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Merge branch 'feature/store' into dynamic_field

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This commit is contained in:
Gabriel Luiz Freitas Almeida 2023-11-21 15:04:44 -03:00
commit 4959d8ecf4
224 changed files with 7150 additions and 3415 deletions
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12
.env.example
View file

@ -71,3 +71,15 @@ LANGFLOW_SUPERUSER=
# Superuser password
# Example: LANGFLOW_SUPERUSER_PASSWORD=123456
LANGFLOW_SUPERUSER_PASSWORD=
# STORE_URL
# Example: LANGFLOW_STORE_URL=https://api.langflow.store
LANGFLOW_STORE_URL=
# DOWNLOAD_WEBHOOK_URL
#
LANGFLOW_DOWNLOAD_WEBHOOK_URL=
# LIKE_WEBHOOK_URL
#
LANGFLOW_LIKE_WEBHOOK_URL=

3
.vscode/launch.json vendored
View file

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

9
Makefile
View file

@ -30,13 +30,12 @@ else
endif
format:
poetry run black .
poetry run ruff . --fix
poetry run ruff format .
cd src/frontend && npm run format
lint:
poetry run mypy src/backend/langflow
poetry run black . --check
poetry run ruff . --fix
install_frontend:
@ -46,6 +45,7 @@ install_frontendc:
cd src/frontend && rm -rf node_modules package-lock.json && npm install
run_frontend:
@-kill -9 `lsof -t -i:3000`
cd src/frontend && npm start
run_cli:
@ -73,12 +73,13 @@ install_backend:
backend:
make install_backend
@-kill -9 `lsof -t -i:7860`
ifeq ($(login),1)
@echo "Running backend without autologin";
poetry run langflow run --backend-only --port 7860 --host 0.0.0.0 --no-open-browser --log-level debug --workers 3
poetry run langflow run --backend-only --port 7860 --host 0.0.0.0 --no-open-browser --env-file .env
else
@echo "Running backend with autologin";
LANGFLOW_AUTO_LOGIN=True poetry run langflow run --backend-only --port 7860 --host 0.0.0.0 --no-open-browser --log-level debug --workers 3
LANGFLOW_AUTO_LOGIN=True poetry run langflow run --backend-only --port 7860 --host 0.0.0.0 --no-open-browser --env-file .env
endif
build_and_run:

932
package-lock.json generated Normal file
View file

@ -0,0 +1,932 @@
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"cmdk": "^0.2.0"
}
}

44
poetry.lock generated
View file

@ -410,48 +410,6 @@ files = [
{file = "billiard-4.2.0.tar.gz", hash = "sha256:9a3c3184cb275aa17a732f93f65b20c525d3d9f253722d26a82194803ade5a2c"},
]
[[package]]
name = "black"
version = "23.11.0"
description = "The uncompromising code formatter."
optional = false
python-versions = ">=3.8"
files = [
{file = "black-23.11.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:dbea0bb8575c6b6303cc65017b46351dc5953eea5c0a59d7b7e3a2d2f433a911"},
{file = "black-23.11.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:412f56bab20ac85927f3a959230331de5614aecda1ede14b373083f62ec24e6f"},
{file = "black-23.11.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d136ef5b418c81660ad847efe0e55c58c8208b77a57a28a503a5f345ccf01394"},
{file = "black-23.11.0-cp310-cp310-win_amd64.whl", hash = "sha256:6c1cac07e64433f646a9a838cdc00c9768b3c362805afc3fce341af0e6a9ae9f"},
{file = "black-23.11.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:cf57719e581cfd48c4efe28543fea3d139c6b6f1238b3f0102a9c73992cbb479"},
{file = "black-23.11.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:698c1e0d5c43354ec5d6f4d914d0d553a9ada56c85415700b81dc90125aac244"},
{file = "black-23.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:760415ccc20f9e8747084169110ef75d545f3b0932ee21368f63ac0fee86b221"},
{file = "black-23.11.0-cp311-cp311-win_amd64.whl", hash = "sha256:58e5f4d08a205b11800332920e285bd25e1a75c54953e05502052738fe16b3b5"},
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{file = "black-23.11.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:4c44b7211a3a0570cc097e81135faa5f261264f4dfaa22bd5ee2875a4e773bd6"},
{file = "black-23.11.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2a9acad1451632021ee0d146c8765782a0c3846e0e0ea46659d7c4f89d9b212b"},
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{file = "black-23.11.0-py3-none-any.whl", hash = "sha256:54caaa703227c6e0c87b76326d0862184729a69b73d3b7305b6288e1d830067e"},
{file = "black-23.11.0.tar.gz", hash = "sha256:4c68855825ff432d197229846f971bc4d6666ce90492e5b02013bcaca4d9ab05"},
]
[package.dependencies]
click = ">=8.0.0"
mypy-extensions = ">=0.4.3"
packaging = ">=22.0"
pathspec = ">=0.9.0"
platformdirs = ">=2"
tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
[package.extras]
colorama = ["colorama (>=0.4.3)"]
d = ["aiohttp (>=3.7.4)"]
jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
uvloop = ["uvloop (>=0.15.2)"]
[[package]]
name = "blinker"
version = "1.7.0"
@ -8927,4 +8885,4 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<3.11"
content-hash = "27f6f86ca62ee5cfdae333daae4f043a745ed2fff0d958a201b514fac36068d4"
content-hash = "ab18ac504d15c9a85e1fe6457df0bef60aeeb96ed78d7281fbe3eb7c9017c8f4"

6
pyproject.toml
View file

@ -87,7 +87,7 @@ passlib = "^1.7.4"
bcrypt = "^4.0.1"
python-jose = "^3.3.0"
metaphor-python = "^0.1.11"
pydantic = "^2.4.0"
pydantic = "^2.0.0"
pydantic-settings = "^2.0.3"
zep-python = { version = "^1.3.0", allow-prereleases = true }
pywin32 = { version = "^306", markers = "sys_platform == 'win32'" }
@ -105,9 +105,8 @@ pgvector = "^0.2.3"
[tool.poetry.group.dev.dependencies]
types-redis = "^4.6.0.5"
black = "^23.10.0"
ipykernel = "^6.21.2"
mypy = "^1.6.1"
mypy = "^1.1.1"
ruff = "^0.1.5"
httpx = "*"
pytest = "^7.4.2"
@ -144,6 +143,7 @@ markers = ["async_test"]
[tool.ruff]
exclude = ["src/backend/langflow/alembic/*"]
line-length = 120
[build-system]

59
src/backend/langflow/__main__.py
View file

@ -11,7 +11,7 @@ import typer
from dotenv import load_dotenv
from langflow.main import setup_app
from langflow.services.database.utils import session_getter
from langflow.services.getters import get_db_service, get_settings_service
from langflow.services.deps import get_db_service, get_settings_service
from langflow.services.utils import initialize_services, initialize_settings_service
from langflow.utils.logger import configure, logger
from multiprocess import Process, cpu_count # type: ignore
@ -72,6 +72,7 @@ def update_settings(
dev: bool = False,
remove_api_keys: bool = False,
components_path: Optional[Path] = None,
store: bool = True,
):
"""Update the settings from a config file."""
@ -90,16 +91,15 @@ def update_settings(
if components_path:
logger.debug(f"Adding component path {components_path}")
settings_service.settings.update_settings(COMPONENTS_PATH=components_path)
if not store:
logger.debug("Setting store to False")
settings_service.settings.update_settings(STORE=False)
@app.command()
def run(
host: str = typer.Option(
"127.0.0.1", help="Host to bind the server to.", envvar="LANGFLOW_HOST"
),
workers: int = typer.Option(
1, help="Number of worker processes.", envvar="LANGFLOW_WORKERS"
),
host: str = typer.Option("127.0.0.1", help="Host to bind the server to.", envvar="LANGFLOW_HOST"),
workers: int = typer.Option(1, help="Number of worker processes.", envvar="LANGFLOW_WORKERS"),
timeout: int = typer.Option(300, help="Worker timeout in seconds."),
port: int = typer.Option(7860, help="Port to listen on.", envvar="LANGFLOW_PORT"),
components_path: Optional[Path] = typer.Option(
@ -107,32 +107,17 @@ def run(
help="Path to the directory containing custom components.",
envvar="LANGFLOW_COMPONENTS_PATH",
),
config: str = typer.Option(
Path(__file__).parent / "config.yaml", help="Path to the configuration file."
),
config: str = typer.Option(Path(__file__).parent / "config.yaml", help="Path to the configuration file."),
# .env file param
env_file: Path = typer.Option(
None, help="Path to the .env file containing environment variables."
),
log_level: str = typer.Option(
"critical", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL"
),
log_file: Path = typer.Option(
"logs/langflow.log", help="Path to the log file.", envvar="LANGFLOW_LOG_FILE"
),
env_file: Path = typer.Option(None, help="Path to the .env file containing environment variables."),
log_level: str = typer.Option("critical", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL"),
log_file: Path = typer.Option("logs/langflow.log", help="Path to the log file.", envvar="LANGFLOW_LOG_FILE"),
cache: Optional[str] = typer.Option(
envvar="LANGFLOW_LANGCHAIN_CACHE",
help="Type of cache to use. (InMemoryCache, SQLiteCache)",
default=None,
),
dev: bool = typer.Option(False, help="Run in development mode (may contain bugs)"),
# This variable does not work but is set by the .env file
# and works with Pydantic
# database_url: str = typer.Option(
# None,
# help="Database URL to connect to. If not provided, a local SQLite database will be used.",
# envvar="LANGFLOW_DATABASE_URL",
# ),
path: str = typer.Option(
None,
help="Path to the frontend directory containing build files. This is for development purposes only.",
@ -153,6 +138,11 @@ def run(
help="Run only the backend server without the frontend.",
envvar="LANGFLOW_BACKEND_ONLY",
),
store: bool = typer.Option(
True,
help="Enables the store features.",
envvar="LANGFLOW_STORE",
),
):
"""
Run the Langflow.
@ -171,6 +161,7 @@ def run(
remove_api_keys=remove_api_keys,
cache=cache,
components_path=components_path,
store=store,
)
# create path object if path is provided
static_files_dir: Optional[Path] = Path(path) if path else None
@ -200,9 +191,7 @@ def run(
def run_on_mac_or_linux(host, port, log_level, options, app, open_browser=True):
webapp_process = Process(
target=run_langflow, args=(host, port, log_level, options, app)
)
webapp_process = Process(target=run_langflow, args=(host, port, log_level, options, app))
webapp_process.start()
status_code = 0
while status_code != 200:
@ -278,9 +267,7 @@ def print_banner(host, port):
)
# Create a panel with the title and the info text, and a border around it
panel = Panel(
f"{title}\n{info_text}", box=box.ROUNDED, border_style="blue", expand=False
)
panel = Panel(f"{title}\n{info_text}", box=box.ROUNDED, border_style="blue", expand=False)
# Print the banner with a separator line before and after
rprint(panel)
@ -312,12 +299,8 @@ def run_langflow(host, port, log_level, options, app):
@app.command()
def superuser(
username: str = typer.Option(..., prompt=True, help="Username for the superuser."),
password: str = typer.Option(
..., prompt=True, hide_input=True, help="Password for the superuser."
),
log_level: str = typer.Option(
"critical", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL"
),
password: str = typer.Option(..., prompt=True, hide_input=True, help="Password for the superuser."),
log_level: str = typer.Option("critical", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL"),
):
"""
Create a superuser.

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

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

48
src/backend/langflow/alembic/versions/7843803a87b5_store_updates.py Normal file
View file

@ -0,0 +1,48 @@
"""Store updates
Revision ID: 7843803a87b5
Revises: eb5866d51fd2
Create Date: 2023-10-18 23:08:57.744906
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
import sqlmodel
# revision identifiers, used by Alembic.
revision: str = "7843803a87b5"
down_revision: Union[str, None] = "eb5866d51fd2"
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! ###
try:
with op.batch_alter_table("flow", schema=None) as batch_op:
batch_op.add_column(sa.Column("is_component", sa.Boolean(), nullable=True))
with op.batch_alter_table("user", schema=None) as batch_op:
batch_op.add_column(
sa.Column(
"store_api_key", sqlmodel.sql.sqltypes.AutoString(), nullable=True
)
)
except Exception:
pass
# ### end Alembic commands ###
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table("user", schema=None) as batch_op:
batch_op.drop_column("store_api_key")
with op.batch_alter_table("flow", schema=None) as batch_op:
batch_op.drop_column("is_component")
# ### end Alembic commands ###

45
src/backend/langflow/alembic/versions/f5ee9749d1a6_user_id_can_be_null_in_flow.py Normal file
View file

@ -0,0 +1,45 @@
"""User id can be null in Flow
Revision ID: f5ee9749d1a6
Revises: 7843803a87b5
Create Date: 2023-10-18 23:12:27.297016
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
import sqlmodel
# revision identifiers, used by Alembic.
revision: str = "f5ee9749d1a6"
down_revision: Union[str, None] = "7843803a87b5"
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! ###
try:
with op.batch_alter_table("flow", schema=None) as batch_op:
batch_op.alter_column(
"user_id", existing_type=sa.CHAR(length=32), nullable=True
)
except Exception:
pass
# ### end Alembic commands ###
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
try:
with op.batch_alter_table("flow", schema=None) as batch_op:
batch_op.alter_column(
"user_id", existing_type=sa.CHAR(length=32), nullable=False
)
except Exception:
pass
# ### end Alembic commands ###

4
src/backend/langflow/api/router.py
View file

@ -5,7 +5,7 @@ from langflow.api.v1 import (
endpoints_router,
validate_router,
flows_router,
component_router,
store_router,
users_router,
api_key_router,
login_router,
@ -17,7 +17,7 @@ router = APIRouter(
router.include_router(chat_router)
router.include_router(endpoints_router)
router.include_router(validate_router)
router.include_router(component_router)
router.include_router(store_router)
router.include_router(flows_router)
router.include_router(users_router)
router.include_router(api_key_router)

18
src/backend/langflow/api/utils.py
View file

@ -2,9 +2,7 @@ API_WORDS = ["api", "key", "token"]
def has_api_terms(word: str):
return "api" in word and (
"key" in word or ("token" in word and "tokens" not in word)
)
return "api" in word and ("key" in word or ("token" in word and "tokens" not in word))
def remove_api_keys(flow: dict):
@ -14,11 +12,7 @@ def remove_api_keys(flow: dict):
node_data = node.get("data").get("node")
template = node_data.get("template")
for value in template.values():
if (
isinstance(value, dict)
and has_api_terms(value["name"])
and value.get("password")
):
if isinstance(value, dict) and has_api_terms(value["name"]) and value.get("password"):
value["value"] = None
return flow
@ -39,9 +33,7 @@ def build_input_keys_response(langchain_object, artifacts):
input_keys_response["input_keys"][key] = value
# If the object has memory, that memory will have a memory_variables attribute
# memory variables should be removed from the input keys
if hasattr(langchain_object, "memory") and hasattr(
langchain_object.memory, "memory_variables"
):
if hasattr(langchain_object, "memory") and hasattr(langchain_object.memory, "memory_variables"):
# Remove memory variables from input keys
input_keys_response["input_keys"] = {
key: value
@ -51,9 +43,7 @@ def build_input_keys_response(langchain_object, artifacts):
# Add memory variables to memory_keys
input_keys_response["memory_keys"] = langchain_object.memory.memory_variables
if hasattr(langchain_object, "prompt") and hasattr(
langchain_object.prompt, "template"
):
if hasattr(langchain_object, "prompt") and hasattr(langchain_object.prompt, "template"):
input_keys_response["template"] = langchain_object.prompt.template
return input_keys_response

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

@ -2,7 +2,7 @@ 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.components import router as component_router
from langflow.api.v1.store import router as store_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
@ -10,7 +10,7 @@ from langflow.api.v1.login import router as login_router
__all__ = [
"chat_router",
"endpoints_router",
"component_router",
"store_router",
"validate_router",
"flows_router",
"users_router",

38
src/backend/langflow/api/v1/api_key.py
View file

@ -1,7 +1,7 @@
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.api.v1.schemas import ApiKeysResponse, ApiKeyCreateRequest
from langflow.services.auth import utils as auth_utils
from langflow.services.database.models.api_key.api_key import (
ApiKeyCreate,
UnmaskedApiKeyRead,
@ -14,9 +14,17 @@ from langflow.services.database.models.api_key.crud import (
delete_api_key,
)
from langflow.services.database.models.user.user import User
from langflow.services.getters import get_session
from langflow.services.deps import (
get_session,
get_settings_service,
)
from typing import TYPE_CHECKING
from sqlmodel import Session
if TYPE_CHECKING:
pass
router = APIRouter(tags=["APIKey"], prefix="/api_key")
@ -24,7 +32,7 @@ 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),
current_user: User = Depends(auth_utils.get_current_active_user),
):
try:
user_id = current_user.id
@ -38,7 +46,7 @@ def get_api_keys_route(
@router.post("/", response_model=UnmaskedApiKeyRead)
def create_api_key_route(
req: ApiKeyCreate,
current_user: User = Depends(get_current_active_user),
current_user: User = Depends(auth_utils.get_current_active_user),
db: Session = Depends(get_session),
):
try:
@ -51,7 +59,7 @@ def create_api_key_route(
@router.delete("/{api_key_id}")
def delete_api_key_route(
api_key_id: UUID,
current_user=Depends(get_current_active_user),
current_user=Depends(auth_utils.get_current_active_user),
db: Session = Depends(get_session),
):
try:
@ -59,3 +67,21 @@ def delete_api_key_route(
return {"detail": "API Key deleted"}
except Exception as e:
raise HTTPException(status_code=400, detail=str(e)) from e
@router.post("/store")
def save_store_api_key(
api_key_request: ApiKeyCreateRequest,
current_user: User = Depends(auth_utils.get_current_active_user),
db: Session = Depends(get_session),
settings_service=Depends(get_settings_service),
):
try:
api_key = api_key_request.api_key
# Encrypt the API key
encrypted = auth_utils.encrypt_api_key(api_key, settings_service=settings_service)
current_user.store_api_key = encrypted
db.commit()
return {"detail": "API Key saved"}
except Exception as e:
raise HTTPException(status_code=400, detail=str(e)) from e

