Add new files and modify existing files

src/backend/base/langflow/api/v1/chat.py

@@ -1,7 +1,7 @@
 import time
 import uuid
 from functools import partial
-from typing import TYPE_CHECKING, Annotated, Callable, Optional
+from typing import TYPE_CHECKING, Annotated, Optional
 
 from fastapi import APIRouter, BackgroundTasks, Body, Depends, HTTPException
 from fastapi.responses import StreamingResponse

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

@@ -1,10 +1,11 @@
-from typing import List, Optional, Union
+from typing import List, Union
+
+from langchain.agents import AgentExecutor, BaseMultiActionAgent, BaseSingleActionAgent
 
-from langchain.agents.agent import AgentExecutor, BaseMultiActionAgent, BaseSingleActionAgent
 from langchain_core.runnables import Runnable
 
+from langflow.custom import CustomComponent
 from langflow.field_typing import BaseMemory, Text, Tool
-from langflow.interface.custom.custom_component import CustomComponent
 
 
 class LCAgentComponent(CustomComponent):
@@ -44,7 +45,7 @@ class LCAgentComponent(CustomComponent):
         inputs: str,
         input_variables: list[str],
         tools: List[Tool],
-        memory: Optional[BaseMemory] = None,
+        memory: BaseMemory = None,
         handle_parsing_errors: bool = True,
         output_key: str = "output",
     ) -> Text:
@@ -52,11 +53,7 @@ class LCAgentComponent(CustomComponent):
             runnable = agent
         else:
             runnable = AgentExecutor.from_agent_and_tools(
-                agent=agent,  # type: ignore
+                agent=agent, tools=tools, verbose=True, memory=memory, handle_parsing_errors=handle_parsing_errors
-                tools=tools,
-                verbose=True,
-                memory=memory,
-                handle_parsing_errors=handle_parsing_errors,
             )
         input_dict = {"input": inputs}
         for var in input_variables:
@@ -72,5 +69,4 @@ class LCAgentComponent(CustomComponent):
             else:
                 raise ValueError("Output key not found in result. Tried 'output'.")
 
-        output: str = result.get("output")
+        return result.get("output")
-        return output

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

@@ -1,7 +1,6 @@
 from typing import Any, Callable, Dict, List, Optional, Union
 
 from langchain_openai.embeddings.base import OpenAIEmbeddings
-from pydantic.v1.types import SecretStr
 
 from langflow.field_typing import NestedDict
 from langflow.interface.custom.custom_component import CustomComponent

src/backend/base/langflow/components/experimental/FlowTool.py

@@ -1,4 +1,4 @@
-from typing import Any, List, Optional
+from typing import Any, List, Optional, Text
 
 from langchain_core.tools import StructuredTool
 from loguru import logger
@@ -8,7 +8,6 @@ from langflow.field_typing import Tool
 from langflow.graph.graph.base import Graph
 from langflow.helpers.flow import build_function_and_schema
 from langflow.schema.dotdict import dotdict
-from langflow.schema.schema import Record
 
 
 class FlowToolComponent(CustomComponent):
@@ -20,7 +19,7 @@ class FlowToolComponent(CustomComponent):
         flow_records = self.list_flows()
         return [flow_record.data["name"] for flow_record in flow_records]
 
-    def get_flow(self, flow_name: str) -> Optional[Record]:
+    def get_flow(self, flow_name: str) -> Optional[Text]:
         """
         Retrieves a flow by its name.
 
@@ -83,4 +82,4 @@ class FlowToolComponent(CustomComponent):
         description_repr = repr(tool.description).strip("'")
         args_str = "\n".join([f"- {arg_name}: {arg_data['description']}" for arg_name, arg_data in tool.args.items()])
         self.status = f"{description_repr}\nArguments:\n{args_str}"
-        return tool  # type: ignore
+        return tool

src/backend/base/langflow/components/experimental/SubFlow.py

@@ -1,10 +1,11 @@
-from typing import Any, List, Optional, Tuple
+from typing import Any, List, Optional
 
 from loguru import logger
 
 from langflow.custom import CustomComponent
 from langflow.graph.graph.base import Graph
 from langflow.graph.schema import ResultData, RunOutputs
+from langflow.graph.vertex.base import Vertex
 from langflow.schema import Record
 from langflow.schema.dotdict import dotdict
 from langflow.template.field.base import TemplateField
@@ -50,7 +51,7 @@ class SubFlowComponent(CustomComponent):
 
         return build_config
 
-    def get_flow_inputs(self, graph: Graph) -> List[Record]:
+    def get_flow_inputs(self, graph: Graph) -> List[Vertex]:
         inputs = []
         for vertex in graph.vertices:
             if vertex.is_input:
@@ -58,13 +59,13 @@ class SubFlowComponent(CustomComponent):
         logger.debug(inputs)
         return inputs
 
