Fixed Sub Flow, Run Flow and Flow as Tool components (#1986)

* feat(langflow): add utility functions to build records from run outputs and result data for better code organization and reusability

* chore: Generate dynamic flow function with user ID parameter for better flow customization and tracking

* chore: Refactor build_records_from_run_outputs and build_records_from_result_data for better code organization and reusability

* chore: Update FlowToolComponent to include user ID parameter in build_function_and_schema method call

* chore: Add conditional check for result_data in build_records_from_run_outputs

* chore: Generate dynamic flow function with optional user ID parameter for better flow customization and tracking

* feat: Add user ID parameter to Graph.from_payload method

* chore: Add FlowTool class for flow processing and customization

* chore: Update FlowToolComponent to use get_flow_inputs instead of build_function_and_schema

* chore: Update FlowTool to handle optional user ID parameter

src/backend/base/langflow/base/flow_processing/utils.py

@@ -0,0 +1,67 @@
+from typing import List
+
+from langflow.graph.schema import ResultData, RunOutputs
+from langflow.schema.schema import Record
+
+
+def build_records_from_run_outputs(run_outputs: RunOutputs) -> List[Record]:
+    """
+    Build a list of records from the given RunOutputs.
+
+    Args:
+        run_outputs (RunOutputs): The RunOutputs object containing the output data.
+
+    Returns:
+        List[Record]: A list of records built from the RunOutputs.
+
+    """
+    if not run_outputs:
+        return []
+    records = []
+    for result_data in run_outputs.outputs:
+        if result_data:
+            records.extend(build_records_from_result_data(result_data))
+    return records
+
+
+def build_records_from_result_data(result_data: ResultData, get_final_results_only: bool = True) -> List[Record]:
+    """
+    Build a list of records from the given ResultData.
+
+    Args:
+        result_data (ResultData): The ResultData object containing the result data.
+        get_final_results_only (bool, optional): Whether to include only final results. Defaults to True.
+
+    Returns:
+        List[Record]: A list of records built from the ResultData.
+
+    """
+    messages = result_data.messages
+    if not messages:
+        return []
+    records = []
+    for message in messages:
+        message_dict = message if isinstance(message, dict) else message.model_dump()
+        if get_final_results_only:
+            result_data_dict = result_data.model_dump()
+            results = result_data_dict.get("results", {})
+            inner_result = results.get("result", {})
+        record = Record(data={"result": inner_result, "message": message_dict}, text_key="result")
+        records.append(record)
+    return records
+
+
+def format_flow_output_records(records: List[Record]) -> str:
+    """
+    Format the flow output records into a string.
+
+    Args:
+        records (List[Record]): The list of records to format.
+
+    Returns:
+        str: The formatted flow output records.
+
+    """
+    result = "Flow run output:\n"
+    results = "\n".join([record.result for record in records if record.data["message"]])
+    return result + results

