refac: fix linting

src/backend/langflow/cache/utils.py

@@ -5,7 +5,7 @@ import os
 import tempfile
 from pathlib import Path
 
-import dill
+import dill  # type: ignore
 
 PREFIX = "langflow_cache"
 

src/backend/langflow/graph/nodes.py

@@ -1,10 +1,8 @@
-import json
 from copy import deepcopy
 from typing import Any, Dict, List, Optional, Union
 
 from langflow.graph.base import Node
 from langflow.graph.utils import extract_input_variables_from_prompt
-from langflow.interface.toolkits.base import toolkits_creator
 
 
 class AgentNode(Node):

src/backend/langflow/interface/agents/custom.py

@@ -1,18 +1,14 @@
-from pathlib import Path
 from typing import Any, Optional
 
 from langchain import LLMChain
 from langchain.agents import AgentExecutor, ZeroShotAgent
 from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX
 from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
-from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
 from langchain.agents.agent_toolkits.pandas.prompt import PREFIX as PANDAS_PREFIX
 from langchain.agents.agent_toolkits.pandas.prompt import SUFFIX as PANDAS_SUFFIX
 from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
-from langchain.llms.base import BaseLLM
 from langchain.schema import BaseLanguageModel
 from langchain.tools.python.tool import PythonAstREPLTool
-from pydantic import BaseModel
 
 
 class JsonAgent(AgentExecutor):
@@ -65,12 +61,12 @@ class CSVAgent(AgentExecutor):
         pandas_kwargs: Optional[dict] = None,
         **kwargs: Any
     ):
-        import pandas as pd
+        import pandas as pd  # type: ignore
 
         _kwargs = pandas_kwargs or {}
         df = pd.DataFrame.from_dict(path, **_kwargs)
 
-        tools = [PythonAstREPLTool(locals={"df": df})]
+        tools = [PythonAstREPLTool(locals={"df": df})]  # type: ignore
         prompt = ZeroShotAgent.create_prompt(
             tools,
             prefix=PANDAS_PREFIX,
@@ -81,7 +77,6 @@ class CSVAgent(AgentExecutor):
         llm_chain = LLMChain(
             llm=llm,
             prompt=partial_prompt,
-            callback_manager=None,
         )
         tool_names = [tool.name for tool in tools]
         agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)

src/backend/langflow/interface/custom_lists.py

@@ -1,18 +1,11 @@
-## LLM
 from typing import Any
 
+## LLM
 from langchain import llms, requests
 from langchain.agents import agent_toolkits
 from langchain.chat_models import ChatOpenAI
 
