[autofix.ci] apply automated fixes

src/backend/base/langflow/base/agents/agent.py

@@ -1,15 +1,11 @@
 from abc import abstractmethod
 from langchain.agents.agent import RunnableAgent
 
-from langchain.agents import AgentExecutor, BaseMultiActionAgent, BaseSingleActionAgent
-from langchain_core.messages import BaseMessage
+from langchain.agents import AgentExecutor
 from langchain_core.runnables import Runnable
 
-from langflow.base.agents.utils import data_to_messages, get_agents_list
-from langflow.custom import CustomComponent, Component
-from langflow.field_typing import Text, Tool
+from langflow.custom import Component
 from langflow.inputs import BoolInput, IntInput, HandleInput
-from langflow.schema import Data
 from langflow.template import Output
 
 
@@ -64,10 +60,7 @@ def get_agent_kwargs(self, flatten: bool = False) -> dict:
                 **base,
                 **agent_kwargs,
             }
-        return {
-            **base,
-            "agent_executor_kwargs": agent_kwargs
-        }
+        return {**base, "agent_executor_kwargs": agent_kwargs}
 
 
 class LCToolsAgentComponent(LCAgentComponent):
@@ -78,20 +71,15 @@ class LCToolsAgentComponent(LCAgentComponent):
             input_types=["Tool"],
             is_list=True,
         ),
-        HandleInput(
-            name="llm",
-            display_name="Language Model",
-            input_types=["LanguageModel"],
-            required=True
-        ),
+        HandleInput(name="llm", display_name="Language Model", input_types=["LanguageModel"], required=True),
     ]
 
     def build_agent(self) -> AgentExecutor:
         agent = self.creat_agent_runnable()
         return AgentExecutor.from_agent_and_tools(
             agent=RunnableAgent(runnable=agent, input_keys_arg=["input"], output_keys_arg=["output"]),
             tools=self.tools,
-            **self.get_agent_kwargs(flatten=True)
+            **self.get_agent_kwargs(flatten=True),
         )
 
     @abstractmethod

src/backend/base/langflow/base/vectorstores/model.py

@@ -8,7 +8,6 @@
 from langflow.helpers.data import docs_to_data
 from langflow.io import Output
 from langflow.schema import Data
-from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo
 
 
 class LCVectorStoreComponent(Component):
@@ -28,7 +27,7 @@ class LCVectorStoreComponent(Component):
             display_name="Vector Store",
             name="vector_store",
             method="cast_vector_store",
-        )
+        ),
     ]
 
     def _validate_outputs(self):

src/backend/base/langflow/components/agents/CSVAgent.py

@@ -3,7 +3,6 @@
 from langflow.base.agents.agent import LCAgentComponent
 from langflow.field_typing import AgentExecutor
 from langflow.inputs import HandleInput, FileInput, DropdownInput
-from langflow.template import Output
 
 
 class CSVAgentComponent(LCAgentComponent):
@@ -13,32 +12,16 @@ class CSVAgentComponent(LCAgentComponent):
     name = "CSVAgent"
 
     inputs = LCAgentComponent._base_inputs + [
-        FileInput(
-            name="path",
-            display_name="File Path",
-            file_types=["csv"],
-            required=True
-        ),
-        HandleInput(
-            name="llm",
-            display_name="Language Model",
-            input_types=["LanguageModel"],
-            required=True
-        ),
+        FileInput(name="path", display_name="File Path", file_types=["csv"], required=True),
+        HandleInput(name="llm", display_name="Language Model", input_types=["LanguageModel"], required=True),
         DropdownInput(
             name="agent_type",
             display_name="Agent Type",
             advanced=True,
             options=["zero-shot-react-description", "openai-functions", "openai-tools"],
             value="openai-tools",
         ),
-
     ]
 
     def build_agent(self) -> AgentExecutor:
-        return create_csv_agent(
-            llm=self.llm,
-            path=self.path,
-            agent_type=self.agent_type,
-            **self.get_agent_kwargs()
-        )
+        return create_csv_agent(llm=self.llm, path=self.path, agent_type=self.agent_type, **self.get_agent_kwargs())

