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refactor: move tests folder structure and update pytest commands (#2785)

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* refactor: move tests folder to src/backend

* chore(Makefile): update pytest commands to run tests from the correct directory paths for unit and integration tests

* refactor: update file path in test_custom_component.py

The file path in the test_custom_component.py file has been updated to use the correct relative path to the component_multiple_outputs.py file. This change ensures that the test code can access the correct file and improves the reliability of the test.
This commit is contained in:
Gabriel Luiz Freitas Almeida 2024-07-18 12:19:43 -03:00 • committed by GitHub
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56 changed files with 5 additions and 5 deletions
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6
Makefile
View file

@ -74,13 +74,13 @@ coverage: ## run the tests and generate a coverage report
# allow passing arguments to pytest
unit_tests: ## run unit tests
poetry run pytest \
--ignore=tests/integration \
poetry run pytest src/backend/tests \
--ignore=src/backend/tests/integration \
--instafail -ra -n auto -m "not api_key_required" \
$(args)
integration_tests: ## run integration tests
poetry run pytest tests/integration \
poetry run pytest src/backend/tests/integration \
--instafail -ra -n auto \
$(args)

466
src/backend/tests/conftest.py Normal file
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@ -0,0 +1,466 @@
import json
import os.path
import shutil
# we need to import tmpdir
import tempfile
from contextlib import contextmanager, suppress
from pathlib import Path
from typing import TYPE_CHECKING, AsyncGenerator
import orjson
import pytest
from dotenv import load_dotenv
from fastapi.testclient import TestClient
from httpx import AsyncClient
from sqlmodel import Session, SQLModel, create_engine, select
from sqlmodel.pool import StaticPool
from typer.testing import CliRunner
from langflow.graph.graph.base import Graph
from langflow.initial_setup.setup import STARTER_FOLDER_NAME
from langflow.services.auth.utils import get_password_hash
from langflow.services.database.models.api_key.model import ApiKey
from langflow.services.database.models.flow.model import Flow, FlowCreate
from langflow.services.database.models.folder.model import Folder
from langflow.services.database.models.user.model import User, UserCreate
from langflow.services.database.utils import session_getter
from langflow.services.deps import get_db_service
if TYPE_CHECKING:
from langflow.services.database.service import DatabaseService
load_dotenv()
def pytest_configure(config):
config.addinivalue_line("markers", "noclient: don't create a client for this test")
config.addinivalue_line("markers", "load_flows: load the flows for this test")
config.addinivalue_line("markers", "api_key_required: run only if the api key is set in the environment variables")
data_path = Path(__file__).parent.absolute() / "data"
pytest.BASIC_EXAMPLE_PATH = data_path / "basic_example.json"
pytest.COMPLEX_EXAMPLE_PATH = data_path / "complex_example.json"
pytest.OPENAPI_EXAMPLE_PATH = data_path / "Openapi.json"
pytest.GROUPED_CHAT_EXAMPLE_PATH = data_path / "grouped_chat.json"
pytest.ONE_GROUPED_CHAT_EXAMPLE_PATH = data_path / "one_group_chat.json"
pytest.VECTOR_STORE_GROUPED_EXAMPLE_PATH = data_path / "vector_store_grouped.json"
pytest.WEBHOOK_TEST = data_path / "WebhookTest.json"
pytest.BASIC_CHAT_WITH_PROMPT_AND_HISTORY = data_path / "BasicChatwithPromptandHistory.json"
pytest.CHAT_INPUT = data_path / "ChatInputTest.json"
pytest.TWO_OUTPUTS = data_path / "TwoOutputsTest.json"
pytest.VECTOR_STORE_PATH = data_path / "Vector_store.json"
pytest.SIMPLE_API_TEST = data_path / "SimpleAPITest.json"
pytest.CODE_WITH_SYNTAX_ERROR = """
def get_text():
retun "Hello World"
"""
# validate that all the paths are correct and the files exist
for path in [
pytest.BASIC_EXAMPLE_PATH,
pytest.COMPLEX_EXAMPLE_PATH,
pytest.OPENAPI_EXAMPLE_PATH,
pytest.GROUPED_CHAT_EXAMPLE_PATH,
pytest.ONE_GROUPED_CHAT_EXAMPLE_PATH,
pytest.VECTOR_STORE_GROUPED_EXAMPLE_PATH,
pytest.BASIC_CHAT_WITH_PROMPT_AND_HISTORY,
pytest.CHAT_INPUT,
pytest.TWO_OUTPUTS,
pytest.VECTOR_STORE_PATH,
]:
assert path.exists(), f"File {path} does not exist. Available files: {list(data_path.iterdir())}"
@pytest.fixture()
async def async_client() -> AsyncGenerator:
from langflow.main import create_app
app = create_app()
async with AsyncClient(app=app, base_url="http://testserver") as client:
yield client
@pytest.fixture(name="session")
def session_fixture():
engine = create_engine("sqlite://", connect_args={"check_same_thread": False}, poolclass=StaticPool)
SQLModel.metadata.create_all(engine)
with Session(engine) as session:
yield session
class Config:
broker_url = "redis://localhost:6379/0"
result_backend = "redis://localhost:6379/0"
@pytest.fixture(name="load_flows_dir")
def load_flows_dir():
tempdir = tempfile.TemporaryDirectory()
yield tempdir.name
@pytest.fixture(name="distributed_env")
def setup_env(monkeypatch):
monkeypatch.setenv("LANGFLOW_CACHE_TYPE", "redis")
monkeypatch.setenv("LANGFLOW_REDIS_HOST", "result_backend")
monkeypatch.setenv("LANGFLOW_REDIS_PORT", "6379")
monkeypatch.setenv("LANGFLOW_REDIS_DB", "0")
monkeypatch.setenv("LANGFLOW_REDIS_EXPIRE", "3600")
monkeypatch.setenv("LANGFLOW_REDIS_PASSWORD", "")
monkeypatch.setenv("FLOWER_UNAUTHENTICATED_API", "True")
monkeypatch.setenv("BROKER_URL", "redis://result_backend:6379/0")
monkeypatch.setenv("RESULT_BACKEND", "redis://result_backend:6379/0")
monkeypatch.setenv("C_FORCE_ROOT", "true")
@pytest.fixture(name="distributed_client")
def distributed_client_fixture(session: Session, monkeypatch, distributed_env):
# Here we load the .env from ../deploy/.env
from langflow.core import celery_app
db_dir = tempfile.mkdtemp()
db_path = Path(db_dir) / "test.db"
monkeypatch.setenv("LANGFLOW_DATABASE_URL", f"sqlite:///{db_path}")
monkeypatch.setenv("LANGFLOW_AUTO_LOGIN", "false")
# monkeypatch langflow.services.task.manager.USE_CELERY to True
# monkeypatch.setattr(manager, "USE_CELERY", True)
monkeypatch.setattr(celery_app, "celery_app", celery_app.make_celery("langflow", Config))
# def get_session_override():
# return session
from langflow.main import create_app
app = create_app()
# app.dependency_overrides[get_session] = get_session_override
with TestClient(app) as client:
yield client
app.dependency_overrides.clear()
monkeypatch.undo()
def get_graph(_type="basic"):
"""Get a graph from a json file"""
if _type == "basic":
path = pytest.BASIC_EXAMPLE_PATH
elif _type == "complex":
path = pytest.COMPLEX_EXAMPLE_PATH
elif _type == "openapi":
path = pytest.OPENAPI_EXAMPLE_PATH
with open(path, "r") as f:
flow_graph = json.load(f)
data_graph = flow_graph["data"]
nodes = data_graph["nodes"]
edges = data_graph["edges"]
return Graph(nodes, edges)
@pytest.fixture
def basic_graph_data():
with open(pytest.BASIC_EXAMPLE_PATH, "r") as f:
return json.load(f)
@pytest.fixture
def basic_graph():
return get_graph()
@pytest.fixture
def complex_graph():
return get_graph("complex")
@pytest.fixture
def openapi_graph():
return get_graph("openapi")
@pytest.fixture
def json_flow():
with open(pytest.BASIC_EXAMPLE_PATH, "r") as f:
return f.read()
@pytest.fixture
def grouped_chat_json_flow():
with open(pytest.GROUPED_CHAT_EXAMPLE_PATH, "r") as f:
return f.read()
@pytest.fixture
def one_grouped_chat_json_flow():
with open(pytest.ONE_GROUPED_CHAT_EXAMPLE_PATH, "r") as f:
return f.read()
@pytest.fixture
def vector_store_grouped_json_flow():
with open(pytest.VECTOR_STORE_GROUPED_EXAMPLE_PATH, "r") as f:
