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Merge branch 'feature/store' into bug/undo-copy and fixed edit node modal

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This commit is contained in:
Lucas Oliveira 2023-11-23 14:39:53 -03:00
commit 807ea46464
295 changed files with 17407 additions and 7429 deletions
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8
.devcontainer/devcontainer.json
View file

@ -15,7 +15,7 @@
// "forwardPorts": [],
// Use 'postCreateCommand' to run commands after the container is created.
"postCreateCommand": "make install_frontend && make install_backend",
"postCreateCommand": "make setup_devcontainer",
"containerEnv": {
"POETRY_VIRTUALENVS_IN_PROJECT": "true"
@ -31,11 +31,13 @@
"sourcery.sourcery",
"eamodio.gitlens",
"ms-vscode.makefile-tools",
"GitHub.vscode-pull-request-github"
"GitHub.vscode-pull-request-github",
"Codium.codium",
"ms-azuretools.vscode-docker"
]
}
}
// Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root.
// "remoteUser": "root"
}
}

20
.env.example
View file

@ -56,6 +56,14 @@ LANGFLOW_REMOVE_API_KEYS=
# LANGFLOW_REDIS_CACHE_EXPIRE (default: 3600)
LANGFLOW_CACHE_TYPE=
# Auto login
# If set to true then a superuser will be logged in automatically
# and the login page will be skipped, keeping the
# default experience of Langflow
# Values: true, false
# Example: LANGFLOW_AUTO_LOGIN=true
LANGFLOW_AUTO_LOGIN=
# Superuser username
# Example: LANGFLOW_SUPERUSER=admin
LANGFLOW_SUPERUSER=
@ -63,3 +71,15 @@ LANGFLOW_SUPERUSER=
# Superuser password
# Example: LANGFLOW_SUPERUSER_PASSWORD=123456
LANGFLOW_SUPERUSER_PASSWORD=
# STORE_URL
# Example: LANGFLOW_STORE_URL=https://api.langflow.store
LANGFLOW_STORE_URL=
# DOWNLOAD_WEBHOOK_URL
#
LANGFLOW_DOWNLOAD_WEBHOOK_URL=
# LIKE_WEBHOOK_URL
#
LANGFLOW_LIKE_WEBHOOK_URL=

2
.github/workflows/lint.yml vendored
View file

@ -15,7 +15,7 @@ on:
- "pyproject.toml"
env:
POETRY_VERSION: "1.4.0"
POETRY_VERSION: "1.7.0"
jobs:
lint:

4
.gitignore vendored
View file

@ -254,4 +254,6 @@ langflow.db
/tmp/*
src/backend/langflow/frontend/
.docker
.docker
.idea

3
.vscode/launch.json vendored
View file

@ -16,7 +16,8 @@
"debug"
],
"jinja": true,
"justMyCode": true
"justMyCode": true,
"envFile": "${workspaceFolder}/.env"
},
{
"name": "Python: Remote Attach",

2
Dockerfile
View file

@ -11,5 +11,5 @@ WORKDIR $HOME/app
COPY --chown=user . $HOME/app
RUN pip install langflow>==0.0.86 -U --user
RUN pip install langflow>==0.5.0 -U --user
CMD ["python", "-m", "langflow", "run", "--host", "0.0.0.0", "--port", "7860"]