src/backend/base/langflow/components/agents/JsonAgent.py

@@ -7,9 +7,7 @@
 from langchain_community.tools.json.tool import JsonSpec
 
 from langflow.base.agents.agent import LCAgentComponent
-from langflow.custom import Component
-from langflow.inputs import BoolInput, HandleInput, FileInput
-from langflow.template import Output
+from langflow.inputs import HandleInput, FileInput
 
 
 class JsonAgentComponent(LCAgentComponent):
@@ -18,19 +16,10 @@ class JsonAgentComponent(LCAgentComponent):
     name = "JsonAgent"
 
     inputs = LCAgentComponent._base_inputs + [
-        FileInput(
-            name="path",
-            display_name="File Path",
-            file_types=["json", "yaml", "yml"],
-            required=True
-        ),
-        HandleInput(
-            name="llm",
-            display_name="Language Model",
-            input_types=["LanguageModel"],
-            required=True
-        )
+        FileInput(name="path", display_name="File Path", file_types=["json", "yaml", "yml"], required=True),
+        HandleInput(name="llm", display_name="Language Model", input_types=["LanguageModel"], required=True),
     ]
+
     def build_agent(self) -> AgentExecutor:
         if self.path.endswith("yaml") or self.path.endswith("yml"):
             yaml_dict = yaml.load(open(self.path, "r"), Loader=yaml.FullLoader)
@@ -39,8 +28,4 @@ def build_agent(self) -> AgentExecutor:
             spec = JsonSpec.from_file(Path(self.path))
         toolkit = JsonToolkit(spec=spec)
 
-        return create_json_agent(
-            llm=self.llm,
-            toolkit=toolkit,
-            **self.get_agent_kwargs()
-        )
+        return create_json_agent(llm=self.llm, toolkit=toolkit, **self.get_agent_kwargs())

src/backend/base/langflow/components/agents/OpenAIToolsAgent.py

@@ -1,7 +1,7 @@
 from langchain.agents import create_openai_tools_agent
 from langchain_core.prompts import ChatPromptTemplate, PromptTemplate, HumanMessagePromptTemplate
 
-from langflow.base.agents.agent import LCAgentComponent, LCToolsAgentComponent
+from langflow.base.agents.agent import LCToolsAgentComponent
 from langflow.inputs import MultilineInput
 
 
@@ -20,10 +20,7 @@ class OpenAIToolsAgentComponent(LCToolsAgentComponent):
             value="You are a helpful assistant",
         ),
         MultilineInput(
-            name="user_prompt",
-            display_name="Prompt",
-            info="This prompt must contain 'input' key.",
-            value="{input}"
+            name="user_prompt", display_name="Prompt", info="This prompt must contain 'input' key.", value="{input}"
         ),
     ]
 
@@ -32,8 +29,8 @@ def creat_agent_runnable(self):
             raise ValueError("Prompt must contain 'input' key.")
         messages = [
             ("system", self.system_prompt),
-            HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['input'], template=self.user_prompt)),
-            ("placeholder", "{agent_scratchpad}")
+            HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=["input"], template=self.user_prompt)),
+            ("placeholder", "{agent_scratchpad}"),
         ]
         prompt = ChatPromptTemplate.from_messages(messages)
-        return create_openai_tools_agent(self.llm, self.tools, prompt)
+        return create_openai_tools_agent(self.llm, self.tools, prompt)

src/backend/base/langflow/components/agents/OpenAPIAgent.py

@@ -3,12 +3,10 @@
 import yaml
 from langchain.agents import AgentExecutor
 from langchain_community.agent_toolkits import create_openapi_agent
-from langchain_community.agent_toolkits.json.toolkit import JsonToolkit
 from langchain_community.tools.json.tool import JsonSpec
-from langchain_community.agent_toolkits.openapi.toolkit import BaseToolkit, OpenAPIToolkit
+from langchain_community.agent_toolkits.openapi.toolkit import OpenAPIToolkit
 
 from langflow.base.agents.agent import LCAgentComponent
-from langflow.custom import Component
 from langflow.inputs import BoolInput, HandleInput, FileInput
 from langchain_community.utilities.requests import TextRequestsWrapper
 