return f.read()
@pytest.fixture
def json_flow_with_prompt_and_history():
with open(pytest.BASIC_CHAT_WITH_PROMPT_AND_HISTORY, "r") as f:
return f.read()
@pytest.fixture
def json_simple_api_test():
with open(pytest.SIMPLE_API_TEST, "r") as f:
return f.read()
@pytest.fixture
def json_vector_store():
with open(pytest.VECTOR_STORE_PATH, "r") as f:
return f.read()
@pytest.fixture
def json_webhook_test():
with open(pytest.WEBHOOK_TEST, "r") as f:
return f.read()
@pytest.fixture(name="client", autouse=True)
def client_fixture(session: Session, monkeypatch, request, load_flows_dir):
# Set the database url to a test database
if "noclient" in request.keywords:
yield
else:
db_dir = tempfile.mkdtemp()
db_path = Path(db_dir) / "test.db"
monkeypatch.setenv("LANGFLOW_DATABASE_URL", f"sqlite:///{db_path}")
monkeypatch.setenv("LANGFLOW_AUTO_LOGIN", "false")
if "load_flows" in request.keywords:
shutil.copyfile(
pytest.BASIC_EXAMPLE_PATH, os.path.join(load_flows_dir, "c54f9130-f2fa-4a3e-b22a-3856d946351b.json")
)
monkeypatch.setenv("LANGFLOW_LOAD_FLOWS_PATH", load_flows_dir)
monkeypatch.setenv("LANGFLOW_AUTO_LOGIN", "true")
from langflow.main import create_app
app = create_app()
# app.dependency_overrides[get_session] = get_session_override
with TestClient(app) as client:
yield client
# app.dependency_overrides.clear()
monkeypatch.undo()
# clear the temp db
with suppress(FileNotFoundError):
db_path.unlink()
# create a fixture for session_getter above
@pytest.fixture(name="session_getter")
def session_getter_fixture(client):
@contextmanager
def blank_session_getter(db_service: "DatabaseService"):
with Session(db_service.engine) as session:
yield session
yield blank_session_getter
@pytest.fixture
def runner():
return CliRunner()
@pytest.fixture
def test_user(client):
user_data = UserCreate(
username="testuser",
password="testpassword",
)
response = client.post("/api/v1/users", json=user_data.model_dump())
assert response.status_code == 201
return response.json()
@pytest.fixture(scope="function")
def active_user(client):
db_manager = get_db_service()
with session_getter(db_manager) as session:
user = User(
username="activeuser",
password=get_password_hash("testpassword"),
is_active=True,
is_superuser=False,
)
# check if user exists
if active_user := session.exec(select(User).where(User.username == user.username)).first():
return active_user
session.add(user)
session.commit()
session.refresh(user)
return user
@pytest.fixture
def logged_in_headers(client, active_user):
login_data = {"username": active_user.username, "password": "testpassword"}
response = client.post("/api/v1/login", data=login_data)
assert response.status_code == 200
tokens = response.json()
a_token = tokens["access_token"]
return {"Authorization": f"Bearer {a_token}"}
@pytest.fixture
def flow(client, json_flow: str, active_user):
from langflow.services.database.models.flow.model import FlowCreate
loaded_json = json.loads(json_flow)
flow_data = FlowCreate(name="test_flow", data=loaded_json.get("data"), user_id=active_user.id)
flow = Flow.model_validate(flow_data)
with session_getter(get_db_service()) as session:
session.add(flow)
session.commit()
session.refresh(flow)
return flow
@pytest.fixture
def json_chat_input():
with open(pytest.CHAT_INPUT, "r") as f:
return f.read()
@pytest.fixture
def json_two_outputs():
with open(pytest.TWO_OUTPUTS, "r") as f:
return f.read()
@pytest.fixture
def added_flow_with_prompt_and_history(client, json_flow_with_prompt_and_history, logged_in_headers):
flow = orjson.loads(json_flow_with_prompt_and_history)
data = flow["data"]
flow = FlowCreate(name="Basic Chat", description="description", data=data)
response = client.post("api/v1/flows/", json=flow.model_dump(), headers=logged_in_headers)
assert response.status_code == 201
assert response.json()["name"] == flow.name
assert response.json()["data"] == flow.data
return response.json()
@pytest.fixture
def added_flow_chat_input(client, json_chat_input, logged_in_headers):
flow = orjson.loads(json_chat_input)
data = flow["data"]
flow = FlowCreate(name="Chat Input", description="description", data=data)
response = client.post("api/v1/flows/", json=flow.model_dump(), headers=logged_in_headers)
assert response.status_code == 201
assert response.json()["name"] == flow.name
assert response.json()["data"] == flow.data
return response.json()
@pytest.fixture
def added_flow_two_outputs(client, json_two_outputs, logged_in_headers):
flow = orjson.loads(json_two_outputs)
data = flow["data"]
flow = FlowCreate(name="Two Outputs", description="description", data=data)
response = client.post("api/v1/flows/", json=flow.model_dump(), headers=logged_in_headers)
assert response.status_code == 201
assert response.json()["name"] == flow.name
assert response.json()["data"] == flow.data
return response.json()
@pytest.fixture
def added_vector_store(client, json_vector_store, logged_in_headers):
vector_store = orjson.loads(json_vector_store)
data = vector_store["data"]
vector_store = FlowCreate(name="Vector Store", description="description", data=data)
response = client.post("api/v1/flows/", json=vector_store.model_dump(), headers=logged_in_headers)
assert response.status_code == 201
assert response.json()["name"] == vector_store.name
assert response.json()["data"] == vector_store.data
return response.json()
@pytest.fixture
def added_webhook_test(client, json_webhook_test, logged_in_headers):
webhook_test = orjson.loads(json_webhook_test)
data = webhook_test["data"]
webhook_test = FlowCreate(
name="Webhook Test", description="description", data=data, endpoint_name=webhook_test["endpoint_name"]
)
response = client.post("api/v1/flows/", json=webhook_test.model_dump(), headers=logged_in_headers)
assert response.status_code == 201
assert response.json()["name"] == webhook_test.name
assert response.json()["data"] == webhook_test.data
return response.json()
@pytest.fixture
def created_api_key(active_user):
hashed = get_password_hash("random_key")
api_key = ApiKey(
name="test_api_key",
user_id=active_user.id,
api_key="random_key",
hashed_api_key=hashed,
)
db_manager = get_db_service()
with session_getter(db_manager) as session:
if existing_api_key := session.exec(select(ApiKey).where(ApiKey.api_key == api_key.api_key)).first():
return existing_api_key
session.add(api_key)
session.commit()
session.refresh(api_key)
return api_key
@pytest.fixture(name="simple_api_test")
def get_simple_api_test(client, logged_in_headers, json_simple_api_test):
# Once the client is created, we can get the starter project
# Just create a new flow with the simple api test
flow = orjson.loads(json_simple_api_test)
data = flow["data"]
flow = FlowCreate(name="Simple API Test", data=data, description="Simple API Test")
response = client.post("api/v1/flows/", json=flow.model_dump(), headers=logged_in_headers)
assert response.status_code == 201
assert response.json()["name"] == flow.name
assert response.json()["data"] == flow.data
return response.json()
@pytest.fixture(name="starter_project")
def get_starter_project(active_user):
# once the client is created, we can get the starter project
with session_getter(get_db_service()) as session:
flow = session.exec(
select(Flow)
.where(Flow.folder.has(Folder.name == STARTER_FOLDER_NAME))
.where(Flow.name == "Basic Prompting (Hello, World)")
).first()
if not flow:
raise ValueError("No starter project found")
new_flow_create = FlowCreate(
name=flow.name,
description=flow.description,
data=flow.data,
user_id=active_user.id,
)
new_flow = Flow.model_validate(new_flow_create, from_attributes=True)
session.add(new_flow)
session.commit()
session.refresh(new_flow)
new_flow_dict = new_flow.model_dump()
return new_flow_dict

