apply ruff

src/backend/base/langflow/components/embeddings/AmazonBedrockEmbeddings.py

@@ -1,9 +1,7 @@
-from typing import Optional
-
 from langchain_community.embeddings import BedrockEmbeddings
 from langflow.base.models.model import LCModelComponent
 from langflow.field_typing import Embeddings
-from langflow.io import DropdownInput, Output, SecretStrInput, TextInput
+from langflow.io import DropdownInput, Output, TextInput
 
 
 class AmazonBedrockEmbeddingsComponent(LCModelComponent):

src/backend/base/langflow/components/embeddings/AzureOpenAIEmbeddings.py

@@ -1,4 +1,3 @@
-from typing import Optional
 from langchain_openai import AzureOpenAIEmbeddings
 from pydantic.v1 import SecretStr
 

src/backend/base/langflow/components/embeddings/CohereEmbeddings.py

@@ -2,7 +2,7 @@ from langchain_community.embeddings.cohere import CohereEmbeddings
 
 from langflow.base.models.model import LCModelComponent
 from langflow.field_typing import Embeddings
-from langflow.io import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, Output, SecretStrInput, TextInput
+from langflow.io import DropdownInput, FloatInput, IntInput, Output, SecretStrInput, TextInput
 
 
 class CohereEmbeddingsComponent(LCModelComponent):

src/backend/base/langflow/components/embeddings/HuggingFaceEmbeddings.py

@@ -1,5 +1,3 @@
-from typing import Dict, Optional
-
 from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings
 
 from langflow.base.models.model import LCModelComponent

src/backend/base/langflow/components/embeddings/HuggingFaceInferenceAPIEmbeddings.py

@@ -1,11 +1,9 @@
-from typing import Dict, Optional
-
 from langchain_community.embeddings.huggingface import HuggingFaceInferenceAPIEmbeddings
 from pydantic.v1.types import SecretStr
 
 from langflow.base.models.model import LCModelComponent
 from langflow.field_typing import Embeddings
-from langflow.io import BoolInput, DictInput, FloatInput, Output, SecretStrInput, TextInput
+from langflow.io import BoolInput, DictInput, Output, SecretStrInput, TextInput
 
 
 class HuggingFaceInferenceAPIEmbeddingsComponent(LCModelComponent):

src/backend/base/langflow/components/embeddings/OllamaEmbeddings.py

@@ -1,5 +1,3 @@
-from typing import Optional
-
 from langchain_community.embeddings import OllamaEmbeddings
 from langflow.base.models.model import LCModelComponent
 from langflow.field_typing import Embeddings

src/backend/base/langflow/components/embeddings/VertexAIEmbeddings.py

@@ -1,5 +1,3 @@
-from typing import List, Optional
-
 from langflow.base.models.model import LCModelComponent
 from langflow.field_typing import Embeddings
 from langflow.io import BoolInput, DictInput, FileInput, FloatInput, IntInput, Output, TextInput