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

@@ -0,0 +1,117 @@
+from typing import Any, List, Optional, Type
+
+from asyncer import syncify
+from langchain.tools import BaseTool
+from langchain_core.runnables import RunnableConfig
+from langchain_core.tools import ToolException
+from pydantic.v1 import BaseModel
+
+from langflow.base.flow_processing.utils import build_records_from_result_data, format_flow_output_records
+from langflow.graph.graph.base import Graph
+from langflow.graph.vertex.base import Vertex
+from langflow.helpers.flow import build_schema_from_inputs, get_arg_names, get_flow_inputs, run_flow
+
+
+class FlowTool(BaseTool):
+    name: str
+    description: str
+    graph: Optional[Graph] = None
+    flow_id: Optional[str] = None
+    user_id: Optional[str] = None
+    inputs: List["Vertex"] = []
+    get_final_results_only: bool = True
+
+    @property
+    def args(self) -> dict:
+        schema = self.get_input_schema()
+        return schema.schema()["properties"]
+
+    def get_input_schema(self, config: Optional[RunnableConfig] = None) -> Type[BaseModel]:
+        """The tool's input schema."""
+        if self.args_schema is not None:
+            return self.args_schema
+        elif self.graph is not None:
+            return build_schema_from_inputs(self.name, get_flow_inputs(self.graph))
+        else:
+            raise ToolException("No input schema available.")
+
+    def _run(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> str:
+        """Use the tool."""
+        args_names = get_arg_names(self.inputs)
+        if len(args_names) == len(args):
+            kwargs = {arg["arg_name"]: arg_value for arg, arg_value in zip(args_names, args)}
+        elif len(args_names) != len(args) and len(args) != 0:
+            raise ToolException(
+                "Number of arguments does not match the number of inputs. Pass keyword arguments instead."
+            )
+        tweaks = {arg["component_name"]: kwargs[arg["arg_name"]] for arg in args_names}
+
+        run_outputs = syncify(run_flow, raise_sync_error=False)(
+            tweaks={key: {"input_value": value} for key, value in tweaks.items()},
+            flow_id=self.flow_id,
+            user_id=self.user_id,
+        )
+        if not run_outputs:
+            return "No output"
+        run_output = run_outputs[0]
+
+        records = []
+        if run_output is not None:
+            for output in run_output.outputs:
+                if output:
+                    records.extend(
+                        build_records_from_result_data(output, get_final_results_only=self.get_final_results_only)
+                    )
+        return format_flow_output_records(records)
+
+    def validate_inputs(self, args_names: List[dict[str, str]], args: Any, kwargs: Any):
+        """Validate the inputs."""
+
+        if len(args) > 0 and len(args) != len(args_names):
+            raise ToolException(
+                "Number of positional arguments does not match the number of inputs. Pass keyword arguments instead."
+            )
+
+        if len(args) == len(args_names):
+            kwargs = {arg_name["arg_name"]: arg_value for arg_name, arg_value in zip(args_names, args)}
+
+        missing_args = [arg["arg_name"] for arg in args_names if arg["arg_name"] not in kwargs]
+        if missing_args:
+            raise ToolException(f"Missing required arguments: {', '.join(missing_args)}")
+
+        return kwargs
+
+    def build_tweaks_dict(self, args, kwargs):
+        args_names = get_arg_names(self.inputs)
+        kwargs = self.validate_inputs(args_names=args_names, args=args, kwargs=kwargs)
+        tweaks = {arg["component_name"]: kwargs[arg["arg_name"]] for arg in args_names}
+        return tweaks
+
+    async def _arun(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> str:
+        """Use the tool asynchronously."""
+        tweaks = self.build_tweaks_dict(args, kwargs)
+        run_outputs = await run_flow(
+            tweaks={key: {"input_value": value} for key, value in tweaks.items()},
+            flow_id=self.flow_id,
+            user_id=self.user_id,
+        )
+        if not run_outputs:
+            return "No output"
+        run_output = run_outputs[0]
+
+        records = []
+        if run_output is not None:
+            for output in run_output.outputs:
+                if output:
+                    records.extend(
+                        build_records_from_result_data(output, get_final_results_only=self.get_final_results_only)
+                    )
+        return format_flow_output_records(records)

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

@@ -1,14 +1,14 @@
 from typing import Any, List, Optional
 
-from asyncer import syncify
-from langchain_core.tools import StructuredTool
+from loguru import logger
+
+from langflow.base.tools.flow_tool import FlowTool
 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.helpers.flow import get_flow_inputs
 from langflow.schema.dotdict import dotdict
 from langflow.schema.schema import Record
-from loguru import logger
 
 
 class FlowToolComponent(CustomComponent):
@@ -68,18 +68,20 @@ class FlowToolComponent(CustomComponent):
         }
 
     async def build(self, flow_name: str, name: str, description: str, return_direct: bool = False) -> Tool:
+        FlowTool.update_forward_refs()
         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(
-            func=syncify(dynamic_flow_function, raise_sync_error=False),  # type: ignore
-            coroutine=dynamic_flow_function,
+        inputs = get_flow_inputs(graph)
+        tool = FlowTool(
             name=name,
             description=description,
+            graph=graph,
             return_direct=return_direct,
-            args_schema=schema,
+            inputs=inputs,
+            flow_id=str(flow_record.id),
+            user_id=str(self._user_id),
         )
         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()])