-from langflow.interface.importing.utils import import_class
-
-llm_type_to_cls_dict = llms.type_to_cls_dict
-llm_type_to_cls_dict["openai-chat"] = ChatOpenAI
-
-
 ## Memory
-
 # from langchain.memory.buffer_window import ConversationBufferWindowMemory
 # from langchain.memory.chat_memory import ChatMessageHistory
 # from langchain.memory.combined import CombinedMemory
@@ -22,108 +15,7 @@ llm_type_to_cls_dict["openai-chat"] = ChatOpenAI
 # from langchain.memory.simple import SimpleMemory
 # from langchain.memory.summary import ConversationSummaryMemory
 # from langchain.memory.summary_buffer import ConversationSummaryBufferMemory
-
-memory_type_to_cls_dict: dict[str, Any] = {
-    # "CombinedMemory": CombinedMemory,
-    # "ConversationBufferWindowMemory": ConversationBufferWindowMemory,
-    # "ConversationBufferMemory": ConversationBufferMemory,
-    # "SimpleMemory": SimpleMemory,
-    # "ConversationSummaryBufferMemory": ConversationSummaryBufferMemory,
-    # "ConversationKGMemory": ConversationKGMemory,
-    # "ConversationEntityMemory": ConversationEntityMemory,
-    # "ConversationSummaryMemory": ConversationSummaryMemory,
-    # "ChatMessageHistory": ChatMessageHistory,
-    # "ConversationStringBufferMemory": ConversationStringBufferMemory,
-    # "ReadOnlySharedMemory": ReadOnlySharedMemory,
-}
-
-
-## Chain
-# from langchain.chains.loading import type_to_loader_dict
-# from langchain.chains.conversation.base import ConversationChain
-
-# chain_type_to_cls_dict = type_to_loader_dict
-# chain_type_to_cls_dict["conversation_chain"] = ConversationChain
-
-toolkit_type_to_loader_dict: dict[str, Any] = {
-    toolkit_name: import_class(f"langchain.agents.agent_toolkits.{toolkit_name}")
-    # if toolkit_name is lower case it is a loader
-    for toolkit_name in agent_toolkits.__all__
-    if toolkit_name.islower()
-}
-
-toolkit_type_to_cls_dict: dict[str, Any] = {
-    toolkit_name: import_class(f"langchain.agents.agent_toolkits.{toolkit_name}")
-    # if toolkit_name is not lower case it is a class
-    for toolkit_name in agent_toolkits.__all__
-    if not toolkit_name.islower()
-}
-
-
-wrapper_type_to_cls_dict: dict[str, Any] = {
-    wrapper.__name__: wrapper for wrapper in [requests.RequestsWrapper]
-}
-
-## Embeddings
-from langchain.embeddings import (
-    CohereEmbeddings,
-    FakeEmbeddings,
-    HuggingFaceEmbeddings,
-    HuggingFaceHubEmbeddings,
-    HuggingFaceInstructEmbeddings,
-    OpenAIEmbeddings,
-    SelfHostedEmbeddings,
-    SelfHostedHuggingFaceEmbeddings,
-    SelfHostedHuggingFaceInstructEmbeddings,
-    # SagemakerEndpointEmbeddings,
-    TensorflowHubEmbeddings,
-)
-
-embedding_type_to_cls_dict = {
-    "OpenAIEmbeddings": OpenAIEmbeddings,
-    "HuggingFaceEmbeddings": HuggingFaceEmbeddings,
-    "CohereEmbeddings": CohereEmbeddings,
-    "HuggingFaceHubEmbeddings": HuggingFaceHubEmbeddings,
-    "TensorflowHubEmbeddings": TensorflowHubEmbeddings,
-    # "SagemakerEndpointEmbeddings": SagemakerEndpointEmbeddings,
-    "HuggingFaceInstructEmbeddings": HuggingFaceInstructEmbeddings,
-    "SelfHostedEmbeddings": SelfHostedEmbeddings,
-    "SelfHostedHuggingFaceEmbeddings": SelfHostedHuggingFaceEmbeddings,
-    "SelfHostedHuggingFaceInstructEmbeddings": SelfHostedHuggingFaceInstructEmbeddings,
-    "FakeEmbeddings": FakeEmbeddings,
-}
-
-## Vector Stores
-from langchain.vectorstores import (
-    FAISS,
-    AtlasDB,
-    Chroma,
-    DeepLake,
-    ElasticVectorSearch,
-    Milvus,
-    OpenSearchVectorSearch,
-    Pinecone,
-    Qdrant,
-    VectorStore,
-    Weaviate,
-)
-
-vectorstores_type_to_cls_dict = {
-    "ElasticVectorSearch": ElasticVectorSearch,
-    "FAISS": FAISS,
-    "VectorStore": VectorStore,
-    "Pinecone": Pinecone,
-    "Weaviate": Weaviate,
-    "Qdrant": Qdrant,
-    "Milvus": Milvus,
-    "Chroma": Chroma,
-    "OpenSearchVectorSearch": OpenSearchVectorSearch,
-    "AtlasDB": AtlasDB,
-    "DeepLake": DeepLake,
-}
-
 ## Document Loaders
-
 from langchain.document_loaders import (
     AirbyteJSONLoader,
     AZLyricsLoader,
@@ -173,6 +65,122 @@ from langchain.document_loaders import (
     YoutubeLoader,
 )
 
