Return quickly when invoking a flow via the API instead of waiting for all the components to finish running.

[rudmac]

How do I return quickly when invoking a flow via API instead of waiting for all the components to finish running?
I don't need to receive the output of processed data.
The entry is Chat Input and there is Chat Output on the end.

[dosubot[bot]]

To ensure that the security key variables are used when running the flow with the /advanced endpoint, you need to include the api_key_user parameter, which is automatically resolved from the API key. This parameter is already included in the experimental_run_flow function definition:

@router.post("/run/advanced/{flow_id}", response_model=RunResponse, response_model_exclude_none=True)
async def experimental_run_flow(
    session: Annotated[Session, Depends(get_session)],
    flow_id: str,
    inputs: Optional[List[InputValueRequest]] = [InputValueRequest(components=[], input_value="")],
    outputs: Optional[List[str]] = [],
    tweaks: Annotated[Optional[Tweaks], Body(embed=True)] = None,
    stream: Annotated[bool, Body(embed=True)] = False,
    session_id: Annotated[Union[None, str], Body(embed=True)] = None,
    api_key_user: User = Depends(api_key_security),
    session_service: SessionService = Depends(get_session_service),
):
    ...

The api_key_user parameter ensures that the security key variables are used during the flow execution. This parameter is automatically populated by the api_key_security dependency, which handles the API key authentication.

To use the API key, include it in your API requests either in the x-api-key header or as a query parameter. Here are examples of how to do this:

Using the x-api-key header

curl -X POST \
  http://localhost:3000/api/v1/run/<your_flow_id> \
  -H 'Content-Type: application/json'\
  -H 'x-api-key: <your api key>'\
  -d '{"inputs": {"text":""}, "tweaks": {}}'

With Python using requests:

import requests
from typing import Optional

BASE_API_URL = "http://localhost:3001/api/v1/process"
FLOW_ID = "4441b773-0724-434e-9cee-19d995d8f2df"
TWEAKS = {}

def run_flow(inputs: dict,
            flow_id: str,
            tweaks: Optional[dict] = None,
            apiKey: Optional[str] = None) -> dict:
    api_url = f"{BASE_API_URL}/{flow_id}"
    payload = {"inputs": inputs}
    headers = {}

    if tweaks:
        payload["tweaks"] = tweaks
    if apiKey:
        headers = {"x-api-key": apiKey}

    response = requests.post(api_url, json=payload, headers=headers)
    return response.json()

inputs = {"text":""}
api_key = "<your api key>"
print(run_flow(inputs, flow_id=FLOW_ID, tweaks=TWEAKS, apiKey=api_key))

Using the query parameter

curl -X POST \
  http://localhost:3000/api/v1/process/<your_flow_id>?x-api-key=<your_api_key> \
  -H 'Content-Type: application/json'\
  -d '{"inputs": {"text":""}, "tweaks": {}}'

Or with Python:

import requests

BASE_API_URL = "http://localhost:3001/api/v1/process"
FLOW_ID = "4441b773-0724-434e-9cee-19d995d8f2df"
TWEAKS = {}

def run_flow(inputs: dict,
            flow_id: str,
            tweaks: Optional[dict] = None,
            apiKey: Optional[str] = None) -> dict:
    api_url = f"{BASE_API_URL}/{flow_id}"
    payload = {"inputs": inputs}
    headers = {}

    if tweaks:
        payload["tweaks"] = tweaks
    if apiKey:
        api_url += f"?x-api-key={apiKey}"

    response = requests.post(api_url, json=payload, headers=headers)
    return response.json()

inputs = {"text":""}
api_key = "<your api key>"
print(run_flow(inputs, flow_id=FLOW_ID, tweaks=TWEAKS, apiKey=api_key))

Additionally, ensure that the necessary environment variables are set, as these are required for the security key variables to be used effectively. The following environment variables should be configured:

VARIABLES_TO_GET_FROM_ENVIRONMENT = [
    "OPENAI_API_KEY",
    "ANTHROPIC_API_KEY",
    "GOOGLE_API_KEY",
    "AZURE_OPENAI_API_KEY",
    "AZURE_OPENAI_API_VERSION",
    "AZURE_OPENAI_API_INSTANCE_NAME",
    "AZURE_OPENAI_API_DEPLOYMENT_NAME",
    "AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME",
    "ASTRA_DB_APPLICATION_TOKEN",
    "ASTRA_DB_API_ENDPOINT",
    "COHERE_API_KEY",
    "HUGGINGFACEHUB_API_TOKEN",
    "SEARCHAPI_API_KEY",
    "SERPAPI_API_KEY",
    "VECTARA_CUSTOMER_ID",
    "VECTARA_CORPUS_ID",
    "VECTARA_API_KEY",
]

By including the API key in your requests and ensuring the necessary environment variables are set, you ensure that the security key variables are used when running the flow with the /advanced endpoint ^[1][2][3][4].