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

@@ -1,8 +1,9 @@
 from typing import Any, List, Optional
 
+from langflow.base.flow_processing.utils import build_records_from_run_outputs
 from langflow.custom import CustomComponent
 from langflow.field_typing import NestedDict, Text
-from langflow.graph.schema import ResultData
+from langflow.graph.schema import RunOutputs
 from langflow.schema import Record, dotdict
 
 
@@ -39,28 +40,17 @@ class RunFlowComponent(CustomComponent):
             },
         }
 
-    def build_records_from_result_data(self, result_data: ResultData) -> List[Record]:
-        messages = result_data.messages
-        if not messages:
-            return []
-        records = []
-        for message in messages:
-            message_dict = message if isinstance(message, dict) else message.model_dump()
-            record = Record(text=message_dict.get("text", ""), data={"result": result_data})
-            records.append(record)
-        return records
-
     async def build(self, input_value: Text, flow_name: str, tweaks: NestedDict) -> List[Record]:
-        results: List[Optional[ResultData]] = await self.run_flow(
+        results: List[Optional[RunOutputs]] = await self.run_flow(
             inputs={"input_value": input_value}, flow_name=flow_name, tweaks=tweaks
         )
         if isinstance(results, list):
             records = []
             for result in results:
                 if result:
-                    records.extend(self.build_records_from_result_data(result))
+                    records.extend(build_records_from_run_outputs(result))
         else:
-            records = self.build_records_from_result_data(results)
+            records = build_records_from_run_outputs()(results)
 
         self.status = records
         return records

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

@@ -2,9 +2,10 @@ from typing import Any, List, Optional
 
 from loguru import logger
 
+from langflow.base.flow_processing.utils import build_records_from_result_data
 from langflow.custom import CustomComponent
 from langflow.graph.graph.base import Graph
-from langflow.graph.schema import ResultData, RunOutputs
+from langflow.graph.schema import RunOutputs
 from langflow.graph.vertex.base import Vertex
 from langflow.helpers.flow import get_flow_inputs
 from langflow.schema import Record
@@ -92,21 +93,6 @@ class SubFlowComponent(CustomComponent):
             },
         }
 
-    def build_records_from_result_data(self, result_data: ResultData, get_final_results_only: bool) -> List[Record]:
-        messages = result_data.messages
-        if not messages:
-            return []
-        records = []
-        for message in messages:
-            message_dict = message if isinstance(message, dict) else message.model_dump()
-            if get_final_results_only:
-                result_data_dict = result_data.model_dump()
-                results = result_data_dict.get("results", {})
-                inner_result = results.get("result", {})
-            record = Record(data={"result": inner_result, "message": message_dict}, text_key="result")
-            records.append(record)
-        return records
-
     async def build(self, flow_name: str, get_final_results_only: bool = True, **kwargs) -> List[Record]:
         tweaks = {key: {"input_value": value} for key, value in kwargs.items()}
         run_outputs: List[Optional[RunOutputs]] = await self.run_flow(
@@ -121,7 +107,7 @@ class SubFlowComponent(CustomComponent):
         if run_output is not None:
             for output in run_output.outputs:
                 if output:
-                    records.extend(self.build_records_from_result_data(output, get_final_results_only))
+                    records.extend(build_records_from_result_data(output, get_final_results_only))
 
         self.status = records
         logger.debug(records)

src/backend/base/langflow/helpers/flow.py

@@ -1,10 +1,13 @@
 from typing import TYPE_CHECKING, Any, Awaitable, Callable, List, Optional, Tuple, Type, Union, cast
+from uuid import UUID
 
+from pydantic.v1 import BaseModel, Field, create_model
+from sqlmodel import select
+
+from langflow.graph.schema import RunOutputs
 from langflow.schema.schema import INPUT_FIELD_NAME, Record
 from langflow.services.database.models.flow.model import Flow
 from langflow.services.deps import session_scope
-from pydantic.v1 import BaseModel, Field, create_model
-from sqlmodel import select
 
 if TYPE_CHECKING:
     from langflow.graph.graph.base import Graph
@@ -51,7 +54,7 @@ async def load_flow(
         raise ValueError(f"Flow {flow_id} not found")
     if tweaks:
         graph_data = process_tweaks(graph_data=graph_data, tweaks=tweaks)
-    graph = Graph.from_payload(graph_data, flow_id=flow_id)
+    graph = Graph.from_payload(graph_data, flow_id=flow_id, user_id=user_id)
     return graph
 
