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merge dev into feat-more

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This commit is contained in:
igorrCarvalho 2023-09-21 15:17:08 -03:00
commit e4ba7364bb
345 changed files with 18856 additions and 8899 deletions
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2
src/backend/Dockerfile
View file

@ -11,4 +11,4 @@ RUN rm *.whl
EXPOSE 80
CMD [ "uvicorn", "--host", "0.0.0.0", "--port", "80", "langflow.backend.app:app" ]
CMD [ "uvicorn", "--host", "0.0.0.0", "--port", "7860", "--factory", "langflow.main:create_app" ]

4
src/backend/langflow/__init__.py
View file

@ -1,5 +1,7 @@
from importlib import metadata
from langflow.cache import cache_manager
# Deactivate cache manager for now
# from langflow.services.cache import cache_manager
from langflow.processing.process import load_flow_from_json
from langflow.interface.custom.custom_component import CustomComponent

110
src/backend/langflow/__main__.py
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@ -1,8 +1,11 @@
import sys
import time
import httpx
from langflow.utils.util import get_number_of_workers
from multiprocess import Process # type: ignore
from langflow.services.database.utils import session_getter
from langflow.services.manager import initialize_services, initialize_settings_manager
from langflow.services.utils import get_db_manager, get_settings_manager
from multiprocess import Process, cpu_count # type: ignore
import platform
from pathlib import Path
from typing import Optional
@ -10,19 +13,49 @@ import socket
from rich.panel import Panel
from rich import box
from rich import print as rprint
from rich.table import Table
import typer
from langflow.main import setup_app
from langflow.settings import settings
from langflow.utils.logger import configure, logger
import webbrowser
from dotenv import load_dotenv
from rich.console import Console
console = Console()
app = typer.Typer()
def get_number_of_workers(workers=None):
if workers == -1 or workers is None:
workers = (cpu_count() * 2) + 1
logger.debug(f"Number of workers: {workers}")
return workers
def display_results(results):
"""
Display the results of the migration.
"""
for table_results in results:
table = Table(title=f"Migration {table_results.table_name}")
table.add_column("Name")
table.add_column("Type")
table.add_column("Status")
for result in table_results.results:
status = "Success" if result.success else "Failure"
color = "green" if result.success else "red"
table.add_row(result.name, result.type, f"[{color}]{status}[/{color}]")
console.print(table)
console.print() # Print a new line
def update_settings(
config: str,
cache: str,
cache: Optional[str] = None,
dev: bool = False,
remove_api_keys: bool = False,
components_path: Optional[Path] = None,
@ -30,19 +63,20 @@ def update_settings(
"""Update the settings from a config file."""
# Check for database_url in the environment variables
initialize_settings_manager()
settings_manager = get_settings_manager()
if config:
logger.debug(f"Loading settings from {config}")
settings.update_from_yaml(config, dev=dev)
settings_manager.settings.update_from_yaml(config, dev=dev)
if remove_api_keys:
logger.debug(f"Setting remove_api_keys to {remove_api_keys}")
settings.update_settings(REMOVE_API_KEYS=remove_api_keys)
settings_manager.settings.update_settings(REMOVE_API_KEYS=remove_api_keys)
if cache:
logger.debug(f"Setting cache to {cache}")
settings.update_settings(CACHE=cache)
settings_manager.settings.update_settings(CACHE=cache)
if components_path:
logger.debug(f"Adding component path {components_path}")
settings.update_settings(COMPONENTS_PATH=components_path)
settings_manager.settings.update_settings(COMPONENTS_PATH=components_path)
def serve_on_jcloud():
@ -92,7 +126,7 @@ def serve_on_jcloud():
@app.command()
def serve(
def run(
host: str = typer.Option(
"127.0.0.1", help="Host to bind the server to.", envvar="LANGFLOW_HOST"
),
@ -106,7 +140,9 @@ def serve(
help="Path to the directory containing custom components.",
envvar="LANGFLOW_COMPONENTS_PATH",
),
config: str = typer.Option("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."
@ -117,10 +153,10 @@ def serve(
log_file: Path = typer.Option(
"logs/langflow.log", help="Path to the log file.", envvar="LANGFLOW_LOG_FILE"
),
cache: str = typer.Option(
cache: Optional[str] = typer.Option(
envvar="LANGFLOW_LANGCHAIN_CACHE",
help="Type of cache to use. (InMemoryCache, SQLiteCache)",
default="SQLiteCache",
default=None,
),
jcloud: bool = typer.Option(False, help="Deploy on Jina AI Cloud"),
dev: bool = typer.Option(False, help="Run in development mode (may contain bugs)"),
@ -146,6 +182,11 @@ def serve(
help="Remove API keys from the projects saved in the database.",
envvar="LANGFLOW_REMOVE_API_KEYS",
),
backend_only: bool = typer.Option(
False,
help="Run only the backend server without the frontend.",
envvar="LANGFLOW_BACKEND_ONLY",
),
):
"""
Run the Langflow server.
@ -167,7 +208,7 @@ def serve(
)
# create path object if path is provided
static_files_dir: Optional[Path] = Path(path) if path else None
app = setup_app(static_files_dir=static_files_dir)
app = setup_app(static_files_dir=static_files_dir, backend_only=backend_only)
# check if port is being used
if is_port_in_use(port, host):
port = get_free_port(port)
@ -179,6 +220,10 @@ def serve(
"timeout": timeout,
}
# Define an env variable to know if we are just testing the server
if "pytest" in sys.modules:
return
if platform.system() in ["Windows"]:
# Run using uvicorn on MacOS and Windows
# Windows doesn't support gunicorn
@ -299,6 +344,43 @@ def run_langflow(host, port, log_level, options, app):
sys.exit(1)
@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."
),
):
initialize_services()
db_manager = get_db_manager()
with session_getter(db_manager) as session:
from langflow.services.auth.utils import create_super_user
if create_super_user(db=session, username=username, password=password):
# Verify that the superuser was created
from langflow.services.database.models.user.user import User
user = session.query(User).filter(User.username == username).first()
if user is None:
typer.echo("Superuser creation failed.")
return
typer.echo("Superuser created successfully.")
else:
typer.echo("Superuser creation failed.")
@app.command()
def migration(test: bool = typer.Option(False, help="Run migrations in test mode.")):
initialize_services()
db_manager = get_db_manager()
if not test:
db_manager.run_migrations()
results = db_manager.run_migrations_test()
display_results(results)
def main():
app()

113
src/backend/langflow/alembic.ini Normal file
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@ -0,0 +1,113 @@
# A generic, single database configuration.
[alembic]
# path to migration scripts
script_location = alembic
# template used to generate migration file names; The default value is %%(rev)s_%%(slug)s
# Uncomment the line below if you want the files to be prepended with date and time
# see https://alembic.sqlalchemy.org/en/latest/tutorial.html#editing-the-ini-file
# for all available tokens
# file_template = %%(year)d_%%(month).2d_%%(day).2d_%%(hour).2d%%(minute).2d-%%(rev)s_%%(slug)s
# sys.path path, will be prepended to sys.path if present.
# defaults to the current working directory.
prepend_sys_path = .
# timezone to use when rendering the date within the migration file
# as well as the filename.
# If specified, requires the python-dateutil library that can be
# installed by adding `alembic[tz]` to the pip requirements
# string value is passed to dateutil.tz.gettz()
# leave blank for localtime
# timezone =
# max length of characters to apply to the
# "slug" field
# truncate_slug_length = 40
# set to 'true' to run the environment during
# the 'revision' command, regardless of autogenerate
# revision_environment = false
# set to 'true' to allow .pyc and .pyo files without
# a source .py file to be detected as revisions in the
# versions/ directory
# sourceless = false
# version location specification; This defaults
# to alembic/versions. When using multiple version
# directories, initial revisions must be specified with --version-path.
# The path separator used here should be the separator specified by "version_path_separator" below.
# version_locations = %(here)s/bar:%(here)s/bat:alembic/versions
# version path separator; As mentioned above, this is the character used to split
# version_locations. The default within new alembic.ini files is "os", which uses os.pathsep.
# If this key is omitted entirely, it falls back to the legacy behavior of splitting on spaces and/or commas.
# Valid values for version_path_separator are:
#
# version_path_separator = :
# version_path_separator = ;
# version_path_separator = space
version_path_separator = os # Use os.pathsep. Default configuration used for new projects.
# set to 'true' to search source files recursively
# in each "version_locations" directory
# new in Alembic version 1.10
# recursive_version_locations = false
# the output encoding used when revision files
# are written from script.py.mako
# output_encoding = utf-8
# This is the path to the db in the root of the project.
# When the user runs the Langflow the database url will
# be set dinamically.
sqlalchemy.url = sqlite:///../../../langflow.db
[post_write_hooks]
# post_write_hooks defines scripts or Python functions that are run
# on newly generated revision scripts. See the documentation for further
# detail and examples
# format using "black" - use the console_scripts runner, against the "black" entrypoint
# hooks = black
# black.type = console_scripts
# black.entrypoint = black
# black.options = -l 79 REVISION_SCRIPT_FILENAME
# Logging configuration
[loggers]
keys = root,sqlalchemy,alembic
[handlers]
keys = console
[formatters]
keys = generic
[logger_root]
level = WARN
handlers = console
qualname =
[logger_sqlalchemy]
level = WARN
handlers =
qualname = sqlalchemy.engine
[logger_alembic]
level = INFO
handlers =
qualname = alembic
[handler_console]
class = StreamHandler
args = (sys.stderr,)
level = NOTSET
formatter = generic
[formatter_generic]
format = %(levelname)-5.5s [%(name)s] %(message)s
datefmt = %H:%M:%S

1
src/backend/langflow/alembic/README Normal file
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@ -0,0 +1 @@
Generic single-database configuration.

81
src/backend/langflow/alembic/env.py Normal file
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@ -0,0 +1,81 @@
from logging.config import fileConfig
from sqlalchemy import engine_from_config
from sqlalchemy import pool
from alembic import context
from langflow.services.database.manager import SQLModel
# this is the Alembic Config object, which provides
# access to the values within the .ini file in use.
config = context.config
# Interpret the config file for Python logging.
# This line sets up loggers basically.
if config.config_file_name is not None:
fileConfig(config.config_file_name)
# add your model's MetaData object here
# for 'autogenerate' support
# from myapp import mymodel
# target_metadata = mymodel.Base.metadata
target_metadata = SQLModel.metadata
# other values from the config, defined by the needs of env.py,
# can be acquired:
# my_important_option = config.get_main_option("my_important_option")
# ... etc.
def run_migrations_offline() -> None:
"""Run migrations in 'offline' mode.
This configures the context with just a URL
and not an Engine, though an Engine is acceptable
here as well. By skipping the Engine creation
we don't even need a DBAPI to be available.
Calls to context.execute() here emit the given string to the
script output.
"""
url = config.get_main_option("sqlalchemy.url")
context.configure(
url=url,
target_metadata=target_metadata,
literal_binds=True,
dialect_opts={"paramstyle": "named"},
render_as_batch=True,
)
with context.begin_transaction():
context.run_migrations()
def run_migrations_online() -> None:
"""Run migrations in 'online' mode.
In this scenario we need to create an Engine
and associate a connection with the context.
"""
connectable = engine_from_config(
config.get_section(config.config_ini_section, {}),
prefix="sqlalchemy.",
poolclass=pool.NullPool,
)
with connectable.connect() as connection:
context.configure(
connection=connection, target_metadata=target_metadata, render_as_batch=True
)
with context.begin_transaction():
context.run_migrations()
if context.is_offline_mode():
run_migrations_offline()
else:
run_migrations_online()

27
src/backend/langflow/alembic/script.py.mako Normal file
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@ -0,0 +1,27 @@
"""${message}
Revision ID: ${up_revision}
Revises: ${down_revision | comma,n}
Create Date: ${create_date}
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
import sqlmodel
${imports if imports else ""}
# revision identifiers, used by Alembic.
revision: str = ${repr(up_revision)}
down_revision: Union[str, None] = ${repr(down_revision)}
branch_labels: Union[str, Sequence[str], None] = ${repr(branch_labels)}
depends_on: Union[str, Sequence[str], None] = ${repr(depends_on)}
def upgrade() -> None:
${upgrades if upgrades else "pass"}
def downgrade() -> None:
${downgrades if downgrades else "pass"}

177
src/backend/langflow/alembic/versions/260dbcc8b680_adds_tables.py Normal file
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@ -0,0 +1,177 @@
"""Adds tables
Revision ID: 260dbcc8b680
Revises:
Create Date: 2023-08-27 19:49:02.681355
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
import sqlmodel
from sqlalchemy.engine.reflection import Inspector
# revision identifiers, used by Alembic.
revision: str = "260dbcc8b680"
down_revision: Union[str, None] = None
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
conn = op.get_bind()
inspector = Inspector.from_engine(conn)
# List existing tables
existing_tables = inspector.get_table_names()
# Drop 'flowstyle' table if it exists
# and other related indices
if "flowstyle" in existing_tables:
op.drop_table("flowstyle")
if "ix_flowstyle_flow_id" in [
index["name"] for index in inspector.get_indexes("flowstyle")
]:
op.drop_index("ix_flowstyle_flow_id", table_name="flowstyle")
existing_indices_flow = []
existing_fks_flow = []
if "flow" in existing_tables:
existing_indices_flow = [
index["name"] for index in inspector.get_indexes("flow")
]
# Existing foreign keys for the 'flow' table, if it exists
existing_fks_flow = [
fk["referred_table"] + "." + fk["referred_columns"][0]
for fk in inspector.get_foreign_keys("flow")
]
# Now check if the columns user_id exists in the 'flow' table
# If it does not exist, we need to create the foreign key
if "user" not in existing_tables:
op.create_table(
"user",
sa.Column("id", sqlmodel.sql.sqltypes.GUID(), nullable=False),
sa.Column("username", sqlmodel.sql.sqltypes.AutoString(), nullable=False),
sa.Column("password", sqlmodel.sql.sqltypes.AutoString(), nullable=False),
sa.Column("is_active", sa.Boolean(), nullable=False),
sa.Column("is_superuser", sa.Boolean(), nullable=False),
sa.Column("create_at", sa.DateTime(), nullable=False),
sa.Column("updated_at", sa.DateTime(), nullable=False),
sa.Column("last_login_at", sa.DateTime(), nullable=True),
sa.PrimaryKeyConstraint("id"),
sa.UniqueConstraint("id"),
)
with op.batch_alter_table("user", schema=None) as batch_op:
batch_op.create_index(
batch_op.f("ix_user_username"), ["username"], unique=True
)
if "apikey" not in existing_tables:
op.create_table(
"apikey",
sa.Column("name", sqlmodel.sql.sqltypes.AutoString(), nullable=True),
sa.Column("created_at", sa.DateTime(), nullable=False),
sa.Column("last_used_at", sa.DateTime(), nullable=True),
sa.Column("total_uses", sa.Integer(), nullable=False, default=0),
sa.Column("is_active", sa.Boolean(), nullable=False, default=True),
sa.Column("id", sqlmodel.sql.sqltypes.GUID(), nullable=False),
sa.Column("api_key", sqlmodel.sql.sqltypes.AutoString(), nullable=False),
sa.Column("user_id", sqlmodel.sql.sqltypes.GUID(), nullable=False),
sa.ForeignKeyConstraint(
["user_id"],
["user.id"],
),
sa.PrimaryKeyConstraint("id"),
sa.UniqueConstraint("id"),
)
with op.batch_alter_table("apikey", schema=None) as batch_op:
batch_op.create_index(
batch_op.f("ix_apikey_api_key"), ["api_key"], unique=True
)
batch_op.create_index(batch_op.f("ix_apikey_name"), ["name"], unique=False)
batch_op.create_index(
batch_op.f("ix_apikey_user_id"), ["user_id"], unique=False
)
if "flow" not in existing_tables:
op.create_table(
"flow",
sa.Column("data", sa.JSON(), nullable=True),
sa.Column("name", sqlmodel.sql.sqltypes.AutoString(), nullable=False),
sa.Column("description", sqlmodel.sql.sqltypes.AutoString(), nullable=True),
sa.Column("id", sqlmodel.sql.sqltypes.GUID(), nullable=False),
sa.Column("user_id", sqlmodel.sql.sqltypes.GUID(), nullable=False),
sa.ForeignKeyConstraint(
["user_id"],
["user.id"],
),
sa.PrimaryKeyConstraint("id"),
sa.UniqueConstraint("id"),
)
# Conditionally create indices for 'flow' table
# if _alembic_tmp_flow exists, then we need to drop it first
# This is to deal with SQLite not being able to ROLLBACK
# for some unknown reason
if "_alembic_tmp_flow" in existing_tables:
op.drop_table("_alembic_tmp_flow")
with op.batch_alter_table("flow", schema=None) as batch_op:
flow_columns = [col["name"] for col in inspector.get_columns("flow")]
if "user_id" not in flow_columns:
batch_op.add_column(
sa.Column(
"user_id",
sqlmodel.sql.sqltypes.GUID(),
nullable=True, # This should be False, but we need to allow NULL values for now
)
)
if "user.id" not in existing_fks_flow:
batch_op.create_foreign_key("fk_flow_user_id", "user", ["user_id"], ["id"])
if "ix_flow_description" not in existing_indices_flow:
batch_op.create_index(
batch_op.f("ix_flow_description"), ["description"], unique=False
)
if "ix_flow_name" not in existing_indices_flow:
batch_op.create_index(batch_op.f("ix_flow_name"), ["name"], unique=False)
with op.batch_alter_table("flow", schema=None) as batch_op:
if "ix_flow_user_id" not in existing_indices_flow:
batch_op.create_index(
batch_op.f("ix_flow_user_id"), ["user_id"], unique=False
)
# ### end Alembic commands ###
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
conn = op.get_bind()
inspector = Inspector.from_engine(conn)
# List existing tables
existing_tables = inspector.get_table_names()
if "flow" in existing_tables:
with op.batch_alter_table("flow", schema=None) as batch_op:
batch_op.drop_index(batch_op.f("ix_flow_user_id"))
batch_op.drop_index(batch_op.f("ix_flow_name"))
batch_op.drop_index(batch_op.f("ix_flow_description"))
op.drop_table("flow")
if "apikey" in existing_tables:
with op.batch_alter_table("apikey", schema=None) as batch_op:
batch_op.drop_index(batch_op.f("ix_apikey_user_id"))
batch_op.drop_index(batch_op.f("ix_apikey_name"))
batch_op.drop_index(batch_op.f("ix_apikey_api_key"))
op.drop_table("apikey")
if "user" in existing_tables:
with op.batch_alter_table("user", schema=None) as batch_op:
batch_op.drop_index(batch_op.f("ix_user_username"))
op.drop_table("user")
if "flowstyle" in existing_tables:
op.drop_table("flowstyle")
if "component" in existing_tables:
op.drop_table("component")
# ### end Alembic commands ###

49
src/backend/langflow/alembic/versions/67cc006d50bf_add_profile_image_column.py Normal file
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@ -0,0 +1,49 @@
"""Add profile-image column
Revision ID: 67cc006d50bf
Revises: 260dbcc8b680
Create Date: 2023-09-08 07:36:13.387318
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
import sqlmodel
from sqlalchemy.engine.reflection import Inspector
# revision identifiers, used by Alembic.
revision: str = "67cc006d50bf"
down_revision: Union[str, None] = "260dbcc8b680"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
conn = op.get_bind()
inspector = Inspector.from_engine(conn)
if "user" in inspector.get_table_names() and "profile_image" not in [
column["name"] for column in inspector.get_columns("user")
]:
with op.batch_alter_table("user", schema=None) as batch_op:
batch_op.add_column(
sa.Column(
"profile_image", sqlmodel.sql.sqltypes.AutoString(), nullable=True
)
)
# ### end Alembic commands ###
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
conn = op.get_bind()
inspector = Inspector.from_engine(conn)
if "user" in inspector.get_table_names() and "profile_image" in [
column["name"] for column in inspector.get_columns("user")
]:
with op.batch_alter_table("user", schema=None) as batch_op:
batch_op.drop_column("profile_image")
# ### end Alembic commands ###