-    def add_inputs_to_build_config(self, inputs: List[Tuple], build_config: dotdict):
+    def add_inputs_to_build_config(self, inputs: List[Vertex], build_config: dotdict):
         new_fields: list[TemplateField] = []
-        for input_id, input_display_name, input_description in inputs:
+        for vertex in inputs:
             field = TemplateField(
-                display_name=input_display_name,
+                display_name=vertex.display_name,
-                name=input_id,
+                name=vertex.id,
-                info=input_description,
+                info=vertex.description,
                 field_type="str",
                 default=None,
             )

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

@@ -5,8 +5,6 @@ from langflow.interface.base import LangChainTypeCreator
 from langflow.interface.tools.constants import ALL_TOOLS_NAMES, CUSTOM_TOOLS, FILE_TOOLS, OTHER_TOOLS
 from langflow.interface.tools.util import get_tool_params
 from langflow.legacy_custom import customs
-from langflow.interface.tools.util import get_tool_params
-from langflow.legacy_custom import customs
 from langflow.services.deps import get_settings_service
 from langflow.template.field.base import TemplateField
 from langflow.template.template.base import Template

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

@@ -1,70 +0,0 @@
-from typing import List, Union
-
-from langchain.agents import (AgentExecutor, BaseMultiActionAgent,
-
-from langflow.custom import CustomComponent
-from langflow.field_typing import BaseMemory, Text, Tool
-
-
-class LCAgentComponent(CustomComponent):
-    def build_config(self):
-        return {
-            "lc": {
-                "display_name": "LangChain",
-                "info": "The LangChain to interact with.",
-            },
-            "handle_parsing_errors": {
-                "display_name": "Handle Parsing Errors",
-                "info": "If True, the agent will handle parsing errors. If False, the agent will raise an error.",
-                "advanced": True,
-            },
-            "output_key": {
-                "display_name": "Output Key",
-                "info": "The key to use to get the output from the agent.",
-                "advanced": True,
-            },
-            "memory": {
-                "display_name": "Memory",
-                "info": "Memory to use for the agent.",
-            },
-            "tools": {
-                "display_name": "Tools",
-                "info": "Tools the agent can use.",
-            },
-            "input_value": {
-                "display_name": "Input",
-                "info": "Input text to pass to the agent.",
-            },
-        }
-
-    async def run_agent(
-        self,
-        agent: Union[BaseSingleActionAgent, BaseMultiActionAgent, AgentExecutor],
-        inputs: str,
-        input_variables: list[str],
-        tools: List[Tool],
-        memory: BaseMemory = None,
-        handle_parsing_errors: bool = True,
-        output_key: str = "output",
-    ) -> Text:
-        if isinstance(agent, AgentExecutor):
-            runnable = agent
-        else:
-            runnable = AgentExecutor.from_agent_and_tools(
-                agent=agent, tools=tools, verbose=True, memory=memory, handle_parsing_errors=handle_parsing_errors
-            )
-        input_dict = {"input": inputs}
-        for var in input_variables:
-            if var not in ["agent_scratchpad", "input"]:
-                input_dict[var] = ""
-        result = await runnable.ainvoke(input_dict)
-        self.status = result
-        if output_key in result:
-            return result.get(output_key)
-        elif "output" not in result:
-            if output_key != "output":
-                raise ValueError(f"Output key not found in result. Tried '{output_key}' and 'output'.")
-            else:
-                raise ValueError("Output key not found in result. Tried 'output'.")
-
-        return result.get("output")