+## Embeddings
+from langchain.embeddings import (
+    CohereEmbeddings,
+    FakeEmbeddings,
+    HuggingFaceEmbeddings,
+    HuggingFaceHubEmbeddings,
+    HuggingFaceInstructEmbeddings,
+    OpenAIEmbeddings,
+    SelfHostedEmbeddings,
+    SelfHostedHuggingFaceEmbeddings,
+    SelfHostedHuggingFaceInstructEmbeddings,
+    # SagemakerEndpointEmbeddings,
+    TensorflowHubEmbeddings,
+)
+
+## Vector Stores
+from langchain.vectorstores import (
+    FAISS,
+    AtlasDB,
+    Chroma,
+    DeepLake,
+    ElasticVectorSearch,
+    Milvus,
+    OpenSearchVectorSearch,
+    Pinecone,
+    Qdrant,
+    VectorStore,
+    Weaviate,
+)
+
+## Toolkits
+from langflow.interface.importing.utils import import_class
+
+## LLM
+
+llm_type_to_cls_dict = llms.type_to_cls_dict
+llm_type_to_cls_dict["openai-chat"] = ChatOpenAI  # type: ignore
+
+
+## Memory
+
+memory_type_to_cls_dict: dict[str, Any] = {
+    # "CombinedMemory": CombinedMemory,
+    # "ConversationBufferWindowMemory": ConversationBufferWindowMemory,
+    # "ConversationBufferMemory": ConversationBufferMemory,
+    # "SimpleMemory": SimpleMemory,
+    # "ConversationSummaryBufferMemory": ConversationSummaryBufferMemory,
+    # "ConversationKGMemory": ConversationKGMemory,
+    # "ConversationEntityMemory": ConversationEntityMemory,
+    # "ConversationSummaryMemory": ConversationSummaryMemory,
+    # "ChatMessageHistory": ChatMessageHistory,
+    # "ConversationStringBufferMemory": ConversationStringBufferMemory,
+    # "ReadOnlySharedMemory": ReadOnlySharedMemory,
+}
+
+
+## Chain
+# from langchain.chains.loading import type_to_loader_dict
+# from langchain.chains.conversation.base import ConversationChain
+
+# chain_type_to_cls_dict = type_to_loader_dict
+# chain_type_to_cls_dict["conversation_chain"] = ConversationChain
+
+toolkit_type_to_loader_dict: dict[str, Any] = {
+    toolkit_name: import_class(f"langchain.agents.agent_toolkits.{toolkit_name}")
+    # if toolkit_name is lower case it is a loader
+    for toolkit_name in agent_toolkits.__all__
+    if toolkit_name.islower()
+}
+
+toolkit_type_to_cls_dict: dict[str, Any] = {
+    toolkit_name: import_class(f"langchain.agents.agent_toolkits.{toolkit_name}")
+    # if toolkit_name is not lower case it is a class
+    for toolkit_name in agent_toolkits.__all__
+    if not toolkit_name.islower()
+}
+
+
+wrapper_type_to_cls_dict: dict[str, Any] = {
+    wrapper.__name__: wrapper for wrapper in [requests.RequestsWrapper]
+}
+
+## Embeddings
+
+embedding_type_to_cls_dict = {
+    "OpenAIEmbeddings": OpenAIEmbeddings,
+    "HuggingFaceEmbeddings": HuggingFaceEmbeddings,
+    "CohereEmbeddings": CohereEmbeddings,
+    "HuggingFaceHubEmbeddings": HuggingFaceHubEmbeddings,
+    "TensorflowHubEmbeddings": TensorflowHubEmbeddings,
+    # "SagemakerEndpointEmbeddings": SagemakerEndpointEmbeddings,
+    "HuggingFaceInstructEmbeddings": HuggingFaceInstructEmbeddings,
+    "SelfHostedEmbeddings": SelfHostedEmbeddings,
+    "SelfHostedHuggingFaceEmbeddings": SelfHostedHuggingFaceEmbeddings,
+    "SelfHostedHuggingFaceInstructEmbeddings": SelfHostedHuggingFaceInstructEmbeddings,
+    "FakeEmbeddings": FakeEmbeddings,
+}
+
+## Vector Stores
+
+vectorstores_type_to_cls_dict = {
+    "ElasticVectorSearch": ElasticVectorSearch,
+    "FAISS": FAISS,
+    "VectorStore": VectorStore,
+    "Pinecone": Pinecone,
+    "Weaviate": Weaviate,
+    "Qdrant": Qdrant,
+    "Milvus": Milvus,
+    "Chroma": Chroma,
+    "OpenSearchVectorSearch": OpenSearchVectorSearch,
+    "AtlasDB": AtlasDB,
+    "DeepLake": DeepLake,
+}
+
+## Document Loaders
+
 documentloaders_type_to_cls_dict = {
     "UnstructuredFileLoader": UnstructuredFileLoader,
     "UnstructuredFileIOLoader": UnstructuredFileIOLoader,