8
src/backend/langflow/api/router.py
View file

@ -5,8 +5,10 @@ from langflow.api.v1 import (
endpoints_router,
validate_router,
flows_router,
flow_styles_router,
component_router,
users_router,
api_key_router,
login_router,
)
router = APIRouter(
@ -17,4 +19,6 @@ router.include_router(endpoints_router)
router.include_router(validate_router)
router.include_router(component_router)
router.include_router(flows_router)
router.include_router(flow_styles_router)
router.include_router(users_router)
router.include_router(api_key_router)
router.include_router(login_router)

27
src/backend/langflow/api/utils.py
View file

@ -66,3 +66,30 @@ def merge_nested_dicts(dict1, dict2):
else:
dict1[key] = value
return dict1
def merge_nested_dicts_with_renaming(dict1, dict2):
for key, value in dict2.items():
if (
key in dict1
and isinstance(value, dict)
and isinstance(dict1.get(key), dict)
):
for sub_key, sub_value in value.items():
if sub_key in dict1[key]:
new_key = get_new_key(dict1[key], sub_key)
dict1[key][new_key] = sub_value
else:
dict1[key][sub_key] = sub_value
else:
dict1[key] = value
return dict1
def get_new_key(dictionary, original_key):
counter = 1
new_key = original_key + " (" + str(counter) + ")"
while new_key in dictionary:
counter += 1
new_key = original_key + " (" + str(counter) + ")"
return new_key

8
src/backend/langflow/api/v1/__init__.py
View file

@ -2,8 +2,10 @@ from langflow.api.v1.endpoints import router as endpoints_router
from langflow.api.v1.validate import router as validate_router
from langflow.api.v1.chat import router as chat_router
from langflow.api.v1.flows import router as flows_router
from langflow.api.v1.flow_styles import router as flow_styles_router
from langflow.api.v1.components import router as component_router
from langflow.api.v1.users import router as users_router
from langflow.api.v1.api_key import router as api_key_router
from langflow.api.v1.login import router as login_router
__all__ = [
"chat_router",
@ -11,5 +13,7 @@ __all__ = [
"component_router",
"validate_router",
"flows_router",
"flow_styles_router",
"users_router",
"api_key_router",
"login_router",
]

61
src/backend/langflow/api/v1/api_key.py Normal file
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@ -0,0 +1,61 @@
from uuid import UUID
from fastapi import APIRouter, HTTPException, Depends
from langflow.api.v1.schemas import ApiKeysResponse
from langflow.services.auth.utils import get_current_active_user
from langflow.services.database.models.api_key.api_key import (
ApiKeyCreate,
UnmaskedApiKeyRead,
)
# Assuming you have these methods in your service layer
from langflow.services.database.models.api_key.crud import (
get_api_keys,
create_api_key,
delete_api_key,
)
from langflow.services.database.models.user.user import User
from langflow.services.utils import get_session
from sqlmodel import Session
router = APIRouter(tags=["APIKey"], prefix="/api_key")
@router.get("/", response_model=ApiKeysResponse)
def get_api_keys_route(
db: Session = Depends(get_session),
current_user: User = Depends(get_current_active_user),
):
try:
user_id = current_user.id
keys = get_api_keys(db, user_id)
return ApiKeysResponse(total_count=len(keys), user_id=user_id, api_keys=keys)
except Exception as exc:
raise HTTPException(status_code=400, detail=str(exc)) from exc
@router.post("/", response_model=UnmaskedApiKeyRead)
def create_api_key_route(
req: ApiKeyCreate,
current_user: User = Depends(get_current_active_user),
db: Session = Depends(get_session),
):
try:
user_id = current_user.id
return create_api_key(db, req, user_id=user_id)
except Exception as e:
raise HTTPException(status_code=400, detail=str(e)) from e
@router.delete("/{api_key_id}")
def delete_api_key_route(
api_key_id: UUID,
current_user=Depends(get_current_active_user),
db: Session = Depends(get_session),
):
try:
delete_api_key(db, api_key_id)
return {"detail": "API Key deleted"}
except Exception as e:
raise HTTPException(status_code=400, detail=str(e)) from e

7
src/backend/langflow/api/v1/base.py
View file

@ -1,3 +1,4 @@
from typing import Optional
from langflow.template.frontend_node.base import FrontendNode
from pydantic import BaseModel, validator
@ -20,7 +21,8 @@ class FrontendNodeRequest(FrontendNode):
class ValidatePromptRequest(BaseModel):
name: str
template: str
frontend_node: FrontendNodeRequest
# optional for tweak call
frontend_node: Optional[FrontendNodeRequest] = None
# Build ValidationResponse class for {"imports": {"errors": []}, "function": {"errors": []}}
@ -39,7 +41,8 @@ class CodeValidationResponse(BaseModel):
class PromptValidationResponse(BaseModel):
input_variables: list
frontend_node: FrontendNodeRequest
# object return for tweak call
frontend_node: Optional[FrontendNodeRequest] = None
INVALID_CHARACTERS = {

2
src/backend/langflow/api/v1/callback.py
View file

@ -10,7 +10,7 @@ from fastapi import WebSocket
from langchain.schema import AgentAction, LLMResult, AgentFinish
from langflow.utils.logger import logger
from loguru import logger
# https://github.com/hwchase17/chat-langchain/blob/master/callback.py

82
src/backend/langflow/api/v1/chat.py
View file

@ -1,37 +1,79 @@
from fastapi import APIRouter, HTTPException, WebSocket, WebSocketException, status
from fastapi import (
APIRouter,
Depends,
HTTPException,
Query,
WebSocket,
WebSocketException,
status,
)
from fastapi.responses import StreamingResponse
from langflow.api.utils import build_input_keys_response
from langflow.api.v1.schemas import BuildStatus, BuiltResponse, InitResponse, StreamData
from langflow.chat.manager import ChatManager
from langflow.graph.graph.base import Graph
from langflow.utils.logger import logger
from langflow.services.auth.utils import get_current_active_user, get_current_user
from loguru import logger
from langflow.services.utils import get_chat_manager, get_session
from cachetools import LRUCache
from sqlmodel import Session
from langflow.services.chat.manager import ChatManager
router = APIRouter(tags=["Chat"])
chat_manager = ChatManager()
flow_data_store: LRUCache = LRUCache(maxsize=10)
@router.websocket("/chat/{client_id}")
async def chat(client_id: str, websocket: WebSocket):
async def chat(
client_id: str,
websocket: WebSocket,
token: str = Query(...),
db: Session = Depends(get_session),
chat_manager: "ChatManager" = Depends(get_chat_manager),
):
"""Websocket endpoint for chat."""
try:
await websocket.accept()
user = await get_current_user(token, db)
if not user:
await websocket.close(
code=status.WS_1008_POLICY_VIOLATION, reason="Unauthorized"
)
if not user.is_active:
await websocket.close(
code=status.WS_1008_POLICY_VIOLATION, reason="Unauthorized"
)
if client_id in chat_manager.in_memory_cache:
await chat_manager.handle_websocket(client_id, websocket)
else:
# We accept the connection but close it immediately
# if the flow is not built yet
await websocket.accept()
message = "Please, build the flow before sending messages"
await websocket.close(code=status.WS_1011_INTERNAL_ERROR, reason=message)
except WebSocketException as exc:
logger.error(f"Websocket error: {exc}")
await websocket.close(code=status.WS_1011_INTERNAL_ERROR, reason=str(exc))
except Exception as exc:
logger.error(f"Error in chat websocket: {exc}")
messsage = exc.detail if isinstance(exc, HTTPException) else str(exc)
if "Could not validate credentials" in str(exc):
await websocket.close(
code=status.WS_1008_POLICY_VIOLATION, reason="Unauthorized"
)
else:
await websocket.close(code=status.WS_1011_INTERNAL_ERROR, reason=messsage)
@router.post("/build/init/{flow_id}", response_model=InitResponse, status_code=201)
async def init_build(graph_data: dict, flow_id: str):
async def init_build(
graph_data: dict,
flow_id: str,
current_user=Depends(get_current_active_user),
chat_manager: "ChatManager" = Depends(get_chat_manager),
):
"""Initialize the build by storing graph data and returning a unique session ID."""
try:
@ -52,6 +94,7 @@ async def init_build(graph_data: dict, flow_id: str):
flow_data_store[flow_id] = {
"graph_data": graph_data,
"status": BuildStatus.STARTED,
"user_id": current_user.id,
}
return InitResponse(flowId=flow_id)
@ -79,7 +122,9 @@ async def build_status(flow_id: str):
@router.get("/build/stream/{flow_id}", response_class=StreamingResponse)
async def stream_build(flow_id: str):
async def stream_build(
flow_id: str, chat_manager: "ChatManager" = Depends(get_chat_manager)
):
"""Stream the build process based on stored flow data."""
async def event_stream(flow_id):
@ -97,6 +142,7 @@ async def stream_build(flow_id: str):
return
graph_data = flow_data_store[flow_id].get("graph_data")
user_id = flow_data_store[flow_id]["user_id"]
if not graph_data:
error_message = "No data provided"
@ -104,14 +150,9 @@ async def stream_build(flow_id: str):
return
logger.debug("Building langchain object")
try:
# Some error could happen when building the graph
graph = Graph.from_payload(graph_data)
except Exception as exc:
logger.exception(exc)
error_message = str(exc)
yield str(StreamData(event="error", data={"error": error_message}))
return
# Some error could happen when building the graph
graph = Graph.from_payload(graph_data)
number_of_nodes = len(graph.nodes)
flow_data_store[flow_id]["status"] = BuildStatus.IN_PROGRESS
@ -122,11 +163,13 @@ async def stream_build(flow_id: str):
"log": f"Building node {vertex.vertex_type}",
}
yield str(StreamData(event="log", data=log_dict))
vertex.build()
vertex.build(user_id)
params = vertex._built_object_repr()
valid = True
logger.debug(f"Building node {str(vertex.vertex_type)}")
logger.debug(f"Output: {params}")
logger.debug(
f"Output: {params[:100]}{'...' if len(params) > 100 else ''}"
)
if vertex.artifacts:
# The artifacts will be prompt variables
# passed to build_input_keys_response
@ -155,12 +198,11 @@ async def stream_build(flow_id: str):
)
else:
input_keys_response = {
"input_keys": {},
"input_keys": None,
"memory_keys": [],
"handle_keys": [],
}
yield str(StreamData(event="message", data=input_keys_response))
chat_manager.set_cache(flow_id, langchain_object)
# We need to reset the chat history
chat_manager.chat_history.empty_history(flow_id)

4
src/backend/langflow/api/v1/components.py
View file

@ -1,8 +1,8 @@
from datetime import timezone
from typing import List
from uuid import UUID
from langflow.database.models.component import Component, ComponentModel
from langflow.database.base import get_session
from langflow.services.database.models.component import Component, ComponentModel
from langflow.services.utils import get_session
from sqlmodel import Session, select
from fastapi import APIRouter, Depends, HTTPException
from sqlalchemy.exc import IntegrityError

118
src/backend/langflow/api/v1/endpoints.py
View file

@ -1,14 +1,15 @@
from http import HTTPStatus
from typing import Annotated, Optional
from typing import Annotated, Any, Optional, Union
from langflow.services.auth.utils import api_key_security, get_current_active_user
from langflow.cache.utils import save_uploaded_file
from langflow.database.models.flow import Flow
from langflow.services.cache.utils import save_uploaded_file
from langflow.services.database.models.flow import Flow
from langflow.processing.process import process_graph_cached, process_tweaks
from langflow.utils.logger import logger
from langflow.settings import settings
from fastapi import APIRouter, Depends, HTTPException, UploadFile, Body
from langflow.services.database.models.user.user import User
from langflow.services.utils import get_settings_manager
from loguru import logger
from fastapi import APIRouter, Depends, HTTPException, UploadFile, Body, status
import sqlalchemy as sa
from langflow.interface.custom.custom_component import CustomComponent
@ -18,7 +19,7 @@ from langflow.api.v1.schemas import (
CustomComponentCode,
)
from langflow.api.utils import merge_nested_dicts
from langflow.api.utils import merge_nested_dicts_with_renaming
from langflow.interface.types import (
build_langchain_types_dict,
@ -26,52 +27,93 @@ from langflow.interface.types import (
build_langchain_custom_component_list_from_path,
)
from langflow.database.base import get_session
from langflow.services.utils import get_session
from sqlmodel import Session
# build router
router = APIRouter(tags=["Base"])
@router.get("/all")
def get_all():
@router.get("/all", dependencies=[Depends(get_current_active_user)])
def get_all(
settings_manager=Depends(get_settings_manager),
):
logger.debug("Building langchain types dict")
native_components = build_langchain_types_dict()
# custom_components is a list of dicts
# need to merge all the keys into one dict
custom_components_from_file = {}
if settings.COMPONENTS_PATH:
logger.info(f"Building custom components from {settings.COMPONENTS_PATH}")
custom_component_dicts = [
build_langchain_custom_component_list_from_path(str(path))
for path in settings.COMPONENTS_PATH
]
logger.info(f"Loading {len(custom_component_dicts)} custom components")
custom_components_from_file: dict[str, Any] = {}
if settings_manager.settings.COMPONENTS_PATH:
logger.info(
f"Building custom components from {settings_manager.settings.COMPONENTS_PATH}"
)
custom_component_dicts = []
processed_paths = []
for path in settings_manager.settings.COMPONENTS_PATH:
if str(path) in processed_paths:
continue
custom_component_dict = build_langchain_custom_component_list_from_path(
str(path)
)
custom_component_dicts.append(custom_component_dict)
processed_paths.append(str(path))
logger.info(f"Loading {len(custom_component_dicts)} category(ies)")
for custom_component_dict in custom_component_dicts:
custom_components_from_file = merge_nested_dicts(
# custom_component_dict is a dict of dicts
if not custom_component_dict:
continue
category = list(custom_component_dict.keys())[0]
logger.info(
f"Loading {len(custom_component_dict[category])} component(s) from category {category}"
)
custom_components_from_file = merge_nested_dicts_with_renaming(
custom_components_from_file, custom_component_dict
)
logger.info(f"Loaded {custom_component_dict}")
return merge_nested_dicts(native_components, custom_components_from_file)
return merge_nested_dicts_with_renaming(
native_components, custom_components_from_file
)
# For backwards compatibility we will keep the old endpoint
@router.post("/predict/{flow_id}", response_model=ProcessResponse)
@router.post("/process/{flow_id}", response_model=ProcessResponse)
@router.post(
"/predict/{flow_id}",
response_model=ProcessResponse,
dependencies=[Depends(api_key_security)],
)
@router.post(
"/process/{flow_id}",
response_model=ProcessResponse,
)
async def process_flow(
session: Annotated[Session, Depends(get_session)],
flow_id: str,
inputs: Optional[dict] = None,
tweaks: Optional[dict] = None,
clear_cache: Annotated[bool, Body(embed=True)] = False, # noqa: F821
session: Session = Depends(get_session),
session_id: Annotated[Union[None, str], Body(embed=True)] = None, # noqa: F821
api_key_user: User = Depends(api_key_security),
):
"""
Endpoint to process an input with a given flow_id.
"""
try:
flow = session.get(Flow, flow_id)
if api_key_user is None:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Invalid API Key",
)
# Get the flow that matches the flow_id and belongs to the user
flow = (
session.query(Flow)
.filter(Flow.id == flow_id)
.filter(Flow.user_id == api_key_user.id)
.first()
)
if flow is None:
raise ValueError(f"Flow {flow_id} not found")
@ -83,10 +125,26 @@ async def process_flow(
graph_data = process_tweaks(graph_data, tweaks)
except Exception as exc:
logger.error(f"Error processing tweaks: {exc}")
response = process_graph_cached(graph_data, inputs, clear_cache)
return ProcessResponse(
result=response,
response, session_id = process_graph_cached(
graph_data, inputs, clear_cache, session_id
)
return ProcessResponse(result=response, session_id=session_id)
except sa.exc.StatementError as exc:
# StatementError('(builtins.ValueError) badly formed hexadecimal UUID string')
if "badly formed hexadecimal UUID string" in str(exc):
# This means the Flow ID is not a valid UUID which means it can't find the flow
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND, detail=str(exc)
) from exc
except ValueError as exc:
if f"Flow {flow_id} not found" in str(exc):
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND, detail=str(exc)
) from exc
else:
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(exc)
) from exc
except Exception as e:
# Log stack trace
logger.exception(e)

83
src/backend/langflow/api/v1/flow_styles.py
View file

@ -1,83 +0,0 @@
from uuid import UUID
from langflow.database.models.flow_style import (
FlowStyle,
FlowStyleCreate,
FlowStyleRead,
FlowStyleUpdate,
)
from langflow.database.base import get_session
from sqlmodel import Session, select
from fastapi import APIRouter, Depends, HTTPException
# build router
router = APIRouter(prefix="/flow_styles", tags=["FlowStyles"])
# FlowStyleCreate:
# class FlowStyleBase(SQLModel):
# color: str = Field(index=True)
# emoji: str = Field(index=False)
# flow_id: UUID = Field(default=None, foreign_key="flow.id")
@router.post("/", response_model=FlowStyleRead)
def create_flow_style(
*, session: Session = Depends(get_session), flow_style: FlowStyleCreate
):
"""Create a new flow_style."""
db_flow_style = FlowStyle.from_orm(flow_style)
session.add(db_flow_style)
session.commit()
session.refresh(db_flow_style)
return db_flow_style
@router.get("/", response_model=list[FlowStyleRead])
def read_flow_styles(*, session: Session = Depends(get_session)):
"""Read all flows."""
try:
flows = session.exec(select(FlowStyle)).all()
except Exception as e:
raise HTTPException(status_code=500, detail=str(e)) from e
return flows
@router.get("/{flow_styles_id}", response_model=FlowStyleRead)
def read_flow_style(*, session: Session = Depends(get_session), flow_styles_id: UUID):
"""Read a flow_style."""
if flow_style := session.get(FlowStyle, flow_styles_id):
return flow_style
else:
raise HTTPException(status_code=404, detail="FlowStyle not found")
@router.patch("/{flow_style_id}", response_model=FlowStyleRead)
def update_flow_style(
*,
session: Session = Depends(get_session),
flow_style_id: UUID,
flow_style: FlowStyleUpdate,
):
"""Update a flow_style."""
db_flow_style = session.get(FlowStyle, flow_style_id)
if not db_flow_style:
raise HTTPException(status_code=404, detail="FlowStyle not found")
flow_data = flow_style.dict(exclude_unset=True)
for key, value in flow_data.items():
if hasattr(db_flow_style, key) and value is not None:
setattr(db_flow_style, key, value)
session.add(db_flow_style)
session.commit()
session.refresh(db_flow_style)
return db_flow_style
@router.delete("/{flow_id}")
def delete_flow_style(*, session: Session = Depends(get_session), flow_id: UUID):
"""Delete a flow_style."""
flow_style = session.get(FlowStyle, flow_id)
if not flow_style:
raise HTTPException(status_code=404, detail="FlowStyle not found")
session.delete(flow_style)
session.commit()
return {"message": "FlowStyle deleted successfully"}