src/backend/base/langflow/components/model_specs/ChatAnthropicSpecs.py

@@ -1,86 +0,0 @@
-from typing import Optional
-
-from langchain_anthropic import ChatAnthropic
-from pydantic.v1.types import SecretStr
-
-from langflow.field_typing import LanguageModel
-
-
-class AnthropicLLM(CustomComponent):
-    display_name: str = "Anthropic"
-    description: str = "Generate text using Anthropic Chat&Completion LLMs."
-    icon = "Anthropic"
-
-    field_order = [
-        "model",
-        "anthropic_api_key",
-        "max_tokens",
-        "temperature",
-        "anthropic_api_url",
-    ]
-
-    def build_config(self):
-        return {
-            "model": {
-                "display_name": "Model Name",
-                "options": [
-                    "claude-3-opus-20240229",
-                    "claude-3-sonnet-20240229",
-                    "claude-3-haiku-20240307",
-                    "claude-2.1",
-                    "claude-2.0",
-                    "claude-instant-1.2",
-                    "claude-instant-1",
-                ],
-                "info": "Name of the model to use.",
-                "required": True,
-                "value": "claude-3-opus-20240229",
-            },
-            "anthropic_api_key": {
-                "display_name": "Anthropic API Key",
-                "required": True,
-                "password": True,
-                "info": "Your Anthropic API key.",
-            },
-            "max_tokens": {
-                "display_name": "Max Tokens",
-                "advanced": True,
-                "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
-            },
-            "temperature": {
-                "display_name": "Temperature",
-                "field_type": "float",
-                "value": 0.1,
-            },
-            "anthropic_api_url": {
-                "display_name": "Anthropic API URL",
-                "advanced": True,
-                "info": "Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.",
-            },
-            "code": {"show": False},
-        }
-
-    def build(
-        self,
-        model: str,
-        anthropic_api_key: Optional[str] = None,
-        max_tokens: Optional[int] = 1000,
-        temperature: Optional[float] = None,
-        anthropic_api_url: Optional[str] = None,
-    ) -> LanguageModel:
-        # Set default API endpoint if not provided
-        if not anthropic_api_url:
-            anthropic_api_url = "https://api.anthropic.com"
-
-        try:
-            output = ChatAnthropic(
-                model_name=model,
-                anthropic_api_key=(SecretStr(anthropic_api_key) if anthropic_api_key else None),
-                max_tokens_to_sample=max_tokens,  # type: ignore
-                temperature=temperature,
-                anthropic_api_url=anthropic_api_url,
-            )
-        except Exception as e:
-            raise ValueError("Could not connect to Anthropic API.") from e
-
-        return output

src/backend/base/langflow/components/models/AmazonBedrockModel.py

@@ -2,7 +2,7 @@ from langchain_aws import ChatBedrock
 
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
-from langflow.field_typing import LanguageModel, Text
+from langflow.field_typing import LanguageModel
 from langflow.io import BoolInput, DictInput, DropdownInput, MessageInput, Output, StrInput
 
 

src/backend/base/langflow/components/models/AzureOpenAIModel.py

@@ -3,7 +3,7 @@ from pydantic.v1 import SecretStr
 
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
-from langflow.field_typing import LanguageModel, Text
+from langflow.field_typing import LanguageModel
 from langflow.io import BoolInput, DropdownInput, FloatInput, IntInput, MessageInput, Output, SecretStrInput, StrInput
 
 

src/backend/base/langflow/components/models/GoogleGenerativeAIModel.py

@@ -2,7 +2,7 @@ from pydantic.v1 import SecretStr
 
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
-from langflow.field_typing import LanguageModel, Text
+from langflow.field_typing import LanguageModel
 from langflow.io import BoolInput, DropdownInput, FloatInput, IntInput, MessageInput, Output, SecretStrInput, StrInput
 
 

src/backend/base/langflow/components/textsplitters/RecursiveCharacterTextSplitter.py

@@ -47,7 +47,7 @@ class RecursiveCharacterTextSplitterComponent(Component):
         Split text into chunks of a specified length.
 
         Args:
-            separators (list[str]): The characters to split on.
+            separators (list[str] | None): The characters to split on.
             chunk_size (int): The maximum length of each chunk.
             chunk_overlap (int): The amount of overlap between chunks.
 
@@ -63,9 +63,9 @@ class RecursiveCharacterTextSplitterComponent(Component):
             self.separators = [unescape_string(x) for x in self.separators]
 
         # Make sure chunk_size and chunk_overlap are ints
-        if isinstance(self.chunk_size, str):
+        if self.chunk_size:
             self.chunk_size = int(self.chunk_size)
-        if isinstance(self.chunk_overlap, str):
+        if self.chunk_overlap:
             self.chunk_overlap = int(self.chunk_overlap)
         splitter = RecursiveCharacterTextSplitter(
             separators=self.separators,

src/backend/base/langflow/components/vectorstores/Cassandra.py

@@ -7,7 +7,6 @@ from langflow.custom import Component
 from langflow.helpers.data import docs_to_data
 from langflow.io import BoolInput, DropdownInput, HandleInput, IntInput, Output, SecretStrInput, StrInput
 from langflow.schema import Data
-from langflow.field_typing import Retriever
 
 
 class CassandraVectorStoreComponent(Component):