src/backend/langflow/interface/importing/utils.py

@@ -9,7 +9,6 @@ from langchain.chains.base import Chain
 from langchain.llms.base import BaseLLM
 from langchain.tools import BaseTool
 
-from langflow.interface.agents.custom import CUSTOM_AGENTS
 from langflow.interface.tools.util import get_tool_by_name
 
 

src/backend/langflow/interface/loading.py

@@ -29,7 +29,7 @@ def instantiate_class(node_type: str, base_type: str, params: Dict) -> Any:
     """Instantiate class from module type and key, and params"""
     if node_type in CUSTOM_AGENTS:
         if custom_agent := CUSTOM_AGENTS.get(node_type):
-            return custom_agent.initialize(**params)
+            return custom_agent.initialize(**params)  # type: ignore
 
     class_object = import_by_type(_type=base_type, name=node_type)
 

src/backend/langflow/interface/wrappers/base.py

@@ -2,9 +2,7 @@ from typing import Dict, List
 
 from langchain import requests
 
-from langflow.custom.customs import get_custom_nodes
 from langflow.interface.base import LangChainTypeCreator
-from langflow.settings import settings
 from langflow.utils.util import build_template_from_class
 
 

tests/test_cache.py

@@ -1,12 +1,10 @@
-import hashlib
 import json
 import tempfile
 from pathlib import Path
 
-import dill
 import pytest
-from langflow.cache.utils import PREFIX, compute_hash, load_cache, save_cache
-from langflow.interface.run import load_langchain_object, process_graph
+from langflow.cache.utils import PREFIX, compute_hash
+from langflow.interface.run import load_langchain_object
 
 
 def get_graph(_type="basic"):

tests/test_creators.py

@@ -10,7 +10,7 @@ def sample_lang_chain_type_creator() -> LangChainTypeCreator:
     class SampleLangChainTypeCreator(LangChainTypeCreator):
         type_name: str = "test_type"
 
-        def type_to_loader_dict(self) -> Dict:
+        def type_to_loader_dict(self) -> Dict:  # type: ignore
             return {"test_type": "TestClass"}
 
         def to_list(self) -> List[str]:

tests/test_frontend_nodes.py

@@ -1,8 +1,4 @@
-from typing import Dict, List
-
 import pytest
-from langflow.interface.agents.base import AgentCreator
-from langflow.interface.base import LangChainTypeCreator
 from langflow.template.base import FrontendNode, Template, TemplateField
 
 
@@ -28,16 +24,16 @@ def sample_frontend_node(sample_template: Template) -> FrontendNode:
 
 def test_template_field_defaults(sample_template_field: TemplateField):
     assert sample_template_field.field_type == "str"
-    assert sample_template_field.required == False
+    assert sample_template_field.required is False
     assert sample_template_field.placeholder == ""
-    assert sample_template_field.is_list == False
-    assert sample_template_field.show == True
-    assert sample_template_field.multiline == False
-    assert sample_template_field.value == None
+    assert sample_template_field.is_list is False
+    assert sample_template_field.show is True
+    assert sample_template_field.multiline is False
+    assert sample_template_field.value is None
     assert sample_template_field.suffixes == []
     assert sample_template_field.file_types == []
-    assert sample_template_field.content == None
-    assert sample_template_field.password == False
+    assert sample_template_field.content is None
+    assert sample_template_field.password is False
     assert sample_template_field.name == "test_field"
 
 

tests/test_template.py

@@ -1,5 +1,4 @@
 import importlib
-import re
 from typing import Dict, List, Optional
 
 import pytest