diff --git a/.githooks/pre-commit b/.githooks/pre-commit old mode 100755 new mode 100644 index f5008c586..ef67eaa37 --- a/.githooks/pre-commit +++ b/.githooks/pre-commit @@ -1,2 +1,6 @@ +#!/bin/sh -make format \ No newline at end of file +added_files=$(git diff --name-only --cached --diff-filter=d) + +make format +git add ${added_files} \ No newline at end of file diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index cc9e15761..2d5284ad3 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -54,4 +54,4 @@ jobs: token: ${{ secrets.SERVE_GITHUB_TOKEN }} repository: jina-ai/langchain-serve event-type: langflow-push - client-payload: '{"push_token": "${{ secrets.LCSERVE_PUSH_TOKEN }}", "branch": "dev"}' + client-payload: '{"push_token": "${{ secrets.LCSERVE_PUSH_TOKEN }}", "branch": "main"}' diff --git a/.gitignore b/.gitignore index cb7805a70..36af35fa2 100644 --- a/.gitignore +++ b/.gitignore @@ -241,3 +241,4 @@ dmypy.json # Poetry .testenv/* +langflow.db diff --git a/.vscode/launch.json b/.vscode/launch.json index b0a48cdc6..3b458aa81 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -1,7 +1,7 @@ { "configurations": [ { - "name": "Python: FastAPI", + "name": "Debug Backend", "type": "python", "request": "launch", "module": "uvicorn", @@ -14,7 +14,7 @@ "debug" ], "jinja": true, - "justMyCode": false + "justMyCode": true }, { "name": "Python: Remote Attach", @@ -30,6 +30,13 @@ "remoteRoot": "." } ] + }, + { + "name": "Debug Frontend", + "type": "chrome", + "request": "launch", + "url": "http://localhost:3000/", + "webRoot": "${workspaceRoot}/src/frontend" } ] } diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 000000000..afe112a40 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,15 @@ +FROM python:3.10-slim + +RUN apt-get update && apt-get install gcc g++ git make -y && apt-get clean \ + && rm -rf /var/lib/apt/lists/* +RUN useradd -m -u 1000 user +USER user +ENV HOME=/home/user \ + PATH=/home/user/.local/bin:$PATH + +WORKDIR $HOME/app + +COPY --chown=user . $HOME/app + +RUN pip install langflow>==0.0.86 -U --user +CMD ["python", "-m", "langflow", "--host", "0.0.0.0", "--port", "7860"] diff --git a/Makefile b/Makefile index fdb09a13e..bfed91f37 100644 --- a/Makefile +++ b/Makefile @@ -43,7 +43,18 @@ install_backend: poetry install backend: - poetry run uvicorn langflow.main:app --port 7860 --reload --log-level debug + make install_backend + poetry run uvicorn src.backend.langflow.main:app --port 7860 --reload --log-level debug + +build_and_run: + echo 'Removing dist folder' + rm -rf dist + make build && poetry run pip install dist/*.tar.gz && poetry run langflow + +build_and_install: + echo 'Removing dist folder' + rm -rf dist + make build && poetry run pip install dist/*.tar.gz build_frontend: cd src/frontend && CI='' npm run build @@ -59,7 +70,7 @@ lcserve_push: make build_frontend @version=$$(poetry version --short); \ lc-serve push --app langflow.lcserve:app --app-dir . \ - --image-name langflow --image-tag $${version} --verbose + --image-name langflow --image-tag $${version} --verbose --public lcserve_deploy: @:$(if $(uses),,$(error `uses` is not set. Please run `make uses=... lcserve_deploy`)) diff --git a/README.md b/README.md index bd377896c..36ea5a20f 100644 --- a/README.md +++ b/README.md @@ -2,10 +2,9 @@ # ⛓️ LangFlow -~ A User Interface For [LangChain](https://github.com/hwchase17/langchain) ~ +~ An effortless way to experiment and prototype [LangChain](https://github.com/hwchase17/langchain) pipelines ~

-HuggingFace Spaces GitHub Contributors GitHub Last Commit @@ -14,10 +13,17 @@ Github License

+

+Discord Server +HuggingFace Spaces +

+ -LangFlow is a GUI for [LangChain](https://github.com/hwchase17/langchain), designed with [react-flow](https://github.com/wbkd/react-flow) to provide an effortless way to experiment and prototype flows with drag-and-drop components and a chat box. + +

+