107
src/backend/langflow/api/v1/flows.py
View file

@ -1,70 +1,101 @@
from typing import List
from uuid import UUID
from langflow.settings import settings
from fastapi.encoders import jsonable_encoder
from langflow.api.utils import remove_api_keys
from langflow.api.v1.schemas import FlowListCreate, FlowListRead
from langflow.database.models.flow import (
from langflow.services.auth.utils import get_current_active_user
from langflow.services.database.models.flow import (
Flow,
FlowCreate,
FlowRead,
FlowReadWithStyle,
FlowUpdate,
)
from langflow.database.base import get_session
from sqlmodel import Session, select
from langflow.services.database.models.user.user import User
from langflow.services.utils import get_session
from langflow.services.utils import get_settings_manager
import orjson
from sqlmodel import Session
from fastapi import APIRouter, Depends, HTTPException
from fastapi.encoders import jsonable_encoder
from fastapi import File, UploadFile
import json
# build router
router = APIRouter(prefix="/flows", tags=["Flows"])
@router.post("/", response_model=FlowRead, status_code=201)
def create_flow(*, session: Session = Depends(get_session), flow: FlowCreate):
def create_flow(
*,
session: Session = Depends(get_session),
flow: FlowCreate,
current_user: User = Depends(get_current_active_user),
):
"""Create a new flow."""
if flow.user_id is None:
flow.user_id = current_user.id
db_flow = Flow.from_orm(flow)
session.add(db_flow)
session.commit()
session.refresh(db_flow)
return db_flow
@router.get("/", response_model=list[FlowReadWithStyle], status_code=200)
def read_flows(*, session: Session = Depends(get_session)):
@router.get("/", response_model=list[FlowRead], status_code=200)
def read_flows(
*,
session: Session = Depends(get_session),
current_user: User = Depends(get_current_active_user),
):
"""Read all flows."""
try:
flows = session.exec(select(Flow)).all()
flows = current_user.flows
except Exception as e:
raise HTTPException(status_code=500, detail=str(e)) from e
return [jsonable_encoder(flow) for flow in flows]
@router.get("/{flow_id}", response_model=FlowReadWithStyle, status_code=200)
def read_flow(*, session: Session = Depends(get_session), flow_id: UUID):
@router.get("/{flow_id}", response_model=FlowRead, status_code=200)
def read_flow(
*,
session: Session = Depends(get_session),
flow_id: UUID,
current_user: User = Depends(get_current_active_user),
):
"""Read a flow."""
if flow := session.get(Flow, flow_id):
return flow
if user_flow := (
session.query(Flow)
.filter(Flow.id == flow_id)
.filter(Flow.user_id == current_user.id)
.first()
):
return user_flow
else:
raise HTTPException(status_code=404, detail="Flow not found")
@router.patch("/{flow_id}", response_model=FlowRead, status_code=200)
def update_flow(
*, session: Session = Depends(get_session), flow_id: UUID, flow: FlowUpdate
*,
session: Session = Depends(get_session),
flow_id: UUID,
flow: FlowUpdate,
current_user: User = Depends(get_current_active_user),
settings_manager=Depends(get_settings_manager),
):
"""Update a flow."""
db_flow = session.get(Flow, flow_id)
db_flow = read_flow(session=session, flow_id=flow_id, current_user=current_user)
if not db_flow:
raise HTTPException(status_code=404, detail="Flow not found")
flow_data = flow.dict(exclude_unset=True)
if settings.REMOVE_API_KEYS:
if settings_manager.settings.REMOVE_API_KEYS:
flow_data = remove_api_keys(flow_data)
for key, value in flow_data.items():
setattr(db_flow, key, value)
if value is not None:
setattr(db_flow, key, value)
session.add(db_flow)
session.commit()
session.refresh(db_flow)
@ -72,9 +103,14 @@ def update_flow(
@router.delete("/{flow_id}", status_code=200)
def delete_flow(*, session: Session = Depends(get_session), flow_id: UUID):
def delete_flow(
*,
session: Session = Depends(get_session),
flow_id: UUID,
current_user: User = Depends(get_current_active_user),
):
"""Delete a flow."""
flow = session.get(Flow, flow_id)
flow = read_flow(session=session, flow_id=flow_id, current_user=current_user)
if not flow:
raise HTTPException(status_code=404, detail="Flow not found")
session.delete(flow)
@ -86,10 +122,16 @@ def delete_flow(*, session: Session = Depends(get_session), flow_id: UUID):
@router.post("/batch/", response_model=List[FlowRead], status_code=201)
def create_flows(*, session: Session = Depends(get_session), flow_list: FlowListCreate):
def create_flows(
*,
session: Session = Depends(get_session),
flow_list: FlowListCreate,
current_user: User = Depends(get_current_active_user),
):
"""Create multiple new flows."""
db_flows = []
for flow in flow_list.flows:
flow.user_id = current_user.id
db_flow = Flow.from_orm(flow)
session.add(db_flow)
db_flows.append(db_flow)
@ -101,20 +143,31 @@ def create_flows(*, session: Session = Depends(get_session), flow_list: FlowList
@router.post("/upload/", response_model=List[FlowRead], status_code=201)
async def upload_file(
*, session: Session = Depends(get_session), file: UploadFile = File(...)
*,
session: Session = Depends(get_session),
file: UploadFile = File(...),
current_user: User = Depends(get_current_active_user),
):
"""Upload flows from a file."""
contents = await file.read()
data = json.loads(contents)
data = orjson.loads(contents)
if "flows" in data:
flow_list = FlowListCreate(**data)
else:
flow_list = FlowListCreate(flows=[FlowCreate(**flow) for flow in data])
return create_flows(session=session, flow_list=flow_list)
# Now we set the user_id for all flows
for flow in flow_list.flows:
flow.user_id = current_user.id
return create_flows(session=session, flow_list=flow_list, current_user=current_user)
@router.get("/download/", response_model=FlowListRead, status_code=200)
async def download_file(*, session: Session = Depends(get_session)):
async def download_file(
*,
session: Session = Depends(get_session),
current_user: User = Depends(get_current_active_user),
):
"""Download all flows as a file."""
flows = read_flows(session=session)
flows = read_flows(session=session, current_user=current_user)
return FlowListRead(flows=flows)

63
src/backend/langflow/api/v1/login.py Normal file
View file

@ -0,0 +1,63 @@
from sqlmodel import Session
from fastapi import APIRouter, Depends, HTTPException, status
from fastapi.security import OAuth2PasswordRequestForm
from langflow.services.utils import get_session
from langflow.api.v1.schemas import Token
from langflow.services.auth.utils import (
authenticate_user,
create_user_tokens,
create_refresh_token,
create_user_longterm_token,
get_current_active_user,
)
from langflow.services.utils import get_settings_manager
router = APIRouter(tags=["Login"])
@router.post("/login", response_model=Token)
async def login_to_get_access_token(
form_data: OAuth2PasswordRequestForm = Depends(),
db: Session = Depends(get_session),
# _: Session = Depends(get_current_active_user)
):
if user := authenticate_user(form_data.username, form_data.password, db):
return create_user_tokens(user_id=user.id, db=db, update_last_login=True)
else:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Incorrect username or password",
headers={"WWW-Authenticate": "Bearer"},
)
@router.get("/auto_login")
async def auto_login(
db: Session = Depends(get_session), settings_manager=Depends(get_settings_manager)
):
if settings_manager.auth_settings.AUTO_LOGIN:
return create_user_longterm_token(db)
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail={
"message": "Auto login is disabled. Please enable it in the settings",
"auto_login": False,
},
)
@router.post("/refresh")
async def refresh_token(
token: str, current_user: Session = Depends(get_current_active_user)
):
if token:
return create_refresh_token(token)
else:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Invalid refresh token",
headers={"WWW-Authenticate": "Bearer"},
)

42
src/backend/langflow/api/v1/schemas.py
View file

@ -1,9 +1,13 @@
from enum import Enum
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
from langflow.database.models.flow import FlowCreate, FlowRead
from uuid import UUID
from langflow.services.database.models.api_key.api_key import ApiKeyRead
from langflow.services.database.models.flow import FlowCreate, FlowRead
from langflow.services.database.models.user import UserRead
from langflow.services.database.models.base import orjson_dumps
from pydantic import BaseModel, Field, validator
import json
class BuildStatus(Enum):
@ -47,6 +51,7 @@ class ProcessResponse(BaseModel):
"""Process response schema."""
result: dict
session_id: Optional[str] = None
class ChatMessage(BaseModel):
@ -115,7 +120,9 @@ class StreamData(BaseModel):
data: dict
def __str__(self) -> str:
return f"event: {self.event}\ndata: {json.dumps(self.data)}\n\n"
return (
f"event: {self.event}\ndata: {orjson_dumps(self.data, indent_2=False)}\n\n"
)
class CustomComponentCode(BaseModel):
@ -133,3 +140,32 @@ class ComponentListCreate(BaseModel):
class ComponentListRead(BaseModel):
flows: List[FlowRead]
class UsersResponse(BaseModel):
total_count: int
users: List[UserRead]
class ApiKeyResponse(BaseModel):
id: str
api_key: str
name: str
created_at: str
last_used_at: str
class ApiKeysResponse(BaseModel):
total_count: int
user_id: UUID
api_keys: List[ApiKeyRead]
class CreateApiKeyRequest(BaseModel):
name: str
class Token(BaseModel):
access_token: str
refresh_token: str
token_type: str

194
src/backend/langflow/api/v1/users.py Normal file
View file

@ -0,0 +1,194 @@
from uuid import UUID
from langflow.api.v1.schemas import UsersResponse
from langflow.services.database.models.user import (
User,
UserCreate,
UserRead,
UserUpdate,
)
from sqlalchemy import func
from sqlalchemy.exc import IntegrityError
from sqlmodel import Session, select
from fastapi import APIRouter, Depends, HTTPException
from langflow.services.utils import get_session
from langflow.services.auth.utils import (
get_current_active_superuser,
get_current_active_user,
get_password_hash,
verify_password,
)
from langflow.services.database.models.user.crud import (
get_user_by_id,
update_user,
)
router = APIRouter(tags=["Users"], prefix="/users")
@router.post("/", response_model=UserRead, status_code=201)
def add_user(
user: UserCreate,
session: Session = Depends(get_session),
) -> User:
"""
Add a new user to the database.
"""
new_user = User.from_orm(user)
try:
new_user.password = get_password_hash(user.password)
session.add(new_user)
session.commit()
session.refresh(new_user)
except IntegrityError as e:
session.rollback()
raise HTTPException(
status_code=400, detail="This username is unavailable."
) from e
return new_user
@router.get("/whoami", response_model=UserRead)
def read_current_user(
current_user: User = Depends(get_current_active_user),
) -> User:
"""
Retrieve the current user's data.
"""
return current_user
@router.get("/", response_model=UsersResponse)
def read_all_users(
skip: int = 0,
limit: int = 10,
current_user: Session = Depends(get_current_active_superuser),
session: Session = Depends(get_session),
) -> UsersResponse:
"""
Retrieve a list of users from the database with pagination.
"""
query = select(User).offset(skip).limit(limit)
users = session.execute(query).fetchall()
count_query = select(func.count()).select_from(User) # type: ignore
total_count = session.execute(count_query).scalar()
return UsersResponse(
total_count=total_count, # type: ignore
users=[UserRead(**dict(user.User)) for user in users],
)
@router.patch("/{user_id}", response_model=UserRead)
def patch_user(
user_id: UUID,
user_update: UserUpdate,
user: User = Depends(get_current_active_user),
session: Session = Depends(get_session),
) -> User:
"""
Update an existing user's data.
"""
if not user.is_superuser and user.id != user_id:
raise HTTPException(
status_code=403, detail="You don't have the permission to update this user"
)
if user_update.password:
raise HTTPException(
status_code=400, detail="You can't change your password here"
)
if user_db := get_user_by_id(session, user_id):
return update_user(user_db, user_update, session)
else:
raise HTTPException(status_code=404, detail="User not found")
@router.patch("/{user_id}/reset-password", response_model=UserRead)
def reset_password(
user_id: UUID,
user_update: UserUpdate,
user: User = Depends(get_current_active_user),
session: Session = Depends(get_session),
) -> User:
"""
Reset a user's password.
"""
if user_id != user.id:
raise HTTPException(
status_code=400, detail="You can't change another user's password"
)
if not user:
raise HTTPException(status_code=404, detail="User not found")
if verify_password(user_update.password, user.password):
raise HTTPException(
status_code=400, detail="You can't use your current password"
)
new_password = get_password_hash(user_update.password)
user.password = new_password
session.commit()
session.refresh(user)
return user
@router.delete("/{user_id}", response_model=dict)
def delete_user(
user_id: UUID,
current_user: User = Depends(get_current_active_superuser),
session: Session = Depends(get_session),
) -> dict:
"""
Delete a user from the database.
"""
if current_user.id == user_id:
raise HTTPException(
status_code=400, detail="You can't delete your own user account"
)
elif not current_user.is_superuser:
raise HTTPException(
status_code=403, detail="You don't have the permission to delete this user"
)
user_db = session.query(User).filter(User.id == user_id).first()
if not user_db:
raise HTTPException(status_code=404, detail="User not found")
session.delete(user_db)
session.commit()
return {"detail": "User deleted"}
# TODO: REMOVE - Just for testing purposes
@router.post("/super_user", response_model=User)
def add_super_user_for_testing_purposes_delete_me_before_merge_into_dev(
session: Session = Depends(get_session),
) -> User:
"""
Add a superuser for testing purposes.
(This should be removed in production)
"""
new_user = User(
username="superuser",
password=get_password_hash("12345"),
is_active=True,
is_superuser=True,
last_login_at=None,
)
try:
session.add(new_user)
session.commit()
session.refresh(new_user)
except IntegrityError as e:
session.rollback()
raise HTTPException(status_code=400, detail="User exists") from e
return new_user

19
src/backend/langflow/api/v1/validate.py
View file

@ -8,7 +8,7 @@ from langflow.api.v1.base import (
validate_prompt,
)
from langflow.template.field.base import TemplateField
from langflow.utils.logger import logger
from loguru import logger
from langflow.utils.validate import validate_code
# build router
@ -31,7 +31,12 @@ def post_validate_code(code: Code):
def post_validate_prompt(prompt_request: ValidatePromptRequest):
try:
input_variables = validate_prompt(prompt_request.template)
# Check if frontend_node is None before proceeding to avoid attempting to update a non-existent node.
if prompt_request.frontend_node is None:
return PromptValidationResponse(
input_variables=input_variables,
frontend_node=None,
)
old_custom_fields = get_old_custom_fields(prompt_request)
add_new_variables_to_template(input_variables, prompt_request)
@ -53,6 +58,16 @@ def post_validate_prompt(prompt_request: ValidatePromptRequest):
def get_old_custom_fields(prompt_request):
try:
if (
len(prompt_request.frontend_node.custom_fields) == 1
and prompt_request.name == ""
):
# If there is only one custom field and the name is empty string
# then we are dealing with the first prompt request after the node was created
prompt_request.name = list(
prompt_request.frontend_node.custom_fields.keys()
)[0]
old_custom_fields = prompt_request.frontend_node.custom_fields[
prompt_request.name
].copy()

7
src/backend/langflow/cache/__init__.py vendored
View file

@ -1,7 +0,0 @@
from langflow.cache.manager import cache_manager
from langflow.cache.flow import InMemoryCache
__all__ = [
"cache_manager",
"InMemoryCache",
]

82
src/backend/langflow/components/agents/OpenAIConversationalAgent.py Normal file
View file

@ -0,0 +1,82 @@
from langflow import CustomComponent
from typing import Optional
from langchain.prompts import SystemMessagePromptTemplate
from langchain.tools import Tool
from langchain.schema.memory import BaseMemory
from langchain.chat_models import ChatOpenAI
from langchain.agents.agent import AgentExecutor
from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
from langchain.memory.token_buffer import ConversationTokenBufferMemory
from langchain.prompts.chat import MessagesPlaceholder
from langchain.agents.agent_toolkits.conversational_retrieval.openai_functions import (
_get_default_system_message,
)
class ConversationalAgent(CustomComponent):
display_name: str = "OpenAI Conversational Agent"
description: str = "Conversational Agent that can use OpenAI's function calling API"
def build_config(self):
openai_function_models = [
"gpt-3.5-turbo-0613",
"gpt-3.5-turbo-16k-0613",
"gpt-4-0613",
"gpt-4-32k-0613",
]
return {
"tools": {"is_list": True, "display_name": "Tools"},
"memory": {"display_name": "Memory"},
"system_message": {"display_name": "System Message"},
"max_token_limit": {"display_name": "Max Token Limit"},
"model_name": {
"display_name": "Model Name",
"options": openai_function_models,
"value": openai_function_models[0],
},
"code": {"show": False},
}
def build(
self,
model_name: str,
openai_api_key: str,
tools: Tool,
openai_api_base: Optional[str] = None,
memory: Optional[BaseMemory] = None,
system_message: Optional[SystemMessagePromptTemplate] = None,
max_token_limit: int = 2000,
) -> AgentExecutor:
llm = ChatOpenAI(
model=model_name,
openai_api_key=openai_api_key,
openai_api_base=openai_api_base,
)
if not memory:
memory_key = "chat_history"
memory = ConversationTokenBufferMemory(
memory_key=memory_key,
return_messages=True,
output_key="output",
llm=llm,
max_token_limit=max_token_limit,
)
else:
memory_key = memory.memory_key # type: ignore
_system_message = system_message or _get_default_system_message()
prompt = OpenAIFunctionsAgent.create_prompt(
system_message=_system_message, # type: ignore
extra_prompt_messages=[MessagesPlaceholder(variable_name=memory_key)],
)
agent = OpenAIFunctionsAgent(
llm=llm, tools=tools, prompt=prompt # type: ignore
)
return AgentExecutor(
agent=agent,
tools=tools, # type: ignore
memory=memory,
verbose=True,
return_intermediate_steps=True,
)

0
src/backend/langflow/chat/__init__.py → src/backend/langflow/components/agents/__init__.py
View file

7
src/backend/langflow/components/chains/prompt_runner.py → src/backend/langflow/components/chains/PromptRunner.py
View file

@ -16,17 +16,14 @@ class PromptRunner(CustomComponent):
"info": "Make sure the prompt has all variables filled.",
},
"code": {"show": False},
"inputs": {"field_type": "code"},
}
def build(
self,
llm: BaseLLM,
prompt: PromptTemplate,
self, llm: BaseLLM, prompt: PromptTemplate, inputs: dict = {}
) -> Document:
chain = prompt | llm
# The input is an empty dict because the prompt is already filled
result = chain.invoke({})
result = chain.invoke(input=inputs)
if hasattr(result, "content"):
result = result.content
self.repr_value = result

42
src/backend/langflow/components/llms/HuggingFaceEndpoints.py Normal file
View file

@ -0,0 +1,42 @@
from typing import Optional
from langflow import CustomComponent
from langchain.llms import HuggingFaceEndpoint
from langchain.llms.base import BaseLLM
class HuggingFaceEndpointsComponent(CustomComponent):
display_name: str = "Hugging Face Inference API"
description: str = "LLM model from Hugging Face Inference API."
def build_config(self):
return {
"endpoint_url": {"display_name": "Endpoint URL", "password": True},
"task": {
"display_name": "Task",
"type": "select",
"options": ["text2text-generation", "text-generation", "summarization"],
},
"huggingfacehub_api_token": {"display_name": "API token", "password": True},
"model_kwargs": {
"display_name": "Model Keyword Arguments",
"field_type": "code",
},
"code": {"show": False},
}
def build(
self,
endpoint_url: str,
task="text2text-generation",
huggingfacehub_api_token: Optional[str] = None,
model_kwargs: Optional[dict] = None,
) -> BaseLLM:
try:
output = HuggingFaceEndpoint(
endpoint_url=endpoint_url,
task=task,
huggingfacehub_api_token=huggingfacehub_api_token,
)
except Exception as e:
raise ValueError("Could not connect to HuggingFace Endpoints API.") from e
return output

0
src/backend/langflow/database/__init__.py → src/backend/langflow/components/llms/__init__.py
View file

28
src/backend/langflow/components/retrievers/MetalRetriever.py Normal file
View file

@ -0,0 +1,28 @@
from typing import Optional
from langflow import CustomComponent
from langchain.retrievers import MetalRetriever
from langchain.schema import BaseRetriever
from metal_sdk.metal import Metal # type: ignore
class MetalRetrieverComponent(CustomComponent):
display_name: str = "Metal Retriever"
description: str = "Retriever that uses the Metal API."
def build_config(self):
return {
"api_key": {"display_name": "API Key", "password": True},
"client_id": {"display_name": "Client ID", "password": True},
"index_id": {"display_name": "Index ID"},
"params": {"display_name": "Parameters"},
"code": {"show": False},
}
def build(
self, api_key: str, client_id: str, index_id: str, params: Optional[dict] = None
) -> BaseRetriever:
try:
metal = Metal(api_key=api_key, client_id=client_id, index_id=index_id)
except Exception as e:
raise ValueError("Could not connect to Metal API.") from e
return MetalRetriever(client=metal, params=params or {})

0
src/backend/langflow/database/models/__init__.py → src/backend/langflow/components/retrievers/__init__.py
View file

80
src/backend/langflow/components/textsplitters/LanguageRecursiveTextSplitter.py Normal file
View file