22
Makefile
View file

@ -23,16 +23,19 @@ tests:
poetry run pytest tests
tests_frontend:
cd src/frontend && ./run-tests.sh
ifeq ($(UI), true)
cd src/frontend && ./run-tests.sh --ui
else
cd src/frontend && ./run-tests.sh
endif
format:
poetry run black .
poetry run ruff . --fix
poetry run ruff format .
cd src/frontend && npm run format
lint:
poetry run mypy src/backend/langflow
poetry run black . --check
poetry run ruff . --fix
install_frontend:
@ -42,18 +45,20 @@ install_frontendc:
cd src/frontend && rm -rf node_modules package-lock.json && npm install
run_frontend:
@-kill -9 `lsof -t -i:3000`
cd src/frontend && npm start
run_cli:
poetry run langflow run --path src/frontend/build
poetry run langflow --path src/frontend/build
run_cli_debug:
poetry run langflow run --path src/frontend/build --log-level debug
poetry run langflow --path src/frontend/build --log-level debug
setup_devcontainer:
make init
make build_frontend
poetry run langflow --path src/frontend/build
@echo 'Run Cli'
make run_cli
frontend:
@-make install_frontend || (echo "An error occurred while installing frontend dependencies. Attempting to fix." && make install_frontendc)
@ -68,12 +73,13 @@ install_backend:
backend:
make install_backend
@-kill -9 `lsof -t -i:7860`
ifeq ($(login),1)
@echo "Running backend without autologin";
poetry run langflow run --backend-only --port 7860 --host 0.0.0.0 --no-open-browser
poetry run langflow run --backend-only --port 7860 --host 0.0.0.0 --no-open-browser --env-file .env
else
@echo "Running backend with autologin";
LANGFLOW_AUTO_LOGIN=True poetry run langflow run --backend-only --port 7860 --host 0.0.0.0 --no-open-browser
LANGFLOW_AUTO_LOGIN=True poetry run langflow run --backend-only --port 7860 --host 0.0.0.0 --no-open-browser --env-file .env
endif
build_and_run:

2
base.Dockerfile
View file

@ -23,7 +23,7 @@ ENV PYTHONUNBUFFERED=1 \
\
# poetry
# https://python-poetry.org/docs/configuration/#using-environment-variables
POETRY_VERSION=1.5.1 \
POETRY_VERSION=1.7 \
# make poetry install to this location
POETRY_HOME="/opt/poetry" \
# make poetry create the virtual environment in the project's root

9
docker_example/Dockerfile
View file

@ -1,14 +1,15 @@
FROM python:3.10-slim
RUN apt-get update && apt-get install gcc g++ git make -y
RUN apt-get update && apt-get install gcc g++ git make -y && apt-get clean \
&& rm -rf /var/lib/apt/lists/*
RUN useradd -m -u 1000 user
USER user
ENV HOME=/home/user \
PATH=/home/user/.local/bin:$PATH
PATH=/home/user/.local/bin:$PATH
WORKDIR $HOME/app
COPY --chown=user . $HOME/app
RUN pip install langflow>==0.0.71 -U --user
CMD ["langflow", "--host", "0.0.0.0", "--port", "7860"]
RUN pip install langflow>==0.5.0 -U --user
CMD ["python", "-m", "langflow", "run", "--host", "0.0.0.0", "--port", "7860"]

2
docker_example/docker-compose.yml
View file

@ -7,4 +7,4 @@ services:
dockerfile: Dockerfile
ports:
- "7860:7860"
command: langflow --host 0.0.0.0
command: langflow run --host 0.0.0.0

16
docs/docs/components/embeddings.mdx
View file

@ -12,6 +12,22 @@ Embeddings are vector representations of text that capture the semantic meaning
---
### BedrockEmbeddings
Used to load [Amazon Bedrocks’s](https://aws.amazon.com/bedrock/) embedding models.
**Params**
- **credentials_profile_name:** The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which has either access keys or role information specified. If not specified, the default credential profile or, if on an EC2 instance, credentials from IMDS will be used. See [the AWS documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html) for more details.
- **model_id:** Id of the model to call, e.g., amazon.titan-embed-text-v1, this is equivalent to the modelId property in the list-foundation-models api.
- **endpoint_url:** Needed if you don’t want to default to us-east-1 endpoint.
- **region_name:** The aws region e.g., us-west-2. Fallsback to AWS_DEFAULT_REGION env variable or region specified in ~/.aws/config in case it is not provided here.
---
### CohereEmbeddings
Used to load [Cohere’s](https://cohere.com/) embedding models.