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

@ -81,9 +81,7 @@ def validate_prompt(template: str):
# Check if there are invalid characters in the input_variables
input_variables = check_input_variables(input_variables)
if any(var in INVALID_NAMES for var in input_variables):
raise ValueError(
f"Invalid input variables. None of the variables can be named {', '.join(input_variables)}. "
)
raise ValueError(f"Invalid input variables. None of the variables can be named {', '.join(input_variables)}. ")
try:
PromptTemplate(template=template, input_variables=input_variables)
@ -134,9 +132,7 @@ def check_input_variables(input_variables: list):
return input_variables
def build_error_message(
input_variables, invalid_chars, wrong_variables, fixed_variables, empty_variables
):
def build_error_message(input_variables, invalid_chars, wrong_variables, fixed_variables, empty_variables):
input_variables_str = ", ".join([f"'{var}'" for var in input_variables])
error_string = f"Invalid input variables: {input_variables_str}. "

34
src/backend/langflow/api/v1/callback.py
View file

@ -1,19 +1,15 @@
import asyncio
from typing import Any, Dict, List, Optional
from uuid import UUID
from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler
from langflow.api.v1.schemas import ChatResponse, PromptResponse
from typing import Any, Dict, List, Optional
from langflow.services.getters import get_chat_service
from langflow.utils.util import remove_ansi_escape_codes
from langchain.schema import AgentAction, AgentFinish
from loguru import logger
from langflow.api.v1.schemas import ChatResponse, PromptResponse
from langflow.services.deps import get_chat_service
from langflow.utils.util import remove_ansi_escape_codes
# https://github.com/hwchase17/chat-langchain/blob/master/callback.py
class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler):
@ -26,18 +22,16 @@ class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler):
async def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
resp = ChatResponse(message=token, type="stream", intermediate_steps="")
await self.websocket.send_json(resp.dict())
await self.websocket.send_json(resp.model_dump())
async def on_tool_start(
self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
) -> Any:
async def on_tool_start(self, serialized: Dict[str, Any], input_str: str, **kwargs: Any) -> Any:
"""Run when tool starts running."""
resp = ChatResponse(
message="",
type="stream",
intermediate_steps=f"Tool input: {input_str}",
)
await self.websocket.send_json(resp.dict())
await self.websocket.send_json(resp.model_dump())
async def on_tool_end(self, output: str, **kwargs: Any) -> Any:
"""Run when tool ends running."""
@ -68,7 +62,7 @@ class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler):
try:
# This is to emulate the stream of tokens
for resp in resps:
await self.websocket.send_json(resp.dict())
await self.websocket.send_json(resp.model_dump())
except Exception as exc:
logger.error(f"Error sending response: {exc}")
@ -94,7 +88,7 @@ class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler):
resp = PromptResponse(
prompt=text,
)
await self.websocket.send_json(resp.dict())
await self.websocket.send_json(resp.model_dump())
self.chat_service.chat_history.add_message(self.client_id, resp)
async def on_agent_action(self, action: AgentAction, **kwargs: Any):
@ -105,10 +99,10 @@ class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler):
logs = log.split("\n")
for log in logs:
resp = ChatResponse(message="", type="stream", intermediate_steps=log)
await self.websocket.send_json(resp.dict())
await self.websocket.send_json(resp.model_dump())
else:
resp = ChatResponse(message="", type="stream", intermediate_steps=log)
await self.websocket.send_json(resp.dict())
await self.websocket.send_json(resp.model_dump())
async def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:
"""Run on agent end."""
@ -117,7 +111,7 @@ class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler):
type="stream",
intermediate_steps=finish.log,
)
await self.websocket.send_json(resp.dict())
await self.websocket.send_json(resp.model_dump())
class StreamingLLMCallbackHandler(BaseCallbackHandler):
@ -132,5 +126,5 @@ class StreamingLLMCallbackHandler(BaseCallbackHandler):
resp = ChatResponse(message=token, type="stream", intermediate_steps="")
loop = asyncio.get_event_loop()
coroutine = self.websocket.send_json(resp.dict())
coroutine = self.websocket.send_json(resp.model_dump())
asyncio.run_coroutine_threadsafe(coroutine, loop)

39
src/backend/langflow/api/v1/chat.py
View file

@ -18,10 +18,10 @@ from langflow.services.auth.utils import (
)
from langflow.services.cache.utils import update_build_status
from loguru import logger
from langflow.services.getters import get_chat_service, get_session, get_cache_service
from langflow.services.deps import get_chat_service, get_session, get_cache_service
from sqlmodel import Session
from langflow.services.chat.manager import ChatService
from langflow.services.cache.manager import BaseCacheService
from langflow.services.chat.service import ChatService
from langflow.services.cache.service import BaseCacheService
router = APIRouter(tags=["Chat"])
@ -40,13 +40,9 @@ async def chat(
user = await get_current_user_by_jwt(token, db)
await websocket.accept()
if not user:
await websocket.close(
code=status.WS_1008_POLICY_VIOLATION, reason="Unauthorized"
)
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"
)
await websocket.close(code=status.WS_1008_POLICY_VIOLATION, reason="Unauthorized")
if client_id in chat_service.cache_service:
await chat_service.handle_websocket(client_id, websocket)
@ -62,9 +58,7 @@ async def chat(
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"
)
await websocket.close(code=status.WS_1008_POLICY_VIOLATION, reason="Unauthorized")
else:
await websocket.close(code=status.WS_1011_INTERNAL_ERROR, reason=messsage)
@ -106,15 +100,10 @@ async def init_build(
@router.get("/build/{flow_id}/status", response_model=BuiltResponse)
async def build_status(
flow_id: str, cache_service: "BaseCacheService" = Depends(get_cache_service)
):
async def build_status(flow_id: str, cache_service: "BaseCacheService" = Depends(get_cache_service)):
"""Check the flow_id is in the cache_service."""
try:
built = (
flow_id in cache_service
and cache_service[flow_id]["status"] == BuildStatus.SUCCESS
)
built = flow_id in cache_service and cache_service[flow_id]["status"] == BuildStatus.SUCCESS
return BuiltResponse(
built=built,
@ -181,9 +170,7 @@ async def stream_build(
params = vertex._built_object_repr()
valid = True
logger.debug(f"Building node {str(vertex.vertex_type)}")
logger.debug(
f"Output: {params[:100]}{'...' if len(params) > 100 else ''}"
)
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
@ -195,9 +182,7 @@ async def stream_build(
valid = False
update_build_status(cache_service, flow_id, BuildStatus.FAILURE)
vertex_id = (
vertex.parent_node_id if vertex.parent_is_top_level else vertex.id
)
vertex_id = vertex.parent_node_id if vertex.parent_is_top_level else vertex.id
if vertex_id in graph.top_level_nodes:
response = {
"valid": valid,
@ -211,9 +196,7 @@ async def stream_build(
langchain_object = graph.build()
# Now we need to check the input_keys to send them to the client
if hasattr(langchain_object, "input_keys"):
input_keys_response = build_input_keys_response(
langchain_object, artifacts
)
input_keys_response = build_input_keys_response(langchain_object, artifacts)
else:
input_keys_response = {
"input_keys": None,

77
src/backend/langflow/api/v1/components.py
View file

@ -1,77 +0,0 @@
from datetime import timezone
from typing import List
from uuid import UUID
from langflow.services.database.models.component import Component, ComponentModel
from langflow.services.getters import get_session
from sqlmodel import Session, select
from fastapi import APIRouter, Depends, HTTPException
from sqlalchemy.exc import IntegrityError
from datetime import datetime
COMPONENT_NOT_FOUND = "Component not found"
COMPONENT_ALREADY_EXISTS = "A component with the same id already exists."
COMPONENT_DELETED = "Component deleted"
router = APIRouter(prefix="/components", tags=["Components"])
@router.post("/", response_model=Component)
def create_component(component: ComponentModel, db: Session = Depends(get_session)):
db_component = Component(**component.dict())
try:
db.add(db_component)
db.commit()
db.refresh(db_component)
except IntegrityError as e:
db.rollback()
raise HTTPException(
status_code=400,
detail=COMPONENT_ALREADY_EXISTS,
) from e
return db_component
@router.get("/{component_id}", response_model=Component)
def read_component(component_id: UUID, db: Session = Depends(get_session)):
if component := db.get(Component, component_id):
return component
else:
raise HTTPException(status_code=404, detail=COMPONENT_NOT_FOUND)
@router.get("/", response_model=List[Component])
def read_components(skip: int = 0, limit: int = 50, db: Session = Depends(get_session)):
query = select(Component)
query = query.offset(skip).limit(limit)
return db.execute(query).fetchall()
@router.patch("/{component_id}", response_model=Component)
def update_component(
component_id: UUID, component: ComponentModel, db: Session = Depends(get_session)
):
db_component = db.get(Component, component_id)
if not db_component:
raise HTTPException(status_code=404, detail=COMPONENT_NOT_FOUND)
component_data = component.dict(exclude_unset=True)
for key, value in component_data.items():
setattr(db_component, key, value)
db_component.update_at = datetime.now(timezone.utc)
db.commit()
db.refresh(db_component)
return db_component
@router.delete("/{component_id}")
def delete_component(component_id: UUID, db: Session = Depends(get_session)):
component = db.get(Component, component_id)
if not component:
raise HTTPException(status_code=404, detail=COMPONENT_NOT_FOUND)
db.delete(component)
db.commit()
return {"detail": COMPONENT_DELETED}

6
src/backend/langflow/api/v1/endpoints.py
View file

@ -7,7 +7,7 @@ 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.services.database.models.user.user import User
from langflow.services.getters import (
from langflow.services.deps import (
get_session_service,
get_settings_service,
get_task_service,
@ -27,7 +27,7 @@ from langflow.api.v1.schemas import (
)
from langflow.services.getters import get_session
from langflow.services.deps import get_session
try:
from langflow.worker import process_graph_cached_task
@ -40,7 +40,7 @@ except ImportError:
from sqlmodel import Session
from langflow.services.task.manager import TaskService
from langflow.services.task.service import TaskService
# build router
router = APIRouter(tags=["Base"])

22
src/backend/langflow/api/v1/flows.py
View file

@ -1,6 +1,10 @@
from typing import List
from uuid import UUID
import orjson
from fastapi import APIRouter, Depends, File, HTTPException, UploadFile
from fastapi.encoders import jsonable_encoder
from sqlmodel import Session
from langflow.api.utils import remove_api_keys
from langflow.api.v1.schemas import FlowListCreate, FlowListRead
@ -12,13 +16,7 @@ from langflow.services.database.models.flow import (
FlowUpdate,
)
from langflow.services.database.models.user.user import User
from langflow.services.getters import get_session
from langflow.services.getters import get_settings_service
import orjson
from sqlmodel import Session
from fastapi import APIRouter, Depends, HTTPException
from fastapi import File, UploadFile
from langflow.services.deps import get_session, get_settings_service
# build router
router = APIRouter(prefix="/flows", tags=["Flows"])
@ -46,7 +44,6 @@ def create_flow(
@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."""
@ -65,12 +62,7 @@ def read_flow(
current_user: User = Depends(get_current_active_user),
):
"""Read a flow."""
if user_flow := (
session.query(Flow)
.filter(Flow.id == flow_id)
.filter(Flow.user_id == current_user.id)
.first()
):
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")
@ -169,5 +161,5 @@ async def download_file(
current_user: User = Depends(get_current_active_user),
):
"""Download all flows as a file."""
flows = read_flows(session=session, current_user=current_user)
flows = read_flows(current_user=current_user)
return FlowListRead(flows=flows)

12
src/backend/langflow/api/v1/login.py
View file

@ -2,7 +2,7 @@ from sqlmodel import Session
from fastapi import APIRouter, Depends, HTTPException, status
from fastapi.security import OAuth2PasswordRequestForm
from langflow.services.getters import get_session
from langflow.services.deps import get_session
from langflow.api.v1.schemas import Token
from langflow.services.auth.utils import (
authenticate_user,
@ -12,7 +12,7 @@ from langflow.services.auth.utils import (
get_current_active_user,
)
from langflow.services.getters import get_settings_service
from langflow.services.deps import get_settings_service
router = APIRouter(tags=["Login"])
@ -44,9 +44,7 @@ async def login_to_get_access_token(
@router.get("/auto_login")
async def auto_login(
db: Session = Depends(get_session), settings_service=Depends(get_settings_service)
):
async def auto_login(db: Session = Depends(get_session), settings_service=Depends(get_settings_service)):
if settings_service.auth_settings.AUTO_LOGIN:
return create_user_longterm_token(db)
@ -60,9 +58,7 @@ async def auto_login(
@router.post("/refresh")
async def refresh_token(
token: str, current_user: Session = Depends(get_current_active_user)
):
async def refresh_token(token: str, current_user: Session = Depends(get_current_active_user)):
if token:
return create_refresh_token(token)
else:

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

@ -151,9 +151,7 @@ class StreamData(BaseModel):
data: dict
def __str__(self) -> str:
return (
f"event: {self.event}\ndata: {orjson_dumps(self.data, indent_2=False)}\n\n"
)
return f"event: {self.event}\ndata: {orjson_dumps(self.data, indent_2=False)}\n\n"
class CustomComponentCode(BaseModel):
@ -200,3 +198,7 @@ class Token(BaseModel):
access_token: str
refresh_token: str
token_type: str
class ApiKeyCreateRequest(BaseModel):
api_key: str

195
src/backend/langflow/api/v1/store.py Normal file
View file

@ -0,0 +1,195 @@
import warnings
from typing import Annotated, List, Optional, Union
from uuid import UUID
from fastapi import APIRouter, Depends, HTTPException, Query
from langflow.services.auth import utils as auth_utils
from langflow.services.database.models.user.user import User
from langflow.services.deps import get_settings_service, get_store_service
from langflow.services.store.exceptions import CustomException
from langflow.services.store.schema import (
CreateComponentResponse,
DownloadComponentResponse,
ListComponentResponseModel,
StoreComponentCreate,
TagResponse,
UsersLikesResponse,
)
from langflow.services.store.service import StoreService
from langflow.services.store.utils import get_lf_version_from_pypi
router = APIRouter(prefix="/store", tags=["Components Store"])
def get_user_store_api_key(
user: User = Depends(auth_utils.get_current_active_user),
settings_service=Depends(get_settings_service),
):
if not user.store_api_key:
raise HTTPException(status_code=400, detail="You must have a store API key set.")
decrypted = auth_utils.decrypt_api_key(user.store_api_key, settings_service)
return decrypted
def get_optional_user_store_api_key(
user: User = Depends(auth_utils.get_current_active_user),
settings_service=Depends(get_settings_service),
):
if not user.store_api_key:
return None
decrypted = auth_utils.decrypt_api_key(user.store_api_key, settings_service)
return decrypted
@router.get("/check/")
def check_if_store_is_enabled(
settings_service=Depends(get_settings_service),
):
return {
"enabled": settings_service.settings.STORE,
}
@router.get("/check/api_key")
async def check_if_store_has_api_key(
api_key: Optional[str] = Depends(get_optional_user_store_api_key),
store_service: StoreService = Depends(get_store_service),
):
if api_key is None:
return {"has_api_key": False, "is_valid": False}
try:
is_valid = await store_service.check_api_key(api_key)
except Exception as e:
raise HTTPException(status_code=400, detail=str(e))
return {"has_api_key": api_key is not None, "is_valid": is_valid}
@router.post("/components/", response_model=CreateComponentResponse, status_code=201)
async def share_component(
component: StoreComponentCreate,
store_service: StoreService = Depends(get_store_service),
store_api_Key: str = Depends(get_user_store_api_key),
):
try:
# Verify if this is the latest version of Langflow
# If not, raise an error
if not component.last_tested_version:
# Get the local version of Langflow
from langflow import __version__ as current_version
component.last_tested_version = current_version
langflow_version = get_lf_version_from_pypi()
if langflow_version is None:
raise HTTPException(
status_code=500,
detail="Unable to verify the latest version of Langflow",
)
elif langflow_version != component.last_tested_version:
# If the user is using an older version of Langflow, we need to raise an error
# raise ValueError(
warnings.warn(
f"Your version of Langflow ({component.last_tested_version}) is outdated."
f" Please update to the latest version ({langflow_version}) and try again."
)
result = await store_service.upload(store_api_Key, component)
return result
except Exception as exc:
raise HTTPException(status_code=400, detail=str(exc))
@router.get("/components/", response_model=ListComponentResponseModel)
async def get_components(
search: Annotated[Optional[str], Query()] = None,
private: Annotated[Optional[bool], Query()] = None,
is_component: Annotated[Optional[bool], Query()] = None,
tags: Annotated[Optional[list[str]], Query()] = None,
sort: Annotated[Union[list[str], None], Query()] = None,
liked: Annotated[bool, Query()] = False,
filter_by_user: Annotated[bool, Query()] = False,
page: int = 1,
limit: int = 10,
store_service: StoreService = Depends(get_store_service),
store_api_Key: Optional[str] = Depends(get_optional_user_store_api_key),
):
try:
return await store_service.get_list_component_response_model(
search=search,
private=private,
is_component=is_component,
tags=tags,
sort=sort,
liked=liked,
filter_by_user=filter_by_user,
page=page,
limit=limit,
store_api_key=store_api_Key,
)
except CustomException as exc:
raise HTTPException(status_code=exc.status_code, detail=str(exc)) from exc
except Exception as exc:
raise HTTPException(status_code=500, detail=str(exc)) from exc
@router.get("/components/{component_id}", response_model=DownloadComponentResponse)
async def download_component(
component_id: UUID,
store_service: StoreService = Depends(get_store_service),
store_api_Key: str = Depends(get_user_store_api_key),
):
try:
component = await store_service.download(store_api_Key, component_id)
except CustomException as exc:
raise HTTPException(status_code=400, detail=str(exc)) from exc
except Exception as exc:
raise HTTPException(status_code=500, detail=str(exc)) from exc
if component is None:
raise HTTPException(status_code=400, detail="Component not found")
return component
@router.get("/tags", response_model=List[TagResponse])
async def get_tags(
store_service: StoreService = Depends(get_store_service),
):
try:
return await store_service.get_tags()
except CustomException as exc:
raise HTTPException(status_code=400, detail=str(exc)) from exc
except Exception as exc:
raise HTTPException(status_code=500, detail=str(exc))
@router.get("/users/likes", response_model=List[UsersLikesResponse])
async def get_list_of_components_liked_by_user(
store_service: StoreService = Depends(get_store_service),
store_api_Key: str = Depends(get_user_store_api_key),
):
try:
return await store_service.get_user_likes(store_api_Key)
except CustomException as exc:
raise HTTPException(status_code=400, detail=str(exc)) from exc
except Exception as exc:
raise HTTPException(status_code=500, detail=str(exc))
@router.post("/users/likes/{component_id}", response_model=UsersLikesResponse)
async def like_component(
component_id: UUID,
store_service: StoreService = Depends(get_store_service),
store_api_Key: str = Depends(get_user_store_api_key),
):
try:
result = await store_service.like_component(store_api_Key, str(component_id))
likes_count = await store_service.get_component_likes_count(str(component_id), store_api_Key)
return UsersLikesResponse(likes_count=likes_count, liked_by_user=result)
except CustomException as exc:
raise HTTPException(status_code=exc.status_code, detail=str(exc)) from exc
except Exception as exc:
raise HTTPException(status_code=500, detail=str(exc))