@ -0,0 +1,80 @@
from typing import Optional
from langflow import CustomComponent
from langchain.text_splitter import Language
from langchain.schema import Document
class LanguageRecursiveTextSplitterComponent(CustomComponent):
display_name: str = "Language Recursive Text Splitter"
description: str = "Split text into chunks of a specified length based on language."
documentation: str = "https://docs.langflow.org/components/text-splitters#languagerecursivetextsplitter"
def build_config(self):
options = [x.value for x in Language]
return {
"documents": {
"display_name": "Documents",
"info": "The documents to split.",
},
"separator_type": {
"display_name": "Separator Type",
"info": "The type of separator to use.",
"field_type": "str",
"options": options,
"value": "Python",
},
"separators": {
"display_name": "Separators",
"info": "The characters to split on.",
"is_list": True,
},
"chunk_size": {
"display_name": "Chunk Size",
"info": "The maximum length of each chunk.",
"field_type": "int",
"value": 1000,
},
"chunk_overlap": {
"display_name": "Chunk Overlap",
"info": "The amount of overlap between chunks.",
"field_type": "int",
"value": 200,
},
"code": {"show": False},
}
def build(
self,
documents: list[Document],
chunk_size: Optional[int] = 1000,
chunk_overlap: Optional[int] = 200,
separator_type: Optional[str] = "Python",
) -> list[Document]:
"""
Split text into chunks of a specified length.
Args:
separators (list[str]): The characters to split on.
chunk_size (int): The maximum length of each chunk.
chunk_overlap (int): The amount of overlap between chunks.
length_function (function): The function to use to calculate the length of the text.
Returns:
list[str]: The chunks of text.
"""
from langchain.text_splitter import RecursiveCharacterTextSplitter
# Make sure chunk_size and chunk_overlap are ints
if isinstance(chunk_size, str):
chunk_size = int(chunk_size)
if isinstance(chunk_overlap, str):
chunk_overlap = int(chunk_overlap)
splitter = RecursiveCharacterTextSplitter.from_language(
language=Language(separator_type),
chunk_size=chunk_size,
chunk_overlap=chunk_overlap,
)
docs = splitter.split_documents(documents)
return docs

79
src/backend/langflow/components/textsplitters/RecursiveCharacterTextSplitter.py Normal file
View file

@ -0,0 +1,79 @@
from typing import Optional
from langflow import CustomComponent
from langchain.schema import Document
from langflow.utils.util import build_loader_repr_from_documents
class RecursiveCharacterTextSplitterComponent(CustomComponent):
display_name: str = "Recursive Character Text Splitter"
description: str = "Split text into chunks of a specified length."
documentation: str = "https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter"
def build_config(self):
return {
"documents": {
"display_name": "Documents",
"info": "The documents to split.",
},
"separators": {
"display_name": "Separators",
"info": 'The characters to split on.\nIf left empty defaults to ["\\n\\n", "\\n", " ", ""].',
"is_list": True,
},
"chunk_size": {
"display_name": "Chunk Size",
"info": "The maximum length of each chunk.",
"field_type": "int",
"value": 1000,
},
"chunk_overlap": {
"display_name": "Chunk Overlap",
"info": "The amount of overlap between chunks.",
"field_type": "int",
"value": 200,
},
"code": {"show": False},
}
def build(
self,
documents: list[Document],
separators: Optional[list[str]] = None,
chunk_size: Optional[int] = 1000,
chunk_overlap: Optional[int] = 200,
) -> list[Document]:
"""
Split text into chunks of a specified length.
Args:
separators (list[str]): The characters to split on.
chunk_size (int): The maximum length of each chunk.
chunk_overlap (int): The amount of overlap between chunks.
length_function (function): The function to use to calculate the length of the text.
Returns:
list[str]: The chunks of text.
"""
from langchain.text_splitter import RecursiveCharacterTextSplitter
if separators == "":
separators = None
elif separators:
# check if the separators list has escaped characters
# if there are escaped characters, unescape them
separators = [x.encode().decode("unicode-escape") for x in separators]
# Make sure chunk_size and chunk_overlap are ints
if isinstance(chunk_size, str):
chunk_size = int(chunk_size)
if isinstance(chunk_overlap, str):
chunk_overlap = int(chunk_overlap)
splitter = RecursiveCharacterTextSplitter(
separators=separators,
chunk_size=chunk_size,
chunk_overlap=chunk_overlap,
)
docs = splitter.split_documents(documents)
self.repr_value = build_loader_repr_from_documents(docs)
return docs

0
src/backend/langflow/components/textsplitters/__init__.py Normal file
View file

56
src/backend/langflow/components/toolkits/Metaphor.py Normal file
View file

@ -0,0 +1,56 @@
from typing import List, Union
from langflow import CustomComponent
from metaphor_python import Metaphor # type: ignore
from langchain.tools import Tool
from langchain.agents import tool
from langchain.agents.agent_toolkits.base import BaseToolkit
class MetaphorToolkit(CustomComponent):
display_name: str = "Metaphor"
description: str = "Metaphor Toolkit"
documentation = (
"https://python.langchain.com/docs/integrations/tools/metaphor_search"
)
beta = True
# api key should be password = True
field_config = {
"metaphor_api_key": {"display_name": "Metaphor API Key", "password": True},
"code": {"advanced": True},
}
def build(
self,
metaphor_api_key: str,
use_autoprompt: bool = True,
search_num_results: int = 5,
similar_num_results: int = 5,
) -> Union[Tool, BaseToolkit]:
# If documents, then we need to create a Vectara instance using .from_documents
client = Metaphor(api_key=metaphor_api_key)
@tool
def search(query: str):
"""Call search engine with a query."""
return client.search(
query, use_autoprompt=use_autoprompt, num_results=search_num_results
)
@tool
def get_contents(ids: List[str]):
"""Get contents of a webpage.
The ids passed in should be a list of ids as fetched from `search`.
"""
return client.get_contents(ids)
@tool
def find_similar(url: str):
"""Get search results similar to a given URL.
The url passed in should be a URL returned from `search`
"""
return client.find_similar(url, num_results=similar_num_results)
return [search, get_contents, find_similar] # type: ignore

0
src/backend/langflow/components/toolkits/__init__.py Normal file
View file

75
src/backend/langflow/components/utilities/GetRequest.py Normal file
View file

@ -0,0 +1,75 @@
from langflow import CustomComponent
from langchain.schema import Document
from langflow.services.database.models.base import orjson_dumps
import requests
from typing import Optional
class GetRequest(CustomComponent):
display_name: str = "GET Request"
description: str = "Make a GET request to the given URL."
output_types: list[str] = ["Document"]
documentation: str = "https://docs.langflow.org/components/utilities#get-request"
beta = True
field_config = {
"url": {
"display_name": "URL",
"info": "The URL to make the request to",
"is_list": True,
},
"headers": {
"display_name": "Headers",
"info": "The headers to send with the request.",
},
"code": {"show": False},
"timeout": {
"display_name": "Timeout",
"field_type": "int",
"info": "The timeout to use for the request.",
"value": 5,
},
}
def get_document(
self, session: requests.Session, url: str, headers: Optional[dict], timeout: int
) -> Document:
try:
response = session.get(url, headers=headers, timeout=int(timeout))
try:
response_json = response.json()
result = orjson_dumps(response_json, indent_2=False)
except Exception:
result = response.text
self.repr_value = result
return Document(
page_content=result,
metadata={
"source": url,
"headers": headers,
"status_code": response.status_code,
},
)
except requests.Timeout:
return Document(
page_content="Request Timed Out",
metadata={"source": url, "headers": headers, "status_code": 408},
)
except Exception as exc:
return Document(
page_content=str(exc),
metadata={"source": url, "headers": headers, "status_code": 500},
)
def build(
self,
url: str,
headers: Optional[dict] = None,
timeout: int = 5,
) -> list[Document]:
if headers is None:
headers = {}
urls = url if isinstance(url, list) else [url]
with requests.Session() as session:
documents = [self.get_document(session, u, headers, timeout) for u in urls]
self.repr_value = documents
return documents

55
src/backend/langflow/components/utilities/JSONDocumentBuilder.py Normal file
View file

@ -0,0 +1,55 @@
### JSON Document Builder
# Build a Document containing a JSON object using a key and another Document page content.
# **Params**
# - **Key:** The key to use for the JSON object.
# - **Document:** The Document page to use for the JSON object.
# **Output**
# - **Document:** The Document containing the JSON object.
from langflow import CustomComponent
from langchain.schema import Document
from langflow.services.database.models.base import orjson_dumps
class JSONDocumentBuilder(CustomComponent):
display_name: str = "JSON Document Builder"
description: str = "Build a Document containing a JSON object using a key and another Document page content."
output_types: list[str] = ["Document"]
beta = True
documentation: str = (
"https://docs.langflow.org/components/utilities#json-document-builder"
)
field_config = {
"key": {"display_name": "Key"},
"document": {"display_name": "Document"},
}
def build(
self,
key: str,
document: Document,
) -> Document:
documents = None
if isinstance(document, list):
documents = [
Document(
page_content=orjson_dumps({key: doc.page_content}, indent_2=False)
)
for doc in document
]
elif isinstance(document, Document):
documents = Document(
page_content=orjson_dumps({key: document.page_content}, indent_2=False)
)
else:
raise TypeError(
f"Expected Document or list of Documents, got {type(document)}"
)
self.repr_value = documents
return documents

80
src/backend/langflow/components/utilities/PostRequest.py Normal file
View file

@ -0,0 +1,80 @@
from langflow import CustomComponent
from langchain.schema import Document
from langflow.services.database.models.base import orjson_dumps
import requests
from typing import Optional
class PostRequest(CustomComponent):
display_name: str = "POST Request"
description: str = "Make a POST request to the given URL."
output_types: list[str] = ["Document"]
documentation: str = "https://docs.langflow.org/components/utilities#post-request"
beta = True
field_config = {
"url": {"display_name": "URL", "info": "The URL to make the request to."},
"headers": {
"display_name": "Headers",
"info": "The headers to send with the request.",
},
"code": {"show": False},
"document": {"display_name": "Document"},
}
def post_document(
self,
session: requests.Session,
document: Document,
url: str,
headers: Optional[dict] = None,
) -> Document:
try:
response = session.post(url, headers=headers, data=document.page_content)
try:
response_json = response.json()
result = orjson_dumps(response_json, indent_2=False)
except Exception:
result = response.text
self.repr_value = result
return Document(
page_content=result,
metadata={
"source": url,
"headers": headers,
"status_code": response,
},
)
except Exception as exc:
return Document(
page_content=str(exc),
metadata={
"source": url,
"headers": headers,
"status_code": 500,
},
)
def build(
self,
document: Document,
url: str,
headers: Optional[dict] = None,
) -> list[Document]:
if headers is None:
headers = {}
if not isinstance(document, list) and isinstance(document, Document):
documents: list[Document] = [document]
elif isinstance(document, list) and all(
isinstance(doc, Document) for doc in document
):
documents = document
else:
raise ValueError("document must be a Document or a list of Documents")
with requests.Session() as session:
documents = [
self.post_document(session, doc, url, headers) for doc in documents
]
self.repr_value = documents
return documents

94
src/backend/langflow/components/utilities/UpdateRequest.py Normal file
View file

@ -0,0 +1,94 @@
from typing import List, Optional
import requests
from langflow import CustomComponent
from langchain.schema import Document
from langflow.services.database.models.base import orjson_dumps
class UpdateRequest(CustomComponent):
display_name: str = "Update Request"
description: str = "Make a PATCH request to the given URL."
output_types: list[str] = ["Document"]
documentation: str = "https://docs.langflow.org/components/utilities#update-request"
beta = True
field_config = {
"url": {"display_name": "URL", "info": "The URL to make the request to."},
"headers": {
"display_name": "Headers",
"field_type": "NestedDict",
"info": "The headers to send with the request.",
},
"code": {"show": False},
"document": {"display_name": "Document"},
"method": {
"display_name": "Method",
"field_type": "str",
"info": "The HTTP method to use.",
"options": ["PATCH", "PUT"],
"value": "PATCH",
},
}
def update_document(
self,
session: requests.Session,
document: Document,
url: str,
headers: Optional[dict] = None,
method: str = "PATCH",
) -> Document:
try:
if method == "PATCH":
response = session.patch(
url, headers=headers, data=document.page_content
)
elif method == "PUT":
response = session.put(url, headers=headers, data=document.page_content)
else:
raise ValueError(f"Unsupported method: {method}")
try:
response_json = response.json()
result = orjson_dumps(response_json, indent_2=False)
except Exception:
result = response.text
self.repr_value = result
return Document(
page_content=result,
metadata={
"source": url,
"headers": headers,
"status_code": response.status_code,
},
)
except Exception as exc:
return Document(
page_content=str(exc),
metadata={"source": url, "headers": headers, "status_code": 500},
)
def build(
self,
method: str,
document: Document,
url: str,
headers: Optional[dict] = None,
) -> List[Document]:
if headers is None:
headers = {}
if not isinstance(document, list) and isinstance(document, Document):
documents: list[Document] = [document]
elif isinstance(document, list) and all(
isinstance(doc, Document) for doc in document
):
documents = document
else:
raise ValueError("document must be a Document or a list of Documents")
with requests.Session() as session:
documents = [
self.update_document(session, doc, url, headers, method)
for doc in documents
]
self.repr_value = documents
return documents

109
src/backend/langflow/components/vectorstores/Chroma.py Normal file
View file

@ -0,0 +1,109 @@
from typing import Optional, Union
from langflow import CustomComponent
from langchain.vectorstores import Chroma
from langchain.schema import Document
from langchain.vectorstores.base import VectorStore
from langchain.schema import BaseRetriever
from langchain.embeddings.base import Embeddings
import chromadb # type: ignore
class ChromaComponent(CustomComponent):
"""
A custom component for implementing a Vector Store using Chroma.
"""
display_name: str = "Chroma (Custom Component)"
description: str = "Implementation of Vector Store using Chroma"
documentation = "https://python.langchain.com/docs/integrations/vectorstores/chroma"
beta = True
def build_config(self):
"""
Builds the configuration for the component.
Returns:
- dict: A dictionary containing the configuration options for the component.
"""
return {
"collection_name": {"display_name": "Collection Name", "value": "langflow"},
"persist": {"display_name": "Persist"},
"persist_directory": {"display_name": "Persist Directory"},
"code": {"show": False, "display_name": "Code"},
"documents": {"display_name": "Documents", "is_list": True},
"embedding": {"display_name": "Embedding"},
"chroma_server_cors_allow_origins": {
"display_name": "Server CORS Allow Origins",
"advanced": True,
},
"chroma_server_host": {"display_name": "Server Host", "advanced": True},
"chroma_server_port": {"display_name": "Server Port", "advanced": True},
"chroma_server_grpc_port": {
"display_name": "Server gRPC Port",
"advanced": True,
},
"chroma_server_ssl_enabled": {
"display_name": "Server SSL Enabled",
"advanced": True,
},
}
def build(
self,
collection_name: str,
persist: bool,
chroma_server_ssl_enabled: bool,
persist_directory: Optional[str] = None,
embedding: Optional[Embeddings] = None,
documents: Optional[Document] = None,
chroma_server_cors_allow_origins: Optional[str] = None,
chroma_server_host: Optional[str] = None,
chroma_server_port: Optional[int] = None,
chroma_server_grpc_port: Optional[int] = None,
) -> Union[VectorStore, BaseRetriever]:
"""
Builds the Vector Store or BaseRetriever object.
Args:
- collection_name (str): The name of the collection.
- persist_directory (Optional[str]): The directory to persist the Vector Store to.
- chroma_server_ssl_enabled (bool): Whether to enable SSL for the Chroma server.
- persist (bool): Whether to persist the Vector Store or not.
- embedding (Optional[Embeddings]): The embeddings to use for the Vector Store.
- documents (Optional[Document]): The documents to use for the Vector Store.
- chroma_server_cors_allow_origins (Optional[str]): The CORS allow origins for the Chroma server.
- chroma_server_host (Optional[str]): The host for the Chroma server.
- chroma_server_port (Optional[int]): The port for the Chroma server.
- chroma_server_grpc_port (Optional[int]): The gRPC port for the Chroma server.
Returns:
- Union[VectorStore, BaseRetriever]: The Vector Store or BaseRetriever object.
"""
# Chroma settings
chroma_settings = None
if chroma_server_host is not None:
chroma_settings = chromadb.config.Settings(
chroma_server_cors_allow_origins=chroma_server_cors_allow_origins
or None,
chroma_server_host=chroma_server_host,
chroma_server_port=chroma_server_port or None,
chroma_server_grpc_port=chroma_server_grpc_port or None,
chroma_server_ssl_enabled=chroma_server_ssl_enabled,
)
# If documents, then we need to create a Chroma instance using .from_documents
if documents is not None and embedding is not None:
return Chroma.from_documents(
documents=documents, # type: ignore
persist_directory=persist_directory if persist else None,
collection_name=collection_name,
embedding=embedding,
client_settings=chroma_settings,
)
return Chroma(
persist_directory=persist_directory, client_settings=chroma_settings
)

50
src/backend/langflow/components/vectorstores/Vectara.py Normal file
View file

@ -0,0 +1,50 @@
from typing import Optional, Union
from langflow import CustomComponent
from langchain.vectorstores import Vectara
from langchain.schema import Document
from langchain.vectorstores.base import VectorStore
from langchain.schema import BaseRetriever
from langchain.embeddings.base import Embeddings
class VectaraComponent(CustomComponent):
display_name: str = "Vectara"
description: str = "Implementation of Vector Store using Vectara"
documentation = (
"https://python.langchain.com/docs/integrations/vectorstores/vectara"
)
beta = True
# api key should be password = True
field_config = {
"vectara_customer_id": {"display_name": "Vectara Customer ID"},
"vectara_corpus_id": {"display_name": "Vectara Corpus ID"},
"vectara_api_key": {"display_name": "Vectara API Key", "password": True},
"code": {"show": False},
"documents": {"display_name": "Documents"},
"embedding": {"display_name": "Embedding"},
}
def build(
self,
vectara_customer_id: str,
vectara_corpus_id: str,
vectara_api_key: str,
embedding: Optional[Embeddings] = None,
documents: Optional[Document] = None,
) -> Union[VectorStore, BaseRetriever]:
# If documents, then we need to create a Vectara instance using .from_documents
if documents is not None and embedding is not None:
return Vectara.from_documents(
documents=documents, # type: ignore
vectara_customer_id=vectara_customer_id,
vectara_corpus_id=vectara_corpus_id,
vectara_api_key=vectara_api_key,
embedding=embedding,
)
return Vectara(
vectara_customer_id=vectara_customer_id,
vectara_corpus_id=vectara_corpus_id,
vectara_api_key=vectara_api_key,
)

0
src/backend/langflow/components/vectorstores/__init__.py Normal file
View file

8
src/backend/langflow/config.yaml
View file

@ -104,6 +104,8 @@ embeddings:
documentation: "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/sentence_transformers"
CohereEmbeddings:
documentation: "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/cohere"
VertexAIEmbeddings:
documentation: "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/google_vertex_ai_palm"
llms:
OpenAI:
documentation: "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai"
@ -127,8 +129,8 @@ llms:
# There's a bug in this component deactivating until we get it sorted: _language_models.py", line 804, in send_message
# is_blocked=safety_attributes.get("blocked", False),
# AttributeError: 'list' object has no attribute 'get'
# ChatVertexAI:
# documentation: "https://python.langchain.com/docs/modules/model_io/models/chat/integrations/google_vertex_ai_palm"
ChatVertexAI:
documentation: "https://python.langchain.com/docs/modules/model_io/models/chat/integrations/google_vertex_ai_palm"
###
memories:
# https://github.com/supabase-community/supabase-py/issues/482
@ -169,8 +171,6 @@ prompts:
textsplitters:
CharacterTextSplitter:
documentation: "https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter"
RecursiveCharacterTextSplitter:
documentation: "https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/recursive_text_splitter"
toolkits:
OpenAPIToolkit:
documentation: ""