38
docs/docs/components/llms.mdx
View file

@ -217,4 +217,40 @@ Vertex AI is a cloud computing platform offered by Google Cloud Platform (GCP).
- **top_k:** How the model selects tokens for output, the next token is selected from – defaults to `40`.
- **top_p:** Tokens are selected from most probable to least until the sum of their – defaults to `0.95`.
- **tuned_model_name:** The name of a tuned model. If provided, model_name is ignored.
- **verbose:** This parameter is used to control the level of detail in the output of the chain. When set to True, it will print out some internal states of the chain while it is being run, which can help debug and understand the chain's behavior. If set to False, it will suppress the verbose output – defaults to `False`.
- **verbose:** This parameter is used to control the level of detail in the output of the chain. When set to True, it will print out some internal states of the chain while it is being run, which can help debug and understand the chain's behavior. If set to False, it will suppress the verbose output – defaults to `False`.
---
### QianfanLLMEndpoint
Wrapper around [Baidu Qianfan](https://cloud.baidu.com/doc/WENXINWORKSHOP/index.html) large language models.
:::info
The Qianfan Big Model Platform is a one-stop platform for enterprise developers to develop and operate large models and services. It provides data management based on ERNIE Bot's underlying model (Ernie Bot), automatic model customization and fine-tuning, and one-stop large-scale model customization services for cloud deployment of prediction services, and provides ERNIE Bot's enterprise level service API that can be quickly called, helping to implement the demand for generative AI applications in various industries.
:::
- **Model Name:** Model name. you could get from https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu preset models are mapping to an endpoint. `Model Name` will be ignored if `Endpoint` is set.
- **Qianfan Ak:** which you could get from https://cloud.baidu.com/product/wenxinworkshop.
- **Qianfan Sk:** which you could get from https://cloud.baidu.com/product/wenxinworkshop.
- **Top p:** Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo. The diversity of the output text is affected, and the larger the value, the stronger the diversity of the generated text - defaults to `0.8`.
- **Temperature:** Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo. Higher values make the output more random, while lower values make it more concentrated and deterministic - defaults to `0.95`.
- **Penalty Score:** Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo. By increasing the penalty for generated tokens, the phenomenon of duplicate generation is reduced. A higher value indicates a higher penalty - defaults to `1.0`.
- **Endpoint:** Endpoint of the Qianfan LLM, required if custom model used.
---
### QianfanChatEndpoint
Wrapper around [Baidu Qianfan](https://cloud.baidu.com/doc/WENXINWORKSHOP/index.html) chat large language models. This component supports some of the LLMs (Large Language Models) available by Baidu qianfan and is used for tasks such as chatbots, Generative Question-Answering (GQA), and summarization.
:::info
The Qianfan Big Model Platform is a one-stop platform for enterprise developers to develop and operate large models and services. It provides data management based on ERNIE Bot's underlying model (Ernie Bot), automatic model customization and fine-tuning, and one-stop large-scale model customization services for cloud deployment of prediction services, and provides ERNIE Bot's enterprise level service API that can be quickly called, helping to implement the demand for generative AI applications in various industries.
:::
- **Model Name:** Model name. you could get from https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu preset models are mapping to an endpoint. `Model Name` will be ignored if `Endpoint` is set.
- **Qianfan Ak:** which you could get from https://cloud.baidu.com/product/wenxinworkshop.
- **Qianfan Sk:** which you could get from https://cloud.baidu.com/product/wenxinworkshop.
- **Top p:** Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo. The diversity of the output text is affected, and the larger the value, the stronger the diversity of the generated text - defaults to `0.8`.
- **Temperature:** Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo. Higher values make the output more random, while lower values make it more concentrated and deterministic - defaults to `0.95`.
- **Penalty Score:** Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo. By increasing the penalty for generated tokens, the phenomenon of duplicate generation is reduced. A higher value indicates a higher penalty - defaults to `1.0`.
- **Endpoint:** Endpoint of the Qianfan LLM, required if custom model used.