30
src/backend/langflow/api/v1/users.py
View file

@ -13,7 +13,7 @@ from sqlalchemy.exc import IntegrityError
from sqlmodel import Session, select
from fastapi import APIRouter, Depends, HTTPException
from langflow.services.getters import get_session, get_settings_service
from langflow.services.deps import get_session, get_settings_service
from langflow.services.auth.utils import (
get_current_active_superuser,
get_current_active_user,
@ -46,9 +46,7 @@ def add_user(
session.refresh(new_user)
except IntegrityError as e:
session.rollback()
raise HTTPException(
status_code=400, detail="This username is unavailable."
) from e
raise HTTPException(status_code=400, detail="This username is unavailable.") from e
return new_user
@ -96,14 +94,10 @@ def patch_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"
)
raise HTTPException(status_code=403, detail="You don't have the permission to update this user")
if user_update.password:
if not user.is_superuser:
raise HTTPException(
status_code=400, detail="You can't change your password here"
)
raise HTTPException(status_code=400, detail="You can't change your password here")
user_update.password = get_password_hash(user_update.password)
if user_db := get_user_by_id(session, user_id):
@ -123,16 +117,12 @@ def reset_password(
Reset a user's password.
"""
if user_id != user.id:
raise HTTPException(
status_code=400, detail="You can't change another user's password"
)
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"
)
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()
@ -151,13 +141,9 @@ def delete_user(
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"
)
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"
)
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:

47
src/backend/langflow/api/v1/validate.py
View file

@ -41,9 +41,7 @@ def post_validate_prompt(prompt_request: ValidatePromptRequest):
add_new_variables_to_template(input_variables, prompt_request)
remove_old_variables_from_template(
old_custom_fields, input_variables, prompt_request
)
remove_old_variables_from_template(old_custom_fields, input_variables, prompt_request)
update_input_variables_field(input_variables, prompt_request)
@ -58,19 +56,12 @@ 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 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]
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()
old_custom_fields = prompt_request.frontend_node.custom_fields[prompt_request.name].copy()
except KeyError:
old_custom_fields = []
prompt_request.frontend_node.custom_fields[prompt_request.name] = []
@ -92,40 +83,26 @@ def add_new_variables_to_template(input_variables, prompt_request):
)
if variable in prompt_request.frontend_node.template:
# Set the new field with the old value
template_field.value = prompt_request.frontend_node.template[variable][
"value"
]
template_field.value = prompt_request.frontend_node.template[variable]["value"]
prompt_request.frontend_node.template[variable] = template_field.to_dict()
# Check if variable is not already in the list before appending
if (
variable
not in prompt_request.frontend_node.custom_fields[prompt_request.name]
):
prompt_request.frontend_node.custom_fields[prompt_request.name].append(
variable
)
if variable not in prompt_request.frontend_node.custom_fields[prompt_request.name]:
prompt_request.frontend_node.custom_fields[prompt_request.name].append(variable)
except Exception as exc:
logger.exception(exc)
raise HTTPException(status_code=500, detail=str(exc)) from exc
def remove_old_variables_from_template(
old_custom_fields, input_variables, prompt_request
):
def remove_old_variables_from_template(old_custom_fields, input_variables, prompt_request):
for variable in old_custom_fields:
if variable not in input_variables:
try:
# Remove the variable from custom_fields associated with the given name
if (
variable
in prompt_request.frontend_node.custom_fields[prompt_request.name]
):
prompt_request.frontend_node.custom_fields[
prompt_request.name
].remove(variable)
if variable in prompt_request.frontend_node.custom_fields[prompt_request.name]:
prompt_request.frontend_node.custom_fields[prompt_request.name].remove(variable)
# Remove the variable from the template
prompt_request.frontend_node.template.pop(variable, None)
@ -137,6 +114,4 @@ def remove_old_variables_from_template(
def update_input_variables_field(input_variables, prompt_request):
if "input_variables" in prompt_request.frontend_node.template:
prompt_request.frontend_node.template["input_variables"][
"value"
] = input_variables
prompt_request.frontend_node.template["input_variables"]["value"] = input_variables

4
src/backend/langflow/components/agents/OpenAIConversationalAgent.py
View file

@ -72,7 +72,9 @@ class ConversationalAgent(CustomComponent):
extra_prompt_messages=[MessagesPlaceholder(variable_name=memory_key)],
)
agent = OpenAIFunctionsAgent(
llm=llm, tools=tools, prompt=prompt # type: ignore
llm=llm,
tools=tools,
prompt=prompt, # type: ignore
)
return AgentExecutor(
agent=agent,

4
src/backend/langflow/components/chains/PromptRunner.py
View file

@ -18,9 +18,7 @@ class PromptRunner(CustomComponent):
"code": {"show": False},
}
def build(
self, llm: BaseLLM, prompt: PromptTemplate, inputs: dict = {}
) -> Document:
def build(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(input=inputs)

12
src/backend/langflow/components/documentloaders/FileLoader.py
View file

@ -122,9 +122,7 @@ class FileLoaderComponent(CustomComponent):
beta = True
def build_config(self):
loader_options = ["Automatic"] + [
loader_info["name"] for loader_info in loaders_info
]
loader_options = ["Automatic"] + [loader_info["name"] for loader_info in loaders_info]
file_types = []
suffixes = []
@ -214,9 +212,7 @@ class FileLoaderComponent(CustomComponent):
if isinstance(selected_loader_info, dict):
loader_import: str = selected_loader_info["import"]
else:
raise ValueError(
f"Loader info for {loader} is not a dict\nLoader info:\n{selected_loader_info}"
)
raise ValueError(f"Loader info for {loader} is not a dict\nLoader info:\n{selected_loader_info}")
module_name, class_name = loader_import.rsplit(".", 1)
try:
@ -224,9 +220,7 @@ class FileLoaderComponent(CustomComponent):
loader_module = __import__(module_name, fromlist=[class_name])
loader_instance = getattr(loader_module, class_name)
except ImportError as e:
raise ValueError(
f"Loader {loader} could not be imported\nLoader info:\n{selected_loader_info}"
) from e
raise ValueError(f"Loader {loader} could not be imported\nLoader info:\n{selected_loader_info}") from e
result = loader_instance(file_path=file_path)
return result.load()

4
src/backend/langflow/components/retrievers/MetalRetriever.py
View file

@ -18,9 +18,7 @@ class MetalRetrieverComponent(CustomComponent):
"code": {"show": False},
}
def build(
self, api_key: str, client_id: str, index_id: str, params: Optional[dict] = None
) -> BaseRetriever:
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:

15
src/backend/langflow/components/toolkits/Metaphor.py
View file

@ -1,18 +1,17 @@
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
from langchain.tools import Tool
from metaphor_python import Metaphor # type: ignore
from langflow import CustomComponent
class MetaphorToolkit(CustomComponent):
display_name: str = "Metaphor"
description: str = "Metaphor Toolkit"
documentation = (
"https://python.langchain.com/docs/integrations/tools/metaphor_search"
)
documentation = "https://python.langchain.com/docs/integrations/tools/metaphor_search"
beta: bool = True
# api key should be password = True
field_config = {
@ -33,9 +32,7 @@ class MetaphorToolkit(CustomComponent):
@tool
def search(query: str):
"""Call search engine with a query."""
return client.search(
query, use_autoprompt=use_autoprompt, num_results=search_num_results
)
return client.search(query, use_autoprompt=use_autoprompt, num_results=search_num_results)
@tool
def get_contents(ids: List[str]):

4
src/backend/langflow/components/utilities/GetRequest.py
View file

@ -30,9 +30,7 @@ class GetRequest(CustomComponent):
},
}
def get_document(
self, session: requests.Session, url: str, headers: Optional[dict], timeout: int
) -> Document:
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:

21
src/backend/langflow/components/utilities/JSONDocumentBuilder.py
View file

@ -11,8 +11,8 @@
# - **Document:** The Document containing the JSON object.
from langflow import CustomComponent
from langchain.schema import Document
from langflow import CustomComponent
from langflow.services.database.models.base import orjson_dumps
@ -20,10 +20,8 @@ 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: bool = True
documentation: str = (
"https://docs.langflow.org/components/utilities#json-document-builder"
)
beta = True
documentation: str = "https://docs.langflow.org/components/utilities#json-document-builder"
field_config = {
"key": {"display_name": "Key"},
@ -38,18 +36,11 @@ class JSONDocumentBuilder(CustomComponent):
documents = None
if isinstance(document, list):
documents = [
Document(
page_content=orjson_dumps({key: doc.page_content}, indent_2=False)
)
for doc in document
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)
)
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)}"
)
raise TypeError(f"Expected Document or list of Documents, got {type(document)}")
self.repr_value = documents
return documents

8
src/backend/langflow/components/utilities/PostRequest.py
View file

@ -65,16 +65,12 @@ class PostRequest(CustomComponent):
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
):
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
]
documents = [self.post_document(session, doc, url, headers) for doc in documents]
self.repr_value = documents
return documents

13
src/backend/langflow/components/utilities/UpdateRequest.py
View file

@ -39,9 +39,7 @@ class UpdateRequest(CustomComponent):
) -> Document:
try:
if method == "PATCH":
response = session.patch(
url, headers=headers, data=document.page_content
)
response = session.patch(url, headers=headers, data=document.page_content)
elif method == "PUT":
response = session.put(url, headers=headers, data=document.page_content)
else:
@ -78,17 +76,12 @@ class UpdateRequest(CustomComponent):
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
):
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
]
documents = [self.update_document(session, doc, url, headers, method) for doc in documents]
self.repr_value = documents
return documents

7
src/backend/langflow/components/vectorstores/Chroma.py
View file

@ -86,8 +86,7 @@ class ChromaComponent(CustomComponent):
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_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,
@ -104,6 +103,4 @@ class ChromaComponent(CustomComponent):
client_settings=chroma_settings,
)
return Chroma(
persist_directory=persist_directory, client_settings=chroma_settings
)
return Chroma(persist_directory=persist_directory, client_settings=chroma_settings)

12
src/backend/langflow/components/vectorstores/Vectara.py
View file

@ -1,19 +1,17 @@
from typing import Optional, Union
from langflow import CustomComponent
from langchain.schema import BaseRetriever, Document
from langchain.vectorstores import Vectara
from langchain.schema import Document
from langchain.vectorstores.base import VectorStore
from langchain.schema import BaseRetriever
from langflow import CustomComponent
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: bool = True
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"},

4
src/backend/langflow/components/vectorstores/pgvector.py
View file

@ -14,9 +14,7 @@ class PostgresqlVectorComponent(CustomComponent):
display_name: str = "PGVector"
description: str = "Implementation of Vector Store using PostgreSQL"
documentation = (
"https://python.langchain.com/docs/integrations/vectorstores/pgvector"
)
documentation = "https://python.langchain.com/docs/integrations/vectorstores/pgvector"
beta = True
def build_config(self):

44
src/backend/langflow/field_typing/__init__.py
View file

@ -1,40 +1,25 @@
# LANGCHAIN_BASE_TYPES = {
# "Chain": Chain,
# "AgentExecutor": AgentExecutor,
# "Tool": Tool,
# "BaseLLM": BaseLLM,
# "PromptTemplate": PromptTemplate,
# "BaseLoader": BaseLoader,
# "Document": Document,
# "TextSplitter": TextSplitter,
# "VectorStore": VectorStore,
# "Embeddings": Embeddings,
# "BaseRetriever": BaseRetriever,
# "BaseOutputParser": BaseOutputParser,
# "BaseMemory": BaseMemory,
# "BaseChatMemory": BaseChatMemory,
# }
from .constants import (
Tool,
PromptTemplate,
ChatPromptTemplate,
BasePromptTemplate,
Chain,
AgentExecutor,
BaseChatMemory,
BaseLanguageModel,
BaseLLM,
BaseLoader,
BaseMemory,
BaseOutputParser,
BasePromptTemplate,
BaseRetriever,
VectorStore,
Embeddings,
TextSplitter,
Document,
AgentExecutor,
NestedDict,
Data,
BaseLanguageModel,
Callable,
Chain,
ChatPromptTemplate,
Data,
Document,
Embeddings,
NestedDict,
Object,
PromptTemplate,
TextSplitter,
Tool,
VectorStore,
)
__all__ = [
@ -55,6 +40,7 @@ __all__ = [
"TextSplitter",
"Document",
"AgentExecutor",
"Object",
"Callable",
"BasePromptTemplate",
"ChatPromptTemplate",

12
src/backend/langflow/field_typing/constants.py
View file

@ -1,21 +1,26 @@
from typing import Callable, Dict, Union
from langchain.agents.agent import AgentExecutor
from langchain.chains.base import Chain
from langchain.document_loaders.base import BaseLoader
from langchain.llms.base import BaseLLM, BaseLanguageModel
from langchain.llms.base import BaseLanguageModel, BaseLLM
from langchain.memory.chat_memory import BaseChatMemory
from langchain.prompts import PromptTemplate, ChatPromptTemplate, BasePromptTemplate
from langchain.prompts import BasePromptTemplate, ChatPromptTemplate, PromptTemplate
from langchain.schema import BaseOutputParser, BaseRetriever, Document
from langchain.schema.embeddings import Embeddings
from langchain.schema.memory import BaseMemory
from langchain.text_splitter import TextSplitter
from langchain.tools import Tool
from langchain.vectorstores.base import VectorStore
from typing import Union, Dict, Callable
# Type alias for more complex dicts
NestedDict = Dict[str, Union[str, Dict]]
class Object:
pass
class Data:
pass
@ -50,5 +55,6 @@ CUSTOM_COMPONENT_SUPPORTED_TYPES = {
"dict": dict,
"NestedDict": NestedDict,
"Data": Data,
"Object": Object,
"Callable": Callable,
}

35
src/backend/langflow/graph/edge/base.py
View file

@ -8,9 +8,7 @@ if TYPE_CHECKING:
class SourceHandle(BaseModel):
baseClasses: List[str] = Field(
..., description="List of base classes for the source handle."
)
baseClasses: List[str] = Field(..., description="List of base classes for the source handle.")
dataType: str = Field(..., description="Data type for the source handle.")
id: str = Field(..., description="Unique identifier for the source handle.")
@ -18,9 +16,7 @@ class SourceHandle(BaseModel):
class TargetHandle(BaseModel):
fieldName: str = Field(..., description="Field name for the target handle.")
id: str = Field(..., description="Unique identifier for the target handle.")
inputTypes: Optional[List[str]] = Field(
None, description="List of input types for the target handle."
)
inputTypes: Optional[List[str]] = Field(None, description="List of input types for the target handle.")
type: str = Field(..., description="Type of the target handle.")
@ -49,23 +45,17 @@ class Edge:
def validate_handles(self) -> None:
if self.target_handle.inputTypes is None:
self.valid_handles = (
self.target_handle.type in self.source_handle.baseClasses
)
self.valid_handles = self.target_handle.type in self.source_handle.baseClasses
else:
self.valid_handles = (
any(
baseClass in self.target_handle.inputTypes
for baseClass in self.source_handle.baseClasses
)
any(baseClass in self.target_handle.inputTypes for baseClass in self.source_handle.baseClasses)
or self.target_handle.type in self.source_handle.baseClasses
)
if not self.valid_handles:
logger.debug(self.source_handle)
logger.debug(self.target_handle)
raise ValueError(
f"Edge between {self.source.vertex_type} and {self.target.vertex_type} "
f"has invalid handles"
f"Edge between {self.source.vertex_type} and {self.target.vertex_type} " f"has invalid handles"
)
def __setstate__(self, state):
@ -87,11 +77,7 @@ class Edge:
# Both lists contain strings and sometimes a string contains the value we are
# looking for e.g. comgin_out=["Chain"] and target_reqs=["LLMChain"]
# so we need to check if any of the strings in source_types is in target_reqs
self.valid = any(
output in target_req
for output in self.source_types
for target_req in self.target_reqs
)
self.valid = any(output in target_req for output in self.source_types for target_req in self.target_reqs)
# Get what type of input the target node is expecting
self.matched_type = next(
@ -103,8 +89,7 @@ class Edge:
logger.debug(self.source_types)
logger.debug(self.target_reqs)
raise ValueError(
f"Edge between {self.source.vertex_type} and {self.target.vertex_type} "
f"has no matched type"
f"Edge between {self.source.vertex_type} and {self.target.vertex_type} " f"has no matched type"
)
def __repr__(self) -> str:
@ -117,8 +102,4 @@ class Edge:
return hash(self.__repr__())
def __eq__(self, __value: object) -> bool:
return (
self.__repr__() == __value.__repr__()
if isinstance(__value, Edge)
else False
)
return self.__repr__() == __value.__repr__() if isinstance(__value, Edge) else False