src/backend/langflow/components/experimental/FlowTool.py

@@ -1,84 +0,0 @@
-from typing import Any, List, Optional, Text
-
-from langchain_core.tools import StructuredTool
-from langflow.custom import CustomComponent
-from langflow.field_typing import Tool
-from langflow.graph.graph.base import Graph
-from langflow.helpers.flow import build_function_and_schema
-from langflow.schema.dotdict import dotdict
-from loguru import logger
-
-
-class FlowToolComponent(CustomComponent):
-    display_name = "Flow as Tool"
-    description = "Construct a Tool from a function that runs the loaded Flow."
-    field_order = ["flow_name", "name", "description", "return_direct"]
-
-    def get_flow_names(self) -> List[str]:
-        flow_records = self.list_flows()
-        return [flow_record.data["name"] for flow_record in flow_records]
-
-    def get_flow(self, flow_name: str) -> Optional[Text]:
-        """
-        Retrieves a flow by its name.
-
-        Args:
-            flow_name (str): The name of the flow to retrieve.
-
-        Returns:
-            Optional[Text]: The flow record if found, None otherwise.
-        """
-        flow_records = self.list_flows()
-        for flow_record in flow_records:
-            if flow_record.data["name"] == flow_name:
-                return flow_record
-        return None
-
-    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):
-        logger.debug(f"Updating build config with field value {field_value} and field name {field_name}")
-        if field_name == "flow_name":
-            build_config["flow_name"]["options"] = self.get_flow_names()
-
-        return build_config
-
-    def build_config(self):
-        return {
-            "flow_name": {
-                "display_name": "Flow Name",
-                "info": "The name of the flow to run.",
-                "options": [],
-                "real_time_refresh": True,
-                "refresh_button": True,
-            },
-            "name": {
-                "display_name": "Name",
-                "description": "The name of the tool.",
-            },
-            "description": {
-                "display_name": "Description",
-                "description": "The description of the tool.",
-            },
-            "return_direct": {
-                "display_name": "Return Direct",
-                "description": "Return the result directly from the Tool.",
-                "advanced": True,
-            },
-        }
-
-    async def build(self, flow_name: str, name: str, description: str, return_direct: bool = False) -> Tool:
-        flow_record = self.get_flow(flow_name)
-        if not flow_record:
-            raise ValueError("Flow not found.")
-        graph = Graph.from_payload(flow_record.data["data"])
-        dynamic_flow_function, schema = build_function_and_schema(flow_record, graph)
-        tool = StructuredTool.from_function(
-            coroutine=dynamic_flow_function,
-            name=name,
-            description=description,
-            return_direct=return_direct,
-            args_schema=schema,
-        )
-        description_repr = repr(tool.description).strip("'")
-        args_str = "\n".join([f"- {arg_name}: {arg_data['description']}" for arg_name, arg_data in tool.args.items()])
-        self.status = f"{description_repr}\nArguments:\n{args_str}"
-        return tool

src/backend/langflow/components/experimental/Schema.py

@@ -1,25 +0,0 @@
-from langflow.custom import CustomComponent
-
-
-class SchemaComponent(CustomComponent):
-    display_name = "Schema"
-    description = "Construct a Schema from a list of fields."
-
-    def build_config(self):
-        return {
-            "fields": {
-                "display_name": "Fields",
-                "info": "The fields to include in the schema.",
-            },
-            "name": {
-                "display_name": "Name",
-                "info": "The name of the schema.",
-            },
-        }
-
-    def build(self, name: str, fields: list[dict]):
-        # The idea for this component is to use create_model from pydantic to create a schema
-        # from a list of fields. This will be useful for creating schemas for the flow tool.
-        pass
-
-        # field is a simple list of dictionaries with the field name and

src/backend/langflow/components/tools/SearchAPITool.py

@@ -1,36 +0,0 @@
-from langchain_community.tools.searchapi import SearchAPIRun
-from langchain_community.utilities.searchapi import SearchApiAPIWrapper
-from langflow.custom import CustomComponent
-from langflow.field_typing import Tool
-
-
-class SearchApiToolComponent(CustomComponent):
-    display_name: str = "SearchApi Tool"
-    description: str = "Real-time search engine results API."
-    documentation: str = "https://www.searchapi.io/docs/google"
-    field_config = {
-        "engine": {
-            "display_name": "Engine",
-            "field_type": "str",
-            "info": "The search engine to use.",
-        },
-        "api_key": {
-            "display_name": "API Key",
-            "field_type": "str",
-            "required": True,
-            "password": True,
-            "info": "The API key to use SearchApi.",
-        },
-    }
-
-    def build(
-        self,
-        engine: str,
-        api_key: str,
-    ) -> Tool:
-        search_api_wrapper = SearchApiAPIWrapper(engine=engine, searchapi_api_key=api_key)
-
-        tool = SearchAPIRun(api_wrapper=search_api_wrapper)
-
-        self.status = tool
-        return tool