2
docs/docs/guidelines/components.mdx
View file

@ -5,7 +5,7 @@ import ReactPlayer from "react-player";
# Component
Components are the building blocks of the flows. They are made of inputs, outputs, and parameters that define their functionality, providing a convenient and straightforward way to compose LLM-based applications. Learn more about components and how they work in the LangChain [documentation](https://docs.langchain.com/docs/category/components) section.
Components are the building blocks of the flows. They are made of inputs, outputs, and parameters that define their functionality, providing a convenient and straightforward way to compose LLM-based applications. Learn more about components and how they work in the LangChain [documentation](https://python.langchain.com/docs/integrations/components) section.
### Component's Features

8
docs/docusaurus.config.js
View file

@ -31,7 +31,7 @@ module.exports = {
[
remarkCodeHike,
{
theme: "github-light",
theme: "github-dark",
showCopyButton: true,
lineNumbers: true,
},
@ -112,8 +112,10 @@ module.exports = {
},
colorMode: {
defaultMode: "light",
disableSwitch: true,
respectPrefersColorScheme: false,
/* Allow users to chose light or dark mode. */
disableSwitch: false,
/* Respect user preferences, such as low light mode in the evening */
respectPrefersColorScheme: true,
},
announcementBar: {
content:

932
package-lock.json generated Normal file
View file

@ -0,0 +1,932 @@
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},
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6
package.json Normal file
View file

@ -0,0 +1,6 @@
{
"dependencies": {
"@radix-ui/react-popover": "^1.0.7",
"cmdk": "^0.2.0"
}
}

5311
poetry.lock generated
View file

File diff suppressed because it is too large Load diff

69
pyproject.toml
View file

@ -1,6 +1,6 @@
[tool.poetry]
name = "langflow"
version = "0.5.1"
version = "0.6.0a0"
description = "A Python package with a built-in web application"
authors = ["Logspace <contact@logspace.ai>"]
maintainers = [
@ -25,30 +25,32 @@ documentation = "https://docs.langflow.org"
langflow = "langflow.__main__:main"
[tool.poetry.dependencies]
python = ">=3.9,<3.11"
fastapi = "^0.103.0"
fastapi = "^0.104.0"
uvicorn = "^0.23.0"
beautifulsoup4 = "^4.12.2"
google-search-results = "^2.4.1"
google-api-python-client = "^2.79.0"
typer = "^0.9.0"
gunicorn = "^21.2.0"
langchain = "^0.0.308"
openai = "^0.27.8"
langchain = "~0.0.338"
openai = "^1.3.4"
pandas = "2.0.3"
chromadb = "^0.3.21"
huggingface-hub = { version = "^0.16.0", extras = ["inference"] }
rich = "^13.5.0"
llama-cpp-python = { version = "~0.1.0", optional = true }
chromadb = "^0.4.0"
huggingface-hub = { version = "^0.19.0", extras = ["inference"] }