16
src/backend/langflow/graph/graph/base.py
View file

@ -104,9 +104,7 @@ class Graph:
return
for node in self.nodes:
if not self._validate_node(node):
raise ValueError(
f"{node.vertex_type} is not connected to any other components"
)
raise ValueError(f"{node.vertex_type} is not connected to any other components")
def _validate_node(self, node: Vertex) -> bool:
"""Validates a node."""
@ -119,9 +117,7 @@ class Graph:
def get_nodes_with_target(self, node: Vertex) -> List[Vertex]:
"""Returns the nodes connected to a node."""
connected_nodes: List[Vertex] = [
edge.source for edge in self.edges if edge.target == node
]
connected_nodes: List[Vertex] = [edge.source for edge in self.edges if edge.target == node]
return connected_nodes
def build(self) -> Chain:
@ -149,9 +145,7 @@ class Graph:
def dfs(node):
if state[node] == 1:
# We have a cycle
raise ValueError(
"Graph contains a cycle, cannot perform topological sort"
)
raise ValueError("Graph contains a cycle, cannot perform topological sort")
if state[node] == 0:
state[node] = 1
for edge in node.edges:
@ -245,7 +239,5 @@ class Graph:
def __repr__(self):
node_ids = [node.id for node in self.nodes]
edges_repr = "\n".join(
[f"{edge.source.id} --> {edge.target.id}" for edge in self.edges]
)
edges_repr = "\n".join([f"{edge.source.id} --> {edge.target.id}" for edge in self.edges])
return f"Graph:\nNodes: {node_ids}\nConnections:\n{edges_repr}"

5
src/backend/langflow/graph/graph/constants.py
View file

@ -47,10 +47,7 @@ class VertexTypesDict(LazyLoadDictBase):
**{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.CustomComponentVertex for t in custom_component_creator.to_list()},
**{t: types.RetrieverVertex for t in retriever_creator.to_list()},
}

41
src/backend/langflow/graph/graph/utils.py
View file

@ -28,23 +28,14 @@ def ungroup_node(group_node_data, base_flow):
g_edges = flow["data"]["edges"]
# Redirect edges to the correct proxy node
updated_edges = get_updated_edges(
base_flow, g_nodes, g_edges, group_node_data["id"]
)
updated_edges = get_updated_edges(base_flow, g_nodes, g_edges, group_node_data["id"])
# Update template values
update_template(template, g_nodes)
nodes = [
n for n in base_flow["nodes"] if n["id"] != group_node_data["id"]
] + g_nodes
nodes = [n for n in base_flow["nodes"] if n["id"] != group_node_data["id"]] + g_nodes
edges = (
[
e
for e in base_flow["edges"]
if e["target"] != group_node_data["id"]
and e["source"] != group_node_data["id"]
]
[e for e in base_flow["edges"] if e["target"] != group_node_data["id"] and e["source"] != group_node_data["id"]]
+ g_edges
+ updated_edges
)
@ -66,11 +57,7 @@ def process_flow(flow_object):
if node_id in processed_nodes:
return
if (
node.get("data")
and node["data"].get("node")
and node["data"]["node"].get("flow")
):
if node.get("data") and node["data"].get("node") and node["data"]["node"].get("flow"):
process_flow(node["data"]["node"]["flow"]["data"])
new_nodes = ungroup_node(node["data"], cloned_flow)
# Add new nodes to the queue for future processing
@ -108,26 +95,16 @@ def update_template(template, g_nodes):
if node_index != -1:
display_name = None
show = g_nodes[node_index]["data"]["node"]["template"][field]["show"]
advanced = g_nodes[node_index]["data"]["node"]["template"][field][
"advanced"
]
advanced = g_nodes[node_index]["data"]["node"]["template"][field]["advanced"]
if "display_name" in g_nodes[node_index]["data"]["node"]["template"][field]:
display_name = g_nodes[node_index]["data"]["node"]["template"][field][
"display_name"
]
display_name = g_nodes[node_index]["data"]["node"]["template"][field]["display_name"]
else:
display_name = g_nodes[node_index]["data"]["node"]["template"][field][
"name"
]
display_name = g_nodes[node_index]["data"]["node"]["template"][field]["name"]
g_nodes[node_index]["data"]["node"]["template"][field] = value
g_nodes[node_index]["data"]["node"]["template"][field]["show"] = show
g_nodes[node_index]["data"]["node"]["template"][field][
"advanced"
] = advanced
g_nodes[node_index]["data"]["node"]["template"][field][
"display_name"
] = display_name
g_nodes[node_index]["data"]["node"]["template"][field]["advanced"] = advanced
g_nodes[node_index]["data"]["node"]["template"][field]["display_name"] = display_name
def update_target_handle(new_edge, g_nodes, group_node_id):

50
src/backend/langflow/graph/vertex/base.py
View file

@ -51,9 +51,7 @@ class Vertex:
self.params.pop(target_param, None)
continue
if target_param in self.params and not is_basic_type(
self.params[target_param]
):
if target_param in self.params and not is_basic_type(self.params[target_param]):
# edge.source.params = {}
edge.source._build_params()
edge.source._built_object = UnbuiltObject()
@ -99,29 +97,17 @@ class Vertex:
def _parse_data(self) -> None:
self.data = self._data["data"]
self.output = self.data["node"]["base_classes"]
template_dicts = {
key: value
for key, value in self.data["node"]["template"].items()
if isinstance(value, dict)
}
template_dicts = {key: value for key, value in self.data["node"]["template"].items() if isinstance(value, dict)}
self.required_inputs = [
template_dicts[key]["type"]
for key, value in template_dicts.items()
if value["required"]
template_dicts[key]["type"] for key, value in template_dicts.items() if value["required"]
]
self.optional_inputs = [
template_dicts[key]["type"]
for key, value in template_dicts.items()
if not value["required"]
template_dicts[key]["type"] for key, value in template_dicts.items() if not value["required"]
]
# Add the template_dicts[key]["input_types"] to the optional_inputs
self.optional_inputs.extend(
[
input_type
for value in template_dicts.values()
for input_type in value.get("input_types", [])
]
[input_type for value in template_dicts.values() for input_type in value.get("input_types", [])]
)
template_dict = self.data["node"]["template"]
@ -160,11 +146,7 @@ class Vertex:
# and use that as the value for the param
# If the type is "str", then we need to get the value of the "value" key
# and use that as the value for the param
template_dict = {
key: value
for key, value in self.data["node"]["template"].items()
if isinstance(value, dict)
}
template_dict = {key: value for key, value in self.data["node"]["template"].items() if isinstance(value, dict)}
params = self.params.copy() if self.params else {}
for edge in self.edges:
@ -209,11 +191,7 @@ class Vertex:
# 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()
}
params[key] = {k: v for item in value.get("value", []) for k, v in item.items()}
elif isinstance(_value, dict):
params[key] = _value
elif value.get("type") == "int" and value.get("value") is not None:
@ -304,9 +282,7 @@ class Vertex:
self._extend_params_list_with_result(key, result)
self.params[key] = result
def _build_list_of_nodes_and_update_params(
self, key, nodes: List["Vertex"], user_id=None
):
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.
"""
@ -358,9 +334,7 @@ class Vertex:
self._update_built_object_and_artifacts(result)
except Exception as exc:
logger.exception(exc)
raise ValueError(
f"Error building node {self.vertex_type}(ID:{self.id}): {str(exc)}"
) from exc
raise ValueError(f"Error building node {self.vertex_type}(ID:{self.id}): {str(exc)}") from exc
def _update_built_object_and_artifacts(self, result):
"""
@ -408,8 +382,4 @@ class Vertex:
def _built_object_repr(self):
# Add a message with an emoji, stars for sucess,
return (
"Built sucessfully ✨"
if self._built_object is not None
else "Failed to build 😵‍💫"
)
return "Built sucessfully ✨" if self._built_object is not None else "Failed to build 😵‍💫"

49
src/backend/langflow/graph/vertex/types.py
View file

@ -107,11 +107,9 @@ class DocumentLoaderVertex(Vertex):
# show how many documents are in the list?
if self._built_object:
avg_length = sum(
len(doc.page_content)
for doc in self._built_object
if hasattr(doc, "page_content")
) / len(self._built_object)
avg_length = sum(len(doc.page_content) for doc in self._built_object if hasattr(doc, "page_content")) / len(
self._built_object
)
return f"""{self.vertex_type}({len(self._built_object)} documents)
\nAvg. Document Length (characters): {int(avg_length)}
Documents: {self._built_object[:3]}..."""
@ -184,9 +182,7 @@ class TextSplitterVertex(Vertex):
# show how many documents are in the list?
if self._built_object:
avg_length = sum(len(doc.page_content) for doc in self._built_object) / len(
self._built_object
)
avg_length = sum(len(doc.page_content) for doc in self._built_object) / len(self._built_object)
return f"""{self.vertex_type}({len(self._built_object)} documents)
\nAvg. Document Length (characters): {int(avg_length)}
\nDocuments: {self._built_object[:3]}..."""
@ -205,6 +201,8 @@ class ChainVertex(Vertex):
**kwargs,
) -> Any:
if not self._built or force:
# Temporarily remove the code from the params
self.params.pop("code", None)
# Check if the chain requires a PromptVertex
# Temporarily remove "code" from the params
@ -234,27 +232,18 @@ class PromptVertex(Vertex):
**kwargs,
) -> Any:
if not self._built or force:
if (
"input_variables" not in self.params
or self.params["input_variables"] is None
):
if "input_variables" not in self.params or self.params["input_variables"] is None:
self.params["input_variables"] = []
# Check if it is a ZeroShotPrompt and needs a tool
if "ShotPrompt" in self.vertex_type:
tools = (
[tool_node.build(user_id=user_id) for tool_node in tools]
if tools is not None
else []
)
tools = [tool_node.build(user_id=user_id) for tool_node in tools] if tools is not None else []
# flatten the list of tools if it is a list of lists
# first check if it is a list
if tools and isinstance(tools, list) and isinstance(tools[0], list):
tools = flatten_list(tools)
self.params["tools"] = tools
prompt_params = [
key
for key, value in self.params.items()
if isinstance(value, str) and key != "format_instructions"
key for key, value in self.params.items() if isinstance(value, str) and key != "format_instructions"
]
else:
prompt_params = ["template"]
@ -264,9 +253,7 @@ class PromptVertex(Vertex):
prompt_text = self.params[param]
variables = extract_input_variables_from_prompt(prompt_text)
self.params["input_variables"].extend(variables)
self.params["input_variables"] = list(
set(self.params["input_variables"])
)
self.params["input_variables"] = list(set(self.params["input_variables"]))
elif isinstance(self.params, dict):
self.params.pop("input_variables", None)
@ -274,11 +261,7 @@ class PromptVertex(Vertex):
return self._built_object
def _built_object_repr(self):
if (
not self.artifacts
or self._built_object is None
or not hasattr(self._built_object, "format")
):
if not self.artifacts or self._built_object is None or not hasattr(self._built_object, "format"):
return super()._built_object_repr()
# We'll build the prompt with the artifacts
# to show the user what the prompt looks like
@ -288,9 +271,7 @@ class PromptVertex(Vertex):
# so the prompt format doesn't break
artifacts.pop("handle_keys", None)
try:
if not hasattr(self._built_object, "template") and hasattr(
self._built_object, "prompt"
):
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
@ -298,11 +279,7 @@ class PromptVertex(Vertex):
if value:
replace_key = "{" + key + "}"
template = template.replace(replace_key, value)
return (
template
if isinstance(template, str)
else f"{self.vertex_type}({template})"
)
return template if isinstance(template, str) else f"{self.vertex_type}({template})"
except KeyError:
return str(self._built_object)

17
src/backend/langflow/interface/agents/base.py
View file

@ -5,7 +5,7 @@ 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.services.getters import get_settings_service
from langflow.services.deps import get_settings_service
from langflow.template.frontend_node.agents import AgentFrontendNode
from loguru import logger
@ -42,9 +42,7 @@ class AgentCreator(LangChainTypeCreator):
add_function=True,
method_name=self.from_method_nodes[name],
)
return build_template_from_class(
name, self.type_to_loader_dict, add_function=True
)
return build_template_from_class(name, self.type_to_loader_dict, add_function=True)
except ValueError as exc:
raise ValueError("Agent not found") from exc
except AttributeError as exc:
@ -56,15 +54,8 @@ class AgentCreator(LangChainTypeCreator):
names = []
settings_service = get_settings_service()
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_service.settings.AGENTS
or settings_service.settings.DEV
):
agent_name = agent.function_name() if hasattr(agent, "function_name") else agent.__name__
if agent_name in settings_service.settings.AGENTS or settings_service.settings.DEV:
names.append(agent_name)
return names

50
src/backend/langflow/interface/agents/custom.py
View file

@ -68,7 +68,8 @@ class JsonAgent(CustomAgentExecutor):
prompt=prompt,
)
agent = ZeroShotAgent(
llm_chain=llm_chain, allowed_tools=tool_names # type: ignore
llm_chain=llm_chain,
allowed_tools=tool_names, # type: ignore
)
return cls.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
@ -92,11 +93,7 @@ class CSVAgent(CustomAgentExecutor):
@classmethod
def from_toolkit_and_llm(
cls,
path: str,
llm: BaseLanguageModel,
pandas_kwargs: Optional[dict] = None,
**kwargs: Any
cls, path: str, llm: BaseLanguageModel, pandas_kwargs: Optional[dict] = None, **kwargs: Any
):
import pandas as pd # type: ignore
@ -117,7 +114,9 @@ class CSVAgent(CustomAgentExecutor):
)
tool_names = {tool.name for tool in tools}
agent = ZeroShotAgent(
llm_chain=llm_chain, allowed_tools=tool_names, **kwargs # type: ignore
llm_chain=llm_chain,
allowed_tools=tool_names,
**kwargs, # type: ignore
)
return cls.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
@ -141,9 +140,7 @@ class VectorStoreAgent(CustomAgentExecutor):
super().__init__(*args, **kwargs)
@classmethod
def from_toolkit_and_llm(
cls, llm: BaseLanguageModel, vectorstoreinfo: VectorStoreInfo, **kwargs: Any
):
def from_toolkit_and_llm(cls, llm: BaseLanguageModel, vectorstoreinfo: VectorStoreInfo, **kwargs: Any):
"""Construct a vectorstore agent from an LLM and tools."""
toolkit = VectorStoreToolkit(vectorstore_info=vectorstoreinfo, llm=llm)
@ -156,11 +153,11 @@ class VectorStoreAgent(CustomAgentExecutor):
)
tool_names = {tool.name for tool in tools}
agent = ZeroShotAgent(
llm_chain=llm_chain, allowed_tools=tool_names, **kwargs # type: ignore
)
return AgentExecutor.from_agent_and_tools(
agent=agent, tools=tools, verbose=True, handle_parsing_errors=True
llm_chain=llm_chain,
allowed_tools=tool_names,
**kwargs, # type: ignore
)
return AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, handle_parsing_errors=True)
def run(self, *args, **kwargs):
return super().run(*args, **kwargs)
@ -181,9 +178,7 @@ class SQLAgent(CustomAgentExecutor):
super().__init__(*args, **kwargs)
@classmethod
def from_toolkit_and_llm(
cls, llm: BaseLanguageModel, database_uri: str, **kwargs: Any
):
def from_toolkit_and_llm(cls, llm: BaseLanguageModel, database_uri: str, **kwargs: Any):
"""Construct an SQL agent from an LLM and tools."""
db = SQLDatabase.from_uri(database_uri)
toolkit = SQLDatabaseToolkit(db=db, llm=llm)
@ -201,9 +196,7 @@ class SQLAgent(CustomAgentExecutor):
llmchain = LLMChain(
llm=llm,
prompt=PromptTemplate(
template=QUERY_CHECKER, input_variables=["query", "dialect"]
),
prompt=PromptTemplate(template=QUERY_CHECKER, input_variables=["query", "dialect"]),
)
tools = [
@ -226,7 +219,9 @@ class SQLAgent(CustomAgentExecutor):
)
tool_names = {tool.name for tool in tools} # type: ignore
agent = ZeroShotAgent(
llm_chain=llm_chain, allowed_tools=tool_names, **kwargs # type: ignore
llm_chain=llm_chain,
allowed_tools=tool_names,
**kwargs, # type: ignore
)
return AgentExecutor.from_agent_and_tools(
agent=agent,
@ -257,10 +252,7 @@ class VectorStoreRouterAgent(CustomAgentExecutor):
@classmethod
def from_toolkit_and_llm(
cls,
llm: BaseLanguageModel,
vectorstoreroutertoolkit: VectorStoreRouterToolkit,
**kwargs: Any
cls, llm: BaseLanguageModel, vectorstoreroutertoolkit: VectorStoreRouterToolkit, **kwargs: Any
):
"""Construct a vector store router agent from an LLM and tools."""
@ -276,11 +268,11 @@ class VectorStoreRouterAgent(CustomAgentExecutor):
)
tool_names = {tool.name for tool in tools}
agent = ZeroShotAgent(
llm_chain=llm_chain, allowed_tools=tool_names, **kwargs # type: ignore
)
return AgentExecutor.from_agent_and_tools(
agent=agent, tools=tools, verbose=True, handle_parsing_errors=True
llm_chain=llm_chain,
allowed_tools=tool_names,
**kwargs, # type: ignore
)
return AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, handle_parsing_errors=True)
def run(self, *args, **kwargs):
return super().run(*args, **kwargs)