1
src/backend/tests/data/BasicChatwithPromptandHistory.json Normal file
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918
src/backend/tests/data/ChatInputTest.json Normal file
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@ -0,0 +1,918 @@
{
"name": "ChatInputTest",
"description": "",
"data": {
"nodes": [
{
"width": 384,
"height": 359,
"id": "PromptTemplate-IKKOx",
"type": "genericNode",
"position": {
"x": 880,
"y": 646.9375
},
"data": {
"type": "PromptTemplate",
"node": {
"template": {
"output_parser": {
"required": false,
"placeholder": "",
"show": false,
"multiline": false,
"password": false,
"name": "output_parser",
"advanced": false,
"dynamic": false,
"info": "",
"type": "BaseOutputParser",
"list": false
},
"input_variables": {
"required": true,
"placeholder": "",
"show": false,
"multiline": false,
"password": false,
"name": "input_variables",
"advanced": false,
"dynamic": false,
"info": "",
"type": "str",
"list": true,
"value": [
"input"
]
},
"partial_variables": {
"required": false,
"placeholder": "",
"show": false,
"multiline": false,
"password": false,
"name": "partial_variables",
"advanced": false,
"dynamic": false,
"info": "",
"type": "code",
"list": false
},
"template": {
"required": true,
"placeholder": "",
"show": true,
"multiline": true,
"password": false,
"name": "template",
"advanced": false,
"dynamic": false,
"info": "",
"type": "prompt",
"list": false,
"value": "Input: {input}\nAI:"
},
"template_format": {
"required": false,
"placeholder": "",
"show": false,
"multiline": false,
"value": "f-string",
"password": false,
"name": "template_format",
"advanced": false,
"dynamic": false,
"info": "",
"type": "str",
"list": false
},
"validate_template": {
"required": false,
"placeholder": "",
"show": false,
"multiline": false,
"value": true,
"password": false,
"name": "validate_template",
"advanced": false,
"dynamic": false,
"info": "",
"type": "bool",
"list": false
},
"_type": "PromptTemplate",
"input": {
"required": false,
"placeholder": "",
"show": true,
"multiline": true,
"value": "",
"password": false,
"name": "input",
"display_name": "input",
"advanced": false,
"input_types": [
"Document",
"BaseOutputParser",
"str"
],
"dynamic": false,
"info": "",
"type": "str",
"list": false
}
},
"description": "A prompt template for a language model.",
"base_classes": [
"BasePromptTemplate",
"PromptTemplate",
"StringPromptTemplate"
],
"name": "",
"display_name": "PromptTemplate",
"documentation": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/",
"custom_fields": {
"": [
"input"
],
"template": [
"input"
]
},
"output_types": [],
"field_formatters": {
"formatters": {
"openai_api_key": {}
},
"base_formatters": {
"kwargs": {},
"optional": {},
"list": {},
"dict": {},
"union": {},
"multiline": {},
"show": {},
"password": {},
"default": {},
"headers": {},
"dict_code_file": {},
"model_fields": {
"MODEL_DICT": {
"OpenAI": [
"text-davinci-003",
"text-davinci-002",
"text-curie-001",
"text-babbage-001",
"text-ada-001"
],
"ChatOpenAI": [
"gpt-3.5-turbo-0613",
"gpt-3.5-turbo",
"gpt-3.5-turbo-16k-0613",
"gpt-3.5-turbo-16k",
"gpt-4-0613",
"gpt-4-32k-0613",
"gpt-4",
"gpt-4-32k"
],
"Anthropic": [
"claude-v1",
"claude-v1-100k",
"claude-instant-v1",
"claude-instant-v1-100k",
"claude-v1.3",
"claude-v1.3-100k",
"claude-v1.2",
"claude-v1.0",
"claude-instant-v1.1",
"claude-instant-v1.1-100k",
"claude-instant-v1.0"
],
"ChatAnthropic": [
"claude-v1",
"claude-v1-100k",
"claude-instant-v1",
"claude-instant-v1-100k",
"claude-v1.3",
"claude-v1.3-100k",
"claude-v1.2",
"claude-v1.0",
"claude-instant-v1.1",
"claude-instant-v1.1-100k",
"claude-instant-v1.0"
]
}
}
}
},
"beta": false,
"error": null
},
"id": "PromptTemplate-IKKOx"
},
"selected": false,
"positionAbsolute": {
"x": 880,
"y": 646.9375
},
"dragging": false
},
{
"width": 384,
"height": 307,
"id": "LLMChain-e2dhN",
"type": "genericNode",
"position": {
"x": 1449.330344958542,
"y": 880.1760221487797
},
"data": {
"type": "LLMChain",
"node": {
"template": {
"callbacks": {
"required": false,
"placeholder": "",
"show": false,
"multiline": false,
"password": false,
"name": "callbacks",
"advanced": false,
"dynamic": false,
"info": "",
"type": "langchain.callbacks.base.BaseCallbackHandler",
"list": true
},
"llm": {
"required": true,
"placeholder": "",
"show": true,
"multiline": false,
"password": false,
"name": "llm",
"advanced": false,
"dynamic": false,
"info": "",
"type": "BaseLanguageModel",
"list": false
},
"memory": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"password": false,
"name": "memory",
"advanced": false,
"dynamic": false,
"info": "",
"type": "BaseMemory",
"list": false
},
"output_parser": {
"required": false,
"placeholder": "",
"show": false,
"multiline": false,
"password": false,
"name": "output_parser",
"advanced": false,
"dynamic": false,
"info": "",
"type": "BaseLLMOutputParser",
"list": false
},
"prompt": {
"required": true,
"placeholder": "",
"show": true,
"multiline": false,
"password": false,
"name": "prompt",
"advanced": false,
"dynamic": false,
"info": "",
"type": "BasePromptTemplate",
"list": false
},
"llm_kwargs": {
"required": false,
"placeholder": "",
"show": false,
"multiline": false,
"password": false,
"name": "llm_kwargs",
"advanced": false,
"dynamic": false,
"info": "",
"type": "code",
"list": false
},
"metadata": {
"required": false,
"placeholder": "",
"show": false,
"multiline": false,
"password": false,
"name": "metadata",
"advanced": false,
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"source": "dndnode_19",
"sourceHandle": "JsonToolkit|dndnode_19|BaseToolkit",
"target": "dndnode_33",
"targetHandle": "BaseToolkit|toolkit|dndnode_33",
"className": "animate-pulse",
"id": "reactflow__edge-dndnode_19JsonToolkit|dndnode_19|BaseToolkit-dndnode_33BaseToolkit|toolkit|dndnode_33",
"selected": false
},
{
"source": "dndnode_33",
"sourceHandle": "JsonAgent|dndnode_33|AgentExecutor",
"target": "dndnode_32",
"targetHandle": "AgentExecutor|json_agent|dndnode_32",
"className": "animate-pulse",
"id": "reactflow__edge-dndnode_33JsonAgent|dndnode_33|AgentExecutor-dndnode_32AgentExecutor|json_agent|dndnode_32",
"selected": false
},
{
"source": "dndnode_34",
"sourceHandle": "TextRequestsWrapper|dndnode_34|TextRequestsWrapper",
"target": "dndnode_32",
"targetHandle": "TextRequestsWrapper|requests_wrapper|dndnode_32",
"className": "animate-pulse",
"id": "reactflow__edge-dndnode_34RequestsWrapper|dndnode_34|TextRequestsWrapper-dndnode_32RequestsWrapper|requests_wrapper|dndnode_32",
"selected": false
},
{
"source": "dndnode_35",
"sourceHandle": "JsonSpec|dndnode_35|Tool|JsonSpec",
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"className": "animate-pulse",
"id": "reactflow__edge-dndnode_35JsonSpec|dndnode_35|Tool|JsonSpec-dndnode_19JsonSpec|spec|dndnode_19",
"selected": false
},
{
"source": "dndnode_36",
"sourceHandle": "ChatOpenAI|dndnode_36|BaseLanguageModel|BaseLLM",
"target": "dndnode_33",
"targetHandle": "BaseLanguageModel|llm|dndnode_33",
"className": "animate-pulse",
"id": "reactflow__edge-dndnode_36OpenAIChat|dndnode_36|BaseLanguageModel|BaseLLM-dndnode_33BaseLanguageModel|llm|dndnode_33"
}
],
"viewport": {
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"zoom": 1
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},
"chat": [
{
"message": "test",
"isSend": true
}
]
}