@@ -19,25 +17,11 @@ class OpenAPIAgentComponent(LCAgentComponent):
     name = "OpenAPIAgent"
 
     inputs = LCAgentComponent._base_inputs + [
-        FileInput(
-            name="path",
-            display_name="File Path",
-            file_types=["json", "yaml", "yml"],
-            required=True
-        ),
-        HandleInput(
-            name="llm",
-            display_name="Language Model",
-            input_types=["LanguageModel"],
-            required=True
-        ),
-        BoolInput(
-            name="allow_dangerous_requests",
-            display_name="Allow Dangerous Requests",
-            value=False,
-            required=True
-        )
+        FileInput(name="path", display_name="File Path", file_types=["json", "yaml", "yml"], required=True),
+        HandleInput(name="llm", display_name="Language Model", input_types=["LanguageModel"], required=True),
+        BoolInput(name="allow_dangerous_requests", display_name="Allow Dangerous Requests", value=False, required=True),
     ]
+
     def build_agent(self) -> AgentExecutor:
         if self.path.endswith("yaml") or self.path.endswith("yml"):
             yaml_dict = yaml.load(open(self.path, "r"), Loader=yaml.FullLoader)
@@ -55,8 +39,4 @@ def build_agent(self) -> AgentExecutor:
         agent_args = self.get_agent_kwargs()
         agent_args["max_iterations"] = agent_args["agent_executor_kwargs"]["max_iterations"]
         del agent_args["agent_executor_kwargs"]["max_iterations"]
-        return create_openapi_agent(
-            llm=self.llm,
-            toolkit=toolkit,
-            **agent_args
-        )
+        return create_openapi_agent(llm=self.llm, toolkit=toolkit, **agent_args)

src/backend/base/langflow/components/agents/SQLAgent.py

@@ -13,17 +13,8 @@ class SQLAgentComponent(LCAgentComponent):
     name = "SQLAgent"
 
     inputs = LCAgentComponent._base_inputs + [
-        MessageTextInput(
-            name="database_uri",
-            display_name="Database URI",
-            required=True
-        ),
-        HandleInput(
-            name="llm",
-            display_name="Language Model",
-            input_types=["LanguageModel"],
-            required=True
-        )
+        MessageTextInput(name="database_uri", display_name="Database URI", required=True),
+        HandleInput(name="llm", display_name="Language Model", input_types=["LanguageModel"], required=True),
     ]
 
     def build_agent(self) -> AgentExecutor:
@@ -32,8 +23,4 @@ def build_agent(self) -> AgentExecutor:
         agent_args = self.get_agent_kwargs()
         agent_args["max_iterations"] = agent_args["agent_executor_kwargs"]["max_iterations"]
         del agent_args["agent_executor_kwargs"]["max_iterations"]
-        return create_sql_agent(
-            llm=self.llm,
-            toolkit=toolkit,
-            **agent_args
-        )
+        return create_sql_agent(llm=self.llm, toolkit=toolkit, **agent_args)

src/backend/base/langflow/components/agents/ToolCallingAgent.py

@@ -1,6 +1,6 @@
 from langchain.agents import create_tool_calling_agent
 from langchain_core.prompts import ChatPromptTemplate, PromptTemplate, HumanMessagePromptTemplate
-from langflow.base.agents.agent import LCAgentComponent, LCToolsAgentComponent
+from langflow.base.agents.agent import LCToolsAgentComponent
 from langflow.inputs import MultilineInput
 