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

@ -2,7 +2,7 @@ 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.getters import get_settings_service
from langflow.services.deps import get_settings_service
from pydantic import BaseModel
from langflow.template.field.base import TemplateField
@ -30,13 +30,8 @@ class LangChainTypeCreator(BaseModel, ABC):
settings_service = get_settings_service()
if self.name_docs_dict is None:
try:
type_settings = getattr(
settings_service.settings, self.type_name.upper()
)
self.name_docs_dict = {
name: value_dict["documentation"]
for name, value_dict in type_settings.items()
}
type_settings = getattr(settings_service.settings, self.type_name.upper())
self.name_docs_dict = {name: value_dict["documentation"] for name, value_dict in type_settings.items()}
except AttributeError as exc:
logger.error(f"Error getting settings for {self.type_name}: {exc}")

18
src/backend/langflow/interface/chains/base.py
View file

@ -3,7 +3,7 @@ from typing import Any, ClassVar, 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.services.getters import get_settings_service
from langflow.services.deps import get_settings_service
from langflow.template.frontend_node.chains import ChainFrontendNode
from loguru import logger
@ -33,8 +33,7 @@ class ChainCreator(LangChainTypeCreator):
if self.type_dict is None:
settings_service = get_settings_service()
self.type_dict: dict[str, Any] = {
chain_name: import_class(f"langchain.chains.{chain_name}")
for chain_name in chains.__all__
chain_name: import_class(f"langchain.chains.{chain_name}") for chain_name in chains.__all__
}
from langflow.interface.chains.custom import CUSTOM_CHAINS
@ -45,8 +44,7 @@ class ChainCreator(LangChainTypeCreator):
self.type_dict = {
name: chain
for name, chain in self.type_dict.items()
if name in settings_service.settings.CHAINS
or settings_service.settings.DEV
if name in settings_service.settings.CHAINS or settings_service.settings.DEV
}
return self.type_dict
@ -61,9 +59,7 @@ class ChainCreator(LangChainTypeCreator):
method_name=self.from_method_nodes[name],
add_function=True,
)
return build_template_from_class(
name, self.type_to_loader_dict, add_function=True
)
return build_template_from_class(name, self.type_to_loader_dict, add_function=True)
except ValueError as exc:
raise ValueError(f"Chain {name} not found: {exc}") from exc
except AttributeError as exc:
@ -73,11 +69,7 @@ class ChainCreator(LangChainTypeCreator):
def to_list(self) -> List[str]:
names = []
for _, chain in self.type_to_loader_dict.items():
chain_name = (
chain.function_name()
if hasattr(chain, "function_name")
else chain.__name__
)
chain_name = chain.function_name() if hasattr(chain, "function_name") else chain.__name__
names.append(chain_name)
return names

16
src/backend/langflow/interface/chains/custom.py
View file

@ -41,9 +41,7 @@ class BaseCustomConversationChain(ConversationChain):
values["template"] = values["template"].format(**format_dict)
values["template"] = values["template"]
values["input_variables"] = extract_input_variables_from_prompt(
values["template"]
)
values["input_variables"] = extract_input_variables_from_prompt(values["template"])
values["prompt"].template = values["template"]
values["prompt"].input_variables = values["input_variables"]
return values
@ -54,9 +52,7 @@ class SeriesCharacterChain(BaseCustomConversationChain):
character: str
series: str
template: Optional[
str
] = """I want you to act like {character} from {series}.
template: Optional[str] = """I want you to act like {character} from {series}.
I want you to respond and answer like {character}. do not write any explanations. only answer like {character}.
You must know all of the knowledge of {character}.
Current conversation:
@ -71,9 +67,7 @@ Human: {input}
class MidJourneyPromptChain(BaseCustomConversationChain):
"""MidJourneyPromptChain is a chain you can use to generate new MidJourney prompts."""
template: Optional[
str
] = """I want you to act as a prompt generator for Midjourney's artificial intelligence program.
template: Optional[str] = """I want you to act as a prompt generator for Midjourney's artificial intelligence program.
Your job is to provide detailed and creative descriptions that will inspire unique and interesting images from the AI.
Keep in mind that the AI is capable of understanding a wide range of language and can interpret abstract concepts, so feel free to be as imaginative and descriptive as possible.
For example, you could describe a scene from a futuristic city, or a surreal landscape filled with strange creatures.
@ -87,9 +81,7 @@ class MidJourneyPromptChain(BaseCustomConversationChain):
class TimeTravelGuideChain(BaseCustomConversationChain):
template: Optional[
str
] = """I want you to act as my time travel guide. You are helpful and creative. I will provide you with the historical period or future time I want to visit and you will suggest the best events, sights, or people to experience. Provide the suggestions and any necessary information.
template: Optional[str] = """I want you to act as my time travel guide. You are helpful and creative. I will provide you with the historical period or future time I want to visit and you will suggest the best events, sights, or people to experience. Provide the suggestions and any necessary information.
Current conversation:
{history}
Human: {input}

36
src/backend/langflow/interface/custom/code_parser.py
View file

@ -1,8 +1,8 @@
import ast
import inspect
import traceback
from typing import Any, Dict, List, Type, Union
from typing import Dict, Any, List, Type, Union
from fastapi import HTTPException
from langflow.interface.custom.schema import CallableCodeDetails, ClassCodeDetails
@ -104,7 +104,7 @@ class CodeParser:
func.args = self.parse_function_args(node)
func.body = self.parse_function_body(node)
return func.dict()
return func.model_dump()
def parse_function_args(self, node: ast.FunctionDef) -> List[Dict[str, Any]]:
"""
@ -127,22 +127,14 @@ class CodeParser:
num_defaults = len(node.args.defaults)
num_missing_defaults = num_args - num_defaults
missing_defaults = [None] * num_missing_defaults
default_values = [
ast.unparse(default).strip("'") if default else None
for default in node.args.defaults
]
default_values = [ast.unparse(default).strip("'") if default else None for default in node.args.defaults]
# Now check all default values to see if there
# are any "None" values in the middle
default_values = [
None if value == "None" else value for value in default_values
]
default_values = [None if value == "None" else value for value in default_values]
defaults = missing_defaults + default_values
args = [
self.parse_arg(arg, default)
for arg, default in zip(node.args.args, defaults)
]
args = [self.parse_arg(arg, default) for arg, default in zip(node.args.args, defaults)]
return args
def parse_varargs(self, node: ast.FunctionDef) -> List[Dict[str, Any]]:
@ -160,17 +152,11 @@ class CodeParser:
"""
Parses the keyword-only arguments of a function or method node.
"""
kw_defaults = [None] * (
len(node.args.kwonlyargs) - len(node.args.kw_defaults)
) + [
ast.unparse(default) if default else None
for default in node.args.kw_defaults
kw_defaults = [None] * (len(node.args.kwonlyargs) - len(node.args.kw_defaults)) + [
ast.unparse(default) if default else None for default in node.args.kw_defaults
]
args = [
self.parse_arg(arg, default)
for arg, default in zip(node.args.kwonlyargs, kw_defaults)
]
args = [self.parse_arg(arg, default) for arg, default in zip(node.args.kwonlyargs, kw_defaults)]
return args
def parse_kwargs(self, node: ast.FunctionDef) -> List[Dict[str, Any]]:
@ -247,16 +233,14 @@ class CodeParser:
else:
class_details.methods.append(method)
self.data["classes"].append(class_details.dict())
self.data["classes"].append(class_details.model_dump())
def parse_global_vars(self, node: ast.Assign) -> None:
"""
Extracts global variables from the code.
"""
global_var = {
"targets": [
t.id if hasattr(t, "id") else ast.dump(t) for t in node.targets
],
"targets": [t.id if hasattr(t, "id") else ast.dump(t) for t in node.targets],
"value": ast.unparse(node.value),
}
self.data["global_vars"].append(global_var)

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

@ -1,9 +1,10 @@
import ast
from typing import Any, ClassVar, Optional
from fastapi import HTTPException
from langflow.utils import validate
from langflow.interface.custom.code_parser import CodeParser
from langflow.utils import validate
class ComponentCodeNullError(HTTPException):
@ -16,9 +17,7 @@ class ComponentFunctionEntrypointNameNullError(HTTPException):
class Component:
ERROR_CODE_NULL: ClassVar[str] = "Python code must be provided."
ERROR_FUNCTION_ENTRYPOINT_NAME_NULL: ClassVar[
str
] = "The name of the entrypoint function must be provided."
ERROR_FUNCTION_ENTRYPOINT_NAME_NULL: ClassVar[str] = "The name of the entrypoint function must be provided."
code: Optional[str] = None
_function_entrypoint_name: str = "build"

1
src/backend/langflow/interface/custom/constants.py
View file

@ -17,6 +17,7 @@ from langflow.field_typing import (
AgentExecutor,
NestedDict,
Data,
Object,
)

47
src/backend/langflow/interface/custom/custom_component.py
View file

@ -1,17 +1,16 @@
from typing import Any, Callable, ClassVar, List, Optional, Union
from uuid import UUID
import yaml
from fastapi import HTTPException
from langflow.field_typing.constants import CUSTOM_COMPONENT_SUPPORTED_TYPES
from langflow.interface.custom.component import Component
from langflow.interface.custom.directory_reader import DirectoryReader
from langflow.services.getters import get_db_service
from langflow.interface.custom.utils import extract_inner_type, extract_union_types
from langflow.utils import validate
from langflow.services.database.utils import session_getter
from langflow.services.database.models.flow import Flow
import yaml
from langflow.services.database.utils import session_getter
from langflow.services.deps import get_db_service
from langflow.utils import validate
class CustomComponent(Component):
@ -54,9 +53,9 @@ class CustomComponent(Component):
reader = DirectoryReader("", False)
for type_hint in TYPE_HINT_LIST:
if reader._is_type_hint_used_in_args(
if reader._is_type_hint_used_in_args(type_hint, code) and not reader._is_type_hint_imported(
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.",
@ -75,20 +74,14 @@ class CustomComponent(Component):
return ""
tree = self.get_code_tree(self.code)
component_classes = [
cls
for cls in tree["classes"]
if self.code_class_base_inheritance in cls["bases"]
]
component_classes = [cls for cls in tree["classes"] if self.code_class_base_inheritance in cls["bases"]]
if not component_classes:
return ""
# Assume the first Component class is the one we're interested in
component_class = component_classes[0]
build_methods = [
method
for method in component_class["methods"]
if method["name"] == self.function_entrypoint_name
method for method in component_class["methods"] if method["name"] == self.function_entrypoint_name
]
if not build_methods:
@ -104,8 +97,7 @@ class CustomComponent(Component):
detail={
"error": "Type hint Error",
"traceback": (
"Prompt type is not supported in the build method."
" Try using PromptTemplate instead."
"Prompt type is not supported in the build method." " Try using PromptTemplate instead."
),
},
)
@ -120,20 +112,14 @@ class CustomComponent(Component):
return []
tree = self.get_code_tree(self.code)
component_classes = [
cls
for cls in tree["classes"]
if self.code_class_base_inheritance in cls["bases"]
]
component_classes = [cls for cls in tree["classes"] if self.code_class_base_inheritance in cls["bases"]]
if not component_classes:
return []
# Assume the first Component class is the one we're interested in
component_class = component_classes[0]
build_methods = [
method
for method in component_class["methods"]
if method["name"] == self.function_entrypoint_name
method for method in component_class["methods"] if method["name"] == self.function_entrypoint_name
]
if not build_methods:
@ -191,8 +177,7 @@ class CustomComponent(Component):
return validate.create_function(self.code, self.function_entrypoint_name)
def load_flow(self, flow_id: str, tweaks: Optional[dict] = None) -> Any:
from langflow.processing.process import build_sorted_vertices
from langflow.processing.process import process_tweaks
from langflow.processing.process import build_sorted_vertices, process_tweaks
db_service = get_db_service()
with session_getter(db_service) as session:
@ -229,11 +214,7 @@ class CustomComponent(Component):
if flow_id:
flow = session.query(Flow).get(flow_id)
elif flow_name:
flow = (
session.query(Flow)
.filter(Flow.name == flow_name)
.filter(Flow.user_id == self.user_id)
).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")

36
src/backend/langflow/interface/custom/directory_reader.py
View file

@ -76,9 +76,7 @@ class DirectoryReader:
for menu in data["menu"]
]
filtered = [menu for menu in items if menu["components"]]
logger.debug(
f'Filtered components {"with errors" if with_errors else ""}: {len(filtered)}'
)
logger.debug(f'Filtered components {"with errors" if with_errors else ""}: {len(filtered)}')
return {"menu": filtered}
def validate_code(self, file_content):
@ -111,9 +109,7 @@ class DirectoryReader:
Walk through the directory path and return a list of all .py files.
"""
if not (safe_path := self.get_safe_path()):
raise CustomComponentPathValueError(
f"The path needs to start with '{self.base_path}'."
)
raise CustomComponentPathValueError(f"The path needs to start with '{self.base_path}'.")
file_list = []
for root, _, files in os.walk(safe_path):
@ -158,9 +154,7 @@ class DirectoryReader:
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
):
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
@ -178,16 +172,14 @@ class DirectoryReader:
and annotation.value.id == type_hint_name
)
def is_type_hint_used_but_not_imported(
self, type_hint_name: str, code: str
) -> bool:
def is_type_hint_used_but_not_imported(self, type_hint_name: str, code: str) -> bool:
"""
Check if a type hint is used but not imported in the given code.
"""
try:
return self._is_type_hint_used_in_args(
return self._is_type_hint_used_in_args(type_hint_name, code) and not self._is_type_hint_imported(
type_hint_name, code
) and not self._is_type_hint_imported(type_hint_name, code)
)
except SyntaxError:
# Returns True if there's something wrong with the code
# TODO : Find a better way to handle this
@ -208,9 +200,9 @@ class DirectoryReader:
return False, "Syntax error"
elif not self.validate_build(file_content):
return False, "Missing build function"
elif self._is_type_hint_used_in_args(
elif self._is_type_hint_used_in_args("Optional", file_content) and not self._is_type_hint_imported(
"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.",
@ -226,9 +218,7 @@ class DirectoryReader:
from the .py files in the directory.
"""
response = {"menu": []}
logger.debug(
"-------------------- Building component menu list --------------------"
)
logger.debug("-------------------- Building component menu list --------------------")
for file_path in file_paths:
menu_name = os.path.basename(os.path.dirname(file_path))
@ -248,9 +238,7 @@ class DirectoryReader:
# first check if it's already CamelCase
if "_" in component_name:
component_name_camelcase = " ".join(
word.title() for word in component_name.split("_")
)
component_name_camelcase = " ".join(word.title() for word in component_name.split("_"))
else:
component_name_camelcase = component_name
@ -266,7 +254,5 @@ class DirectoryReader:
logger.debug(f"Component info: {component_info}")
if menu_result not in response["menu"]:
response["menu"].append(menu_result)
logger.debug(
"-------------------- Component menu list built --------------------"
)
logger.debug("-------------------- Component menu list built --------------------")
return response

18
src/backend/langflow/interface/custom_lists.py
View file

@ -46,34 +46,26 @@ toolkit_type_to_cls_dict: dict[str, Any] = {
# Memories
memory_type_to_cls_dict: dict[str, Any] = {
memory_name: import_class(f"langchain.memory.{memory_name}")
for memory_name in memory.__all__
memory_name: import_class(f"langchain.memory.{memory_name}") for memory_name in memory.__all__
}
# Wrappers
wrapper_type_to_cls_dict: dict[str, Any] = {
wrapper.__name__: wrapper for wrapper in [requests.RequestsWrapper]
}
wrapper_type_to_cls_dict: dict[str, Any] = {wrapper.__name__: wrapper for wrapper in [requests.RequestsWrapper]}
# Embeddings
embedding_type_to_cls_dict: dict[str, Any] = {
embedding_name: import_class(f"langchain.embeddings.{embedding_name}")
for embedding_name in embeddings.__all__
embedding_name: import_class(f"langchain.embeddings.{embedding_name}") for embedding_name in embeddings.__all__
}
# Document Loaders
documentloaders_type_to_cls_dict: dict[str, Any] = {
documentloader_name: import_class(
f"langchain.document_loaders.{documentloader_name}"
)
documentloader_name: import_class(f"langchain.document_loaders.{documentloader_name}")
for documentloader_name in document_loaders.__all__
}
# Text Splitters
textsplitter_type_to_cls_dict: dict[str, Any] = dict(
inspect.getmembers(text_splitter, inspect.isclass)
)
textsplitter_type_to_cls_dict: dict[str, Any] = dict(inspect.getmembers(text_splitter, inspect.isclass))
# merge CUSTOM_AGENTS and CUSTOM_CHAINS
CUSTOM_NODES = {**CUSTOM_AGENTS, **CUSTOM_CHAINS} # type: ignore

5
src/backend/langflow/interface/document_loaders/base.py
View file

@ -1,7 +1,7 @@
from typing import Dict, List, Optional, Type
from langflow.interface.base import LangChainTypeCreator
from langflow.services.getters import get_settings_service
from langflow.services.deps import get_settings_service
from langflow.template.frontend_node.documentloaders import DocumentLoaderFrontNode
from langflow.interface.custom_lists import documentloaders_type_to_cls_dict
@ -35,8 +35,7 @@ class DocumentLoaderCreator(LangChainTypeCreator):
return [
documentloader.__name__
for documentloader in self.type_to_loader_dict.values()
if documentloader.__name__ in settings_service.settings.DOCUMENTLOADERS
or settings_service.settings.DEV
if documentloader.__name__ in settings_service.settings.DOCUMENTLOADERS or settings_service.settings.DEV
]

5
src/backend/langflow/interface/embeddings/base.py
View file

@ -2,7 +2,7 @@ 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.services.getters import get_settings_service
from langflow.services.deps import get_settings_service
from langflow.template.frontend_node.base import FrontendNode
from langflow.template.frontend_node.embeddings import EmbeddingFrontendNode
@ -37,8 +37,7 @@ class EmbeddingCreator(LangChainTypeCreator):
return [
embedding.__name__
for embedding in self.type_to_loader_dict.values()
if embedding.__name__ in settings_service.settings.EMBEDDINGS
or settings_service.settings.DEV
if embedding.__name__ in settings_service.settings.EMBEDDINGS or settings_service.settings.DEV
]