 
@@ -67,25 +70,29 @@ async def run_flow(
     flow_id: Optional[str] = None,
     flow_name: Optional[str] = None,
     user_id: Optional[str] = None,
-) -> Any:
+) -> List[RunOutputs]:
     if user_id is None:
         raise ValueError("Session is invalid")
     graph = await load_flow(user_id, flow_id, flow_name, tweaks)
 
     if inputs is None:
         inputs = []
+    if isinstance(inputs, dict):
+        inputs = [inputs]
     inputs_list = []
     inputs_components = []
     types = []
     for input_dict in inputs:
         inputs_list.append({INPUT_FIELD_NAME: cast(str, input_dict.get("input_value"))})
         inputs_components.append(input_dict.get("components", []))
-        types.append(input_dict.get("type", []))
+        types.append(input_dict.get("type", "chat"))
 
     return await graph.arun(inputs_list, inputs_components=inputs_components, types=types)
 
 
-def generate_function_for_flow(inputs: List["Vertex"], flow_id: str) -> Callable[..., Awaitable[Any]]:
+def generate_function_for_flow(
+    inputs: List["Vertex"], flow_id: str, user_id: str | UUID | None
+) -> Callable[..., Awaitable[Any]]:
     """
     Generate a dynamic flow function based on the given inputs and flow ID.
 
@@ -129,11 +136,23 @@ async def flow_function({func_args}):
     tweaks = {{ {arg_mappings} }}
     from langflow.helpers.flow import run_flow
     from langchain_core.tools import ToolException
+    from langflow.base.flow_processing.utils import build_records_from_result_data, format_flow_output_records
     try:
-        return await run_flow(
+        run_outputs = await run_flow(
             tweaks={{key: {{'input_value': value}} for key, value in tweaks.items()}},
             flow_id="{flow_id}",
+            user_id="{user_id}"
         )
+        if not run_outputs:
+                return []
+        run_output = run_outputs[0]
+
+        records = []
+        if run_output is not None:
+            for output in run_output.outputs:
+                if output:
+                    records.extend(build_records_from_result_data(output, get_final_results_only=True))
+        return format_flow_output_records(records)
     except Exception as e:
         raise ToolException(f'Error running flow: ' + e)
 """
@@ -145,7 +164,7 @@ async def flow_function({func_args}):
 
 
 def build_function_and_schema(
-    flow_record: Record, graph: "Graph"
+    flow_record: Record, graph: "Graph", user_id: str | UUID | None
 ) -> Tuple[Callable[..., Awaitable[Any]], Type[BaseModel]]:
     """
     Builds a dynamic function and schema for a given flow.
@@ -159,7 +178,7 @@ def build_function_and_schema(
     """
     flow_id = flow_record.id
     inputs = get_flow_inputs(graph)
-    dynamic_flow_function = generate_function_for_flow(inputs, flow_id)
+    dynamic_flow_function = generate_function_for_flow(inputs, flow_id, user_id=user_id)
     schema = build_schema_from_inputs(flow_record.name, inputs)
     return dynamic_flow_function, schema
 
@@ -200,3 +219,19 @@ def build_schema_from_inputs(name: str, inputs: List["Vertex"]) -> Type[BaseMode
         description = input_.description
         fields[field_name] = (str, Field(default="", description=description))
     return create_model(name, **fields)  # type: ignore
+
+
+def get_arg_names(inputs: List["Vertex"]) -> List[dict[str, str]]:
+    """
+    Returns a list of dictionaries containing the component name and its corresponding argument name.
+
+    Args:
+        inputs (List[Vertex]): A list of Vertex objects representing the inputs.
+
+    Returns:
+        List[dict[str, str]]: A list of dictionaries, where each dictionary contains the component name and its argument name.
+    """
+    return [
+        {"component_name": input_.display_name, "arg_name": input_.display_name.lower().replace(" ", "_")}
+        for input_ in inputs
+    ]