567
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@ -0,0 +1,567 @@
{
"id": "e9380216-9300-41a1-bc35-7ee92fe4b30d",
"data": {
"nodes": [
{
"id": "ChatInput-irFJf",
"type": "genericNode",
"position": {
"x": 180,
"y": 200.296875
},
"data": {
"type": "ChatInput",
"node": {
"template": {
"_type": "Component",
"files": {
"trace_as_metadata": true,
"file_path": "",
"fileTypes": [
"txt",
"md",
"mdx",
"csv",
"json",
"yaml",
"yml",
"xml",
"html",
"htm",
"pdf",
"docx",
"py",
"sh",
"sql",
"js",
"ts",
"tsx",
"jpg",
"jpeg",
"png",
"bmp",
"image"
],
"list": true,
"required": false,
"placeholder": "",
"show": true,
"value": "",
"name": "files",
"display_name": "Files",
"advanced": true,
"dynamic": false,
"info": "Files to be sent with the message.",
"title_case": false,
"type": "file"
},
"code": {
"type": "code",
"required": true,
"placeholder": "",
"list": false,
"show": true,
"multiline": true,
"value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.memory import store_message\nfrom langflow.schema.message import Message\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n name = \"ChatInput\"\n\n inputs = [\n MultilineInput(\n name=\"input_value\",\n display_name=\"Text\",\n value=\"\",\n info=\"Message to be passed as input.\",\n ),\n BoolInput(\n name=\"store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"User\",\n info=\"Type of sender.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=\"User\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n FileInput(\n name=\"files\",\n display_name=\"Files\",\n file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n info=\"Files to be sent with the message.\",\n advanced=True,\n is_list=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n ]\n\n def message_response(self) -> Message:\n message = Message(\n text=self.input_value,\n sender=self.sender,\n sender_name=self.sender_name,\n session_id=self.session_id,\n files=self.files,\n )\n\n if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n store_message(\n message,\n flow_id=self.graph.flow_id,\n )\n self.message.value = message\n\n self.status = message\n return message\n",
"fileTypes": [],
"file_path": "",
"password": false,
"name": "code",
"advanced": true,
"dynamic": true,
"info": "",
"load_from_db": false,
"title_case": false
},
"input_value": {
"trace_as_input": true,
"multiline": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"value": "",
"name": "input_value",
"display_name": "Text",
"advanced": false,
"input_types": [
"Message"
],
"dynamic": false,
"info": "Message to be passed as input.",
"title_case": false,
"type": "str"
},
"sender": {
"trace_as_metadata": true,
"options": [
"Machine",
"User"
],
"required": false,
"placeholder": "",
"show": true,
"value": "User",
"name": "sender",
"display_name": "Sender Type",
"advanced": true,
"dynamic": false,
"info": "Type of sender.",
"title_case": false,
"type": "str"
},
"sender_name": {
"trace_as_input": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"value": "User",
"name": "sender_name",
"display_name": "Sender Name",
"advanced": true,
"input_types": [
"Message"
],
"dynamic": false,
"info": "Name of the sender.",
"title_case": false,
"type": "str"
},
"session_id": {
"trace_as_input": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"value": "",
"name": "session_id",
"display_name": "Session ID",
"advanced": true,
"input_types": [
"Message"
],
"dynamic": false,
"info": "Session ID for the message.",
"title_case": false,
"type": "str"
},
"store_message": {
"trace_as_metadata": true,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"value": true,
"name": "store_message",
"display_name": "Store Messages",
"advanced": true,
"dynamic": false,
"info": "Store the message in the history.",
"title_case": false,
"type": "bool"
}
},
"description": "Get chat inputs from the Playground.",
"icon": "ChatInput",
"base_classes": [
"Message"
],
"display_name": "Chat Input",
"documentation": "",
"custom_fields": {},
"output_types": [],
"pinned": false,
"conditional_paths": [],
"frozen": false,
"outputs": [
{
"types": [
"Message"
],
"selected": "Message",
"name": "message",
"display_name": "Message",
"method": "message_response",
"value": "__UNDEFINED__",
"cache": true
}
],
"field_order": [
"input_value",
"store_message",
"sender",
"sender_name",
"session_id",
"files"
],
"beta": false,
"edited": false
},
"id": "ChatInput-irFJf",
"description": "Get chat inputs from the Playground.",
"display_name": "Chat Input"
},
"selected": false,
"width": 384,
"height": 309
},
{
"id": "TextInput-tcoZg",
"type": "genericNode",
"position": {
"x": 186,
"y": 549.296875
},
"data": {
"type": "TextInput",
"node": {
"template": {
"_type": "Component",
"code": {
"type": "code",
"required": true,
"placeholder": "",
"list": false,
"show": true,
"multiline": true,
"value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import MessageTextInput, Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n display_name = \"Text Input\"\n description = \"Get text inputs from the Playground.\"\n icon = \"type\"\n name = \"TextInput\"\n\n inputs = [\n MessageTextInput(\n name=\"input_value\",\n display_name=\"Text\",\n info=\"Text to be passed as input.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Text\", name=\"text\", method=\"text_response\"),\n ]\n\n def text_response(self) -> Message:\n message = Message(\n text=self.input_value,\n )\n return message\n",
"fileTypes": [],
"file_path": "",
"password": false,
"name": "code",
"advanced": true,
"dynamic": true,
"info": "",
"load_from_db": false,
"title_case": false
},
"input_value": {
"trace_as_input": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"value": "AI",
"name": "input_value",
"display_name": "Text",
"advanced": false,
"input_types": [
"Message"
],
"dynamic": false,
"info": "Text to be passed as input.",
"title_case": false,
"type": "str"
}
},
"description": "Get text inputs from the Playground.",
"icon": "type",
"base_classes": [
"Message"
],
"display_name": "Text Input",
"documentation": "",
"custom_fields": {},
"output_types": [],
"pinned": false,
"conditional_paths": [],
"frozen": false,
"outputs": [
{
"types": [
