/src/haystack_integrations/components/embedders/optimum/__init__.py

@@ -10,10 +10,10 @@
 
 __all__ = [
     "OptimumDocumentEmbedder",
-    "OptimumEmbedderOptimizationMode",
     "OptimumEmbedderOptimizationConfig",
+    "OptimumEmbedderOptimizationMode",
     "OptimumEmbedderPooling",
-    "OptimumEmbedderQuantizationMode",
     "OptimumEmbedderQuantizationConfig",
+    "OptimumEmbedderQuantizationMode",
     "OptimumTextEmbedder",
 ]

integrations/optimum/src/haystack_integrations/components/embedders/optimum/optimum_document_embedder.py

@@ -208,7 +208,7 @@ def run(self, documents: List[Document]):
         if not self._initialized:
             msg = "The embedding model has not been loaded. Please call warm_up() before running."
             raise RuntimeError(msg)
-        if not isinstance(documents, list) or documents and not isinstance(documents[0], Document):
+        if not isinstance(documents, list) or (documents and not isinstance(documents[0], Document)):
             msg = (
                 "OptimumDocumentEmbedder expects a list of Documents as input."
                 " In case you want to embed a string, please use the OptimumTextEmbedder."

integrations/qdrant/src/haystack_integrations/components/retrievers/qdrant/__init__.py

@@ -4,4 +4,4 @@
 
 from .retriever import QdrantEmbeddingRetriever, QdrantHybridRetriever, QdrantSparseEmbeddingRetriever
 
-__all__ = ("QdrantEmbeddingRetriever", "QdrantSparseEmbeddingRetriever", "QdrantHybridRetriever")
+__all__ = ("QdrantEmbeddingRetriever", "QdrantHybridRetriever", "QdrantSparseEmbeddingRetriever")

integrations/weaviate/src/haystack_integrations/document_stores/weaviate/__init__.py

@@ -5,10 +5,10 @@
 from .document_store import WeaviateDocumentStore
 
 __all__ = [
-    "WeaviateDocumentStore",
     "AuthApiKey",
     "AuthBearerToken",
     "AuthClientCredentials",
     "AuthClientPassword",
     "AuthCredentials",
+    "WeaviateDocumentStore",
 ]