rich = "^13.7.0"
llama-cpp-python = { version = "~0.2.0", optional = true }
networkx = "^3.1"
unstructured = "^0.10.0"
pypdf = "^3.15.0"
pypdf = "^3.17.0"
lxml = "^4.9.2"
pysrt = "^1.1.2"
fake-useragent = "^1.2.1"
fake-useragent = "^1.3.0"
docstring-parser = "^0.15"
psycopg2-binary = "^2.9.6"
pyarrow = "^12.0.0"
pyarrow = "^14.0.0"
tiktoken = "~0.5.0"
wikipedia = "^1.4.0"
qdrant-client = "^1.4.0"
@ -57,25 +59,26 @@ weaviate-client = "^3.23.0"
jina = "3.15.2"
sentence-transformers = { version = "^2.2.2", optional = true }
ctransformers = { version = "^0.2.10", optional = true }
cohere = "^4.27.0"
cohere = "^4.32.0"
python-multipart = "^0.0.6"
sqlmodel = "^0.0.8"
# install sqlmodel using https://github.com/honglei/sqlmodel.git
sqlmodel = { git = "https://github.com/honglei/sqlmodel.git", branch = "main" }
faiss-cpu = "^1.7.4"
anthropic = "^0.3.0"
anthropic = "^0.5.0"
orjson = "3.9.3"
multiprocess = "^0.70.14"
cachetools = "^5.3.1"
types-cachetools = "^5.3.0.5"
appdirs = "^1.4.4"
platformdirs = "^3.11.0"
pinecone-client = "^2.2.2"
supabase = "^1.0.3"
pymongo = "^4.4.0"
pymongo = "^4.5.0"
supabase = "^2.0.3"
certifi = "^2023.5.7"
google-cloud-aiplatform = "^1.26.1"
google-cloud-aiplatform = "^1.36.0"
psycopg = "^3.1.9"
psycopg-binary = "^3.1.9"
fastavro = "^1.8.0"
langchain-experimental = "^0.0.8"
langchain-experimental = "*"
celery = { extras = ["redis"], version = "^5.3.1", optional = true }
redis = { version = "^4.6.0", optional = true }
flower = { version = "^2.0.0", optional = true }
@ -84,28 +87,35 @@ passlib = "^1.7.4"
bcrypt = "^4.0.1"
python-jose = "^3.3.0"
metaphor-python = "^0.1.11"
pydantic = "^2.0.0"
pydantic-settings = "^2.0.3"
zep-python = { version = "^1.3.0", allow-prereleases = true }
pywin32 = { version = "^306", markers = "sys_platform == 'win32'" }
loguru = "^0.7.1"
langfuse = "^1.0.13"
langfuse = "^1.1.11"
pillow = "^10.0.0"
metal-sdk = "^2.2.0"
metal-sdk = "^2.4.0"
markupsafe = "^2.1.3"
extract-msg = "^0.45.0"
jq = "^1.6.0"
boto3 = "^1.28.63"
numexpr = "^2.8.6"
qianfan = "0.0.5"
pgvector = "^0.2.3"
[tool.poetry.group.dev.dependencies]
pytest-asyncio = "^0.21.1"
types-redis = "^4.6.0.5"
black = "^23.1.0"
ipykernel = "^6.21.2"
mypy = "^1.1.1"
ruff = "^0.0.254"
ruff = "^0.1.5"
httpx = "*"
pytest = "^7.2.2"
types-requests = "^2.28.11"
requests = "^2.28.0"
pytest-cov = "^4.0.0"
pytest = "^7.4.2"
types-requests = "^2.31.0"
requests = "^2.31.0"
pytest-cov = "^4.1.0"
pandas-stubs = "^2.0.0.230412"
types-pillow = "^9.5.0.2"
types-appdirs = "^1.4.3.5"
types-pyyaml = "^6.0.12.8"
types-python-jose = "^3.3.4.8"
types-passlib = "^1.7.7.13"
@ -134,6 +144,7 @@ markers = ["async_test"]
[tool.ruff]
exclude = ["src/backend/langflow/alembic/*"]
line-length = 120
[build-system]