"Message"
],
"selected": "Message",
"name": "text",
"display_name": "Text",
"method": "text_response",
"value": "__UNDEFINED__",
"cache": true
}
],
"field_order": [
"input_value"
],
"beta": false,
"edited": false
},
"id": "TextInput-tcoZg"
},
"selected": true,
"width": 384,
"height": 309,
"positionAbsolute": {
"x": 186,
"y": 549.296875
},
"dragging": false
},
{
"id": "ChatOutput-dJRst",
"type": "genericNode",
"position": {
"x": 820,
"y": 224.296875
},
"data": {
"type": "ChatOutput",
"node": {
"template": {
"_type": "Component",
"code": {
"type": "code",
"required": true,
"placeholder": "",
"list": false,
"show": true,
"multiline": true,
"value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.memory import store_message\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n name = \"ChatOutput\"\n\n inputs = [\n MessageTextInput(\n name=\"input_value\",\n display_name=\"Text\",\n info=\"Message to be passed as output.\",\n ),\n BoolInput(\n name=\"store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n ),\n MessageTextInput(\n name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n ]\n\n def message_response(self) -> Message:\n message = Message(\n text=self.input_value,\n sender=self.sender,\n sender_name=self.sender_name,\n session_id=self.session_id,\n )\n if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n store_message(\n message,\n flow_id=self.graph.flow_id,\n )\n self.message.value = message\n\n self.status = message\n return message\n",
"fileTypes": [],
"file_path": "",
"password": false,
"name": "code",
"advanced": true,
"dynamic": true,
"info": "",
"load_from_db": false,
"title_case": false
},
"data_template": {
"trace_as_input": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"value": "{text}",
"name": "data_template",
"display_name": "Data Template",
"advanced": true,
"input_types": [
"Message"
],
"dynamic": false,
"info": "Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.",
"title_case": false,
"type": "str"
},
"input_value": {
"trace_as_input": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"value": "",
"name": "input_value",
"display_name": "Text",
"advanced": false,
"input_types": [
"Message"
],
"dynamic": false,
"info": "Message to be passed as output.",
"title_case": false,
"type": "str"
},
"sender": {
"trace_as_metadata": true,
"options": [
"Machine",
"User"
],
"required": false,
"placeholder": "",
"show": true,
"value": "Machine",
"name": "sender",
"display_name": "Sender Type",
"advanced": true,
"dynamic": false,
"info": "Type of sender.",
"title_case": false,
"type": "str"
},
"sender_name": {
"trace_as_input": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"value": "",
"name": "sender_name",
"display_name": "Sender Name",
"advanced": true,
"input_types": [
"Message"
],
"dynamic": false,
"info": "Name of the sender.",
"title_case": false,
"type": "str"
},
"session_id": {
"trace_as_input": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"value": "",
"name": "session_id",
"display_name": "Session ID",
"advanced": true,
"input_types": [
"Message"
],
"dynamic": false,
"info": "Session ID for the message.",
"title_case": false,
"type": "str"
},
"store_message": {
"trace_as_metadata": true,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"value": true,
"name": "store_message",
"display_name": "Store Messages",
"advanced": true,
"dynamic": false,
"info": "Store the message in the history.",
"title_case": false,
"type": "bool"
}
},
"description": "Display a chat message in the Playground.",
"icon": "ChatOutput",
"base_classes": [
"Message"
],
"display_name": "Chat Output",
"documentation": "",
"custom_fields": {},
"output_types": [],
"pinned": false,
"conditional_paths": [],
"frozen": false,
"outputs": [
{
"types": [
"Message"
],
"selected": "Message",
"name": "message",
"display_name": "Message",
"method": "message_response",
"value": "__UNDEFINED__",
"cache": true
}
],
"field_order": [
"input_value",
"store_message",
"sender",
"sender_name",
"session_id",
"data_template"
],
"beta": false,
"edited": false
},
"id": "ChatOutput-dJRst",
"description": "Display a chat message in the Playground.",
"display_name": "Chat Output"
},
"selected": false,
"width": 384,
"height": 403,
"positionAbsolute": {
"x": 820,
"y": 224.296875
},
"dragging": false
}
],
"edges": [
{
"source": "ChatInput-irFJf",
"sourceHandle": "{œdataTypeœ:œChatInputœ,œidœ:œChatInput-irFJfœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}",
"target": "ChatOutput-dJRst",
"targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-dJRstœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
"data": {
"targetHandle": {
"fieldName": "input_value",
"id": "ChatOutput-dJRst",
"inputTypes": [
"Message"
],
"type": "str"
},
"sourceHandle": {
"dataType": "ChatInput",
"id": "ChatInput-irFJf",
"name": "message",
"output_types": [
"Message"
]
}
},
"id": "reactflow__edge-ChatInput-irFJf{œdataTypeœ:œChatInputœ,œidœ:œChatInput-irFJfœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-dJRst{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-dJRstœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
"className": ""
},
{
"source": "TextInput-tcoZg",
"sourceHandle": "{œdataTypeœ:œTextInputœ,œidœ:œTextInput-tcoZgœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}",
"target": "ChatOutput-dJRst",
"targetHandle": "{œfieldNameœ:œsender_nameœ,œidœ:œChatOutput-dJRstœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
"data": {
"targetHandle": {
"fieldName": "sender_name",
"id": "ChatOutput-dJRst",
"inputTypes": [
"Message"
],
"type": "str"
},
"sourceHandle": {
"dataType": "TextInput",
"id": "TextInput-tcoZg",
"name": "text",
"output_types": [
"Message"
]
}
},
"id": "reactflow__edge-TextInput-tcoZg{œdataTypeœ:œTextInputœ,œidœ:œTextInput-tcoZgœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-dJRst{œfieldNameœ:œsender_nameœ,œidœ:œChatOutput-dJRstœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
"className": ""
}
],
"viewport": {
"x": -117,
"y": -69,
"zoom": 1
}
},
"description": "Nurture NLP Nodes Here.",
"name": "Simple API Test",
"last_tested_version": "1.0.9",
"endpoint_name": null,
"is_component": false
}