5
src/backend/langflow/interface/importing/utils.py
View file

@ -104,10 +104,7 @@ def import_prompt(prompt: str) -> Type[PromptTemplate]:
def import_wrapper(wrapper: str) -> Any:
"""Import wrapper from wrapper name"""
if (
isinstance(wrapper_creator.type_dict, dict)
and wrapper in wrapper_creator.type_dict
):
if isinstance(wrapper_creator.type_dict, dict) and wrapper in wrapper_creator.type_dict:
return wrapper_creator.type_dict.get(wrapper)

4
src/backend/langflow/interface/initialize/llm.py
View file

@ -2,8 +2,6 @@ def initialize_vertexai(class_object, params):
if credentials_path := params.get("credentials"):
from google.oauth2 import service_account # type: ignore
credentials_object = service_account.Credentials.from_service_account_file(
filename=credentials_path
)
credentials_object = service_account.Credentials.from_service_account_file(filename=credentials_path)
params["credentials"] = credentials_object
return class_object(**params)

104
src/backend/langflow/interface/initialize/loading.py
View file

@ -1,40 +1,37 @@
import json
from typing import TYPE_CHECKING, Any, Callable, Dict, Sequence, Type
import orjson
from typing import Any, Callable, Dict, Sequence, Type, TYPE_CHECKING
from langchain.schema import Document
from langchain.agents import agent as agent_module
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.agents.tools import BaseTool
from langchain.chains.base import Chain
from langchain.document_loaders.base import BaseLoader
from langchain.schema import Document
from langchain.vectorstores.base import VectorStore
from loguru import logger
from pydantic import ValidationError
from langflow.interface.custom_lists import CUSTOM_NODES
from langflow.interface.importing.utils import (
get_function,
get_function_custom,
import_by_type,
)
from langflow.interface.initialize.llm import initialize_vertexai
from langflow.interface.initialize.utils import (
handle_format_kwargs,
handle_node_type,
handle_partial_variables,
)
from langflow.interface.initialize.vector_store import vecstore_initializer
from pydantic import ValidationError
from langflow.interface.importing.utils import (
get_function,
get_function_custom,
import_by_type,
)
from langflow.interface.custom_lists import CUSTOM_NODES
from langflow.interface.agents.base import agent_creator
from langflow.interface.toolkits.base import toolkits_creator
from langflow.interface.chains.base import chain_creator
from langflow.interface.output_parsers.base import output_parser_creator
from langflow.interface.retrievers.base import retriever_creator
from langflow.interface.wrappers.base import wrapper_creator
from langflow.interface.toolkits.base import toolkits_creator
from langflow.interface.utils import load_file_into_dict
from langflow.interface.wrappers.base import wrapper_creator
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 loguru import logger
if TYPE_CHECKING:
from langflow import CustomComponent
@ -44,15 +41,10 @@ def build_vertex_in_params(params: Dict) -> Dict:
from langflow.graph.vertex.base import Vertex
# If any of the values in params is a Vertex, we will build it
return {
key: value.build() if isinstance(value, Vertex) else value
for key, value in params.items()
}
return {key: value.build() if isinstance(value, Vertex) else value for key, value in params.items()}
def instantiate_class(
node_type: str, base_type: str, params: Dict, user_id=None
) -> 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)
@ -64,9 +56,7 @@ def instantiate_class(
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, user_id=user_id
)
return instantiate_based_on_type(class_object, base_type, node_type, params, user_id=user_id)
def convert_params_to_sets(params):
@ -194,9 +184,7 @@ def instantiate_memory(node_type, class_object, params):
# I want to catch a specific attribute error that happens
# when the object does not have a cursor attribute
except Exception as exc:
if "object has no attribute 'cursor'" in str(
exc
) or 'object has no field "conn"' in str(exc):
if "object has no attribute 'cursor'" in str(exc) or 'object has no field "conn"' in str(exc):
raise AttributeError(
(
"Failed to build connection to database."
@ -218,6 +206,8 @@ def instantiate_retriever(node_type, class_object, params):
def instantiate_chains(node_type, class_object: Type[Chain], params: Dict):
from langflow.interface.chains.base import chain_creator
if "retriever" in params and hasattr(params["retriever"], "as_retriever"):
params["retriever"] = params["retriever"].as_retriever()
if node_type in chain_creator.from_method_nodes:
@ -230,14 +220,14 @@ def instantiate_chains(node_type, class_object: Type[Chain], params: Dict):
def instantiate_agent(node_type, class_object: Type[agent_module.Agent], params: Dict):
from langflow.interface.agents.base import agent_creator
if node_type in agent_creator.from_method_nodes:
method = agent_creator.from_method_nodes[node_type]
if class_method := getattr(class_object, method, None):
agent = class_method(**params)
tools = params.get("tools", [])
return AgentExecutor.from_agent_and_tools(
agent=agent, tools=tools, handle_parsing_errors=True
)
return AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, handle_parsing_errors=True)
return load_agent_executor(class_object, params)
@ -290,11 +280,7 @@ def instantiate_embedding(node_type, class_object, params: Dict):
try:
return class_object(**params)
except ValidationError:
params = {
key: value
for key, value in params.items()
if key in class_object.model_fields
}
params = {key: value for key, value in params.items() if key in class_object.model_fields}
return class_object(**params)
@ -306,9 +292,7 @@ def instantiate_vectorstore(class_object: Type[VectorStore], params: Dict):
if "texts" in params:
params["documents"] = params.pop("texts")
if "documents" in params:
params["documents"] = [
doc for doc in params["documents"] if isinstance(doc, Document)
]
params["documents"] = [doc for doc in params["documents"] if isinstance(doc, Document)]
if initializer := vecstore_initializer.get(class_object.__name__):
vecstore = initializer(class_object, params)
else:
@ -323,9 +307,7 @@ def instantiate_vectorstore(class_object: Type[VectorStore], params: Dict):
return vecstore
def instantiate_documentloader(
node_type: str, class_object: Type[BaseLoader], params: Dict
):
def instantiate_documentloader(node_type: str, class_object: Type[BaseLoader], params: Dict):
if "file_filter" in params:
# file_filter will be a string but we need a function
# that will be used to filter the files using file_filter
@ -334,17 +316,13 @@ def instantiate_documentloader(
# in x and if it is, we will return True
file_filter = params.pop("file_filter")
extensions = file_filter.split(",")
params["file_filter"] = lambda x: any(
extension.strip() in x for extension in extensions
)
params["file_filter"] = lambda x: any(extension.strip() in x for extension in extensions)
metadata = params.pop("metadata", None)
if metadata and isinstance(metadata, str):
try:
metadata = orjson.loads(metadata)
except json.JSONDecodeError as exc:
raise ValueError(
"The metadata you provided is not a valid JSON string."
) from exc
raise ValueError("The metadata you provided is not a valid JSON string.") from exc
if node_type == "WebBaseLoader":
if web_path := params.pop("web_path", None):
@ -377,16 +355,12 @@ def instantiate_textsplitter(
"Try changing the chunk_size of the Text Splitter."
) from exc
if (
"separator_type" in params and params["separator_type"] == "Text"
) or "separator_type" not in params:
if ("separator_type" in params and params["separator_type"] == "Text") or "separator_type" not in params:
params.pop("separator_type", None)
# separators might come in as an escaped string like \\n
# so we need to convert it to a string
if "separators" in params:
params["separators"] = (
params["separators"].encode().decode("unicode-escape")
)
params["separators"] = params["separators"].encode().decode("unicode-escape")
text_splitter = class_object(**params)
else:
from langchain.text_splitter import Language
@ -413,8 +387,7 @@ def replace_zero_shot_prompt_with_prompt_template(nodes):
tools = [
tool
for tool in nodes
if tool["type"] != "chatOutputNode"
and "Tool" in tool["data"]["node"]["base_classes"]
if tool["type"] != "chatOutputNode" and "Tool" in tool["data"]["node"]["base_classes"]
]
node["data"] = build_prompt_template(prompt=node["data"], tools=tools)
break
@ -428,9 +401,7 @@ def load_agent_executor(agent_class: type[agent_module.Agent], params, **kwargs)
# agent has hidden args for memory. might need to be support
# memory = params["memory"]
# if allowed_tools is not a list or set, make it a list
if not isinstance(allowed_tools, (list, set)) and isinstance(
allowed_tools, BaseTool
):
if not isinstance(allowed_tools, (list, set)) and isinstance(allowed_tools, BaseTool):
allowed_tools = [allowed_tools]
tool_names = [tool.name for tool in allowed_tools]
# Agent class requires an output_parser but Agent classes
@ -458,10 +429,7 @@ def build_prompt_template(prompt, tools):
format_instructions = prompt["node"]["template"]["format_instructions"]["value"]
tool_strings = "\n".join(
[
f"{tool['data']['node']['name']}: {tool['data']['node']['description']}"
for tool in tools
]
[f"{tool['data']['node']['name']}: {tool['data']['node']['description']}" for tool in tools]
)
tool_names = ", ".join([tool["data"]["node"]["name"] for tool in tools])
format_instructions = format_instructions.format(tool_names=tool_names)

15
src/backend/langflow/interface/initialize/utils.py
View file

@ -30,9 +30,7 @@ def check_tools_in_params(params: Dict):
def instantiate_from_template(class_object, params: Dict):
from_template_params = {
"template": params.pop("prompt", params.pop("template", ""))
}
from_template_params = {"template": params.pop("prompt", params.pop("template", ""))}
if not from_template_params.get("template"):
raise ValueError("Prompt template is required")
return class_object.from_template(**from_template_params)
@ -48,9 +46,7 @@ def handle_format_kwargs(prompt, params: Dict):
def handle_partial_variables(prompt, format_kwargs: Dict):
partial_variables = format_kwargs.copy()
partial_variables = {
key: value for key, value in partial_variables.items() if value
}
partial_variables = {key: value for key, value in partial_variables.items() if value}
# Remove handle_keys otherwise LangChain raises an error
partial_variables.pop("handle_keys", None)
if partial_variables and hasattr(prompt, "partial"):
@ -62,9 +58,7 @@ def handle_variable(params: Dict, input_variable: str, format_kwargs: Dict):
variable = params[input_variable]
if isinstance(variable, str):
format_kwargs[input_variable] = variable
elif isinstance(variable, BaseOutputParser) and hasattr(
variable, "get_format_instructions"
):
elif isinstance(variable, BaseOutputParser) and hasattr(variable, "get_format_instructions"):
format_kwargs[input_variable] = variable.get_format_instructions()
elif is_instance_of_list_or_document(variable):
format_kwargs = format_document(variable, input_variable, format_kwargs)
@ -107,8 +101,7 @@ def try_to_load_json(content):
def needs_handle_keys(variable):
return is_instance_of_list_or_document(variable) or (
isinstance(variable, BaseOutputParser)
and hasattr(variable, "get_format_instructions")
isinstance(variable, BaseOutputParser) and hasattr(variable, "get_format_instructions")
)

22
src/backend/langflow/interface/initialize/vector_store.py
View file

@ -17,9 +17,7 @@ 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,
If any of them is not an empty list, return True, else return False"""
return ("documents" in params and params["documents"]) or (
"texts" in params and params["texts"]
)
return ("documents" in params and params["documents"]) or ("texts" in params and params["texts"])
def initialize_mongodb(class_object: Type[MongoDBAtlasVectorSearch], params: dict):
@ -31,9 +29,7 @@ def initialize_mongodb(class_object: Type[MongoDBAtlasVectorSearch], params: dic
from pymongo import MongoClient
import certifi
client: MongoClient = MongoClient(
MONGODB_ATLAS_CLUSTER_URI, tlsCAFile=certifi.where()
)
client: MongoClient = MongoClient(MONGODB_ATLAS_CLUSTER_URI, tlsCAFile=certifi.where())
db_name = params.pop("db_name", None)
collection_name = params.pop("collection_name", None)
if not db_name or not collection_name:
@ -141,9 +137,7 @@ def initialize_pinecone(class_object: Type[Pinecone], params: dict):
pinecone_env = os.getenv("PINECONE_ENV")
if pinecone_api_key is None or pinecone_env is None:
raise ValueError(
"Pinecone API key and environment must be provided in the params"
)
raise ValueError("Pinecone API key and environment must be provided in the params")
# initialize pinecone
pinecone.init(
@ -177,19 +171,13 @@ def initialize_chroma(class_object: Type[Chroma], params: dict):
import chromadb # type: ignore
settings_params = {
key: params[key]
for key, value_ in params.items()
if key.startswith("chroma_server_") and value_
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_")
}
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):

2
src/backend/langflow/interface/listing.py
View file

@ -1,4 +1,4 @@
from langflow.services.getters import get_settings_service
from langflow.services.deps import get_settings_service
from langflow.utils.lazy_load import LazyLoadDictBase

5
src/backend/langflow/interface/llms/base.py
View file

@ -2,7 +2,7 @@ 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.services.getters import get_settings_service
from langflow.services.deps import get_settings_service
from langflow.template.frontend_node.llms import LLMFrontendNode
from loguru import logger
@ -38,8 +38,7 @@ class LLMCreator(LangChainTypeCreator):
return [
llm.__name__
for llm in self.type_to_loader_dict.values()
if llm.__name__ in settings_service.settings.LLMS
or settings_service.settings.DEV
if llm.__name__ in settings_service.settings.LLMS or settings_service.settings.DEV
]

5
src/backend/langflow/interface/memories/base.py
View file

@ -2,7 +2,7 @@ from typing import ClassVar, Dict, List, Optional, Type
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.custom_lists import memory_type_to_cls_dict
from langflow.services.getters import get_settings_service
from langflow.services.deps import get_settings_service
from langflow.template.frontend_node.base import FrontendNode
from langflow.template.frontend_node.memories import MemoryFrontendNode
@ -53,8 +53,7 @@ class MemoryCreator(LangChainTypeCreator):
return [
memory.__name__
for memory in self.type_to_loader_dict.values()
if memory.__name__ in settings_service.settings.MEMORIES
or settings_service.settings.DEV
if memory.__name__ in settings_service.settings.MEMORIES or settings_service.settings.DEV
]

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

@ -4,7 +4,7 @@ from langchain import output_parsers
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.importing.utils import import_class
from langflow.services.getters import get_settings_service
from langflow.services.deps import get_settings_service
from langflow.template.frontend_node.output_parsers import OutputParserFrontendNode
from loguru import logger
@ -26,17 +26,14 @@ class OutputParserCreator(LangChainTypeCreator):
if self.type_dict is None:
settings_service = get_settings_service()
self.type_dict = {
output_parser_name: import_class(
f"langchain.output_parsers.{output_parser_name}"
)
output_parser_name: import_class(f"langchain.output_parsers.{output_parser_name}")
# if output_parser_name is not lower case it is a class
for output_parser_name in output_parsers.__all__
}
self.type_dict = {
name: output_parser
for name, output_parser in self.type_dict.items()
if name in settings_service.settings.OUTPUT_PARSERS
or settings_service.settings.DEV
if name in settings_service.settings.OUTPUT_PARSERS or settings_service.settings.DEV
}
return self.type_dict

5
src/backend/langflow/interface/prompts/base.py
View file

@ -5,7 +5,7 @@ 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.services.getters import get_settings_service
from langflow.services.deps import get_settings_service
from langflow.template.frontend_node.prompts import PromptFrontendNode
from loguru import logger
@ -36,8 +36,7 @@ class PromptCreator(LangChainTypeCreator):
self.type_dict = {
name: prompt
for name, prompt in self.type_dict.items()
if name in settings_service.settings.PROMPTS
or settings_service.settings.DEV
if name in settings_service.settings.PROMPTS or settings_service.settings.DEV
}
return self.type_dict

12
src/backend/langflow/interface/prompts/custom.py
View file

@ -42,17 +42,13 @@ class BaseCustomPrompt(PromptTemplate):
values["template"] = values["template"].format(**format_dict)
values["template"] = values["template"]
values["input_variables"] = extract_input_variables_from_prompt(
values["template"]
)
values["input_variables"] = extract_input_variables_from_prompt(values["template"])
return values
class SeriesCharacterPrompt(BaseCustomPrompt):
# Add a very descriptive description for the prompt generator
description: Optional[
str
] = "A prompt that asks the AI to act like a character from a series."
description: Optional[str] = "A prompt that asks the AI to act like a character from a series."
character: str
series: str
template: str = """I want you to act like {character} from {series}.
@ -68,6 +64,4 @@ Human: {input}
input_variables: List[str] = ["character", "series"]
CUSTOM_PROMPTS: Dict[str, Type[BaseCustomPrompt]] = {
"SeriesCharacterPrompt": SeriesCharacterPrompt
}
CUSTOM_PROMPTS: Dict[str, Type[BaseCustomPrompt]] = {"SeriesCharacterPrompt": SeriesCharacterPrompt}

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

@ -4,7 +4,7 @@ from langchain import retrievers
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.importing.utils import import_class
from langflow.services.getters import get_settings_service
from langflow.services.deps import get_settings_service
from langflow.template.frontend_node.retrievers import RetrieverFrontendNode
from loguru import logger
@ -42,9 +42,7 @@ class RetrieverCreator(LangChainTypeCreator):
method_name=self.from_method_nodes[name],
)
else:
return build_template_from_class(
name, type_to_cls_dict=self.type_to_loader_dict
)
return build_template_from_class(name, type_to_cls_dict=self.type_to_loader_dict)
except ValueError as exc:
raise ValueError(f"Retriever {name} not found") from exc
except AttributeError as exc:
@ -56,8 +54,7 @@ class RetrieverCreator(LangChainTypeCreator):
return [
retriever
for retriever in self.type_to_loader_dict.keys()
if retriever in settings_service.settings.RETRIEVERS
or settings_service.settings.DEV
if retriever in settings_service.settings.RETRIEVERS or settings_service.settings.DEV
]