 
@@ -19,21 +19,17 @@ class ToolCallingAgentComponent(LCToolsAgentComponent):
             value="You are a helpful assistant",
         ),
         MultilineInput(
-            name="user_prompt",
-            display_name="Prompt",
-            info="This prompt must contain 'input' key.",
-            value="{input}"
+            name="user_prompt", display_name="Prompt", info="This prompt must contain 'input' key.", value="{input}"
         ),
     ]
 
-
     def creat_agent_runnable(self):
         if "input" not in self.user_prompt:
             raise ValueError("Prompt must contain 'input' key.")
         messages = [
             ("system", self.system_prompt),
-            HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['input'], template=self.user_prompt)),
-            ("placeholder", "{agent_scratchpad}")
+            HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=["input"], template=self.user_prompt)),
+            ("placeholder", "{agent_scratchpad}"),
         ]
         prompt = ChatPromptTemplate.from_messages(messages)
-        return create_tool_calling_agent(self.llm, self.tools, prompt)
+        return create_tool_calling_agent(self.llm, self.tools, prompt)

src/backend/base/langflow/components/agents/VectorStoreAgent.py

@@ -1,5 +1,3 @@
-from typing import Callable, Union
-
 from langchain.agents import AgentExecutor, create_vectorstore_agent
 from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreToolkit
 from langflow.base.agents.agent import LCAgentComponent
@@ -12,21 +10,10 @@ class VectorStoreAgentComponent(LCAgentComponent):
     name = "VectorStoreAgent"
 
     inputs = LCAgentComponent._base_inputs + [
-        HandleInput(
-            name="llm",
-            display_name="Language Model",
-            input_types=["LanguageModel"],
-            required=True
-        ),
-        HandleInput(
-            name="vectorstore",
-            display_name="Vector Store",
-            input_types=["VectorStoreInfo"],
-            required=True
-        )
+        HandleInput(name="llm", display_name="Language Model", input_types=["LanguageModel"], required=True),
+        HandleInput(name="vectorstore", display_name="Vector Store", input_types=["VectorStoreInfo"], required=True),
     ]
 
     def build_agent(self) -> AgentExecutor:
         toolkit = VectorStoreToolkit(vectorstore_info=self.vectorstore, llm=self.llm)
-        return create_vectorstore_agent(llm=self.llm, toolkit=toolkit,
-            **self.get_agent_kwargs())
+        return create_vectorstore_agent(llm=self.llm, toolkit=toolkit, **self.get_agent_kwargs())

src/backend/base/langflow/components/agents/VectorStoreRouterAgent.py

@@ -1,8 +1,8 @@
 from langchain.agents import create_vectorstore_router_agent
-from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreRouterToolkit, VectorStoreInfo
+from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreRouterToolkit
 
 from langflow.base.agents.agent import LCAgentComponent
-from langchain.agents import AgentExecutor, BaseMultiActionAgent, BaseSingleActionAgent
+from langchain.agents import AgentExecutor
 from langflow.inputs import HandleInput
 
 
@@ -12,22 +12,16 @@ class VectorStoreRouterAgentComponent(LCAgentComponent):
     name = "VectorStoreRouterAgent"
 
     inputs = LCAgentComponent._base_inputs + [
-        HandleInput(
-            name="llm",
-            display_name="Language Model",
-            input_types=["LanguageModel"],
-            required=True
-        ),
+        HandleInput(name="llm", display_name="Language Model", input_types=["LanguageModel"], required=True),
         HandleInput(
             name="vectorstores",
             display_name="Vector Stores",
             input_types=["VectorStoreInfo"],
             is_list=True,
-            required=True
-        )
+            required=True,
+        ),
     ]
 
     def build_agent(self) -> AgentExecutor:
         toolkit = VectorStoreRouterToolkit(vectorstores=self.vectorstores, llm=self.llm)
-        return create_vectorstore_router_agent(llm=self.llm, toolkit=toolkit,
-            **self.get_agent_kwargs())
+        return create_vectorstore_router_agent(llm=self.llm, toolkit=toolkit, **self.get_agent_kwargs())