78
src/backend/langflow/database/base.py
View file

@ -1,78 +0,0 @@
from contextlib import contextmanager
import os
from sqlmodel import SQLModel, Session, create_engine
from langflow.utils.logger import logger
class Engine:
_instance = None
@classmethod
def get(cls):
logger.debug("Getting database engine")
if cls._instance is None:
cls.create()
return cls._instance
@classmethod
def create(cls):
logger.debug("Creating database engine")
from langflow.settings import settings
if langflow_database_url := os.getenv("LANGFLOW_DATABASE_URL"):
settings.DATABASE_URL = langflow_database_url
logger.debug("Using LANGFLOW_DATABASE_URL")
if settings.DATABASE_URL and settings.DATABASE_URL.startswith("sqlite"):
connect_args = {"check_same_thread": False}
else:
connect_args = {}
if not settings.DATABASE_URL:
raise RuntimeError("No database_url provided")
cls._instance = create_engine(settings.DATABASE_URL, connect_args=connect_args)
@classmethod
def update(cls):
logger.debug("Updating database engine")
cls._instance = None
cls.create()
def create_db_and_tables():
logger.debug("Creating database and tables")
try:
SQLModel.metadata.create_all(Engine.get())
except Exception as exc:
logger.error(f"Error creating database and tables: {exc}")
raise RuntimeError("Error creating database and tables") from exc
# Now check if the table Flow exists, if not, something went wrong
# and we need to create the tables again.
from sqlalchemy import inspect
inspector = inspect(Engine.get())
if "flow" not in inspector.get_table_names():
logger.error("Something went wrong creating the database and tables.")
logger.error("Please check your database settings.")
raise RuntimeError("Something went wrong creating the database and tables.")
else:
logger.debug("Database and tables created successfully")
@contextmanager
def session_getter():
try:
session = Session(Engine.get())
yield session
except Exception as e:
print("Session rollback because of exception:", e)
session.rollback()
raise
finally:
session.close()
def get_session():
with session_getter() as session:
yield session

14
src/backend/langflow/database/models/base.py
View file

@ -1,14 +0,0 @@
from sqlmodel import SQLModel
import orjson
def orjson_dumps(v, *, default):
# orjson.dumps returns bytes, to match standard json.dumps we need to decode
return orjson.dumps(v, default=default).decode()
class SQLModelSerializable(SQLModel):
class Config:
orm_mode = True
json_loads = orjson.loads
json_dumps = orjson_dumps

33
src/backend/langflow/database/models/flow_style.py
View file

@ -1,33 +0,0 @@
# Path: src/backend/langflow/database/models/flowstyle.py
from langflow.database.models.base import SQLModelSerializable
from sqlmodel import Field, Relationship
from uuid import UUID, uuid4
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from langflow.database.models.flow import Flow
class FlowStyleBase(SQLModelSerializable):
color: str
emoji: str
flow_id: UUID = Field(default=None, foreign_key="flow.id")
class FlowStyle(FlowStyleBase, table=True):
id: UUID = Field(default_factory=uuid4, primary_key=True, unique=True)
flow: "Flow" = Relationship(back_populates="style")
class FlowStyleUpdate(SQLModelSerializable):
color: Optional[str] = None
emoji: Optional[str] = None
class FlowStyleCreate(FlowStyleBase):
pass
class FlowStyleRead(FlowStyleBase):
id: UUID

3
src/backend/langflow/field_typing/__init__.py Normal file
View file

@ -0,0 +1,3 @@
from .base import NestedDict
__all__ = ["NestedDict"]

4
src/backend/langflow/field_typing/base.py Normal file
View file

@ -0,0 +1,4 @@
from typing import Union, Dict
# Type alias for more complex dicts
NestedDict = Dict[str, Union[str, Dict]]

3
src/backend/langflow/graph/edge/base.py
View file

@ -1,4 +1,4 @@
from langflow.utils.logger import logger
from loguru import logger
from typing import TYPE_CHECKING
if TYPE_CHECKING:
@ -40,7 +40,6 @@ class Edge:
if no_matched_type:
logger.debug(self.source_types)
logger.debug(self.target_reqs)
if no_matched_type:
raise ValueError(
f"Edge between {self.source.vertex_type} and {self.target.vertex_type} "
f"has no matched type"

14
src/backend/langflow/graph/graph/base.py
View file

@ -1,7 +1,7 @@
from typing import Dict, Generator, List, Type, Union
from langflow.graph.edge.base import Edge
from langflow.graph.graph.constants import VERTEX_TYPE_MAP
from langflow.graph.graph.constants import lazy_load_vertex_dict
from langflow.graph.vertex.base import Vertex
from langflow.graph.vertex.types import (
FileToolVertex,
@ -10,7 +10,7 @@ from langflow.graph.vertex.types import (
)
from langflow.interface.tools.constants import FILE_TOOLS
from langflow.utils import payload
from langflow.utils.logger import logger
from loguru import logger
from langchain.chains.base import Chain
@ -144,7 +144,7 @@ class Graph:
return list(reversed(sorted_vertices))
def generator_build(self) -> Generator:
def generator_build(self) -> Generator[Vertex, None, None]:
"""Builds each vertex in the graph and yields it."""
sorted_vertices = self.topological_sort()
logger.debug("Sorted vertices: %s", sorted_vertices)
@ -187,10 +187,12 @@ class Graph:
"""Returns the node class based on the node type."""
if node_type in FILE_TOOLS:
return FileToolVertex
if node_type in VERTEX_TYPE_MAP:
return VERTEX_TYPE_MAP[node_type]
if node_type in lazy_load_vertex_dict.VERTEX_TYPE_MAP:
return lazy_load_vertex_dict.VERTEX_TYPE_MAP[node_type]
return (
VERTEX_TYPE_MAP[node_lc_type] if node_lc_type in VERTEX_TYPE_MAP else Vertex
lazy_load_vertex_dict.VERTEX_TYPE_MAP[node_lc_type]
if node_lc_type in lazy_load_vertex_dict.VERTEX_TYPE_MAP
else Vertex
)
def _build_vertices(self) -> List[Vertex]:

59
src/backend/langflow/graph/graph/constants.py
View file

@ -1,4 +1,3 @@
from langflow.graph.vertex.base import Vertex
from langflow.graph.vertex import types
from langflow.interface.agents.base import agent_creator
from langflow.interface.chains.base import chain_creator
@ -15,23 +14,45 @@ from langflow.interface.wrappers.base import wrapper_creator
from langflow.interface.output_parsers.base import output_parser_creator
from langflow.interface.retrievers.base import retriever_creator
from langflow.interface.custom.base import custom_component_creator
from typing import Dict, Type
from langflow.utils.lazy_load import LazyLoadDictBase
VERTEX_TYPE_MAP: Dict[str, Type[Vertex]] = {
**{t: types.PromptVertex for t in prompt_creator.to_list()},
**{t: types.AgentVertex for t in agent_creator.to_list()},
**{t: types.ChainVertex for t in chain_creator.to_list()},
**{t: types.ToolVertex for t in tool_creator.to_list()},
**{t: types.ToolkitVertex for t in toolkits_creator.to_list()},
**{t: types.WrapperVertex for t in wrapper_creator.to_list()},
**{t: types.LLMVertex for t in llm_creator.to_list()},
**{t: types.MemoryVertex for t in memory_creator.to_list()},
**{t: types.EmbeddingVertex for t in embedding_creator.to_list()},
**{t: types.VectorStoreVertex for t in vectorstore_creator.to_list()},
**{t: types.DocumentLoaderVertex for t in documentloader_creator.to_list()},
**{t: types.TextSplitterVertex for t in textsplitter_creator.to_list()},
**{t: types.OutputParserVertex for t in output_parser_creator.to_list()},
**{t: types.CustomComponentVertex for t in custom_component_creator.to_list()},
**{t: types.RetrieverVertex for t in retriever_creator.to_list()},
}
class VertexTypesDict(LazyLoadDictBase):
def __init__(self):
self._all_types_dict = None
@property
def VERTEX_TYPE_MAP(self):
return self.all_types_dict
def _build_dict(self):
langchain_types_dict = self.get_type_dict()
return {
**langchain_types_dict,
"Custom": ["Custom Tool", "Python Function"],
}
def get_type_dict(self):
return {
**{t: types.PromptVertex for t in prompt_creator.to_list()},
**{t: types.AgentVertex for t in agent_creator.to_list()},
**{t: types.ChainVertex for t in chain_creator.to_list()},
**{t: types.ToolVertex for t in tool_creator.to_list()},
**{t: types.ToolkitVertex for t in toolkits_creator.to_list()},
**{t: types.WrapperVertex for t in wrapper_creator.to_list()},
**{t: types.LLMVertex for t in llm_creator.to_list()},
**{t: types.MemoryVertex for t in memory_creator.to_list()},
**{t: types.EmbeddingVertex for t in embedding_creator.to_list()},
**{t: types.VectorStoreVertex for t in vectorstore_creator.to_list()},
**{t: types.DocumentLoaderVertex for t in documentloader_creator.to_list()},
**{t: types.TextSplitterVertex for t in textsplitter_creator.to_list()},
**{t: types.OutputParserVertex for t in output_parser_creator.to_list()},
**{
t: types.CustomComponentVertex
for t in custom_component_creator.to_list()
},
**{t: types.RetrieverVertex for t in retriever_creator.to_list()},
}
lazy_load_vertex_dict = VertexTypesDict()

4
src/backend/langflow/graph/utils.py
View file

@ -3,6 +3,10 @@ from typing import Any, Union
from langflow.interface.utils import extract_input_variables_from_prompt
class UnbuiltObject:
pass
def validate_prompt(prompt: str):
"""Validate prompt."""
if extract_input_variables_from_prompt(prompt):

53
src/backend/langflow/graph/vertex/base.py
View file

@ -1,8 +1,9 @@
import ast
from langflow.graph.utils import UnbuiltObject
from langflow.interface.initialize import loading
from langflow.interface.listing import ALL_TYPES_DICT
from langflow.interface.listing import lazy_load_dict
from langflow.utils.constants import DIRECT_TYPES
from langflow.utils.logger import logger
from loguru import logger
from langflow.utils.util import sync_to_async
@ -22,7 +23,7 @@ class Vertex:
self.edges: List["Edge"] = []
self.base_type: Optional[str] = base_type
self._parse_data()
self._built_object = None
self._built_object = UnbuiltObject()
self._built = False
self.artifacts: Dict[str, Any] = {}
@ -62,7 +63,7 @@ class Vertex:
)
if self.base_type is None:
for base_type, value in ALL_TYPES_DICT.items():
for base_type, value in lazy_load_dict.ALL_TYPES_DICT.items():
if self.vertex_type in value:
self.base_type = base_type
break
@ -121,6 +122,19 @@ class Vertex:
except Exception as exc:
logger.debug(f"Error parsing code: {exc}")
params[key] = value.get("value")
elif value.get("type") in ["dict", "NestedDict"]:
# When dict comes from the frontend it comes as a
# list of dicts, so we need to convert it to a dict
# before passing it to the build method
_value = value.get("value")
if isinstance(_value, list):
params[key] = {
k: v
for item in value.get("value", [])
for k, v in item.items()
}
elif isinstance(_value, dict):
params[key] = _value
else:
params[key] = value.get("value")
@ -132,13 +146,13 @@ class Vertex:
# Add _type to params
self.params = params
def _build(self):
def _build(self, user_id=None):
"""
Initiate the build process.
"""
logger.debug(f"Building {self.vertex_type}")
self._build_each_node_in_params_dict()
self._get_and_instantiate_class()
self._get_and_instantiate_class(user_id)
self._validate_built_object()
self._built = True
@ -168,23 +182,25 @@ class Vertex:
"""
return all(self._is_node(node) for node in value)
def _build_node_and_update_params(self, key, node):
def _build_node_and_update_params(self, key, node, user_id=None):
"""
Builds a given node and updates the params dictionary accordingly.
"""
result = node.build()
result = node.build(user_id)
self._handle_func(key, result)
if isinstance(result, list):
self._extend_params_list_with_result(key, result)
self.params[key] = result
def _build_list_of_nodes_and_update_params(self, key, nodes):
def _build_list_of_nodes_and_update_params(
self, key, nodes: List["Vertex"], user_id=None
):
"""
Iterates over a list of nodes, builds each and updates the params dictionary.
"""
self.params[key] = []
for node in nodes:
built = node.build()
built = node.build(user_id)
if isinstance(built, list):
if key not in self.params:
self.params[key] = []
@ -214,7 +230,7 @@ class Vertex:
if isinstance(self.params[key], list):
self.params[key].extend(result)
def _get_and_instantiate_class(self):
def _get_and_instantiate_class(self, user_id=None):
"""
Gets the class from a dictionary and instantiates it with the params.
"""
@ -225,6 +241,7 @@ class Vertex:
node_type=self.vertex_type,
base_type=self.base_type,
params=self.params,
user_id=user_id,
)
self._update_built_object_and_artifacts(result)
except Exception as exc:
@ -245,12 +262,18 @@ class Vertex:
"""
Checks if the built object is None and raises a ValueError if so.
"""
if self._built_object is None:
raise ValueError(f"Node type {self.vertex_type} not found")
if isinstance(self._built_object, UnbuiltObject):
raise ValueError(f"{self.vertex_type}: {self._built_object_repr()}")
elif self._built_object is None:
message = f"{self.vertex_type} returned None."
if self.base_type == "custom_components":
message += " Make sure your build method returns a component."
def build(self, force: bool = False) -> Any:
raise ValueError(message)
def build(self, force: bool = False, user_id=None, *args, **kwargs) -> Any:
if not self._built or force:
self._build()
self._build(user_id, *args, **kwargs)
return self._built_object

37
src/backend/langflow/graph/vertex/types.py
View file

@ -21,18 +21,18 @@ class AgentVertex(Vertex):
elif isinstance(source_node, ChainVertex):
self.chains.append(source_node)
def build(self, force: bool = False) -> Any:
def build(self, force: bool = False, user_id=None, *args, **kwargs) -> Any:
if not self._built or force:
self._set_tools_and_chains()
# First, build the tools
for tool_node in self.tools:
tool_node.build()
tool_node.build(user_id=user_id)
# Next, build the chains and the rest
for chain_node in self.chains:
chain_node.build(tools=self.tools)
chain_node.build(tools=self.tools, user_id=user_id)
self._build()
self._build(user_id=user_id)
return self._built_object
@ -49,13 +49,13 @@ class LLMVertex(Vertex):
def __init__(self, data: Dict):
super().__init__(data, base_type="llms")
def build(self, force: bool = False) -> Any:
def build(self, force: bool = False, user_id=None, *args, **kwargs) -> Any:
# LLM is different because some models might take up too much memory
# or time to load. So we only load them when we need them.ß
if self.vertex_type == self.built_node_type:
return self.class_built_object
if not self._built or force:
self._build()
self._build(user_id=user_id)
self.built_node_type = self.vertex_type
self.class_built_object = self._built_object
# Avoid deepcopying the LLM
@ -77,11 +77,11 @@ class WrapperVertex(Vertex):
def __init__(self, data: Dict):
super().__init__(data, base_type="wrappers")
def build(self, force: bool = False) -> Any:
def build(self, force: bool = False, user_id=None, *args, **kwargs) -> Any:
if not self._built or force:
if "headers" in self.params:
self.params["headers"] = ast.literal_eval(self.params["headers"])
self._build()
self._build(user_id=user_id)
return self._built_object
@ -148,16 +148,19 @@ class ChainVertex(Vertex):
def build(
self,
force: bool = False,
tools: Optional[List[Union[ToolkitVertex, ToolVertex]]] = None,
user_id=None,
*args,
**kwargs,
) -> Any:
if not self._built or force:
# Check if the chain requires a PromptVertex
for key, value in self.params.items():
if isinstance(value, PromptVertex):
# Build the PromptVertex, passing the tools if available
tools = kwargs.get("tools", None)
self.params[key] = value.build(tools=tools, force=force)
self._build()
self._build(user_id=user_id)
return self._built_object
@ -169,7 +172,10 @@ class PromptVertex(Vertex):
def build(
self,
force: bool = False,
user_id=None,
tools: Optional[List[Union[ToolkitVertex, ToolVertex]]] = None,
*args,
**kwargs,
) -> Any:
if not self._built or force:
if (
@ -180,7 +186,7 @@ class PromptVertex(Vertex):
# Check if it is a ZeroShotPrompt and needs a tool
if "ShotPrompt" in self.vertex_type:
tools = (
[tool_node.build() for tool_node in tools]
[tool_node.build(user_id=user_id) for tool_node in tools]
if tools is not None
else []
)
@ -208,7 +214,7 @@ class PromptVertex(Vertex):
else:
self.params.pop("input_variables", None)
self._build()
self._build(user_id=user_id)
return self._built_object
def _built_object_repr(self):
@ -226,7 +232,12 @@ class PromptVertex(Vertex):
# so the prompt format doesn't break
artifacts.pop("handle_keys", None)
try:
template = self._built_object.template
if not hasattr(self._built_object, "template") and hasattr(
self._built_object, "prompt"
):
template = self._built_object.prompt.template
else:
template = self._built_object.template
for key, value in artifacts.items():
if value:
replace_key = "{" + key + "}"

11
src/backend/langflow/interface/agents/base.py
View file

@ -5,9 +5,10 @@ from langchain.agents import types
from langflow.custom.customs import get_custom_nodes
from langflow.interface.agents.custom import CUSTOM_AGENTS
from langflow.interface.base import LangChainTypeCreator
from langflow.settings import settings
from langflow.services.utils import get_settings_manager
from langflow.template.frontend_node.agents import AgentFrontendNode
from langflow.utils.logger import logger
from loguru import logger
from langflow.utils.util import build_template_from_class, build_template_from_method
@ -53,13 +54,17 @@ class AgentCreator(LangChainTypeCreator):
# Now this is a generator
def to_list(self) -> List[str]:
names = []
settings_manager = get_settings_manager()
for _, agent in self.type_to_loader_dict.items():
agent_name = (
agent.function_name()
if hasattr(agent, "function_name")
else agent.__name__
)
if agent_name in settings.AGENTS or settings.DEV:
if (
agent_name in settings_manager.settings.AGENTS
or settings_manager.settings.DEV
):
names.append(agent_name)
return names

10
src/backend/langflow/interface/base.py
View file

@ -2,13 +2,14 @@ from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional, Type, Union
from langchain.chains.base import Chain
from langchain.agents import AgentExecutor
from langflow.services.utils import get_settings_manager
from pydantic import BaseModel
from langflow.template.field.base import TemplateField
from langflow.template.frontend_node.base import FrontendNode
from langflow.template.template.base import Template
from langflow.utils.logger import logger
from langflow.settings import settings
from loguru import logger
# Assuming necessary imports for Field, Template, and FrontendNode classes
@ -26,9 +27,12 @@ class LangChainTypeCreator(BaseModel, ABC):
@property
def docs_map(self) -> Dict[str, str]:
"""A dict with the name of the component as key and the documentation link as value."""
settings_manager = get_settings_manager()
if self.name_docs_dict is None:
try:
type_settings = getattr(settings, self.type_name.upper())
type_settings = getattr(
settings_manager.settings, self.type_name.upper()
)
self.name_docs_dict = {
name: value_dict["documentation"]
for name, value_dict in type_settings.items()

9
src/backend/langflow/interface/chains/base.py
View file

@ -3,9 +3,10 @@ from typing import Any, Dict, List, Optional, Type
from langflow.custom.customs import get_custom_nodes
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.importing.utils import import_class
from langflow.settings import settings
from langflow.services.utils import get_settings_manager
from langflow.template.frontend_node.chains import ChainFrontendNode
from langflow.utils.logger import logger
from loguru import logger
from langflow.utils.util import build_template_from_class, build_template_from_method
from langchain import chains
from langchain_experimental.sql import SQLDatabaseChain # type: ignore
@ -30,6 +31,7 @@ class ChainCreator(LangChainTypeCreator):
@property
def type_to_loader_dict(self) -> Dict:
if self.type_dict is None:
settings_manager = get_settings_manager()
self.type_dict: dict[str, Any] = {
chain_name: import_class(f"langchain.chains.{chain_name}")
for chain_name in chains.__all__
@ -43,7 +45,8 @@ class ChainCreator(LangChainTypeCreator):
self.type_dict = {
name: chain
for name, chain in self.type_dict.items()
if name in settings.CHAINS or settings.DEV
if name in settings_manager.settings.CHAINS
or settings_manager.settings.DEV
}
return self.type_dict

2
src/backend/langflow/interface/custom/base.py
View file

@ -8,7 +8,7 @@ from langflow.interface.custom.custom_component import CustomComponent
from langflow.template.frontend_node.custom_components import (
CustomComponentFrontendNode,
)
from langflow.utils.logger import logger
from loguru import logger
# Assuming necessary imports for Field, Template, and FrontendNode classes