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749
src/backend/tests/data/WebhookTest.json Normal file
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@ -0,0 +1,749 @@
{
"id": "b00c375e-c858-42b5-a352-561d3f40bd15",
"data": {
"nodes": [
{
"id": "CustomComponent-aF0h1",
"type": "genericNode",
"position": {
"x": 888.0012384532345,
"y": 272.41352212880344
},
"data": {
"type": "CustomComponent",
"node": {
"template": {
"_type": "Component",
"code": {
"type": "code",
"required": true,
"placeholder": "",
"list": false,
"show": true,
"multiline": true,
"value": "# from langflow.field_typing import Data\nfrom langflow.custom import Component\nfrom langflow.io import StrInput\nfrom langflow.schema import Data\nfrom langflow.io import Output\nfrom pathlib import Path\nimport aiofiles\n\nclass CustomComponent(Component):\n display_name = \"Async Component\"\n description = \"Use as a template to create your own component.\"\n documentation: str = \"http://docs.langflow.org/components/custom\"\n icon = \"custom_components\"\n\n inputs = [\n StrInput(name=\"input_value\", display_name=\"Input Value\", value=\"Hello, World!\", input_types=[\"Data\"]),\n ]\n\n outputs = [\n Output(display_name=\"Output\", name=\"output\", method=\"build_output\"),\n ]\n\n async def build_output(self) -> Data:\n if isinstance(self.input_value, Data):\n data = self.input_value\n else:\n data = Data(value=self.input_value)\n \n if \"path\" in data:\n path = self.resolve_path(data.path)\n path_obj = Path(path)\n async with aiofiles.open(path, \"w\") as f:\n await f.write(data.model_dump())\n \n self.status = data\n return data",
"fileTypes": [],
"file_path": "",
"password": false,
"name": "code",
"advanced": true,
"dynamic": true,
"info": "",
"load_from_db": false,
"title_case": false
},
"input_value": {
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"value": "Hello, World!",
"name": "input_value",
"display_name": "Input Value",
"advanced": false,
"input_types": [
"Data"
],
"dynamic": false,
"info": "",
"title_case": false,
"type": "str"
}
},
"description": "Use as a template to create your own component.",
"icon": "custom_components",
"base_classes": [
"Data"
],
"display_name": "Custom Component",
"documentation": "http://docs.langflow.org/components/custom",
"custom_fields": {},
"output_types": [],
"pinned": false,
"conditional_paths": [],
"frozen": false,
"outputs": [
{
"types": [
"Data"
],
"selected": "Data",
"name": "output",
"display_name": "Output",
"method": "build_output",
"value": "__UNDEFINED__",
"cache": true,
"hidden": false
}
],
"field_order": [
"input_value"
],
"beta": false,
"edited": false
},
"id": "CustomComponent-aF0h1",
"description": "Use as a template to create your own component.",
"display_name": "Custom Component"
},
"selected": false,
"width": 384,
"height": 337,
"positionAbsolute": {
"x": 888.0012384532345,
"y": 272.41352212880344
},
"dragging": false
},
{
"id": "Webhook-BeRcd",
"type": "genericNode",
"position": {
"x": 418,
"y": 270.2890625
},
"data": {
"type": "Webhook",
"node": {
"template": {
"_type": "Component",
"code": {
"type": "code",
"required": true,
"placeholder": "",
"list": false,
"show": true,
"multiline": true,
"value": "import json\n\nfrom langflow.custom import Component\nfrom langflow.io import MultilineInput, Output\nfrom langflow.schema import Data\n\n\nclass WebhookComponent(Component):\n display_name = \"Webhook Input\"\n description = \"Defines a webhook input for the flow.\"\n name = \"Webhook\"\n\n inputs = [\n MultilineInput(\n name=\"data\",\n display_name=\"Data\",\n info=\"Use this field to quickly test the webhook component by providing a JSON payload.\",\n )\n ]\n outputs = [\n Output(display_name=\"Data\", name=\"output_data\", method=\"build_data\"),\n ]\n\n def build_data(self) -> Data:\n message: str | Data = \"\"\n if not self.data:\n self.status = \"No data provided.\"\n return Data(data={})\n try:\n body = json.loads(self.data or \"{}\")\n except json.JSONDecodeError:\n body = {\"payload\": self.data}\n message = f\"Invalid JSON payload. Please check the format.\\n\\n{self.data}\"\n data = Data(data=body)\n if not message:\n message = data\n self.status = message\n return data\n",
"fileTypes": [],
"file_path": "",
"password": false,
"name": "code",
"advanced": true,
"dynamic": true,
"info": "",
"load_from_db": false,
"title_case": false
},
"data": {
"trace_as_input": true,
"multiline": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"value": "{\"test\":1}",
"name": "data",
"display_name": "Data",
"advanced": false,
"input_types": [
"Message"
],
"dynamic": false,
"info": "Use this field to quickly test the webhook component by providing a JSON payload.",
"title_case": false,
"type": "str"
}
},
"description": "Defines a webhook input for the flow.",
"base_classes": [
"Data"
],
"display_name": "Webhook Input",
"documentation": "",
"custom_fields": {},
"output_types": [],
"pinned": false,
"conditional_paths": [],
"frozen": false,
"outputs": [
{
"types": [
"Data"
],
"selected": "Data",
"name": "output_data",
"display_name": "Data",
"method": "build_data",
"value": "__UNDEFINED__",
"cache": true,
"hidden": false
}
],
"field_order": [
"data"
],
"beta": false,
"edited": false
},
"id": "Webhook-BeRcd",
"description": "Defines a webhook input for the flow.",
"display_name": "Webhook Input"
},
"selected": false,
"width": 384,
"height": 309,
"dragging": true,
"positionAbsolute": {
"x": 418,
"y": 270.2890625
}
},
{
"id": "ChatInput-QivBB",
"type": "genericNode",
"position": {
"x": 419.7235078147726,
"y": 646.9863203129902
},
"data": {
"type": "ChatInput",
"node": {
"template": {
"_type": "Component",
"files": {
"trace_as_metadata": true,
"file_path": "",
"fileTypes": [
"txt",
"md",
"mdx",
"csv",
"json",
"yaml",
"yml",
"xml",
"html",
"htm",
"pdf",
"docx",
"py",
"sh",
"sql",
"js",
"ts",
"tsx",
"jpg",
"jpeg",
"png",
"bmp",
"image"
],
"list": true,
"required": false,
"placeholder": "",
"show": true,
"value": "",
"name": "files",
"display_name": "Files",
"advanced": true,
"dynamic": false,
"info": "Files to be sent with the message.",
"title_case": false,
"type": "file"
},
"code": {
"type": "code",
"required": true,
"placeholder": "",
"list": false,
"show": true,
"multiline": true,
"value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n name = \"ChatInput\"\n\n inputs = [\n MultilineInput(\n name=\"input_value\",\n display_name=\"Text\",\n value=\"\",\n info=\"Message to be passed as input.\",\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"User\",\n info=\"Type of sender.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=\"User\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n FileInput(\n name=\"files\",\n display_name=\"Files\",\n file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n info=\"Files to be sent with the message.\",\n advanced=True,\n is_list=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n ]\n\n def message_response(self) -> Message:\n message = Message(\n text=self.input_value,\n sender=self.sender,\n sender_name=self.sender_name,\n session_id=self.session_id,\n files=self.files,\n )\n if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n self.store_message(message)\n self.message.value = message\n\n self.status = message\n return message\n",
"fileTypes": [],
"file_path": "",
"password": false,
"name": "code",
"advanced": true,
"dynamic": true,
"info": "",
"load_from_db": false,
"title_case": false
},
"input_value": {
"trace_as_input": true,
"multiline": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"value": "Should not run",
"name": "input_value",
"display_name": "Text",
"advanced": false,
"input_types": [
"Message"
],
"dynamic": false,
"info": "Message to be passed as input.",
"title_case": false,
"type": "str"
},
"sender": {
"trace_as_metadata": true,
"options": [
"Machine",
"User"
],
"required": false,
"placeholder": "",
"show": true,
"value": "User",
"name": "sender",
"display_name": "Sender Type",
"advanced": true,
"dynamic": false,
"info": "Type of sender.",
"title_case": false,
"type": "str"
},
"sender_name": {
"trace_as_input": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"value": "User",
"name": "sender_name",
"display_name": "Sender Name",
"advanced": true,
"input_types": [
"Message"
],
"dynamic": false,
"info": "Name of the sender.",
"title_case": false,
"type": "str"
},
"session_id": {
"trace_as_input": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"value": "",
"name": "session_id",
"display_name": "Session ID",
"advanced": true,
"input_types": [
"Message"
],
"dynamic": false,
"info": "Session ID for the message.",
"title_case": false,
"type": "str"
}
},
"description": "Get chat inputs from the Playground.",
"icon": "ChatInput",
"base_classes": [
"Message"
],
"display_name": "Chat Input",
"documentation": "",
"custom_fields": {},
"output_types": [],
"pinned": false,
"conditional_paths": [],
"frozen": false,
"outputs": [
{
"types": [
"Message"
],
"selected": "Message",
"name": "message",
"display_name": "Message",
"method": "message_response",
"value": "__UNDEFINED__",