61
src/backend/langflow/__main__.py
View file

@ -11,7 +11,7 @@ import typer
from dotenv import load_dotenv
from langflow.main import setup_app
from langflow.services.database.utils import session_getter
from langflow.services.getters import get_db_service, get_settings_service
from langflow.services.deps import get_db_service, get_settings_service
from langflow.services.utils import initialize_services, initialize_settings_service
from langflow.utils.logger import configure, logger
from multiprocess import Process, cpu_count # type: ignore
@ -61,6 +61,8 @@ def set_var_for_macos_issue():
import os
os.environ["OBJC_DISABLE_INITIALIZE_FORK_SAFETY"] = "YES"
# https://stackoverflow.com/questions/75747888/uwsgi-segmentation-fault-with-flask-python-app-behind-nginx-after-running-for-2 # noqa
os.environ["no_proxy"] = "*" # to avoid error with gunicorn
logger.debug("Set OBJC_DISABLE_INITIALIZE_FORK_SAFETY to YES to avoid error")
@ -70,6 +72,7 @@ def update_settings(
dev: bool = False,
remove_api_keys: bool = False,
components_path: Optional[Path] = None,
store: bool = True,
):
"""Update the settings from a config file."""
@ -88,16 +91,15 @@ def update_settings(
if components_path:
logger.debug(f"Adding component path {components_path}")
settings_service.settings.update_settings(COMPONENTS_PATH=components_path)
if not store:
logger.debug("Setting store to False")
settings_service.settings.update_settings(STORE=False)
@app.command()
def run(
host: str = typer.Option(
"127.0.0.1", help="Host to bind the server to.", envvar="LANGFLOW_HOST"
),
workers: int = typer.Option(
1, help="Number of worker processes.", envvar="LANGFLOW_WORKERS"
),
host: str = typer.Option("127.0.0.1", help="Host to bind the server to.", envvar="LANGFLOW_HOST"),
workers: int = typer.Option(1, help="Number of worker processes.", envvar="LANGFLOW_WORKERS"),
timeout: int = typer.Option(300, help="Worker timeout in seconds."),
port: int = typer.Option(7860, help="Port to listen on.", envvar="LANGFLOW_PORT"),
components_path: Optional[Path] = typer.Option(
@ -105,32 +107,17 @@ def run(
help="Path to the directory containing custom components.",
envvar="LANGFLOW_COMPONENTS_PATH",
),
config: str = typer.Option(
Path(__file__).parent / "config.yaml", help="Path to the configuration file."
),
config: str = typer.Option(Path(__file__).parent / "config.yaml", help="Path to the configuration file."),
# .env file param
env_file: Path = typer.Option(
None, help="Path to the .env file containing environment variables."
),
log_level: str = typer.Option(
"critical", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL"
),
log_file: Path = typer.Option(
"logs/langflow.log", help="Path to the log file.", envvar="LANGFLOW_LOG_FILE"
),
env_file: Path = typer.Option(None, help="Path to the .env file containing environment variables."),
log_level: str = typer.Option("critical", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL"),
log_file: Path = typer.Option("logs/langflow.log", help="Path to the log file.", envvar="LANGFLOW_LOG_FILE"),
cache: Optional[str] = typer.Option(
envvar="LANGFLOW_LANGCHAIN_CACHE",
help="Type of cache to use. (InMemoryCache, SQLiteCache)",
default=None,
),
dev: bool = typer.Option(False, help="Run in development mode (may contain bugs)"),
# This variable does not work but is set by the .env file
# and works with Pydantic
# database_url: str = typer.Option(
# None,
# help="Database URL to connect to. If not provided, a local SQLite database will be used.",
# envvar="LANGFLOW_DATABASE_URL",
# ),
path: str = typer.Option(
None,
help="Path to the frontend directory containing build files. This is for development purposes only.",
@ -151,6 +138,11 @@ def run(
help="Run only the backend server without the frontend.",
envvar="LANGFLOW_BACKEND_ONLY",
),
store: bool = typer.Option(
True,
help="Enables the store features.",
envvar="LANGFLOW_STORE",
),
):
"""
Run the Langflow.
@ -169,6 +161,7 @@ def run(
remove_api_keys=remove_api_keys,
cache=cache,
components_path=components_path,
store=store,
)
# create path object if path is provided
static_files_dir: Optional[Path] = Path(path) if path else None
@ -198,9 +191,7 @@ def run(
def run_on_mac_or_linux(host, port, log_level, options, app, open_browser=True):
webapp_process = Process(
target=run_langflow, args=(host, port, log_level, options, app)
)
webapp_process = Process(target=run_langflow, args=(host, port, log_level, options, app))
webapp_process.start()
status_code = 0
while status_code != 200:
@ -276,9 +267,7 @@ def print_banner(host, port):
)
# Create a panel with the title and the info text, and a border around it
panel = Panel(
f"{title}\n{info_text}", box=box.ROUNDED, border_style="blue", expand=False
)
panel = Panel(f"{title}\n{info_text}", box=box.ROUNDED, border_style="blue", expand=False)
# Print the banner with a separator line before and after
rprint(panel)
@ -310,12 +299,8 @@ def run_langflow(host, port, log_level, options, app):
@app.command()
def superuser(
username: str = typer.Option(..., prompt=True, help="Username for the superuser."),
password: str = typer.Option(
..., prompt=True, hide_input=True, help="Password for the superuser."
),
log_level: str = typer.Option(
"critical", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL"
),
password: str = typer.Option(..., prompt=True, hide_input=True, help="Password for the superuser."),
log_level: str = typer.Option("critical", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL"),
):
"""
Create a superuser.

2
src/backend/langflow/alembic/env.py
View file

@ -5,7 +5,7 @@ from sqlalchemy import pool
from alembic import context
from langflow.services.database.manager import SQLModel
from langflow.services.database.service import SQLModel
# this is the Alembic Config object, which provides
# access to the values within the .ini file in use.

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