src/backend/base/langflow/components/vectorstores/Chroma.py

@@ -1,5 +1,5 @@
 from copy import deepcopy
-from typing import TYPE_CHECKING, List
+from typing import TYPE_CHECKING
 
 from chromadb.config import Settings
 from langchain_chroma.vectorstores import Chroma

src/backend/base/langflow/components/vectorstores/Couchbase.py

@@ -8,7 +8,6 @@ from langflow.custom import Component
 from langflow.helpers.data import docs_to_data
 from langflow.io import BoolInput, HandleInput, IntInput, Output, SecretStrInput, StrInput
 from langflow.schema import Data
-from langflow.field_typing import Retriever
 
 
 class CouchbaseVectorStoreComponent(Component):

src/backend/base/langflow/components/vectorstores/MongoDBAtlasVector.py

@@ -8,8 +8,6 @@ from langflow.helpers.data import docs_to_data
 from langflow.io import BoolInput, HandleInput, IntInput, Output, StrInput
 from langflow.schema import Data
 
-from langflow.field_typing import Retriever
-
 
 class MongoVectorStoreComponent(Component):
     display_name = "MongoDB Atlas"

src/backend/base/langflow/components/vectorstores/Qdrant.py

@@ -4,7 +4,6 @@ from langchain_community.vectorstores import Qdrant
 from langchain_core.retrievers import BaseRetriever
 
 from langflow.custom import Component
-from langflow.field_typing import Retriever
 from langflow.helpers.data import docs_to_data
 from langflow.io import BoolInput, DropdownInput, HandleInput, IntInput, Output, SecretStrInput, StrInput
 from langflow.schema import Data

src/backend/base/langflow/components/vectorstores/SupabaseVectorStore.py

@@ -5,7 +5,6 @@ from langchain_core.retrievers import BaseRetriever
 from supabase.client import Client, create_client
 
 from langflow.custom import Component
-from langflow.field_typing import Retriever
 from langflow.helpers.data import docs_to_data
 from langflow.io import HandleInput, IntInput, Output, StrInput
 from langflow.schema import Data

src/backend/base/langflow/components/vectorstores/Upstash.py

@@ -4,7 +4,6 @@ from langchain_community.vectorstores import UpstashVectorStore
 from langchain_core.retrievers import BaseRetriever
 
 from langflow.custom import Component
-from langflow.field_typing import Retriever
 from langflow.helpers.data import docs_to_data
 from langflow.io import BoolInput, HandleInput, IntInput, Output, StrInput
 from langflow.schema import Data

src/backend/base/langflow/components/vectorstores/Weaviate.py

@@ -5,7 +5,6 @@ from langchain_community.vectorstores import Weaviate
 from langchain_core.retrievers import BaseRetriever
 
 from langflow.custom import Component
-from langflow.field_typing import Retriever
 from langflow.helpers.data import docs_to_data
 from langflow.io import BoolInput, HandleInput, IntInput, Output, SecretStrInput, StrInput
 from langflow.schema import Data

src/backend/base/langflow/components/vectorstores/pgvector.py

@@ -8,8 +8,6 @@ from langflow.helpers.data import docs_to_data
 from langflow.io import BoolInput, HandleInput, IntInput, Output, StrInput
 from langflow.schema import Data
 
-from langflow.field_typing import Retriever
-
 
 class PGVectorStoreComponent(Component):
     display_name = "PGVector"

src/backend/base/langflow/graph/graph/base.py

@@ -6,7 +6,6 @@ from itertools import chain
 from typing import TYPE_CHECKING, Dict, Generator, List, Optional, Tuple, Type, Union
 from loguru import logger
 
-from langflow.exceptions.component import ComponentBuildException
 from langflow.exceptions.component import ComponentBuildException
 from langflow.graph.edge.base import ContractEdge
 from langflow.graph.graph.constants import lazy_load_vertex_dict

src/backend/base/langflow/template/template/base.py

@@ -11,7 +11,6 @@ class Template(BaseModel):
     type_name: str = Field(serialization_alias="_type")
     fields: list[Union[Input, InputTypes]]
 
-
     def process_fields(
         self,
         format_field_func: Union[Callable, None] = None,