"cache": true,
"hidden": false
}
],
"field_order": [
"input_value",
"sender",
"sender_name",
"session_id",
"files"
],
"beta": false,
"edited": false
},
"id": "ChatInput-QivBB"
},
"selected": false,
"width": 384,
"height": 309,
"positionAbsolute": {
"x": 419.7235078147726,
"y": 646.9863203129902
},
"dragging": false
},
{
"id": "ChatOutput-mN2VY",
"type": "genericNode",
"position": {
"x": 884.7327265656637,
"y": 662.4287265670896
},
"data": {
"type": "ChatOutput",
"node": {
"template": {
"_type": "Component",
"code": {
"type": "code",
"required": true,
"placeholder": "",
"list": false,
"show": true,
"multiline": true,
"value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n name = \"ChatOutput\"\n\n inputs = [\n MessageTextInput(\n name=\"input_value\",\n display_name=\"Text\",\n info=\"Message to be passed as output.\",\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[\"Machine\", \"User\"],\n value=\"Machine\",\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n ),\n MessageTextInput(\n name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n ]\n\n def message_response(self) -> Message:\n message = Message(\n text=self.input_value,\n sender=self.sender,\n sender_name=self.sender_name,\n session_id=self.session_id,\n )\n if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n self.store_message(message)\n self.message.value = message\n\n self.status = message\n return message\n",
"fileTypes": [],
"file_path": "",
"password": false,
"name": "code",
"advanced": true,
"dynamic": true,
"info": "",
"load_from_db": false,
"title_case": false
},
"data_template": {
"trace_as_input": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"value": "{text}",
"name": "data_template",
"display_name": "Data Template",
"advanced": true,
"input_types": [
"Message"
],
"dynamic": false,
"info": "Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.",
"title_case": false,
"type": "str"
},
"input_value": {
"trace_as_input": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"value": "",
"name": "input_value",
"display_name": "Text",
"advanced": false,
"input_types": [
"Message"
],
"dynamic": false,
"info": "Message to be passed as output.",
"title_case": false,
"type": "str"
},
"sender": {
"trace_as_metadata": true,
"options": [
"Machine",
"User"
],
"required": false,
"placeholder": "",
"show": true,
"value": "Machine",
"name": "sender",
"display_name": "Sender Type",
"advanced": true,
"dynamic": false,
"info": "Type of sender.",
"title_case": false,
"type": "str"
},
"sender_name": {
"trace_as_input": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"value": "AI",
"name": "sender_name",
"display_name": "Sender Name",
"advanced": true,
"input_types": [
"Message"
],
"dynamic": false,
"info": "Name of the sender.",
"title_case": false,
"type": "str"
},
"session_id": {
"trace_as_input": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"value": "",
"name": "session_id",
"display_name": "Session ID",
"advanced": true,
"input_types": [
"Message"
],
"dynamic": false,
"info": "Session ID for the message.",
"title_case": false,
"type": "str"
}
},
"description": "Display a chat message in the Playground.",
"icon": "ChatOutput",
"base_classes": [
"Message"
],
"display_name": "Chat Output",
"documentation": "",
"custom_fields": {},
"output_types": [],
"pinned": false,
"conditional_paths": [],
"frozen": false,
"outputs": [
{
"types": [
"Message"
],
"selected": "Message",
"name": "message",
"display_name": "Message",
"method": "message_response",
"value": "__UNDEFINED__",
"cache": true
}
],
"field_order": [
"input_value",
"sender",
"sender_name",
"session_id",
"data_template"
],
"beta": false,
"edited": false
},
"id": "ChatOutput-mN2VY"
},
"selected": false,
"width": 384,
"height": 309,
"positionAbsolute": {
"x": 884.7327265656637,
"y": 662.4287265670896
},
"dragging": false
},
{
"id": "CustomComponent-Ntw7h",
"type": "genericNode",
"position": {
"x": 1396.7134608749789,
"y": 284.91367968123217
},
"data": {
"type": "CustomComponent",
"node": {
"template": {
"_type": "Component",
"code": {
"type": "code",
"required": true,
"placeholder": "",
"list": false,
"show": true,
"multiline": true,
"value": "# from langflow.field_typing import Data\nfrom langflow.custom import Component\nfrom langflow.io import StrInput\nfrom langflow.schema import Data\nfrom langflow.io import Output\nfrom pathlib import Path\nimport httpx\nclass CustomComponent(Component):\n display_name = \"Async Component\"\n description = \"Use as a template to create your own component.\"\n documentation: str = \"http://docs.langflow.org/components/custom\"\n icon = \"custom_components\"\n\n inputs = [\n StrInput(name=\"input_value\", display_name=\"Input Value\", value=\"Hello, World!\", input_types=[\"Data\"]),\n ]\n\n outputs = [\n Output(display_name=\"Output\", name=\"output\", method=\"build_output\"),\n ]\n\n async def build_output(self) -> Data:\n async with httpx.AsyncClient() as client:\n response = await client.get(\"https://www.google.com\")\n response.raise_for_status()\n return Data(response=response.text)",
"fileTypes": [],
"file_path": "",
"password": false,
"name": "code",
"advanced": true,
"dynamic": true,
"info": "",
"load_from_db": false,
"title_case": false
},
"input_value": {
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"value": "Hello, World!",
"name": "input_value",
"display_name": "Input Value",
"advanced": false,
"input_types": [
"Data"
],
"dynamic": false,
"info": "",
"title_case": false,
"type": "str"
}
},
"description": "Use as a template to create your own component.",
"icon": "custom_components",
"base_classes": [],
"display_name": "Custom Component",
"documentation": "http://docs.langflow.org/components/custom",
"custom_fields": {},
"output_types": [],
"pinned": false,
"conditional_paths": [],
"frozen": false,
"outputs": [
{
"types": [],
"name": "output",
"display_name": "Output",
"method": "build_output",
"value": "__UNDEFINED__",
"cache": true
}
],
"field_order": [
"input_value"
],
"beta": false,
"edited": true
},
"id": "CustomComponent-Ntw7h",
"description": "Use as a template to create your own component.",
"display_name": "Custom Component"
},
"selected": true,
"width": 384,
"height": 337,
"positionAbsolute": {
"x": 1396.7134608749789,
"y": 284.91367968123217
},
"dragging": false
}
],
"edges": [
{
"source": "Webhook-BeRcd",
"sourceHandle": "{œdataTypeœ:œWebhookœ,œidœ:œWebhook-BeRcdœ,œnameœ:œoutput_dataœ,œoutput_typesœ:[œDataœ]}",
"target": "CustomComponent-aF0h1",
"targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œCustomComponent-aF0h1œ,œinputTypesœ:[œDataœ],œtypeœ:œstrœ}",
"data": {
"targetHandle": {
"fieldName": "input_value",
"id": "CustomComponent-aF0h1",
"inputTypes": [
"Data"
],
"type": "str"
},
"sourceHandle": {
"dataType": "Webhook",
"id": "Webhook-BeRcd",
"name": "output_data",
"output_types": [
"Data"
]
}
},
"id": "reactflow__edge-Webhook-BeRcd{œdataTypeœ:œWebhookœ,œidœ:œWebhook-BeRcdœ,œnameœ:œoutput_dataœ,œoutput_typesœ:[œDataœ]}-CustomComponent-aF0h1{œfieldNameœ:œinput_valueœ,œidœ:œCustomComponent-aF0h1œ,œinputTypesœ:[œDataœ],œtypeœ:œstrœ}",
"className": ""
},
{
"source": "ChatInput-QivBB",
"sourceHandle": "{œdataTypeœ:œChatInputœ,œidœ:œChatInput-QivBBœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}",
"target": "ChatOutput-mN2VY",
"targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-mN2VYœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
"data": {
"targetHandle": {
"fieldName": "input_value",
"id": "ChatOutput-mN2VY",
"inputTypes": [
"Message"
],
"type": "str"
},
"sourceHandle": {
"dataType": "ChatInput",
"id": "ChatInput-QivBB",
"name": "message",
"output_types": [
"Message"
]
}
},
"id": "reactflow__edge-ChatInput-QivBB{œdataTypeœ:œChatInputœ,œidœ:œChatInput-QivBBœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-mN2VY{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-mN2VYœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
"className": ""
},
{
"source": "CustomComponent-aF0h1",
"sourceHandle": "{œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-aF0h1œ,œnameœ:œoutputœ,œoutput_typesœ:[œDataœ]}",
"target": "CustomComponent-Ntw7h",
"targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œCustomComponent-Ntw7hœ,œinputTypesœ:[œDataœ],œtypeœ:œstrœ}",
"data": {
"targetHandle": {
"fieldName": "input_value",
"id": "CustomComponent-Ntw7h",
"inputTypes": [
"Data"
],
"type": "str"
},
"sourceHandle": {
"dataType": "CustomComponent",
"id": "CustomComponent-aF0h1",
"name": "output",
"output_types": [
"Data"
]
}
},
"id": "reactflow__edge-CustomComponent-aF0h1{œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-aF0h1œ,œnameœ:œoutputœ,œoutput_typesœ:[œDataœ]}-CustomComponent-Ntw7h{œfieldNameœ:œinput_valueœ,œidœ:œCustomComponent-Ntw7hœ,œinputTypesœ:[œDataœ],œtypeœ:œstrœ}"
}
],
"viewport": {
"x": -7.6743264594028915,
"y": 186.6544574916296,
"zoom": 0.520510798842055
}
},
"description": "The Power of Language at Your Fingertips.",
"name": "Webhook Test",
"last_tested_version": "1.0.7",
"endpoint_name": "webhook-test",
"is_component": false
}