3
src/backend/langflow/interface/custom/component.py
View file

@ -66,6 +66,9 @@ class Component(BaseModel):
elif "beta" in item_name:
template_config["beta"] = ast.literal_eval(item_value)
elif "documentation" in item_name:
template_config["documentation"] = ast.literal_eval(item_value)
return template_config
def build(self, *args: Any, **kwargs: Any) -> Any:

7
src/backend/langflow/interface/custom/constants.py
View file

@ -8,10 +8,13 @@ from langchain.text_splitter import TextSplitter
from langchain.tools import Tool
from langchain.vectorstores.base import VectorStore
from langchain.schema import BaseOutputParser
from langchain.schema.memory import BaseMemory
from langchain.memory.chat_memory import BaseChatMemory
from langchain.agents.agent import AgentExecutor
LANGCHAIN_BASE_TYPES = {
"Chain": Chain,
"AgentExecutor": AgentExecutor,
"Tool": Tool,
"BaseLLM": BaseLLM,
"PromptTemplate": PromptTemplate,
@ -22,6 +25,8 @@ LANGCHAIN_BASE_TYPES = {
"Embeddings": Embeddings,
"BaseRetriever": BaseRetriever,
"BaseOutputParser": BaseOutputParser,
"BaseMemory": BaseMemory,
"BaseChatMemory": BaseChatMemory,
}
# Langchain base types plus Python base types

67
src/backend/langflow/interface/custom/custom_component.py
View file

@ -1,13 +1,16 @@
from typing import Any, Callable, List, Optional
from typing import Any, Callable, List, Optional, Union
from uuid import UUID
from fastapi import HTTPException
from langflow.interface.custom.constants import CUSTOM_COMPONENT_SUPPORTED_TYPES
from langflow.interface.custom.component import Component
from langflow.interface.custom.directory_reader import DirectoryReader
from langflow.services.utils import get_db_manager
from langflow.interface.custom.utils import extract_inner_type
from langflow.utils import validate
from langflow.database.base import session_getter
from langflow.database.models.flow import Flow
from langflow.services.database.utils import session_getter
from langflow.services.database.models.flow import Flow
from pydantic import Extra
import yaml
@ -19,7 +22,8 @@ class CustomComponent(Component, extra=Extra.allow):
function_entrypoint_name = "build"
function: Optional[Callable] = None
return_type_valid_list = list(CUSTOM_COMPONENT_SUPPORTED_TYPES.keys())
repr_value: Optional[str] = ""
repr_value: Optional[Any] = ""
user_id: Optional[Union[UUID, str]] = None
def __init__(self, **data):
super().__init__(**data)
@ -49,7 +53,9 @@ class CustomComponent(Component, extra=Extra.allow):
reader = DirectoryReader("", False)
for type_hint in TYPE_HINT_LIST:
if reader.is_type_hint_used_but_not_imported(type_hint, code):
if reader._is_type_hint_used_in_args(
type_hint, code
) and not reader._is_type_hint_imported(type_hint, code):
error_detail = {
"error": "Type hint Error",
"traceback": f"Type hint '{type_hint}' is used but not imported in the code.",
@ -92,9 +98,9 @@ class CustomComponent(Component, extra=Extra.allow):
return build_method["args"]
@property
def get_function_entrypoint_return_type(self) -> str:
def get_function_entrypoint_return_type(self) -> List[str]:
if not self.code:
return ""
return []
tree = self.get_code_tree(self.code)
component_classes = [
@ -103,7 +109,7 @@ class CustomComponent(Component, extra=Extra.allow):
if self.code_class_base_inheritance in cls["bases"]
]
if not component_classes:
return ""
return []
# Assume the first Component class is the one we're interested in
component_class = component_classes[0]
@ -114,11 +120,25 @@ class CustomComponent(Component, extra=Extra.allow):
]
if not build_methods:
return ""
return []
build_method = build_methods[0]
return_type = build_method["return_type"]
if not return_type:
return []
# If list or List is in the return type, then we remove it and return the inner type
if return_type.startswith("list") or return_type.startswith("List"):
return_type = extract_inner_type(return_type)
return build_method["return_type"]
# If the return type is not a Union, then we just return it as a list
if "Union" not in return_type:
return [return_type] if return_type in self.return_type_valid_list else []
# If the return type is a Union, then we need to parse it
return_type = return_type.replace("Union", "").replace("[", "").replace("]", "")
return_type = return_type.split(",")
return_type = [item.strip() for item in return_type]
return [item for item in return_type if item in self.return_type_valid_list]
@property
def get_main_class_name(self):
@ -159,7 +179,8 @@ class CustomComponent(Component, extra=Extra.allow):
from langflow.processing.process import build_sorted_vertices_with_caching
from langflow.processing.process import process_tweaks
with session_getter() as session:
db_manager = get_db_manager()
with session_getter(db_manager) as session:
graph_data = flow.data if (flow := session.get(Flow, flow_id)) else None
if not graph_data:
raise ValueError(f"Flow {flow_id} not found")
@ -168,10 +189,16 @@ class CustomComponent(Component, extra=Extra.allow):
return build_sorted_vertices_with_caching(graph_data)
def list_flows(self, *, get_session: Optional[Callable] = None) -> List[Flow]:
get_session = get_session or session_getter
with get_session() as session:
flows = session.query(Flow).all()
return flows
if not self.user_id:
raise ValueError("Session is invalid")
try:
get_session = get_session or session_getter
db_manager = get_db_manager()
with get_session(db_manager) as session:
flows = session.query(Flow).filter(Flow.user_id == self.user_id).all()
return flows
except Exception as e:
raise ValueError("Session is invalid") from e
def get_flow(
self,
@ -182,12 +209,16 @@ class CustomComponent(Component, extra=Extra.allow):
get_session: Optional[Callable] = None,
) -> Flow:
get_session = get_session or session_getter
with get_session() as session:
db_manager = get_db_manager()
with get_session(db_manager) as session:
if flow_id:
flow = session.query(Flow).get(flow_id)
elif flow_name:
flow = session.query(Flow).filter(Flow.name == flow_name).first()
flow = (
session.query(Flow)
.filter(Flow.name == flow_name)
.filter(Flow.user_id == self.user_id)
).first()
else:
raise ValueError("Either flow_name or flow_id must be provided")

33
src/backend/langflow/interface/custom/directory_reader.py
View file

@ -1,7 +1,7 @@
import os
import ast
import zlib
from langflow.utils.logger import logger
from loguru import logger
class CustomComponentPathValueError(ValueError):
@ -77,7 +77,7 @@ class DirectoryReader:
]
filtered = [menu for menu in items if menu["components"]]
logger.debug(
f'Filtered components {"with errors" if with_errors else ""}: {filtered}'
f'Filtered components {"with errors" if with_errors else ""}: {len(filtered)}'
)
return {"menu": filtered}
@ -152,15 +152,19 @@ class DirectoryReader:
Check if a specific type hint is used in the
function definitions within the given code.
"""
module = ast.parse(code)
try:
module = ast.parse(code)
for node in ast.walk(module):
if isinstance(node, ast.FunctionDef):
for arg in node.args.args:
if self._is_type_hint_in_arg_annotation(
arg.annotation, type_hint_name
):
return True
for node in ast.walk(module):
if isinstance(node, ast.FunctionDef):
for arg in node.args.args:
if self._is_type_hint_in_arg_annotation(
arg.annotation, type_hint_name
):
return True
except SyntaxError:
# Returns False if the code is not valid Python
return False
return False
def _is_type_hint_in_arg_annotation(self, annotation, type_hint_name: str) -> bool:
@ -204,8 +208,13 @@ class DirectoryReader:
return False, "Syntax error"
elif not self.validate_build(file_content):
return False, "Missing build function"
elif self.is_type_hint_used_but_not_imported("Optional", file_content):
return False, "Type hint 'Optional' is used but not imported in the code."
elif self._is_type_hint_used_in_args(
"Optional", file_content
) and not self._is_type_hint_imported("Optional", file_content):
return (
False,
"Type hint 'Optional' is used but not imported in the code.",
)
else:
if self.compress_code_field:
file_content = str(StringCompressor(file_content).compress_string())

10
src/backend/langflow/interface/custom/utils.py Normal file
View file

@ -0,0 +1,10 @@
import re
def extract_inner_type(return_type: str) -> str:
"""
Extracts the inner type from a type hint that is a list.
"""
if match := re.match(r"list\[(.*)\]", return_type, re.IGNORECASE):
return match[1]
return return_type

9
src/backend/langflow/interface/document_loaders/base.py
View file

@ -1,10 +1,11 @@
from typing import Dict, List, Optional, Type
from langflow.interface.base import LangChainTypeCreator
from langflow.services.utils import get_settings_manager
from langflow.template.frontend_node.documentloaders import DocumentLoaderFrontNode
from langflow.interface.custom_lists import documentloaders_type_to_cls_dict
from langflow.settings import settings
from langflow.utils.logger import logger
from loguru import logger
from langflow.utils.util import build_template_from_class
@ -30,10 +31,12 @@ class DocumentLoaderCreator(LangChainTypeCreator):
return None
def to_list(self) -> List[str]:
settings_manager = get_settings_manager()
return [
documentloader.__name__
for documentloader in self.type_to_loader_dict.values()
if documentloader.__name__ in settings.DOCUMENTLOADERS or settings.DEV
if documentloader.__name__ in settings_manager.settings.DOCUMENTLOADERS
or settings_manager.settings.DEV
]

9
src/backend/langflow/interface/embeddings/base.py
View file

@ -2,10 +2,11 @@ from typing import Dict, List, Optional, Type
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.custom_lists import embedding_type_to_cls_dict
from langflow.settings import settings
from langflow.services.utils import get_settings_manager
from langflow.template.frontend_node.base import FrontendNode
from langflow.template.frontend_node.embeddings import EmbeddingFrontendNode
from langflow.utils.logger import logger
from loguru import logger
from langflow.utils.util import build_template_from_class
@ -32,10 +33,12 @@ class EmbeddingCreator(LangChainTypeCreator):
return None
def to_list(self) -> List[str]:
settings_manager = get_settings_manager()
return [
embedding.__name__
for embedding in self.type_to_loader_dict.values()
if embedding.__name__ in settings.EMBEDDINGS or settings.DEV
if embedding.__name__ in settings_manager.settings.EMBEDDINGS
or settings_manager.settings.DEV
]

45
src/backend/langflow/interface/initialize/loading.py
View file

@ -1,5 +1,6 @@
import json
from typing import Any, Callable, Dict, Sequence, Type
import orjson
from typing import Any, Callable, Dict, Sequence, Type, TYPE_CHECKING
from langchain.agents import agent as agent_module
from langchain.agents.agent import AgentExecutor
@ -33,10 +34,15 @@ from langflow.utils import validate
from langchain.chains.base import Chain
from langchain.vectorstores.base import VectorStore
from langchain.document_loaders.base import BaseLoader
from langflow.utils.logger import logger
from loguru import logger
if TYPE_CHECKING:
from langflow import CustomComponent
def instantiate_class(node_type: str, base_type: str, params: Dict) -> Any:
def instantiate_class(
node_type: str, base_type: str, params: Dict, user_id=None
) -> Any:
"""Instantiate class from module type and key, and params"""
params = convert_params_to_sets(params)
params = convert_kwargs(params)
@ -47,7 +53,9 @@ def instantiate_class(node_type: str, base_type: str, params: Dict) -> Any:
return custom_node(**params)
logger.debug(f"Instantiating {node_type} of type {base_type}")
class_object = import_by_type(_type=base_type, name=node_type)
return instantiate_based_on_type(class_object, base_type, node_type, params)
return instantiate_based_on_type(
class_object, base_type, node_type, params, user_id=user_id
)
def convert_params_to_sets(params):
@ -66,7 +74,7 @@ def convert_kwargs(params):
for key in kwargs_keys:
if isinstance(params[key], str):
try:
params[key] = json.loads(params[key])
params[key] = orjson.loads(params[key])
except json.JSONDecodeError:
# if the string is not a valid json string, we will
# remove the key from the params
@ -74,7 +82,7 @@ def convert_kwargs(params):
return params
def instantiate_based_on_type(class_object, base_type, node_type, params):
def instantiate_based_on_type(class_object, base_type, node_type, params, user_id):
if base_type == "agents":
return instantiate_agent(node_type, class_object, params)
elif base_type == "prompts":
@ -88,7 +96,7 @@ def instantiate_based_on_type(class_object, base_type, node_type, params):
elif base_type == "toolkits":
return instantiate_toolkit(node_type, class_object, params)
elif base_type == "embeddings":
return instantiate_embedding(class_object, params)
return instantiate_embedding(node_type, class_object, params)
elif base_type == "vectorstores":
return instantiate_vectorstore(class_object, params)
elif base_type == "documentloaders":
@ -108,17 +116,20 @@ def instantiate_based_on_type(class_object, base_type, node_type, params):
elif base_type == "memory":
return instantiate_memory(node_type, class_object, params)
elif base_type == "custom_components":
return instantiate_custom_component(node_type, class_object, params)
return instantiate_custom_component(node_type, class_object, params, user_id)
elif base_type == "wrappers":
return instantiate_wrapper(node_type, class_object, params)
else:
return class_object(**params)
def instantiate_custom_component(node_type, class_object, params):
class_object = get_function_custom(params.pop("code"))
custom_component = class_object()
built_object = custom_component.build(**params)
def instantiate_custom_component(node_type, class_object, params, user_id):
# we need to make a copy of the params because we will be
# modifying it
params_copy = params.copy()
class_object: "CustomComponent" = get_function_custom(params_copy.pop("code"))
custom_component = class_object(user_id=user_id)
built_object = custom_component.build(**params_copy)
return built_object, {"repr": custom_component.custom_repr()}
@ -144,7 +155,7 @@ def instantiate_llm(node_type, class_object, params: Dict):
# This is a workaround so JinaChat works until streaming is implemented
# if "openai_api_base" in params and "jina" in params["openai_api_base"]:
# False if condition is True
if node_type == "VertexAI":
if "VertexAI" in node_type:
return initialize_vertexai(class_object=class_object, params=params)
# max_tokens sometimes is a string and should be an int
if "max_tokens" in params:
@ -258,9 +269,13 @@ def instantiate_toolkit(node_type, class_object: Type[BaseToolkit], params: Dict
return loaded_toolkit
def instantiate_embedding(class_object, params: Dict):
def instantiate_embedding(node_type, class_object, params: Dict):
params.pop("model", None)
params.pop("headers", None)
if "VertexAI" in node_type:
return initialize_vertexai(class_object=class_object, params=params)
try:
return class_object(**params)
except ValidationError:
@ -303,7 +318,7 @@ def instantiate_documentloader(class_object: Type[BaseLoader], params: Dict):
metadata = params.pop("metadata", None)
if metadata and isinstance(metadata, str):
try:
metadata = json.loads(metadata)
metadata = orjson.loads(metadata)
except json.JSONDecodeError as exc:
raise ValueError(
"The metadata you provided is not a valid JSON string."

10
src/backend/langflow/interface/initialize/utils.py
View file

@ -1,5 +1,7 @@
import contextlib
import json
from langflow.services.database.models.base import orjson_dumps
import orjson
from typing import Any, Dict, List
from langchain.agents import ZeroShotAgent
@ -51,7 +53,9 @@ def handle_partial_variables(prompt, format_kwargs: Dict):
}
# Remove handle_keys otherwise LangChain raises an error
partial_variables.pop("handle_keys", None)
return prompt.partial(**partial_variables)
if partial_variables and hasattr(prompt, "partial"):
return prompt.partial(**partial_variables)
return prompt
def handle_variable(params: Dict, input_variable: str, format_kwargs: Dict):
@ -93,9 +97,11 @@ def format_content(variable):
def try_to_load_json(content):
with contextlib.suppress(json.JSONDecodeError):
content = json.loads(content)
content = orjson.loads(content)
if isinstance(content, list):
content = ",".join([str(item) for item in content])
else:
content = orjson_dumps(content)
return content

29
src/backend/langflow/interface/initialize/vector_store.py
View file

@ -1,4 +1,3 @@
import json
from typing import Any, Callable, Dict, Type
from langchain.vectorstores import (
Pinecone,
@ -12,6 +11,8 @@ from langchain.vectorstores import (
import os
import orjson
def docs_in_params(params: dict) -> bool:
"""Check if params has documents OR texts and one of them is not an empty list,
@ -92,7 +93,7 @@ def initialize_weaviate(class_object: Type[Weaviate], params: dict):
import weaviate # type: ignore
client_kwargs_json = params.get("client_kwargs", "{}")
client_kwargs = json.loads(client_kwargs_json)
client_kwargs = orjson.loads(client_kwargs_json)
client_params = {
"url": params.get("weaviate_url"),
}
@ -130,8 +131,8 @@ def initialize_pinecone(class_object: Type[Pinecone], params: dict):
import pinecone # type: ignore
pinecone_api_key = params.get("pinecone_api_key")
pinecone_env = params.get("pinecone_env")
pinecone_api_key = params.pop("pinecone_api_key")
pinecone_env = params.pop("pinecone_env")
if pinecone_api_key is None or pinecone_env is None:
if os.getenv("PINECONE_API_KEY") is not None:
@ -170,6 +171,26 @@ def initialize_pinecone(class_object: Type[Pinecone], params: dict):
def initialize_chroma(class_object: Type[Chroma], params: dict):
"""Initialize a ChromaDB object from the params"""
if ( # type: ignore
"chroma_server_host" in params or "chroma_server_http_port" in params
):
import chromadb # type: ignore
settings_params = {
key: params[key]
for key, value_ in params.items()
if key.startswith("chroma_server_") and value_
}
chroma_settings = chromadb.config.Settings(**settings_params)
params["client_settings"] = chroma_settings
else:
# remove all chroma_server_ keys from params
params = {
key: value
for key, value in params.items()
if not key.startswith("chroma_server_")
}
persist = params.pop("persist", False)
if not docs_in_params(params):
params.pop("documents", None)

63
src/backend/langflow/interface/listing.py
View file

@ -14,34 +14,43 @@ from langflow.interface.wrappers.base import wrapper_creator
from langflow.interface.output_parsers.base import output_parser_creator
from langflow.interface.retrievers.base import retriever_creator
from langflow.interface.custom.base import custom_component_creator
from langflow.utils.lazy_load import LazyLoadDictBase
def get_type_dict():
return {
"agents": agent_creator.to_list(),
"prompts": prompt_creator.to_list(),
"llms": llm_creator.to_list(),
"tools": tool_creator.to_list(),
"chains": chain_creator.to_list(),
"memory": memory_creator.to_list(),
"toolkits": toolkits_creator.to_list(),
"wrappers": wrapper_creator.to_list(),
"documentLoaders": documentloader_creator.to_list(),
"vectorStore": vectorstore_creator.to_list(),
"embeddings": embedding_creator.to_list(),
"textSplitters": textsplitter_creator.to_list(),
"utilities": utility_creator.to_list(),
"outputParsers": output_parser_creator.to_list(),
"retrievers": retriever_creator.to_list(),
"custom_components": custom_component_creator.to_list(),
}
class AllTypesDict(LazyLoadDictBase):
def __init__(self):
self._all_types_dict = None
@property
def ALL_TYPES_DICT(self):
return self.all_types_dict
def _build_dict(self):
langchain_types_dict = self.get_type_dict()
return {
**langchain_types_dict,
"Custom": ["Custom Tool", "Python Function"],
}
def get_type_dict(self):
return {
"agents": agent_creator.to_list(),
"prompts": prompt_creator.to_list(),
"llms": llm_creator.to_list(),
"tools": tool_creator.to_list(),
"chains": chain_creator.to_list(),
"memory": memory_creator.to_list(),
"toolkits": toolkits_creator.to_list(),
"wrappers": wrapper_creator.to_list(),
"documentLoaders": documentloader_creator.to_list(),
"vectorStore": vectorstore_creator.to_list(),
"embeddings": embedding_creator.to_list(),
"textSplitters": textsplitter_creator.to_list(),
"utilities": utility_creator.to_list(),
"outputParsers": output_parser_creator.to_list(),
"retrievers": retriever_creator.to_list(),
"custom_components": custom_component_creator.to_list(),
}
LANGCHAIN_TYPES_DICT = get_type_dict()
# Now we'll build a dict with Langchain types and ours
ALL_TYPES_DICT = {
**LANGCHAIN_TYPES_DICT,
"Custom": ["Custom Tool", "Python Function"],
}
lazy_load_dict = AllTypesDict()