4
src/backend/langflow/interface/run.py
View file

@ -4,9 +4,7 @@ from loguru import logger
from uuid import UUID
def build_sorted_vertices(
data_graph, user_id: Optional[Union[str, UUID]] = None
) -> Tuple[Graph, Dict]:
def build_sorted_vertices(data_graph, user_id: Optional[Union[str, UUID]] = None) -> Tuple[Graph, Dict]:
"""
Build langchain object from data_graph.
"""

5
src/backend/langflow/interface/text_splitters/base.py
View file

@ -1,7 +1,7 @@
from typing import Dict, List, Optional, Type
from langflow.interface.base import LangChainTypeCreator
from langflow.services.getters import get_settings_service
from langflow.services.deps import get_settings_service
from langflow.template.frontend_node.textsplitters import TextSplittersFrontendNode
from langflow.interface.custom_lists import textsplitter_type_to_cls_dict
@ -35,8 +35,7 @@ class TextSplitterCreator(LangChainTypeCreator):
return [
textsplitter.__name__
for textsplitter in self.type_to_loader_dict.values()
if textsplitter.__name__ in settings_service.settings.TEXTSPLITTERS
or settings_service.settings.DEV
if textsplitter.__name__ in settings_service.settings.TEXTSPLITTERS or settings_service.settings.DEV
]

13
src/backend/langflow/interface/toolkits/base.py
View file

@ -4,7 +4,7 @@ from langchain.agents import agent_toolkits
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.importing.utils import import_class, import_module
from langflow.services.getters import get_settings_service
from langflow.services.deps import get_settings_service
from loguru import logger
from langflow.utils.util import build_template_from_class
@ -32,13 +32,10 @@ class ToolkitCreator(LangChainTypeCreator):
if self.type_dict is None:
settings_service = get_settings_service()
self.type_dict = {
toolkit_name: import_class(
f"langchain.agents.agent_toolkits.{toolkit_name}"
)
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_service.settings.TOOLKITS
if not toolkit_name.islower() and toolkit_name in settings_service.settings.TOOLKITS
}
return self.type_dict
@ -61,9 +58,7 @@ class ToolkitCreator(LangChainTypeCreator):
def get_create_function(self, name: str) -> Callable:
if loader_name := self.create_functions.get(name):
return import_module(
f"from langchain.agents.agent_toolkits import {loader_name[0]}"
)
return import_module(f"from langchain.agents.agent_toolkits import {loader_name[0]}")
else:
raise ValueError("Toolkit not found")

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

@ -15,7 +15,7 @@ from langflow.interface.tools.constants import (
OTHER_TOOLS,
)
from langflow.interface.tools.util import get_tool_params
from langflow.services.getters import get_settings_service
from langflow.services.deps import get_settings_service
from langflow.template.field.base import TemplateField
from langflow.template.template.base import Template
@ -32,9 +32,7 @@ TOOL_INPUTS = {
placeholder="",
value="",
),
"llm": TemplateField(
field_type="BaseLanguageModel", required=True, is_list=False, show=True
),
"llm": TemplateField(field_type="BaseLanguageModel", required=True, is_list=False, show=True),
"func": TemplateField(
field_type="Callable",
required=True,
@ -81,10 +79,7 @@ class ToolCreator(LangChainTypeCreator):
tool_name = tool_params.get("name") or tool
if (
tool_name in settings_service.settings.TOOLS
or settings_service.settings.DEV
):
if tool_name in settings_service.settings.TOOLS or settings_service.settings.DEV:
if tool_name == "JsonSpec":
tool_params["path"] = tool_params.pop("dict_") # type: ignore
all_tools[tool_name] = {

12
src/backend/langflow/interface/tools/util.py
View file

@ -21,16 +21,12 @@ def get_func_tool_params(func, **kwargs) -> Union[Dict, None]:
for keyword in tool.keywords:
if keyword.arg == "name":
try:
tool_params["name"] = ast.literal_eval(
keyword.value
)
tool_params["name"] = ast.literal_eval(keyword.value)
except ValueError:
break
elif keyword.arg == "description":
try:
tool_params["description"] = ast.literal_eval(
keyword.value
)
tool_params["description"] = ast.literal_eval(keyword.value)
except ValueError:
continue
@ -43,9 +39,7 @@ def get_func_tool_params(func, **kwargs) -> Union[Dict, None]:
else:
# get the class object from the return statement
try:
class_obj = eval(
compile(ast.Expression(tool), "<string>", "eval")
)
class_obj = eval(compile(ast.Expression(tool), "<string>", "eval"))
except Exception:
return None

3
src/backend/langflow/interface/types.py
View file

@ -7,6 +7,8 @@ from typing import Any, Dict, List, Optional, Union
from uuid import UUID
from fastapi import HTTPException
from loguru import logger
from langflow.api.utils import get_new_key
from langflow.field_typing.constants import CUSTOM_COMPONENT_SUPPORTED_TYPES
from langflow.interface.agents.base import agent_creator
@ -33,7 +35,6 @@ from langflow.template.field.base import TemplateField
from langflow.template.frontend_node.constants import CLASSES_TO_REMOVE
from langflow.template.frontend_node.custom_components import CustomComponentFrontendNode
from langflow.utils.util import get_base_classes
from loguru import logger
# Used to get the base_classes list

8
src/backend/langflow/interface/utilities/base.py
View file

@ -1,14 +1,13 @@
from typing import Dict, List, Optional, Type
from langchain import utilities
from loguru import logger
from langflow.custom.customs import get_custom_nodes
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.importing.utils import import_class
from langflow.services.getters import get_settings_service
from langflow.services.deps import get_settings_service
from langflow.template.frontend_node.utilities import UtilitiesFrontendNode
from loguru import logger
from langflow.utils.util import build_template_from_class
@ -41,8 +40,7 @@ class UtilityCreator(LangChainTypeCreator):
self.type_dict = {
name: utility
for name, utility in self.type_dict.items()
if name in settings_service.settings.UTILITIES
or settings_service.settings.DEV
if name in settings_service.settings.UTILITIES or settings_service.settings.DEV
}
return self.type_dict

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

@ -10,7 +10,7 @@ from langchain.base_language import BaseLanguageModel
from PIL.Image import Image
from loguru import logger
from langflow.services.chat.config import ChatConfig
from langflow.services.getters import get_settings_service
from langflow.services.deps import get_settings_service
def load_file_into_dict(file_path: str) -> dict:
@ -43,9 +43,7 @@ def try_setting_streaming_options(langchain_object):
llm = None
if hasattr(langchain_object, "llm"):
llm = langchain_object.llm
elif hasattr(langchain_object, "llm_chain") and hasattr(
langchain_object.llm_chain, "llm"
):
elif hasattr(langchain_object, "llm_chain") and hasattr(langchain_object.llm_chain, "llm"):
llm = langchain_object.llm_chain.llm
if isinstance(llm, BaseLanguageModel):
@ -79,9 +77,7 @@ def set_langchain_cache(settings):
if cache_type := os.getenv("LANGFLOW_LANGCHAIN_CACHE"):
try:
cache_class = import_class(
f"langchain.cache.{cache_type or settings.LANGCHAIN_CACHE}"
)
cache_class = import_class(f"langchain.cache.{cache_type or settings.LANGCHAIN_CACHE}")
logger.debug(f"Setting up LLM caching with {cache_class.__name__}")
langchain.llm_cache = cache_class()

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

@ -4,7 +4,7 @@ from langchain import vectorstores
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.importing.utils import import_class
from langflow.services.getters import get_settings_service
from langflow.services.deps import get_settings_service
from langflow.template.frontend_node.vectorstores import VectorStoreFrontendNode
from loguru import logger
@ -22,9 +22,7 @@ class VectorstoreCreator(LangChainTypeCreator):
def type_to_loader_dict(self) -> Dict:
if self.type_dict is None:
self.type_dict: dict[str, Any] = {
vectorstore_name: import_class(
f"langchain.vectorstores.{vectorstore_name}"
)
vectorstore_name: import_class(f"langchain.vectorstores.{vectorstore_name}")
for vectorstore_name in vectorstores.__all__
}
return self.type_dict
@ -48,8 +46,7 @@ class VectorstoreCreator(LangChainTypeCreator):
return [
vectorstore
for vectorstore in self.type_to_loader_dict.keys()
if vectorstore in settings_service.settings.VECTORSTORES
or settings_service.settings.DEV
if vectorstore in settings_service.settings.VECTORSTORES or settings_service.settings.DEV
]

3
src/backend/langflow/interface/wrappers/base.py
View file

@ -16,8 +16,7 @@ class WrapperCreator(LangChainTypeCreator):
def type_to_loader_dict(self) -> Dict:
if self.type_dict is None:
self.type_dict = {
wrapper.__name__: wrapper
for wrapper in [requests.TextRequestsWrapper, sql_database.SQLDatabase]
wrapper.__name__: wrapper for wrapper in [requests.TextRequestsWrapper, sql_database.SQLDatabase]
}
return self.type_dict

27
src/backend/langflow/main.py
View file

@ -1,19 +1,16 @@
from pathlib import Path
from typing import Optional
from fastapi import FastAPI
from urllib.parse import urlencode
from fastapi import FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import FileResponse
from fastapi.staticfiles import StaticFiles
from langflow.api import router
from langflow.interface.utils import setup_llm_caching
from langflow.services.utils import initialize_services
from langflow.services.plugins.langfuse import LangfuseInstance
from langflow.services.utils import (
teardown_services,
)
from langflow.services.utils import initialize_services, teardown_services
from langflow.utils.logger import configure
@ -23,7 +20,6 @@ def create_app():
configure()
app = FastAPI()
origins = ["*"]
app.add_middleware(
@ -34,6 +30,16 @@ def create_app():
allow_headers=["*"],
)
@app.middleware("http")
async def flatten_query_string_lists(request: Request, call_next):
flattened = []
for key, value in request.query_params.multi_items():
flattened.extend((key, entry) for entry in value.split(","))
request.scope["query_string"] = urlencode(flattened, doseq=True).encode("utf-8")
return await call_next(request)
@app.get("/health")
def health():
return {"status": "ok"}
@ -78,9 +84,7 @@ def get_static_files_dir():
return frontend_path / "frontend"
def setup_app(
static_files_dir: Optional[Path] = None, backend_only: bool = False
) -> 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:
@ -96,6 +100,7 @@ def setup_app(
if __name__ == "__main__":
import uvicorn
from langflow.__main__ import get_number_of_workers
configure()

18
src/backend/langflow/processing/base.py
View file

@ -34,9 +34,7 @@ def get_langfuse_callback(trace_id):
if langfuse := LangfuseInstance.get():
logger.debug("Langfuse credentials found")
try:
trace = langfuse.trace(
CreateTrace(name="langflow-" + trace_id, id=trace_id)
)
trace = langfuse.trace(CreateTrace(name="langflow-" + trace_id, id=trace_id))
return trace.getNewHandler()
except Exception as exc:
logger.error(f"Error initializing langfuse callback: {exc}")
@ -44,9 +42,7 @@ def get_langfuse_callback(trace_id):
return None
def flush_langfuse_callback_if_present(
callbacks: List[Union[BaseCallbackHandler, "CallbackHandler"]]
):
def flush_langfuse_callback_if_present(callbacks: List[Union[BaseCallbackHandler, "CallbackHandler"]]):
"""
If langfuse callback is present, run callback.langfuse.flush()
"""
@ -88,15 +84,9 @@ async def get_result_and_steps(langchain_object, inputs: Union[dict, str], **kwa
# 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 []
)
intermediate_steps = output.get("intermediate_steps", []) if isinstance(output, dict) else []
result = (
output.get(langchain_object.output_keys[0])
if isinstance(output, dict)
else output
)
result = output.get(langchain_object.output_keys[0]) if isinstance(output, dict) else output
try:
thought = format_actions(intermediate_steps) if intermediate_steps else ""
except Exception as exc:

38
src/backend/langflow/processing/process.py
View file

@ -6,7 +6,7 @@ from langflow.interface.run import (
get_memory_key,
update_memory_keys,
)
from langflow.services.getters import get_session_service
from langflow.services.deps import get_session_service
from loguru import logger
from langflow.graph import Graph
from langchain.chains.base import Chain
@ -112,9 +112,7 @@ def load_langchain_object(
logger.debug("Loaded LangChain object")
if langchain_object is None:
raise ValueError(
"There was an error loading the langchain_object. Please, check all the nodes and try again."
)
raise ValueError("There was an error loading the langchain_object. Please, check all the nodes and try again.")
return langchain_object, artifacts, session_id
@ -164,9 +162,7 @@ async def process_graph_cached(
if clear_cache:
session_service.clear_session(session_id)
if session_id is None:
session_id = session_service.generate_key(
session_id=session_id, data_graph=data_graph
)
session_id = session_service.generate_key(session_id=session_id, data_graph=data_graph)
# Load the graph using SessionService
graph, artifacts = session_service.load_session(session_id, data_graph)
built_object = graph.build()
@ -179,9 +175,7 @@ async def process_graph_cached(
return Result(result=result, session_id=session_id)
def load_flow_from_json(
flow: Union[Path, str, dict], tweaks: Optional[dict] = None, build=True
):
def load_flow_from_json(flow: Union[Path, str, dict], tweaks: Optional[dict] = None, build=True):
"""
Load flow from a JSON file or a JSON object.
@ -198,9 +192,7 @@ def load_flow_from_json(
elif isinstance(flow, dict):
flow_graph = flow
else:
raise TypeError(
"Input must be either a file path (str) or a JSON object (dict)"
)
raise TypeError("Input must be either a file path (str) or a JSON object (dict)")
graph_data = flow_graph["data"]
if tweaks is not None:
@ -226,18 +218,14 @@ def load_flow_from_json(
return graph
def validate_input(
graph_data: Dict[str, Any], tweaks: Dict[str, Dict[str, Any]]
) -> List[Dict[str, Any]]:
def validate_input(graph_data: Dict[str, Any], tweaks: Dict[str, Dict[str, Any]]) -> List[Dict[str, Any]]:
if not isinstance(graph_data, dict) or not isinstance(tweaks, dict):
raise ValueError("graph_data and tweaks should be dictionaries")
nodes = graph_data.get("data", {}).get("nodes") or graph_data.get("nodes")
if not isinstance(nodes, list):
raise ValueError(
"graph_data should contain a list of nodes under 'data' key or directly under 'nodes' key"
)
raise ValueError("graph_data should contain a list of nodes under 'data' key or directly under 'nodes' key")
return nodes
@ -246,9 +234,7 @@ def apply_tweaks(node: Dict[str, Any], node_tweaks: Dict[str, Any]) -> None:
template_data = node.get("data", {}).get("node", {}).get("template")
if not isinstance(template_data, dict):
logger.warning(
f"Template data for node {node.get('id')} should be a dictionary"
)
logger.warning(f"Template data for node {node.get('id')} should be a dictionary")
return
for tweak_name, tweak_value in node_tweaks.items():
@ -257,9 +243,7 @@ def apply_tweaks(node: Dict[str, Any], node_tweaks: Dict[str, Any]) -> None:
template_data[tweak_name][key] = tweak_value
def process_tweaks(
graph_data: Dict[str, Any], tweaks: Dict[str, Dict[str, Any]]
) -> Dict[str, Any]:
def process_tweaks(graph_data: Dict[str, Any], tweaks: Dict[str, Dict[str, Any]]) -> Dict[str, Any]:
"""
This function is used to tweak the graph data using the node id and the tweaks dict.
@ -280,8 +264,6 @@ def process_tweaks(
if node_tweaks := tweaks.get(node_id):
apply_tweaks(node, node_tweaks)
else:
logger.warning(
"Each node should be a dictionary with an 'id' key of type str"
)
logger.warning("Each node should be a dictionary with an 'id' key of type str")
return graph_data

6
src/backend/langflow/server.py
View file

@ -10,11 +10,7 @@ class LangflowApplication(BaseApplication):
super().__init__()
def load_config(self):
config = {
key: value
for key, value in self.options.items()
if key in self.cfg.settings and value is not None
}
config = {key: value for key, value in self.options.items() if key in self.cfg.settings and value is not None}
for key, value in config.items():
self.cfg.set(key.lower(), value)

2
src/backend/langflow/services/auth/service.py
View file

@ -2,7 +2,7 @@ from langflow.services.base import Service
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from langflow.services.settings.manager import SettingsService
from langflow.services.settings.service import SettingsService
class AuthService(Service):