To continue talking to Dosu, mention @dosu.

[rudmac]

[dosubot[bot]]

To resolve the issue where the components cannot read the key variables when invoking a flow via the API using the x-api-key header with the API key, ensure that you are correctly including the x-api-key header in your API request. Here is an example using Python with the requests library:

import requests
from typing import Optional

BASE_API_URL = "http://localhost:3001/api/v1/process"
FLOW_ID = "4441b773-0724-434e-9cee-19d995d8f2df"
# You can tweak the flow by adding a tweaks dictionary
# e.g {"OpenAI-XXXXX": {"model_name": "gpt-4"}}
TWEAKS = {}

def run_flow(inputs: dict,
            flow_id: str,
            tweaks: Optional[dict] = None,
            apiKey: Optional[str] = None) -> dict:
    """
    Run a flow with a given message and optional tweaks.

    :param message: The message to send to the flow
    :param flow_id: The ID of the flow to run
    :param tweaks: Optional tweaks to customize the flow
    :param apiKey: The API key for authentication
    :return: The JSON response from the flow
    """
    api_url = f"{BASE_API_URL}/{flow_id}"

    payload = {"inputs": inputs}
    headers = {}
    if tweaks:
        payload["tweaks"] = tweaks
    if apiKey:
        headers = {"x-api-key": apiKey}

    response = requests.post(api_url, json=payload, headers=headers)
    return response.json()

# Setup any tweaks you want to apply to the flow
inputs = {"text":""}
api_key = "<your api key>"
print(run_flow(inputs, flow_id=FLOW_ID, tweaks=TWEAKS, apiKey=api_key))

Make sure that:

1. The x-api-key header is correctly set with your API key.
2. The API key is valid and has not been revoked.
3. The API endpoint URL is correct.

If the issue persists, verify that the API key has the necessary permissions to access the components and flows. Additionally, ensure that the LANGFLOW_AUTO_LOGIN environment variable is set to False, as this is required for the API Key functionality to work. The default user and password are set using the LANGFLOW_SUPERUSER and LANGFLOW_SUPERUSER_PASSWORD environment variables, with default values of langflow and langflow respectively ^[1][2].

To continue talking to Dosu, mention @dosu.

[rudmac]

@dosu I managed to call the API using /run/advanced, it worked but it only returns after the entire flow has completed and I need it to return immediately with code 200 without waiting for the flow to complete.

[dosubot[bot]]

To make the /run/advanced endpoint return immediately with a status code 200 without waiting for the flow to complete, you can modify the endpoint to start the flow execution in the background and return a response immediately. Here is an example of how you can achieve this:

from fastapi import BackgroundTasks, HTTPException

@router.post("/run/advanced/{flow_id}", response_model=RunResponse, response_model_exclude_none=True)
async def experimental_run_flow(
    background_tasks: BackgroundTasks,
    session: Annotated[Session, Depends(get_session)],
    flow_id: UUID,
    inputs: Optional[List[InputValueRequest]] = [InputValueRequest(components=[], input_value="")],
    outputs: Optional[List[str]] = [],
    tweaks: Annotated[Optional[Tweaks], Body(embed=True)] = None,  # noqa: F821
    stream: Annotated[bool, Body(embed=True)] = False,  # noqa: F821
    session_id: Annotated[Union[None, str], Body(embed=True)] = None,  # noqa: F821
    api_key_user: User = Depends(api_key_security),
    session_service: SessionService = Depends(get_session_service),
):
    """
    Executes a specified flow by ID with optional input values, output selection, tweaks, and streaming capability.
    This endpoint supports running flows with caching to enhance performance and efficiency.
    """
    async def run_flow_in_background():
        try:
            flow_id_str = str(flow_id)
            if outputs is None:
                outputs = []