9
src/backend/langflow/interface/llms/base.py
View file

@ -2,9 +2,10 @@ from typing import Dict, List, Optional, Type
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.custom_lists import llm_type_to_cls_dict
from langflow.settings import settings
from langflow.services.utils import get_settings_manager
from langflow.template.frontend_node.llms import LLMFrontendNode
from langflow.utils.logger import logger
from loguru import logger
from langflow.utils.util import build_template_from_class
@ -33,10 +34,12 @@ class LLMCreator(LangChainTypeCreator):
return None
def to_list(self) -> List[str]:
settings_manager = get_settings_manager()
return [
llm.__name__
for llm in self.type_to_loader_dict.values()
if llm.__name__ in settings.LLMS or settings.DEV
if llm.__name__ in settings_manager.settings.LLMS
or settings_manager.settings.DEV
]

9
src/backend/langflow/interface/memories/base.py
View file

@ -2,10 +2,11 @@ from typing import Dict, List, Optional, Type
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.custom_lists import memory_type_to_cls_dict
from langflow.settings import settings
from langflow.services.utils import get_settings_manager
from langflow.template.frontend_node.base import FrontendNode
from langflow.template.frontend_node.memories import MemoryFrontendNode
from langflow.utils.logger import logger
from loguru import logger
from langflow.utils.util import build_template_from_class, build_template_from_method
from langflow.custom.customs import get_custom_nodes
@ -48,10 +49,12 @@ class MemoryCreator(LangChainTypeCreator):
return None
def to_list(self) -> List[str]:
settings_manager = get_settings_manager()
return [
memory.__name__
for memory in self.type_to_loader_dict.values()
if memory.__name__ in settings.MEMORIES or settings.DEV
if memory.__name__ in settings_manager.settings.MEMORIES
or settings_manager.settings.DEV
]

9
src/backend/langflow/interface/output_parsers/base.py
View file

@ -4,9 +4,10 @@ from langchain import output_parsers
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.importing.utils import import_class
from langflow.settings import settings
from langflow.services.utils import get_settings_manager
from langflow.template.frontend_node.output_parsers import OutputParserFrontendNode
from langflow.utils.logger import logger
from loguru import logger
from langflow.utils.util import build_template_from_class, build_template_from_method
@ -23,6 +24,7 @@ class OutputParserCreator(LangChainTypeCreator):
@property
def type_to_loader_dict(self) -> Dict:
if self.type_dict is None:
settings_manager = get_settings_manager()
self.type_dict = {
output_parser_name: import_class(
f"langchain.output_parsers.{output_parser_name}"
@ -33,7 +35,8 @@ class OutputParserCreator(LangChainTypeCreator):
self.type_dict = {
name: output_parser
for name, output_parser in self.type_dict.items()
if name in settings.OUTPUT_PARSERS or settings.DEV
if name in settings_manager.settings.OUTPUT_PARSERS
or settings_manager.settings.DEV
}
return self.type_dict

9
src/backend/langflow/interface/prompts/base.py
View file

@ -5,9 +5,10 @@ from langchain import prompts
from langflow.custom.customs import get_custom_nodes
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.importing.utils import import_class
from langflow.settings import settings
from langflow.services.utils import get_settings_manager
from langflow.template.frontend_node.prompts import PromptFrontendNode
from langflow.utils.logger import logger
from loguru import logger
from langflow.utils.util import build_template_from_class
@ -20,6 +21,7 @@ class PromptCreator(LangChainTypeCreator):
@property
def type_to_loader_dict(self) -> Dict:
settings_manager = get_settings_manager()
if self.type_dict is None:
self.type_dict = {
prompt_name: import_class(f"langchain.prompts.{prompt_name}")
@ -34,7 +36,8 @@ class PromptCreator(LangChainTypeCreator):
self.type_dict = {
name: prompt
for name, prompt in self.type_dict.items()
if name in settings.PROMPTS or settings.DEV
if name in settings_manager.settings.PROMPTS
or settings_manager.settings.DEV
}
return self.type_dict

9
src/backend/langflow/interface/retrievers/base.py
View file

@ -4,9 +4,10 @@ from langchain import retrievers
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.importing.utils import import_class
from langflow.settings import settings
from langflow.services.utils import get_settings_manager
from langflow.template.frontend_node.retrievers import RetrieverFrontendNode
from langflow.utils.logger import logger
from loguru import logger
from langflow.utils.util import build_template_from_method, build_template_from_class
@ -48,10 +49,12 @@ class RetrieverCreator(LangChainTypeCreator):
return None
def to_list(self) -> List[str]:
settings_manager = get_settings_manager()
return [
retriever
for retriever in self.type_to_loader_dict.keys()
if retriever in settings.RETRIEVERS or settings.DEV
if retriever in settings_manager.settings.RETRIEVERS
or settings_manager.settings.DEV
]

15
src/backend/langflow/interface/run.py
View file

@ -1,6 +1,7 @@
from langflow.cache.utils import memoize_dict
from typing import Any, Dict, Tuple
from langflow.services.cache.utils import memoize_dict
from langflow.graph import Graph
from langflow.utils.logger import logger
from loguru import logger
@memoize_dict(maxsize=10)
@ -15,7 +16,7 @@ def build_langchain_object_with_caching(data_graph):
@memoize_dict(maxsize=10)
def build_sorted_vertices_with_caching(data_graph):
def build_sorted_vertices_with_caching(data_graph) -> Tuple[Any, Dict]:
"""
Build langchain object from data_graph.
"""
@ -57,8 +58,12 @@ def get_memory_key(langchain_object):
"chat_history": "history",
"history": "chat_history",
}
memory_key = langchain_object.memory.memory_key
return mem_key_dict.get(memory_key)
# Check if memory_key attribute exists
if hasattr(langchain_object.memory, "memory_key"):
memory_key = langchain_object.memory.memory_key
return mem_key_dict.get(memory_key)
else:
return None # or some other default value or action
def update_memory_keys(langchain_object, possible_new_mem_key):

9
src/backend/langflow/interface/text_splitters/base.py
View file

@ -1,10 +1,11 @@
from typing import Dict, List, Optional, Type
from langflow.interface.base import LangChainTypeCreator
from langflow.services.utils import get_settings_manager
from langflow.template.frontend_node.textsplitters import TextSplittersFrontendNode
from langflow.interface.custom_lists import textsplitter_type_to_cls_dict
from langflow.settings import settings
from langflow.utils.logger import logger
from loguru import logger
from langflow.utils.util import build_template_from_class
@ -30,10 +31,12 @@ class TextSplitterCreator(LangChainTypeCreator):
return None
def to_list(self) -> List[str]:
settings_manager = get_settings_manager()
return [
textsplitter.__name__
for textsplitter in self.type_to_loader_dict.values()
if textsplitter.__name__ in settings.TEXTSPLITTERS or settings.DEV
if textsplitter.__name__ in settings_manager.settings.TEXTSPLITTERS
or settings_manager.settings.DEV
]

9
src/backend/langflow/interface/toolkits/base.py
View file

@ -4,8 +4,9 @@ from langchain.agents import agent_toolkits
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.importing.utils import import_class, import_module
from langflow.settings import settings
from langflow.utils.logger import logger
from langflow.services.utils import get_settings_manager
from loguru import logger
from langflow.utils.util import build_template_from_class
@ -29,13 +30,15 @@ class ToolkitCreator(LangChainTypeCreator):
@property
def type_to_loader_dict(self) -> Dict:
if self.type_dict is None:
settings_manager = get_settings_manager()
self.type_dict = {
toolkit_name: import_class(
f"langchain.agents.agent_toolkits.{toolkit_name}"
)
# if toolkit_name is not lower case it is a class
for toolkit_name in agent_toolkits.__all__
if not toolkit_name.islower() and toolkit_name in settings.TOOLKITS
if not toolkit_name.islower()
and toolkit_name in settings_manager.settings.TOOLKITS
}
return self.type_dict

9
src/backend/langflow/interface/tools/base.py
View file

@ -15,7 +15,8 @@ from langflow.interface.tools.constants import (
OTHER_TOOLS,
)
from langflow.interface.tools.util import get_tool_params
from langflow.settings import settings
from langflow.services.utils import get_settings_manager
from langflow.template.field.base import TemplateField
from langflow.template.template.base import Template
from langflow.utils import util
@ -66,6 +67,7 @@ class ToolCreator(LangChainTypeCreator):
@property
def type_to_loader_dict(self) -> Dict:
settings_manager = get_settings_manager()
if self.tools_dict is None:
all_tools = {}
@ -74,7 +76,10 @@ class ToolCreator(LangChainTypeCreator):
tool_name = tool_params.get("name") or tool
if tool_name in settings.TOOLS or settings.DEV:
if (
tool_name in settings_manager.settings.TOOLS
or settings_manager.settings.DEV
):
if tool_name == "JsonSpec":
tool_params["path"] = tool_params.pop("dict_") # type: ignore
all_tools[tool_name] = {

9
src/backend/langflow/interface/tools/util.py
View file

@ -3,6 +3,7 @@ import inspect
from typing import Dict, Union
from langchain.agents.tools import Tool
from loguru import logger
def get_func_tool_params(func, **kwargs) -> Union[Dict, None]:
@ -57,7 +58,13 @@ def get_func_tool_params(func, **kwargs) -> Union[Dict, None]:
def get_class_tool_params(cls, **kwargs) -> Union[Dict, None]:
tree = ast.parse(inspect.getsource(cls))
try:
tree = ast.parse(inspect.getsource(cls))
except IndentationError:
logger.error(
f"Error parsing class {cls.__name__}. Make sure there are no tabs in the code."
)
return None
tool_params = {}

132
src/backend/langflow/interface/types.py
View file

@ -1,9 +1,11 @@
import ast
import contextlib
from typing import Any
from typing import Any, List
from langflow.api.utils import merge_nested_dicts_with_renaming
from langflow.interface.agents.base import agent_creator
from langflow.interface.chains.base import chain_creator
from langflow.interface.custom.constants import CUSTOM_COMPONENT_SUPPORTED_TYPES
from langflow.interface.custom.utils import extract_inner_type
from langflow.interface.document_loaders.base import documentloader_creator
from langflow.interface.embeddings.base import embedding_creator
from langflow.interface.importing.utils import get_function_custom
@ -28,9 +30,8 @@ from langflow.template.frontend_node.custom_components import (
from langflow.interface.retrievers.base import retriever_creator
from langflow.interface.custom.directory_reader import DirectoryReader
from langflow.utils.logger import logger
from loguru import logger
from langflow.utils.util import get_base_classes
from langflow.api.utils import merge_nested_dicts
import re
import warnings
@ -84,6 +85,8 @@ def build_langchain_types_dict(): # sourcery skip: dict-assign-update-to-union
def process_type(field_type: str):
if field_type.startswith("list") or field_type.startswith("List"):
return extract_inner_type(field_type)
return "prompt" if field_type == "Prompt" else field_type
@ -100,6 +103,7 @@ def add_new_custom_field(
# if it is, update the value
display_name = field_config.pop("display_name", field_name)
field_type = field_config.pop("field_type", field_type)
field_contains_list = "list" in field_type.lower()
field_type = process_type(field_type)
field_value = field_config.pop("value", field_value)
field_advanced = field_config.pop("advanced", False)
@ -110,7 +114,9 @@ def add_new_custom_field(
# If options is a list, then it's a dropdown
# If options is None, then it's a list of strings
is_list = isinstance(field_config.get("options"), list)
field_config["is_list"] = is_list or field_config.get("is_list", False)
field_config["is_list"] = (
is_list or field_config.get("is_list", False) or field_contains_list
)
if "name" in field_config:
warnings.warn(
@ -172,7 +178,7 @@ def extract_type_from_optional(field_type):
Returns:
str: The extracted type, or an empty string if no type was found.
"""
match = re.search(r"\[(.*?)\]", field_type)
match = re.search(r"\[(.*?)\]$", field_type)
return match[1] if match else None
@ -190,14 +196,16 @@ def build_frontend_node(custom_component: CustomComponent):
def update_attributes(frontend_node, template_config):
"""Update the display name and description of a frontend node"""
if "display_name" in template_config:
frontend_node["display_name"] = template_config["display_name"]
if "description" in template_config:
frontend_node["description"] = template_config["description"]
if "beta" in template_config:
frontend_node["beta"] = template_config["beta"]
attributes = [
"display_name",
"description",
"beta",
"documentation",
"output_types",
]
for attribute in attributes:
if attribute in template_config:
frontend_node[attribute] = template_config[attribute]
def build_field_config(custom_component: CustomComponent):
@ -257,55 +265,67 @@ def get_field_properties(extra_field):
return field_name, field_type, field_value, field_required
def add_base_classes(frontend_node, return_type):
def add_base_classes(frontend_node, return_types: List[str]):
"""Add base classes to the frontend node"""
if return_type not in CUSTOM_COMPONENT_SUPPORTED_TYPES or return_type is None:
raise HTTPException(
status_code=400,
detail={
"error": (
"Invalid return type should be one of: "
f"{list(CUSTOM_COMPONENT_SUPPORTED_TYPES.keys())}"
),
"traceback": traceback.format_exc(),
},
)
for return_type in return_types:
if return_type not in CUSTOM_COMPONENT_SUPPORTED_TYPES or return_type is None:
raise HTTPException(
status_code=400,
detail={
"error": (
"Invalid return type should be one of: "
f"{list(CUSTOM_COMPONENT_SUPPORTED_TYPES.keys())}"
),
"traceback": traceback.format_exc(),
},
)
return_type_instance = CUSTOM_COMPONENT_SUPPORTED_TYPES.get(return_type)
base_classes = get_base_classes(return_type_instance)
return_type_instance = CUSTOM_COMPONENT_SUPPORTED_TYPES.get(return_type)
base_classes = get_base_classes(return_type_instance)
for base_class in base_classes:
if base_class not in CLASSES_TO_REMOVE:
frontend_node.get("base_classes").append(base_class)
for base_class in base_classes:
if base_class not in CLASSES_TO_REMOVE:
frontend_node.get("base_classes").append(base_class)
def build_langchain_template_custom_component(custom_component: CustomComponent):
"""Build a custom component template for the langchain"""
logger.debug("Building custom component template")
frontend_node = build_frontend_node(custom_component)
try:
logger.debug("Building custom component template")
frontend_node = build_frontend_node(custom_component)
if frontend_node is None:
return None
logger.debug("Built base frontend node")
template_config = custom_component.build_template_config
if frontend_node is None:
return None
logger.debug("Built base frontend node")
template_config = custom_component.build_template_config
update_attributes(frontend_node, template_config)
logger.debug("Updated attributes")
field_config = build_field_config(custom_component)
logger.debug("Built field config")
add_extra_fields(
frontend_node, field_config, custom_component.get_function_entrypoint_args
)
logger.debug("Added extra fields")
frontend_node = add_code_field(
frontend_node, custom_component.code, field_config.get("code", {})
)
logger.debug("Added code field")
add_base_classes(
frontend_node, custom_component.get_function_entrypoint_return_type
)
logger.debug("Added base classes")
return frontend_node
update_attributes(frontend_node, template_config)
logger.debug("Updated attributes")
field_config = build_field_config(custom_component)
logger.debug("Built field config")
add_extra_fields(
frontend_node, field_config, custom_component.get_function_entrypoint_args
)
logger.debug("Added extra fields")
frontend_node = add_code_field(
frontend_node, custom_component.code, field_config.get("code", {})
)
logger.debug("Added code field")
add_base_classes(
frontend_node, custom_component.get_function_entrypoint_return_type
)
logger.debug("Added base classes")
return frontend_node
except Exception as exc:
raise HTTPException(
status_code=400,
detail={
"error": (
"Invalid type convertion. Please check your code and try again."
),
"traceback": traceback.format_exc(),
},
) from exc
def load_files_from_path(path: str):
@ -334,7 +354,9 @@ def build_valid_menu(valid_components):
valid_menu[menu_name] = {}
for component in menu_item["components"]:
logger.debug(f"Building component: {component}")
logger.debug(
f"Building component: {component.get('name'), component.get('output_types')}"
)
try:
component_name = component["name"]
component_code = component["code"]
@ -423,4 +445,4 @@ def build_langchain_custom_component_list_from_path(path: str):
valid_menu = build_valid_menu(valid_components)
invalid_menu = build_invalid_menu(invalid_components)
return merge_nested_dicts(valid_menu, invalid_menu)
return merge_nested_dicts_with_renaming(valid_menu, invalid_menu)

9
src/backend/langflow/interface/utilities/base.py
View file

@ -5,9 +5,10 @@ from langchain import SQLDatabase, utilities
from langflow.custom.customs import get_custom_nodes
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.importing.utils import import_class
from langflow.settings import settings
from langflow.services.utils import get_settings_manager
from langflow.template.frontend_node.utilities import UtilitiesFrontendNode
from langflow.utils.logger import logger
from loguru import logger
from langflow.utils.util import build_template_from_class
@ -26,6 +27,7 @@ class UtilityCreator(LangChainTypeCreator):
from the langchain.chains module and filtering them according to the settings.utilities list.
"""
if self.type_dict is None:
settings_manager = get_settings_manager()
self.type_dict = {
utility_name: import_class(f"langchain.utilities.{utility_name}")
for utility_name in utilities.__all__
@ -35,7 +37,8 @@ class UtilityCreator(LangChainTypeCreator):
self.type_dict = {
name: utility
for name, utility in self.type_dict.items()
if name in settings.UTILITIES or settings.DEV
if name in settings_manager.settings.UTILITIES
or settings_manager.settings.DEV
}
return self.type_dict

30
src/backend/langflow/interface/utils.py
View file

@ -8,8 +8,9 @@ import re
import yaml
from langchain.base_language import BaseLanguageModel
from PIL.Image import Image
from langflow.utils.logger import logger
from langflow.chat.config import ChatConfig
from loguru import logger
from langflow.services.chat.config import ChatConfig
from langflow.services.utils import get_settings_manager
def load_file_into_dict(file_path: str) -> dict:
@ -63,13 +64,11 @@ def extract_input_variables_from_prompt(prompt: str) -> list[str]:
def setup_llm_caching():
"""Setup LLM caching."""
from langflow.settings import settings
settings_manager = get_settings_manager()
try:
set_langchain_cache(settings)
set_langchain_cache(settings_manager.settings)
except ImportError:
logger.warning(f"Could not import {settings.CACHE}. ")
logger.warning(f"Could not import {settings_manager.settings.CACHE}. ")
except Exception as exc:
logger.warning(f"Could not setup LLM caching. Error: {exc}")
@ -78,9 +77,16 @@ def set_langchain_cache(settings):
import langchain
from langflow.interface.importing.utils import import_class
cache_type = os.getenv("LANGFLOW_LANGCHAIN_CACHE")
cache_class = import_class(f"langchain.cache.{cache_type or settings.CACHE}")
if cache_type := os.getenv("LANGFLOW_LANGCHAIN_CACHE"):
try:
cache_class = import_class(
f"langchain.cache.{cache_type or settings.CACHE}"
)
logger.debug(f"Setting up LLM caching with {cache_class.__name__}")
langchain.llm_cache = cache_class()
logger.info(f"LLM caching setup with {cache_class.__name__}")
logger.debug(f"Setting up LLM caching with {cache_class.__name__}")
langchain.llm_cache = cache_class()
logger.info(f"LLM caching setup with {cache_class.__name__}")
except ImportError:
logger.warning(f"Could not import {cache_type}. ")
else:
logger.info("No LLM cache set.")