510
src/backend/tests/data/basic_example.json Normal file
View file

@ -0,0 +1,510 @@
{
"description": "",
"name": "BasicExample",
"id": "a53f9130-f2fa-4a3e-b22a-3856d946351a",
"data": {
"nodes": [
{
"width": 384,
"height": 267,
"id": "dndnode_81",
"type": "genericNode",
"position": {
"x": 1030,
"y": 694
},
"data": {
"type": "TimeTravelGuideChain",
"node": {
"template": {
"llm": {
"required": true,
"placeholder": "",
"show": true,
"multiline": false,
"password": false,
"name": "llm",
"advanced": false,
"type": "BaseLanguageModel",
"list": false
},
"memory": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"password": false,
"name": "memory",
"advanced": false,
"type": "BaseChatMemory",
"list": false
},
"_type": "TimeTravelGuideChain"
},
"description": "Time travel guide chain to be used in the flow.",
"base_classes": [
"LLMChain",
"BaseCustomChain",
"TimeTravelGuideChain",
"Chain",
"ConversationChain"
]
},
"id": "dndnode_81",
"value": null
},
"selected": false,
"positionAbsolute": {
"x": 1030,
"y": 694
},
"dragging": false
},
{
"width": 384,
"height": 597,
"id": "dndnode_82",
"type": "genericNode",
"position": {
"x": 520,
"y": 732
},
"data": {
"type": "OpenAI",
"node": {
"template": {
"cache": {
"required": false,
"placeholder": "",
"show": false,
"multiline": false,
"password": false,
"name": "cache",
"advanced": false,
"type": "bool",
"list": false
},
"verbose": {
"required": false,
"placeholder": "",
"show": false,
"multiline": false,
"password": false,
"name": "verbose",
"advanced": false,
"type": "bool",
"list": false
},
"callbacks": {
"required": false,
"placeholder": "",
"show": false,
"multiline": false,
"password": false,
"name": "callbacks",
"advanced": false,
"type": "langchain.callbacks.base.BaseCallbackHandler",
"list": true
},
"client": {
"required": false,
"placeholder": "",
"show": false,
"multiline": false,
"password": false,
"name": "client",
"advanced": false,
"type": "Any",
"list": false
},
"model_name": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"value": "text-davinci-003",
"password": false,
"options": [
"text-davinci-003",
"text-davinci-002",
"text-curie-001",
"text-babbage-001",
"text-ada-001"
],
"name": "model_name",
"advanced": false,
"type": "str",
"list": true
},
"temperature": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"value": 0.7,
"password": false,
"name": "temperature",
"advanced": false,
"type": "float",
"list": false
},
"max_tokens": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"value": 256,
"password": true,
"name": "max_tokens",
"advanced": false,
"type": "int",
"list": false
},
"top_p": {
"required": false,
"placeholder": "",
"show": false,
"multiline": false,
"value": 1,
"password": false,
"name": "top_p",
"advanced": false,
"type": "float",
"list": false
},
"frequency_penalty": {
"required": false,
"placeholder": "",
"show": false,
"multiline": false,
"value": 0,
"password": false,
"name": "frequency_penalty",
"advanced": false,
"type": "float",
"list": false
},
"presence_penalty": {
"required": false,
"placeholder": "",
"show": false,
"multiline": false,
"value": 0,
"password": false,
"name": "presence_penalty",
"advanced": false,
"type": "float",
"list": false
},
"n": {
"required": false,
"placeholder": "",
"show": false,
"multiline": false,
"value": 1,
"password": false,
"name": "n",
"advanced": false,
"type": "int",
"list": false
},
"best_of": {
"required": false,
"placeholder": "",
"show": false,
"multiline": false,
"value": 1,
"password": false,
"name": "best_of",
"advanced": false,
"type": "int",
"list": false
},
"model_kwargs": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"password": false,
"name": "model_kwargs",
"advanced": true,
"type": "code",
"list": false
},
"openai_api_key": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"value": null,
"password": true,
"name": "openai_api_key",
"display_name": "OpenAI API Key",
"advanced": false,
"type": "str",
"list": false
},
"openai_api_base": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"password": true,
"name": "openai_api_base",
"advanced": false,
"type": "str",
"list": false
},
"openai_organization": {
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"show": false,
"multiline": false,
"password": false,
"name": "openai_organization",
"advanced": false,
"type": "str",
"list": false
},
"batch_size": {
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"show": false,
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"value": 20,
"password": false,
"name": "batch_size",
"advanced": false,
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},
"request_timeout": {
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"name": "request_timeout",
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},
"logit_bias": {
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"type": "code",
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},
"max_retries": {
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"name": "max_retries",
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},
"streaming": {
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"name": "streaming",
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},
"allowed_special": {
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"name": "allowed_special",
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"type": "Literal'all'",
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},
"disallowed_special": {
"required": false,
"placeholder": "",
"show": false,
"multiline": false,
"value": "all",
"password": false,
"name": "disallowed_special",
"advanced": false,
"type": "Literal'all'",
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},
"_type": "OpenAI"
},
"description": "Wrapper around OpenAI large language models.",
"base_classes": [
"BaseLLM",
"OpenAI",
"BaseOpenAI",
"BaseLanguageModel"
]
},
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"value": null
},
"selected": false,
"positionAbsolute": {
"x": 520,
"y": 732
},
"dragging": false
},
{
"width": 384,
"height": 273,
"id": "dndnode_83",
"type": "genericNode",
"position": {
"x": 512,
"y": 402
},
"data": {
"type": "ConversationBufferMemory",
"node": {
"template": {
"chat_memory": {
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"placeholder": "",
"show": false,
"multiline": false,
"password": false,
"name": "chat_memory",
"advanced": false,
"type": "BaseChatMessageHistory",
"list": false
},
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"name": "output_key",
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},
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},
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},
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"value": "Human",
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"name": "human_prefix",
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},
"ai_prefix": {
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"multiline": false,
"value": "AI",
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"name": "ai_prefix",
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},
"memory_key": {
"required": false,
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"show": true,
"multiline": false,
"value": "history",
"password": false,
"name": "memory_key",
"advanced": false,
"type": "str",
"list": false
},
"_type": "ConversationBufferMemory"
},
"description": "Buffer for storing conversation memory.",
"base_classes": [
"ConversationBufferMemory",
"BaseChatMemory",
"BaseMemory"
]
},
"id": "dndnode_83",
"value": null
},
"selected": false,
"positionAbsolute": {
"x": 512,
"y": 402
},
"dragging": false
}
],
"edges": [
{
"source": "dndnode_82",
"sourceHandle": "OpenAI|dndnode_82|BaseLLM|OpenAI|BaseOpenAI|BaseLanguageModel",
"target": "dndnode_81",
"targetHandle": "BaseLanguageModel|llm|dndnode_81",
"className": "animate-pulse",
"id": "reactflow__edge-dndnode_82OpenAI|dndnode_82|BaseLLM|OpenAI|BaseOpenAI|BaseLanguageModel-dndnode_81BaseLanguageModel|llm|dndnode_81",
"selected": false
},
{
"source": "dndnode_83",
"sourceHandle": "ConversationBufferMemory|dndnode_83|ConversationBufferMemory|BaseChatMemory|BaseMemory",
"target": "dndnode_81",
"targetHandle": "BaseChatMemory|memory|dndnode_81",
"className": "animate-pulse",
"id": "reactflow__edge-dndnode_83ConversationBufferMemory|dndnode_83|ConversationBufferMemory|BaseChatMemory|BaseMemory-dndnode_81BaseChatMemory|memory|dndnode_81"
}
],
"viewport": {
"x": 1,
"y": 0,
"zoom": 0.5
}
},
"last_tested_version": "0.6.2"
}

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src/backend/tests/data/component.py Normal file
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import random
from langflow.custom import CustomComponent
class TestComponent(CustomComponent):
def refresh_values(self):
# This is a function that will be called every time the component is updated
# and should return a list of random strings
return [f"Random {random.randint(1, 100)}" for _ in range(5)]
def build_config(self):
return {"param": {"display_name": "Param", "options": self.refresh_values}}
def build(self, param: int):
return param

20
src/backend/tests/data/component_multiple_outputs.py Normal file
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from langflow.custom import Component
from langflow.inputs.inputs import IntInput, MessageTextInput
from langflow.template.field.base import Output
class MultipleOutputsComponent(Component):
inputs = [
MessageTextInput(display_name="Input", name="input"),
IntInput(display_name="Number", name="number"),
]
outputs = [
Output(display_name="Certain Output", name="certain_output", method="certain_output"),
Output(display_name="Other Output", name="other_output", method="other_output"),
]
def certain_output(self) -> str:
return f"This is my string input: {self.input}"
def other_output(self) -> int:
return f"This is my int input multiplied by 2: {self.number * 2}"

22
src/backend/tests/data/component_nested_call.py Normal file
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from random import randint
from langflow.custom import Component
from langflow.inputs.inputs import IntInput, MessageTextInput
from langflow.template.field.base import Output
class MultipleOutputsComponent(Component):
inputs = [
MessageTextInput(display_name="Input", name="input"),
IntInput(display_name="Number", name="number"),
]
outputs = [
Output(display_name="Certain Output", name="certain_output", method="certain_output"),
Output(display_name="Other Output", name="other_output", method="other_output"),
]
def certain_output(self) -> int:
return randint(0, self.number)
def other_output(self) -> int:
return self.certain_output()

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src/backend/tests/data/component_with_templatefield.py Normal file
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import random
from langflow.custom import CustomComponent
from langflow.field_typing import Input
class TestComponent(CustomComponent):
def refresh_values(self):
# This is a function that will be called every time the component is updated
# and should return a list of random strings
return [f"Random {random.randint(1, 100)}" for _ in range(5)]
def build_config(self):
return {"param": Input(display_name="Param", options=self.refresh_values)}
def build(self, param: int):
return param

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src/backend/tests/data/vector_store_grouped.json Normal file
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