            artifacts = {}
            if session_id:
                session_data = await session_service.load_session(session_id, flow_id=flow_id_str)
                graph, artifacts = session_data if session_data else (None, None)
                if graph is None:
                    raise ValueError(f"Session {session_id} not found")
            else:
                flow = session.exec(
                    select(Flow).where(Flow.id == flow_id_str).where(Flow.user_id == api_key_user.id)
                ).first()
                if flow is None:
                    raise ValueError(f"Flow {flow_id_str} not found")
                if flow.data is None:
                    raise ValueError(f"Flow {flow_id_str} has no data")
                graph_data = flow.data
                graph_data = process_tweaks(graph_data, tweaks or {})
                graph = Graph.from_payload(graph_data, flow_id=flow_id_str)
            task_result, session_id = await run_graph_internal(
                graph=graph,
                flow_id=flow_id_str,
                session_id=session_id,
                inputs=inputs,
                outputs=outputs,
                stream=stream,
            )
        except Exception as exc:
            logger.exception(exc)

    background_tasks.add_task(run_flow_in_background)
    return {"message": "Task started in the background", "status": "in progress"}

Additionally, starting from version 0.5, Langflow introduces a new feature to its API: the sync flag. This flag allows users to opt for asynchronous processing of their flows. When set to false, the API will initiate an asynchronous task instead of processing the flow synchronously.

Here is an example of how to use the sync flag in your API request:

curl -X POST \
  http://localhost:3000/api/v1/process/<your_flow_id> \
  -H 'Content-Type: application/json' \
  -H 'x-api-key: <your_api_key>' \
  -d '{"inputs": {"text": ""}, "tweaks": {}, "sync": false}'

The response will include a task ID that you can use to check the status of the asynchronous task:

{
  "result": {
    "output": "..."
  },
  "task": {
    "id": "...",
    "href": "api/v1/task/<task_id>"
  },
  "session_id": "...",
  "backend": "..." // celery or anyio
}

You can check the status of the task by making a GET request to the /task/{task_id} endpoint:

curl -X GET \
  http://localhost:3000/api/v1/task/<task_id> \
  -H 'x-api-key: <your_api_key>'

The response will include the current status of the task and, if completed, the result of the task. Possible statuses include PENDING, SUCCESS, and FAILURE ^[1][2][3][4].

To continue talking to Dosu, mention @dosu.

[rudmac]

@dosu this is the error I'm getting after call the api using the /process URI, 500 Internal Server Error: message: """TaskService.init() missing 1 required positional argument: 'settings_service'"""

[dosubot[bot]]

To resolve the TaskService.__init__() missing 1 required positional argument: 'settings_service' error, you need to ensure that when you instantiate the TaskService class, you provide an instance of SettingsService as an argument.

Here is an example of how you can instantiate TaskService correctly:

from langflow.services.settings.service import SettingsService
from langflow.services.task.service import TaskService

# Assuming you have a SettingsService instance
settings = Settings()
auth_settings = AuthSettings(CONFIG_DIR=settings.config_dir)
settings_service = SettingsService(settings, auth_settings)

# Instantiate TaskService with the settings_service
task_service = TaskService(settings_service)

To ensure that the flow execution starts in the background and the API returns immediately, you can use the launch_task method of the TaskService class. This method launches the task using the backend and returns immediately.

Here is an example of how to use the launch_task method:

import asyncio
from some_module import some_task_function  # Replace with your actual task function

async def main():
    # Assuming you have a SettingsService instance
    settings = Settings()
    auth_settings = AuthSettings(CONFIG_DIR=settings.config_dir)
    settings_service = SettingsService(settings, auth_settings)

    # Instantiate TaskService with the settings_service
    task_service = TaskService(settings_service)

    # Launch the task in the background
    task_id = await task_service.launch_task(some_task_function, arg1, arg2, kwarg1=value1)

    # The API can return immediately after this
    print(f"Task launched with ID: {task_id}")

# Run the main function
asyncio.run(main())

In this example, some_task_function is the function you want to run as a task, and arg1, arg2, kwarg1=value1 are the arguments and keyword arguments you want to pass to the task function. The launch_task method will start the task in the background, and the API can return immediately after the task is launched. The TaskService class uses either Celery or AnyIO as the backend, depending on the configuration in SettingsService ^[1][2].

To continue talking to Dosu, mention @dosu.