9
src/backend/langflow/interface/vector_store/base.py
View file

@ -4,9 +4,10 @@ from langchain import vectorstores
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.importing.utils import import_class
from langflow.settings import settings
from langflow.services.utils import get_settings_manager
from langflow.template.frontend_node.vectorstores import VectorStoreFrontendNode
from langflow.utils.logger import logger
from loguru import logger
from langflow.utils.util import build_template_from_method
@ -43,10 +44,12 @@ class VectorstoreCreator(LangChainTypeCreator):
return None
def to_list(self) -> List[str]:
settings_manager = get_settings_manager()
return [
vectorstore
for vectorstore in self.type_to_loader_dict.keys()
if vectorstore in settings.VECTORSTORES or settings.DEV
if vectorstore in settings_manager.settings.VECTORSTORES
or settings_manager.settings.DEV
]

2
src/backend/langflow/interface/wrappers/base.py
View file

@ -3,7 +3,7 @@ from typing import Dict, List, Optional
from langchain import requests, sql_database
from langflow.interface.base import LangChainTypeCreator
from langflow.utils.logger import logger
from loguru import logger
from langflow.utils.util import build_template_from_class, build_template_from_method

38
src/backend/langflow/main.py
View file

@ -6,24 +6,23 @@ from fastapi.responses import FileResponse
from fastapi.staticfiles import StaticFiles
from langflow.api import router
from langflow.database.base import create_db_and_tables, Engine
from langflow.interface.utils import setup_llm_caching
from langflow.services.database.utils import initialize_database
from langflow.services.manager import initialize_services, teardown_services
from langflow.services.plugins.langfuse import LangfuseInstance
from langflow.utils.logger import configure
def create_app():
"""Create the FastAPI app and include the router."""
configure()
app = FastAPI()
origins = [
"*",
]
@app.get("/health")
def get_health():
return {"status": "OK"}
origins = ["*"]
app.add_middleware(
CORSMiddleware,
@ -33,10 +32,18 @@ def create_app():
allow_headers=["*"],
)
@app.get("/health")
def health():
return {"status": "ok"}
app.include_router(router)
app.on_event("startup")(Engine.update)
app.on_event("startup")(create_db_and_tables)
app.on_event("startup")(initialize_services)
app.on_event("startup")(initialize_database)
app.on_event("startup")(setup_llm_caching)
app.on_event("shutdown")(teardown_services)
app.on_event("startup")(LangfuseInstance.update)
app.on_event("shutdown")(LangfuseInstance.teardown)
return app
@ -68,22 +75,25 @@ def get_static_files_dir():
return frontend_path / "frontend"
def setup_app(static_files_dir: Optional[Path] = None) -> FastAPI:
def setup_app(
static_files_dir: Optional[Path] = None, backend_only: bool = False
) -> FastAPI:
"""Setup the FastAPI app."""
# get the directory of the current file
if not static_files_dir:
static_files_dir = get_static_files_dir()
if not static_files_dir or not static_files_dir.exists():
if not backend_only and (not static_files_dir or not static_files_dir.exists()):
raise RuntimeError(f"Static files directory {static_files_dir} does not exist.")
app = create_app()
setup_static_files(app, static_files_dir)
if not backend_only and static_files_dir is not None:
setup_static_files(app, static_files_dir)
return app
if __name__ == "__main__":
import uvicorn
from langflow.utils.util import get_number_of_workers
from langflow.__main__ import get_number_of_workers
configure()
uvicorn.run(

67
src/backend/langflow/processing/base.py
View file

@ -1,10 +1,57 @@
from typing import Union
from typing import List, Union, TYPE_CHECKING
from langflow.api.v1.callback import (
AsyncStreamingLLMCallbackHandler,
StreamingLLMCallbackHandler,
)
from langflow.processing.process import fix_memory_inputs, format_actions
from langflow.utils.logger import logger
from loguru import logger
from langchain.agents.agent import AgentExecutor
from langchain.callbacks.base import BaseCallbackHandler
if TYPE_CHECKING:
from langfuse.callback import CallbackHandler # type: ignore
def setup_callbacks(sync, trace_id, **kwargs):
"""Setup callbacks for langchain object"""
callbacks = []
if sync:
callbacks.append(StreamingLLMCallbackHandler(**kwargs))
else:
callbacks.append(AsyncStreamingLLMCallbackHandler(**kwargs))
if langfuse_callback := get_langfuse_callback(trace_id=trace_id):
logger.debug("Langfuse callback loaded")
callbacks.append(langfuse_callback)
return callbacks
def get_langfuse_callback(trace_id):
from langflow.services.plugins.langfuse import LangfuseInstance
from langfuse.callback import CreateTrace
logger.debug("Initializing langfuse callback")
if langfuse := LangfuseInstance.get():
logger.debug("Langfuse credentials found")
try:
trace = langfuse.trace(CreateTrace(id=trace_id))
return trace.getNewHandler()
except Exception as exc:
logger.error(f"Error initializing langfuse callback: {exc}")
return None
def flush_langfuse_callback_if_present(
callbacks: List[Union[BaseCallbackHandler, "CallbackHandler"]]
):
"""
If langfuse callback is present, run callback.langfuse.flush()
"""
for callback in callbacks:
if hasattr(callback, "langfuse"):
callback.langfuse.flush()
break
async def get_result_and_steps(langchain_object, inputs: Union[dict, str], **kwargs):
@ -20,18 +67,24 @@ async def get_result_and_steps(langchain_object, inputs: Union[dict, str], **kwa
# to display intermediate steps
langchain_object.return_intermediate_steps = True
try:
fix_memory_inputs(langchain_object)
if not isinstance(langchain_object, AgentExecutor):
fix_memory_inputs(langchain_object)
except Exception as exc:
logger.error(f"Error fixing memory inputs: {exc}")
try:
async_callbacks = [AsyncStreamingLLMCallbackHandler(**kwargs)]
output = await langchain_object.acall(inputs, callbacks=async_callbacks)
trace_id = kwargs.pop("session_id", None)
callbacks = setup_callbacks(sync=False, trace_id=trace_id, **kwargs)
output = await langchain_object.acall(inputs, callbacks=callbacks)
except Exception as exc:
# make the error message more informative
logger.debug(f"Error: {str(exc)}")
sync_callbacks = [StreamingLLMCallbackHandler(**kwargs)]
output = langchain_object(inputs, callbacks=sync_callbacks)
trace_id = kwargs.pop("session_id", None)
callbacks = setup_callbacks(sync=True, trace_id=trace_id, **kwargs)
output = langchain_object(inputs, callbacks=callbacks)
# if langfuse callback is present, run callback.langfuse.flush()
flush_langfuse_callback_if_present(callbacks)
intermediate_steps = (
output.get("intermediate_steps", []) if isinstance(output, dict) else []

93
src/backend/langflow/processing/process.py
View file

@ -1,16 +1,17 @@
import json
from pathlib import Path
from langchain.schema import AgentAction
import json
from langflow.interface.run import (
build_sorted_vertices_with_caching,
get_memory_key,
update_memory_keys,
)
from langflow.utils.logger import logger
from loguru import logger
from langflow.graph import Graph
from langchain.chains.base import Chain
from langchain.vectorstores.base import VectorStore
from typing import Any, Dict, List, Optional, Tuple, Union
from langchain.schema import Document
def fix_memory_inputs(langchain_object):
@ -85,39 +86,55 @@ def get_input_str_if_only_one_input(inputs: dict) -> Optional[str]:
return list(inputs.values())[0] if len(inputs) == 1 else None
def process_graph_cached(
data_graph: Dict[str, Any], inputs: Optional[dict] = None, clear_cache=False
):
"""
Process graph by extracting input variables and replacing ZeroShotPrompt
with PromptTemplate,then run the graph and return the result and thought.
"""
# Load langchain object
def get_build_result(data_graph, session_id):
# If session_id is provided, load the langchain_object from the session
# using build_sorted_vertices_with_caching.get_result_by_session_id
# if it returns something different than None, return it
# otherwise, build the graph and return the result
if session_id:
logger.debug(f"Loading LangChain object from session {session_id}")
result = build_sorted_vertices_with_caching.get_result_by_session_id(session_id)
if result is not None:
logger.debug("Loaded LangChain object")
return result
logger.debug("Building langchain object")
return build_sorted_vertices_with_caching(data_graph)
def clear_caches_if_needed(clear_cache: bool):
if clear_cache:
build_sorted_vertices_with_caching.clear_cache()
logger.debug("Cleared cache")
langchain_object, artifacts = build_sorted_vertices_with_caching(data_graph)
logger.debug("Loaded LangChain object")
if inputs is None:
inputs = {}
# Add artifacts to inputs
# artifacts can be documents loaded when building
# the flow
for (
key,
value,
) in artifacts.items():
if key not in inputs or not inputs[key]:
inputs[key] = value
def load_langchain_object(
data_graph: Dict[str, Any], session_id: str
) -> Tuple[Union[Chain, VectorStore], Dict[str, Any], str]:
langchain_object, artifacts = get_build_result(data_graph, session_id)
session_id = build_sorted_vertices_with_caching.hash
logger.debug("Loaded LangChain object")
if langchain_object is None:
# Raise user facing error
raise ValueError(
"There was an error loading the langchain_object. Please, check all the nodes and try again."
)
# Generate result and thought
return langchain_object, artifacts, session_id
def process_inputs(inputs: Optional[dict], artifacts: Dict[str, Any]) -> dict:
if inputs is None:
inputs = {}
for key, value in artifacts.items():
if key not in inputs or not inputs[key]:
inputs[key] = value
return inputs
def generate_result(langchain_object: Union[Chain, VectorStore], inputs: dict):
if isinstance(langchain_object, Chain):
if inputs is None:
raise ValueError("Inputs must be provided for a Chain")
@ -126,13 +143,33 @@ def process_graph_cached(
logger.debug("Generated result and thought")
elif isinstance(langchain_object, VectorStore):
result = langchain_object.search(**inputs)
elif isinstance(langchain_object, Document):
result = langchain_object.dict()
else:
raise ValueError(
f"Unknown langchain_object type: {type(langchain_object).__name__}"
)
logger.warning(f"Unknown langchain_object type: {type(langchain_object)}")
result = langchain_object
return result
def process_graph_cached(
data_graph: Dict[str, Any],
inputs: Optional[dict] = None,
clear_cache=False,
session_id=None,
) -> Tuple[Any, str]:
clear_caches_if_needed(clear_cache)
# If session_id is provided, load the langchain_object from the session
# else build the graph and return the result and the new session_id
langchain_object, artifacts, session_id = load_langchain_object(
data_graph, session_id
)
processed_inputs = process_inputs(inputs, artifacts)
result = generate_result(langchain_object, processed_inputs)
return result, session_id
def load_flow_from_json(
flow: Union[Path, str, dict], tweaks: Optional[dict] = None, build=True
):

4
src/backend/langflow/services/__init__.py Normal file
View file

@ -0,0 +1,4 @@
from .manager import service_manager
from .schema import ServiceType
__all__ = ["service_manager", "ServiceType"]

0
src/backend/langflow/services/auth/__init__.py Normal file
View file

12
src/backend/langflow/services/auth/factory.py Normal file
View file

@ -0,0 +1,12 @@
from langflow.services.factory import ServiceFactory
from langflow.services.auth.service import AuthManager
class AuthManagerFactory(ServiceFactory):
name = "auth_manager"
def __init__(self):
super().__init__(AuthManager)
def create(self, settings_manager):
return AuthManager(settings_manager)

12
src/backend/langflow/services/auth/service.py Normal file
View file

@ -0,0 +1,12 @@
from langflow.services.base import Service
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from langflow.services.settings.manager import SettingsManager
class AuthManager(Service):
name = "auth_manager"
def __init__(self, settings_manager: "SettingsManager"):
self.settings_manager = settings_manager

298
src/backend/langflow/services/auth/utils.py Normal file
View file

@ -0,0 +1,298 @@
from datetime import datetime, timedelta, timezone
from fastapi import Depends, HTTPException, Security, status
from fastapi.security import APIKeyHeader, APIKeyQuery, OAuth2PasswordBearer
from jose import JWTError, jwt
from typing import Annotated, Coroutine, Optional, Union
from uuid import UUID
from langflow.services.database.models.api_key.api_key import ApiKey
from langflow.services.database.models.api_key.crud import check_key
from langflow.services.database.models.user.user import User
from langflow.services.database.models.user.crud import (
get_user_by_id,
get_user_by_username,
update_user_last_login_at,
)
from langflow.services.utils import get_session, get_settings_manager
from sqlmodel import Session
oauth2_login = OAuth2PasswordBearer(tokenUrl="api/v1/login")
API_KEY_NAME = "api-key"
api_key_query = APIKeyQuery(
name=API_KEY_NAME, scheme_name="API key query", auto_error=False
)
api_key_header = APIKeyHeader(
name=API_KEY_NAME, scheme_name="API key header", auto_error=False
)
# Source: https://github.com/mrtolkien/fastapi_simple_security/blob/master/fastapi_simple_security/security_api_key.py
async def api_key_security(
query_param: str = Security(api_key_query),
header_param: str = Security(api_key_header),
db: Session = Depends(get_session),
) -> Optional[User]:
settings_manager = get_settings_manager()
result: Optional[Union[ApiKey, User]] = None
if settings_manager.auth_settings.AUTO_LOGIN:
# Get the first user
if not settings_manager.auth_settings.FIRST_SUPERUSER:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail="Missing first superuser credentials",
)
result = get_user_by_username(
db, settings_manager.auth_settings.FIRST_SUPERUSER
)
elif not query_param and not header_param:
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail="An API key must be passed as query or header",
)
elif query_param:
result = check_key(db, query_param)
else:
result = check_key(db, header_param)
if not result:
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail="Invalid or missing API key",
)
if isinstance(result, ApiKey):
return result.user
elif isinstance(result, User):
return result
async def get_current_user(
token: Annotated[str, Depends(oauth2_login)],
db: Session = Depends(get_session),
) -> User:
settings_manager = get_settings_manager()
credentials_exception = HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Could not validate credentials",
headers={"WWW-Authenticate": "Bearer"},
)
if isinstance(token, Coroutine):
token = await token
if settings_manager.auth_settings.SECRET_KEY is None:
raise credentials_exception
try:
payload = jwt.decode(
token,
settings_manager.auth_settings.SECRET_KEY,
algorithms=[settings_manager.auth_settings.ALGORITHM],
)
user_id: UUID = payload.get("sub") # type: ignore
token_type: str = payload.get("type") # type: ignore
if expires := payload.get("exp", None):
expires_datetime = datetime.fromtimestamp(expires, timezone.utc)
# TypeError: can't compare offset-naive and offset-aware datetimes
if datetime.now(timezone.utc) > expires_datetime:
raise credentials_exception
if user_id is None or token_type:
raise credentials_exception
except JWTError as e:
raise credentials_exception from e
user = get_user_by_id(db, user_id) # type: ignore
if user is None or not user.is_active:
raise credentials_exception
return user
def get_current_active_user(current_user: Annotated[User, Depends(get_current_user)]):
if not current_user.is_active:
raise HTTPException(status_code=400, detail="Inactive user")
return current_user
def get_current_active_superuser(
current_user: Annotated[User, Depends(get_current_user)]
) -> User:
if not current_user.is_active:
raise HTTPException(status_code=401, detail="Inactive user")
if not current_user.is_superuser:
raise HTTPException(
status_code=400, detail="The user doesn't have enough privileges"
)
return current_user
def verify_password(plain_password, hashed_password):
settings_manager = get_settings_manager()
return settings_manager.auth_settings.pwd_context.verify(
plain_password, hashed_password
)
def get_password_hash(password):
settings_manager = get_settings_manager()
return settings_manager.auth_settings.pwd_context.hash(password)
def create_token(data: dict, expires_delta: timedelta):
settings_manager = get_settings_manager()
to_encode = data.copy()
expire = datetime.now(timezone.utc) + expires_delta
to_encode["exp"] = expire
return jwt.encode(
to_encode,
settings_manager.auth_settings.SECRET_KEY,
algorithm=settings_manager.auth_settings.ALGORITHM,
)
def create_super_user(
username: str,
password: str,
db: Session = Depends(get_session),
) -> User:
super_user = get_user_by_username(db, username)
if not super_user:
super_user = User(
username=username,
password=get_password_hash(password),
is_superuser=True,
is_active=True,
last_login_at=None,
)
db.add(super_user)
db.commit()
db.refresh(super_user)
return super_user
def create_user_longterm_token(db: Session = Depends(get_session)) -> dict:
settings_manager = get_settings_manager()
username = settings_manager.auth_settings.FIRST_SUPERUSER
password = settings_manager.auth_settings.FIRST_SUPERUSER_PASSWORD
if not username or not password:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail="Missing first superuser credentials",
)
super_user = create_super_user(db=db, username=username, password=password)
access_token_expires_longterm = timedelta(days=365)
access_token = create_token(
data={"sub": str(super_user.id)},
expires_delta=access_token_expires_longterm,
)
# Update: last_login_at
update_user_last_login_at(super_user.id, db)
return {
"access_token": access_token,
"refresh_token": None,
"token_type": "bearer",
}
def create_user_api_key(user_id: UUID) -> dict:
access_token = create_token(
data={"sub": str(user_id), "role": "api_key"},
expires_delta=timedelta(days=365 * 2),
)
return {"api_key": access_token}
def get_user_id_from_token(token: str) -> UUID:
try:
user_id = jwt.get_unverified_claims(token)["sub"]
return UUID(user_id)
except (KeyError, JWTError, ValueError):
return UUID(int=0)
def create_user_tokens(
user_id: UUID, db: Session = Depends(get_session), update_last_login: bool = False
) -> dict:
settings_manager = get_settings_manager()
access_token_expires = timedelta(
minutes=settings_manager.auth_settings.ACCESS_TOKEN_EXPIRE_MINUTES
)
access_token = create_token(
data={"sub": str(user_id)},
expires_delta=access_token_expires,
)
refresh_token_expires = timedelta(
minutes=settings_manager.auth_settings.REFRESH_TOKEN_EXPIRE_MINUTES
)
refresh_token = create_token(
data={"sub": str(user_id), "type": "rf"},
expires_delta=refresh_token_expires,
)
# Update: last_login_at
if update_last_login:
update_user_last_login_at(user_id, db)
return {
"access_token": access_token,
"refresh_token": refresh_token,
"token_type": "bearer",
}
def create_refresh_token(refresh_token: str, db: Session = Depends(get_session)):
settings_manager = get_settings_manager()
try:
payload = jwt.decode(
refresh_token,
settings_manager.auth_settings.SECRET_KEY,
algorithms=[settings_manager.auth_settings.ALGORITHM],
)
user_id: UUID = payload.get("sub") # type: ignore
token_type: str = payload.get("type") # type: ignore
if user_id is None or token_type is None:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid refresh token"
)
return create_user_tokens(user_id, db)
except JWTError as e:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Invalid refresh token",
) from e
def authenticate_user(
username: str, password: str, db: Session = Depends(get_session)
) -> Optional[User]:
user = get_user_by_username(db, username)
if not user:
return None
if not user.is_active:
if not user.last_login_at:
raise HTTPException(status_code=400, detail="Waiting for approval")
raise HTTPException(status_code=400, detail="Inactive user")
return user if verify_password(password, user.password) else None

8
src/backend/langflow/services/base.py Normal file
View file

@ -0,0 +1,8 @@
from abc import ABC
class Service(ABC):
name: str
def teardown(self):
pass

11
src/backend/langflow/services/cache/__init__.py vendored Normal file
View file

@ -0,0 +1,11 @@
from . import factory, manager
from langflow.services.cache.manager import cache_manager
from langflow.services.cache.flow import InMemoryCache
__all__ = [
"cache_manager",
"factory",
"manager",
"InMemoryCache",
]

0
src/backend/langflow/cache/base.py → src/backend/langflow/services/cache/base.py vendored
View file

11
src/backend/langflow/services/cache/factory.py vendored Normal file
View file

@ -0,0 +1,11 @@
from langflow.services.cache.manager import CacheManager
from langflow.services.factory import ServiceFactory
class CacheManagerFactory(ServiceFactory):
def __init__(self):
super().__init__(CacheManager)
def create(self):
# Here you would have logic to create and configure a CacheManager
return CacheManager()

2
src/backend/langflow/cache/flow.py → src/backend/langflow/services/cache/flow.py vendored
View file

@ -2,7 +2,7 @@ import threading
import time
from collections import OrderedDict
from langflow.cache.base import BaseCache
from langflow.services.cache.base import BaseCache
class InMemoryCache(BaseCache):

5
src/backend/langflow/cache/manager.py → src/backend/langflow/services/cache/manager.py vendored
View file

@ -1,5 +1,6 @@
from contextlib import contextmanager
from typing import Any, Awaitable, Callable, List, Optional
from langflow.services.base import Service
import pandas as pd
from PIL import Image
@ -49,9 +50,11 @@ class AsyncSubject:
await observer()
class CacheManager(Subject):
class CacheManager(Subject, Service):
"""Manages cache for different clients and notifies observers on changes."""
name = "cache_manager"
def __init__(self):
super().__init__()
self._cache = {}

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