73
src/backend/langflow/services/auth/utils.py
View file

@ -12,19 +12,16 @@ from langflow.services.database.models.user.crud import (
get_user_by_username,
update_user_last_login_at,
)
from langflow.services.getters import get_session, get_settings_service
from langflow.services.deps import get_session, get_settings_service
from sqlmodel import Session
from cryptography.fernet import Fernet
oauth2_login = OAuth2PasswordBearer(tokenUrl="api/v1/login", auto_error=False)
API_KEY_NAME = "x-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
)
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
@ -141,23 +138,17 @@ def get_current_active_user(current_user: Annotated[User, Depends(get_current_us
return current_user
def get_current_active_superuser(
current_user: Annotated[User, Depends(get_current_user)]
) -> 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"
)
raise HTTPException(status_code=400, detail="The user doesn't have enough privileges")
return current_user
def verify_password(plain_password, hashed_password):
settings_service = get_settings_service()
return settings_service.auth_settings.pwd_context.verify(
plain_password, hashed_password
)
return settings_service.auth_settings.pwd_context.verify(plain_password, hashed_password)
def get_password_hash(password):
@ -246,22 +237,16 @@ def get_user_id_from_token(token: str) -> UUID:
return UUID(int=0)
def create_user_tokens(
user_id: UUID, db: Session = Depends(get_session), update_last_login: bool = False
) -> dict:
def create_user_tokens(user_id: UUID, db: Session = Depends(get_session), update_last_login: bool = False) -> dict:
settings_service = get_settings_service()
access_token_expires = timedelta(
minutes=settings_service.auth_settings.ACCESS_TOKEN_EXPIRE_MINUTES
)
access_token_expires = timedelta(minutes=settings_service.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_service.auth_settings.REFRESH_TOKEN_EXPIRE_MINUTES
)
refresh_token_expires = timedelta(minutes=settings_service.auth_settings.REFRESH_TOKEN_EXPIRE_MINUTES)
refresh_token = create_token(
data={"sub": str(user_id), "type": "rf"},
expires_delta=refresh_token_expires,
@ -291,9 +276,7 @@ def create_refresh_token(refresh_token: str, db: Session = Depends(get_session))
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"
)
raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid refresh token")
return create_user_tokens(user_id, db)
@ -304,9 +287,7 @@ def create_refresh_token(refresh_token: str, db: Session = Depends(get_session))
) from e
def authenticate_user(
username: str, password: str, db: Session = Depends(get_session)
) -> Optional[User]:
def authenticate_user(username: str, password: str, db: Session = Depends(get_session)) -> Optional[User]:
user = get_user_by_username(db, username)
if not user:
@ -318,3 +299,33 @@ def authenticate_user(
raise HTTPException(status_code=400, detail="Inactive user")
return user if verify_password(password, user.password) else None
def add_padding(s):
# Calculate the number of padding characters needed
padding_needed = 4 - len(s) % 4
return s + "=" * padding_needed
def get_fernet(settings_service=Depends(get_settings_service)):
SECRET_KEY = settings_service.auth_settings.SECRET_KEY
# It's important that your secret key is 32 url-safe base64-encoded bytes
padded_secret_key = add_padding(SECRET_KEY)
fernet = Fernet(padded_secret_key)
return fernet
def encrypt_api_key(api_key: str, settings_service=Depends(get_settings_service)):
fernet = get_fernet(settings_service)
# Two-way encryption
encrypted_key = fernet.encrypt(api_key.encode())
return encrypted_key
def decrypt_api_key(encrypted_api_key: str, settings_service=Depends(get_settings_service)):
fernet = get_fernet(settings_service)
# Two-way decryption
if isinstance(encrypted_api_key, str):
encrypted_api_key = encrypted_api_key.encode()
decrypted_key = fernet.decrypt(encrypted_api_key).decode()
return decrypted_key

6
src/backend/langflow/services/cache/__init__.py vendored
View file

@ -1,9 +1,9 @@
from . import factory, manager
from langflow.services.cache.manager import InMemoryCache
from . import factory, service
from langflow.services.cache.service import InMemoryCache
__all__ = [
"factory",
"manager",
"service",
"InMemoryCache",
]

8
src/backend/langflow/services/cache/factory.py vendored
View file

@ -1,10 +1,10 @@
from langflow.services.cache.manager import InMemoryCache, RedisCache, BaseCacheService
from langflow.services.cache.service import InMemoryCache, RedisCache, BaseCacheService
from langflow.services.factory import ServiceFactory
from langflow.utils.logger import logger
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from langflow.services.settings.manager import SettingsService
from langflow.services.settings.service import SettingsService
class CacheServiceFactory(ServiceFactory):
@ -26,9 +26,7 @@ class CacheServiceFactory(ServiceFactory):
if redis_cache.is_connected():
logger.debug("Redis cache is connected")
return redis_cache
logger.warning(
"Redis cache is not connected, falling back to in-memory cache"
)
logger.warning("Redis cache is not connected, falling back to in-memory cache")
return InMemoryCache()
elif settings_service.settings.CACHE_TYPE == "memory":

21
src/backend/langflow/services/cache/manager.py → src/backend/langflow/services/cache/service.py vendored
View file

@ -68,10 +68,7 @@ class InMemoryCache(BaseCacheService, Service):
Retrieve an item from the cache without acquiring the lock.
"""
if item := self._cache.get(key):
if (
self.expiration_time is None
or time.time() - item["time"] < self.expiration_time
):
if self.expiration_time is None or time.time() - item["time"] < self.expiration_time:
# Move the key to the end to make it recently used
self._cache.move_to_end(key)
# Check if the value is pickled
@ -118,11 +115,7 @@ class InMemoryCache(BaseCacheService, Service):
"""
with self._lock:
existing_value = self._get_without_lock(key)
if (
existing_value is not None
and isinstance(existing_value, dict)
and isinstance(value, dict)
):
if existing_value is not None and isinstance(existing_value, dict) and isinstance(value, dict):
existing_value.update(value)
value = existing_value
@ -276,9 +269,7 @@ class RedisCache(BaseCacheService, Service):
if not result:
raise ValueError("RedisCache could not set the value.")
except TypeError as exc:
raise TypeError(
"RedisCache only accepts values that can be pickled. "
) from exc
raise TypeError("RedisCache only accepts values that can be pickled. ") from exc
def upsert(self, key, value):
"""
@ -290,11 +281,7 @@ class RedisCache(BaseCacheService, Service):
value: The value to insert or update.
"""
existing_value = self.get(key)
if (
existing_value is not None
and isinstance(existing_value, dict)
and isinstance(value, dict)
):
if existing_value is not None and isinstance(existing_value, dict) and isinstance(value, dict):
existing_value.update(value)
value = existing_value

4
src/backend/langflow/services/cache/utils.py vendored
View file

@ -83,9 +83,7 @@ def clear_old_cache_files(max_cache_size: int = 3):
cache_files = list(cache_dir.glob("*.dill"))
if len(cache_files) > max_cache_size:
cache_files_sorted_by_mtime = sorted(
cache_files, key=lambda x: x.stat().st_mtime, reverse=True
)
cache_files_sorted_by_mtime = sorted(cache_files, key=lambda x: x.stat().st_mtime, reverse=True)
for cache_file in cache_files_sorted_by_mtime[max_cache_size:]:
with contextlib.suppress(OSError):

2
src/backend/langflow/services/chat/factory.py
View file

@ -1,4 +1,4 @@
from langflow.services.chat.manager import ChatService
from langflow.services.chat.service import ChatService
from langflow.services.factory import ServiceFactory

38
src/backend/langflow/services/chat/manager.py → src/backend/langflow/services/chat/service.py
View file

@ -1,20 +1,20 @@
from collections import defaultdict
import asyncio
import uuid
from collections import defaultdict
from typing import Any, Dict, List
import orjson
from fastapi import WebSocket, status
from starlette.websockets import WebSocketState
from langflow.api.v1.schemas import ChatMessage, ChatResponse, FileResponse
from langflow.interface.utils import pil_to_base64
from langflow.services import ServiceType, service_manager
from langflow.services.base import Service
from langflow.services.chat.cache import Subject
from langflow.services.chat.utils import process_graph
from loguru import logger
from starlette.websockets import WebSocketState
from .cache import cache_service
import asyncio
from typing import Any, Dict, List
from langflow.services import service_manager, ServiceType
import orjson
class ChatHistory(Subject):
@ -59,9 +59,7 @@ class ChatService(Service):
"""Send the last chat message to the client."""
client_id = self.chat_cache.current_client_id
if client_id in self.active_connections:
chat_response = self.chat_history.get_history(
client_id, filter_messages=False
)[-1]
chat_response = self.chat_history.get_history(client_id, filter_messages=False)[-1]
if chat_response.is_bot:
# Process FileResponse
if isinstance(chat_response, FileResponse):
@ -88,9 +86,7 @@ class ChatService(Service):
data_type=self.last_cached_object_dict["type"],
)
self.chat_history.add_message(
self.chat_cache.current_client_id, chat_response
)
self.chat_history.add_message(self.chat_cache.current_client_id, chat_response)
async def connect(self, client_id: str, websocket: WebSocket):
self.active_connections[client_id] = websocket
@ -108,7 +104,7 @@ class ChatService(Service):
async def send_json(self, client_id: str, message: ChatMessage):
websocket = self.active_connections[client_id]
await websocket.send_json(message.dict())
await websocket.send_json(message.model_dump())
async def close_connection(self, client_id: str, code: int, reason: str):
if websocket := self.active_connections[client_id]:
@ -121,9 +117,7 @@ class ChatService(Service):
if "after sending" in str(exc):
logger.error(f"Error closing connection: {exc}")
async def process_message(
self, client_id: str, payload: Dict, langchain_object: Any
):
async def process_message(self, client_id: str, payload: Dict, langchain_object: Any):
# Process the graph data and chat message
chat_inputs = payload.pop("inputs", {})
chatkey = payload.pop("chatKey", None)
@ -197,7 +191,7 @@ class ChatService(Service):
try:
chat_history = self.chat_history.get_history(client_id)
# iterate and make BaseModel into dict
chat_history = [chat.dict() for chat in chat_history]
chat_history = [chat.model_dump() for chat in chat_history]
await websocket.send_json(chat_history)
while True:
@ -211,15 +205,11 @@ class ChatService(Service):
continue
with self.chat_cache.set_client_id(client_id):
if langchain_object := self.cache_service.get(client_id).get(
"result"
):
if langchain_object := self.cache_service.get(client_id).get("result"):
await self.process_message(client_id, payload, langchain_object)
else:
raise RuntimeError(
f"Could not find a build result for client_id {client_id}"
)
raise RuntimeError(f"Could not find a build result for client_id {client_id}")
except Exception as exc:
# Handle any exceptions that might occur
logger.exception(f"Error handling websocket: {exc}")

4
src/backend/langflow/services/chat/utils.py
View file

@ -15,9 +15,7 @@ async def process_graph(
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."
)
raise ValueError("There was an error loading the langchain_object. Please, check all the nodes and try again.")
# Generate result and thought
try:

4
src/backend/langflow/services/database/factory.py
View file

@ -1,9 +1,9 @@
from typing import TYPE_CHECKING
from langflow.services.database.manager import DatabaseService
from langflow.services.database.service import DatabaseService
from langflow.services.factory import ServiceFactory
if TYPE_CHECKING:
from langflow.services.settings.manager import SettingsService
from langflow.services.settings.service import SettingsService
class DatabaseServiceFactory(ServiceFactory):

4
src/backend/langflow/services/database/models/api_key/crud.py
View file

@ -18,9 +18,7 @@ def get_api_keys(session: Session, user_id: UUID) -> List[ApiKeyRead]:
return [ApiKeyRead.from_orm(api_key) for api_key in api_keys]
def create_api_key(
session: Session, api_key_create: ApiKeyCreate, user_id: UUID
) -> UnmaskedApiKeyRead:
def create_api_key(session: Session, api_key_create: ApiKeyCreate, user_id: UUID) -> UnmaskedApiKeyRead:
# Generate a random API key with 32 bytes of randomness
generated_api_key = f"sk-{secrets.token_urlsafe(32)}"

3
src/backend/langflow/services/database/models/flow/flow.py
View file

@ -15,6 +15,7 @@ class FlowBase(SQLModelSerializable):
name: str = Field(index=True)
description: Optional[str] = Field(index=True, nullable=True, default=None)
data: Optional[Dict] = Field(default=None, nullable=True)
is_component: Optional[bool] = Field(default=False, nullable=True)
@field_validator("data")
def validate_json(v):
@ -35,7 +36,7 @@ class FlowBase(SQLModelSerializable):
class Flow(FlowBase, table=True):
id: UUID = Field(default_factory=uuid4, primary_key=True, unique=True)
data: Optional[Dict] = Field(default=None, sa_column=Column(JSON))
user_id: UUID = Field(index=True, foreign_key="user.id")
user_id: UUID = Field(index=True, foreign_key="user.id", nullable=True)
user: "User" = Relationship(back_populates="flows")

10
src/backend/langflow/services/database/models/user/crud.py
View file

@ -3,7 +3,7 @@ from typing import Union
from uuid import UUID
from fastapi import Depends, HTTPException, status
from langflow.services.database.models.user.user import User, UserUpdate
from langflow.services.getters import get_session
from langflow.services.deps import get_session
from sqlalchemy.exc import IntegrityError
from sqlmodel import Session
from typing import Optional
@ -19,9 +19,7 @@ def get_user_by_id(db: Session, id: UUID) -> Union[User, None]:
return db.query(User).filter(User.id == id).first()
def update_user(
user_db: Optional[User], user: UserUpdate, db: Session = Depends(get_session)
) -> User:
def update_user(user_db: Optional[User], user: UserUpdate, db: Session = Depends(get_session)) -> User:
if not user_db:
raise HTTPException(status_code=404, detail="User not found")
@ -37,9 +35,7 @@ def update_user(
changed = True
if not changed:
raise HTTPException(
status_code=status.HTTP_304_NOT_MODIFIED, detail="Nothing to update"
)
raise HTTPException(status_code=status.HTTP_304_NOT_MODIFIED, detail="Nothing to update")
user_db.updated_at = datetime.now(timezone.utc)
flag_modified(user_db, "updated_at")

1
src/backend/langflow/services/database/models/user/user.py
View file

@ -25,6 +25,7 @@ class User(SQLModelSerializable, table=True):
back_populates="user",
sa_relationship_kwargs={"cascade": "delete"},
)
store_api_key: str = Field(default=None, nullable=True)
flows: list["Flow"] = Relationship(back_populates="user")

63
src/backend/langflow/services/database/manager.py → src/backend/langflow/services/database/service.py
View file

@ -3,15 +3,17 @@ from typing import TYPE_CHECKING
from langflow.services.base import Service
from langflow.services.database.models.user.crud import get_user_by_username
from langflow.services.database.utils import Result, TableResults
from langflow.services.getters import get_settings_service
from langflow.services.deps import get_settings_service
from langflow.services.utils import teardown_superuser
from sqlalchemy import inspect
import sqlalchemy as sa
from sqlalchemy.exc import OperationalError
from sqlmodel import SQLModel, Session, create_engine
from loguru import logger
from alembic.config import Config
from alembic import command
from alembic import command, util
from langflow.services.database import models # noqa
import time
if TYPE_CHECKING:
from sqlalchemy.engine import Engine
@ -32,10 +34,7 @@ class DatabaseService(Service):
def _create_engine(self) -> "Engine":
"""Create the engine for the database."""
settings_service = get_settings_service()
if (
settings_service.settings.DATABASE_URL
and settings_service.settings.DATABASE_URL.startswith("sqlite")
):
if settings_service.settings.DATABASE_URL and settings_service.settings.DATABASE_URL.startswith("sqlite"):
connect_args = {"check_same_thread": False}
else:
connect_args = {}
@ -47,9 +46,7 @@ class DatabaseService(Service):
def __exit__(self, exc_type, exc_value, traceback):
if exc_type is not None: # If an exception has been raised
logger.error(
f"Session rollback because of exception: {exc_type.__name__} {exc_value}"
)
logger.error(f"Session rollback because of exception: {exc_type.__name__} {exc_value}")
self._session.rollback()
else:
self._session.commit()
@ -97,9 +94,7 @@ class DatabaseService(Service):
expected_columns = list(model.model_fields.keys())
try:
available_columns = [
col["name"] for col in inspector.get_columns(table)
]
available_columns = [col["name"] for col in inspector.get_columns(table)]
except sa.exc.NoSuchTableError:
logger.error(f"Missing table: {table}")
return False
@ -148,17 +143,29 @@ class DatabaseService(Service):
alembic_cfg = Config()
alembic_cfg.set_main_option("script_location", str(self.script_location))
alembic_cfg.set_main_option("sqlalchemy.url", self.database_url)
command.upgrade(alembic_cfg, "head")
try:
command.check(alembic_cfg)
except Exception as exc:
if isinstance(exc, util.exc.CommandError) or isinstance(exc, util.exc.AutogenerateDiffsDetected):
command.upgrade(alembic_cfg, "head")
# We should check the schema health after running migrations
try:
command.check(alembic_cfg)
except util.exc.AutogenerateDiffsDetected:
# downgrade to base and upgrade again
logger.warning("Autogenerate diffs detected, downgrading and upgrading")
command.downgrade(alembic_cfg, "-1")
# wait for the database to be ready
time.sleep(5)
command.upgrade(alembic_cfg, "head")
def run_migrations_test(self):
# This method is used for testing purposes only
# We will check that all models are in the database
# and that the database is up to date with all columns
sql_models = [models.Flow, models.User, models.ApiKey]
return [
TableResults(sql_model.__tablename__, self.check_table(sql_model))
for sql_model in sql_models
]
return [TableResults(sql_model.__tablename__, self.check_table(sql_model)) for sql_model in sql_models]
def check_table(self, model):
results = []
@ -166,9 +173,7 @@ class DatabaseService(Service):
table_name = model.__tablename__
expected_columns = list(model.__fields__.keys())
try:
available_columns = [
col["name"] for col in inspector.get_columns(table_name)
]
available_columns = [col["name"] for col in inspector.get_columns(table_name)]
results.append(Result(name=table_name, type="table", success=True))
except sa.exc.NoSuchTableError:
logger.error(f"Missing table: {table_name}")
@ -199,9 +204,7 @@ class DatabaseService(Service):
try:
table.create(self.engine, checkfirst=True)
except OperationalError as oe:
logger.warning(
f"Table {table} already exists, skipping. Exception: {oe}"
)
logger.warning(f"Table {table} already exists, skipping. Exception: {oe}")
except Exception as exc:
logger.error(f"Error creating table {table}: {exc}")
raise RuntimeError(f"Error creating table {table}") from exc
@ -213,9 +216,7 @@ class DatabaseService(Service):
if table not in 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."
)
raise RuntimeError("Something went wrong creating the database and tables.")
logger.debug("Database and tables created successfully")
@ -225,14 +226,8 @@ class DatabaseService(Service):
settings_service = get_settings_service()
# remove the default superuser if auto_login is enabled
# using the SUPERUSER to get the user
if settings_service.auth_settings.AUTO_LOGIN:
logger.debug("Removing default superuser")
username = settings_service.auth_settings.SUPERUSER
with Session(self.engine) as session:
user = get_user_by_username(session, username)
session.delete(user)
session.commit()
logger.debug("Default superuser removed")
with Session(self.engine) as session:
teardown_superuser(settings_service, session)
except Exception as exc:
logger.error(f"Error tearing down database: {exc}")

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