## 📦 Installation ### Locally @@ -50,11 +56,11 @@ Alternatively, click the **"Open in Cloud Shell"** button below to launch Google Langflow integrates with langchain-serve to provide a one-command deployment to Jina AI Cloud. -Start by installing `langchain-serve` with +Start by installing `langchain-serve` with ```bash pip install -U langchain-serve -``` +``` Then, run: @@ -109,24 +115,38 @@ You can use Langflow directly on your browser, or use the API endpoints on Jina Show API usage (with python) ```python - import json - import requests +import requests - FLOW_PATH = "Time_traveller.json" +BASE_API_URL = "https://langflow-e3dd8820ec.wolf.jina.ai/api/v1/predict" +FLOW_ID = "864c4f98-2e59-468b-8e13-79cd8da07468" +# You can tweak the flow by adding a tweaks dictionary +# e.g {"OpenAI-XXXXX": {"model_name": "gpt-4"}} +TWEAKS = { + "ChatOpenAI-g4jEr": {}, + "ConversationChain-UidfJ": {} +} - # HOST = 'http://localhost:7860' - HOST = 'https://langflow-f1ed20e309.wolf.jina.ai' - API_URL = f'{HOST}/predict' +def run_flow(message: str, flow_id: str, tweaks: dict = None) -> dict: + """ + Run a flow with a given message and optional tweaks. - def predict(message): - with open(FLOW_PATH, "r") as f: - json_data = json.load(f) - payload = {'exported_flow': json_data, 'message': message} - response = requests.post(API_URL, json=payload) - return response.json() + :param message: The message to send to the flow + :param flow_id: The ID of the flow to run + :param tweaks: Optional tweaks to customize the flow + :return: The JSON response from the flow + """ + api_url = f"{BASE_API_URL}/{flow_id}" + payload = {"message": message} - predict('Take me to 1920s Bangalore') + if tweaks: + payload["tweaks"] = tweaks + + response = requests.post(api_url, json=payload) + return response.json() + +# Setup any tweaks you want to apply to the flow +print(run_flow("Your message", flow_id=FLOW_ID, tweaks=TWEAKS)) ``` ```json @@ -164,6 +184,11 @@ flow("Hey, have you heard of LangFlow?") We welcome contributions from developers of all levels to our open-source project on GitHub. If you'd like to contribute, please check our [contributing guidelines](./CONTRIBUTING.md) and help make LangFlow more accessible. +Join our [Discord](https://discord.com/invite/EqksyE2EX9) server to ask questions, make suggestions and showcase your projects! 🦾 + +

+

+ [![Star History Chart](https://api.star-history.com/svg?repos=logspace-ai/langflow&type=Timeline)](https://star-history.com/#logspace-ai/langflow&Date) diff --git a/package-lock.json b/package-lock.json deleted file mode 100644 index 6d722a3cc..000000000 --- a/package-lock.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "name": "reactFlow", - "lockfileVersion": 3, - "requires": true, - "packages": {} -} diff --git a/package.json b/package.json deleted file mode 100644 index 091eb76c4..000000000 --- a/package.json +++ /dev/null @@ -1,5 +0,0 @@ -{ - "dependencies": { - "vite-plugin-svgr": "^3.2.0" - } -} diff --git a/poetry.lock b/poetry.lock index 0e4d3d10e..5cdeda786 100644 --- a/poetry.lock +++ b/poetry.lock @@ -148,6 +148,27 @@ files = [ {file = "aiostream-0.4.5.tar.gz", hash = "sha256:3ecbf87085230fbcd9605c32ca20c4fb41af02c71d076eab246ea22e35947d88"}, ] +[[package]] +name = "anthropic" +version = "0.2.10" +description = "Library for accessing the anthropic API" +category = "main" 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"Gabriel Almeida ", - "Ibis Prevedello ", + "Gustavo Schaedler ", "Lucas Eduoli ", "Otávio Anovazzi ", ] @@ -22,22 +23,21 @@ langflow = "langflow.__main__:main" [tool.poetry.dependencies] python = ">=3.9,<3.12" -fastapi = "^0.95.0" -uvicorn = "^0.20.0" +fastapi = "^0.97.0" +uvicorn = "^0.22.0" beautifulsoup4 = "^4.11.2" google-search-results = "^2.4.1" google-api-python-client = "^2.79.0" -typer = "^0.7.0" +typer = "^0.9.0" gunicorn = "^20.1.0" -langchain = "^0.0.186" -openai = "^0.27.7" +langchain = "^0.0.202" +openai = "^0.27.8" types-pyyaml = "^6.0.12.8" -dill = "^0.3.6" pandas = "^1.5.3" chromadb = "^0.3.21" huggingface-hub = "^0.13.3" -rich = "^13.3.3" -llama-cpp-python = "^0.1.50" +rich = "^13.4.2" +llama-cpp-python = "~0.1.0" networkx = "^3.1" unstructured = "^0.5.11" pypdf = "^3.7.1" @@ -46,18 +46,25 @@ pysrt = "^1.1.2" fake-useragent = "^1.1.3" docstring-parser = "^0.15" psycopg2-binary = "^2.9.6" -pyarrow = "^11.0.0" -tiktoken = "^0.3.3" +pyarrow = "^12.0.0" +tiktoken = "~0.4.0" wikipedia = "^1.4.0" -langchain-serve = { version = "^0.0.38", optional = true } +langchain-serve = { version = ">0.0.39", optional = true } qdrant-client = "^1.2.0" websockets = "^11.0.3" -weaviate-client = "^3.19.2" +weaviate-client = "^3.21.0" jina = "3.15.2" sentence-transformers = "^2.2.2" ctransformers = "^0.2.2" cohere = "^4.6.0" python-multipart = "^0.0.6" +sqlmodel = "^0.0.8" +faiss-cpu = "^1.7.4" +anthropic = "^0.2.10" +orjson = "^3.9.1" +multiprocess = "^0.70.14" +cachetools = "^5.3.1" +types-cachetools = "^5.3.0.5" [tool.poetry.group.dev.dependencies] @@ -77,6 +84,15 @@ types-pillow = "^9.5.0.2" [tool.poetry.extras] deploy = ["langchain-serve"] +[tool.pytest.ini_options] +minversion = "6.0" +addopts = "-ra" +testpaths = ["tests", "integration"] +console_output_style = "progress" +filterwarnings = ["ignore::DeprecationWarning"] +log_cli = true + + [tool.ruff] line-length = 120 diff --git a/src/backend/langflow/__init__.py b/src/backend/langflow/__init__.py index 35fe814d2..9b80c2ea7 100644 --- a/src/backend/langflow/__init__.py +++ b/src/backend/langflow/__init__.py @@ -1,4 +1,12 @@ +from importlib import metadata from langflow.cache import cache_manager -from langflow.interface.loading import load_flow_from_json +from langflow.processing.process import load_flow_from_json + +try: + __version__ = metadata.version(__package__) +except metadata.PackageNotFoundError: + # Case where package metadata is not available. + __version__ = "" +del metadata # optional, avoids polluting the results of dir(__package__) __all__ = ["load_flow_from_json", "cache_manager"] diff --git a/src/backend/langflow/__main__.py b/src/backend/langflow/__main__.py index af57ed381..29f60ed23 100644 --- a/src/backend/langflow/__main__.py +++ b/src/backend/langflow/__main__.py @@ -1,27 +1,46 @@ -import multiprocessing +import sys +import time +from fastapi import FastAPI +import httpx +from multiprocess import Process, cpu_count # type: ignore import platform from pathlib import Path - +from typing import Optional +import socket +from rich.panel import Panel +from rich import box +from rich import print as rprint import typer from fastapi.staticfiles import StaticFiles - +from fastapi.responses import FileResponse from langflow.main import create_app from langflow.settings import settings -from langflow.utils.logger import configure +from langflow.utils.logger import configure, logger +import webbrowser +from dotenv import load_dotenv app = typer.Typer() def get_number_of_workers(workers=None): if workers == -1: - workers = (multiprocessing.cpu_count() * 2) + 1 + workers = (cpu_count() * 2) + 1 return workers -def update_settings(config: str, dev: bool = False): +def update_settings( + config: str, + dev: bool = False, + database_url: Optional[str] = None, + remove_api_keys: bool = False, +): """Update the settings from a config file.""" if config: settings.update_from_yaml(config, dev=dev) + if database_url: + settings.update_settings(database_url=database_url) + if remove_api_keys: + settings.update_settings(remove_api_keys=remove_api_keys) def serve_on_jcloud(): @@ -77,10 +96,28 @@ def serve( timeout: int = typer.Option(60, help="Worker timeout in seconds."), port: int = typer.Option(7860, help="Port to listen on."), config: str = typer.Option("config.yaml", help="Path to the configuration file."), - log_level: str = typer.Option("info", help="Logging level."), + # .env file param + env_file: Path = typer.Option( + ".env", help="Path to the .env file containing environment variables." + ), + log_level: str = typer.Option("critical", help="Logging level."), log_file: Path = typer.Option("logs/langflow.log", help="Path to the log file."), jcloud: bool = typer.Option(False, help="Deploy on Jina AI Cloud"), dev: bool = typer.Option(False, help="Run in development mode (may contain bugs)"), + database_url: str = typer.Option( + None, + help="Database URL to connect to. If not provided, a local SQLite database will be used.", + ), + path: str = typer.Option( + None, + help="Path to the frontend directory containing build files. This is for development purposes only.", + ), + open_browser: bool = typer.Option( + True, help="Open the browser after starting the server." + ), + remove_api_keys: bool = typer.Option( + False, help="Remove API keys from the projects saved in the database." + ), ): """ Run the Langflow server. @@ -89,17 +126,25 @@ def serve( if jcloud: return serve_on_jcloud() + load_dotenv(env_file) + configure(log_level=log_level, log_file=log_file) - update_settings(config, dev=dev) - app = create_app() - # get the directory of the current file - path = Path(__file__).parent - static_files_dir = path / "frontend" - app.mount( - "/", - StaticFiles(directory=static_files_dir, html=True), - name="static", + update_settings( + config, dev=dev, database_url=database_url, remove_api_keys=remove_api_keys ) + # get the directory of the current file + if not path: + frontend_path = Path(__file__).parent + static_files_dir = frontend_path / "frontend" + else: + static_files_dir = Path(path) + + app = create_app() + setup_static_files(app, static_files_dir) + # check if port is being used + if is_port_in_use(port, host): + port = get_free_port(port) + options = { "bind": f"{host}:{port}", "workers": get_number_of_workers(workers), @@ -107,17 +152,147 @@ def serve( "timeout": timeout, } - if platform.system() in ["Darwin", "Windows"]: + if platform.system() in ["Windows"]: # Run using uvicorn on MacOS and Windows # Windows doesn't support gunicorn # MacOS requires an env variable to be set to use gunicorn - import uvicorn - - uvicorn.run(app, host=host, port=port, log_level=log_level) + run_on_windows(host, port, log_level, options, app) else: - from langflow.server import LangflowApplication + # Run using gunicorn on Linux + run_on_mac_or_linux(host, port, log_level, options, app, open_browser) - LangflowApplication(app, options).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.start() + status_code = 0 + while status_code != 200: + try: + status_code = httpx.get(f"http://{host}:{port}/health").status_code + + except Exception: + time.sleep(1) + + print_banner(host, port) + if open_browser: + webbrowser.open(f"http://{host}:{port}") + + +def run_on_windows(host, port, log_level, options, app): + """ + Run the Langflow server on Windows. + """ + print_banner(host, port) + run_langflow(host, port, log_level, options, app) + + +def setup_static_files(app: FastAPI, static_files_dir: Path): + """ + Setup the static files directory. + + Args: + app (FastAPI): FastAPI app. + path (str): Path to the static files directory. + """ + app.mount( + "/", + StaticFiles(directory=static_files_dir, html=True), + name="static", + ) + + @app.exception_handler(404) + async def custom_404_handler(request, __): + path = static_files_dir / "index.html" + + if not path.exists(): + raise RuntimeError(f"File at path {path} does not exist.") + return FileResponse(path) + + +def is_port_in_use(port, host="localhost"): + """ + Check if a port is in use. + + Args: + port (int): The port number to check. + host (str): The host to check the port on. Defaults to 'localhost'. + + Returns: + bool: True if the port is in use, False otherwise. + """ + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + return s.connect_ex((host, port)) == 0 + + +def get_free_port(port): + """ + Given a used port, find a free port. + + Args: + port (int): The port number to check. + + Returns: + int: A free port number. + """ + while is_port_in_use(port): + port += 1 + return port + + +def print_banner(host, port): + # console = Console() + + word = "LangFlow" + colors = ["#3300cc"] + + styled_word = "" + + for i, char in enumerate(word): + color = colors[i % len(colors)] + styled_word += f"[{color}]{char}[/]" + + # Title with emojis and gradient text + title = ( + f"[bold]Welcome to :chains: {styled_word} [/bold]\n\n" + f"Access [link=http://{host}:{port}]http://{host}:{port}[/link]" + ) + info_text = ( + "Collaborate, and contribute at our " + "[bold][link=https://github.com/logspace-ai/langflow]GitHub Repo[/link][/bold] :rocket:" + ) + + # 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 + ) + + # Print the banner with a separator line before and after + rprint(panel) + + +def run_langflow(host, port, log_level, options, app): + """ + Run Langflow server on localhost + """ + try: + if platform.system() in ["Darwin", "Windows"]: + # Run using uvicorn on MacOS and Windows + # Windows doesn't support gunicorn + # MacOS requires an env variable to be set to use gunicorn + import uvicorn + + uvicorn.run(app, host=host, port=port, log_level=log_level) + else: + from langflow.server import LangflowApplication + + LangflowApplication(app, options).run() + except KeyboardInterrupt: + pass + except Exception as e: + logger.error(e) + sys.exit(1) def main(): diff --git a/src/backend/langflow/api/__init__.py b/src/backend/langflow/api/__init__.py index e69de29bb..f887c47e1 100644 --- a/src/backend/langflow/api/__init__.py +++ b/src/backend/langflow/api/__init__.py @@ -0,0 +1,3 @@ +from langflow.api.router import router + +__all__ = ["router"] diff --git a/src/backend/langflow/api/chat.py b/src/backend/langflow/api/chat.py deleted file mode 100644 index 4afa6c22f..000000000 --- a/src/backend/langflow/api/chat.py +++ /dev/null @@ -1,26 +0,0 @@ -from fastapi import ( - APIRouter, - WebSocket, - WebSocketDisconnect, - WebSocketException, - status, -) - -from langflow.api.chat_manager import ChatManager -from langflow.utils.logger import logger - -router = APIRouter() -chat_manager = ChatManager() - - -@router.websocket("/chat/{client_id}") -async def websocket_endpoint(client_id: str, websocket: WebSocket): - """Websocket endpoint for chat.""" - try: - await chat_manager.handle_websocket(client_id, websocket) - except WebSocketException as exc: - logger.error(exc) - await websocket.close(code=status.WS_1011_INTERNAL_ERROR, reason=str(exc)) - except WebSocketDisconnect as exc: - logger.error(exc) - await websocket.close(code=status.WS_1000_NORMAL_CLOSURE, reason=str(exc)) diff --git a/src/backend/langflow/api/endpoints.py b/src/backend/langflow/api/endpoints.py deleted file mode 100644 index d01d18064..000000000 --- a/src/backend/langflow/api/endpoints.py +++ /dev/null @@ -1,57 +0,0 @@ -import logging -from importlib.metadata import version - -from fastapi import APIRouter, HTTPException, UploadFile - -from langflow.api.schemas import ( - ExportedFlow, - GraphData, - PredictRequest, - PredictResponse, -) -from langflow.cache.base import save_uploaded_file -from langflow.interface.run import process_graph_cached -from langflow.interface.types import build_langchain_types_dict - -# build router -router = APIRouter() -logger = logging.getLogger(__name__) - - -@router.get("/all") -def get_all(): - return build_langchain_types_dict() - - -@router.post("/predict", response_model=PredictResponse) -async def get_load(predict_request: PredictRequest): - try: - exported_flow: ExportedFlow = predict_request.exported_flow - graph_data: GraphData = exported_flow.data - data = graph_data.dict() - response = process_graph_cached(data, predict_request.message) - return PredictResponse(result=response.get("result", "")) - except Exception as e: - # Log stack trace - logger.exception(e) - raise HTTPException(status_code=500, detail=str(e)) from e - - -# Endpoint to upload file -@router.post("/upload/{client_id}") -async def create_upload_file(file: UploadFile, client_id: str): - # Cache file - file_path = save_uploaded_file(file.file, file_name=client_id) - - return {"file_path": file_path} - - -# get endpoint to return version of langflow -@router.get("/version") -def get_version(): - return {"version": version("langflow")} - - -@router.get("/health") -def get_health(): - return {"status": "OK"} diff --git a/src/backend/langflow/api/router.py b/src/backend/langflow/api/router.py new file mode 100644 index 000000000..f090abe74 --- /dev/null +++ b/src/backend/langflow/api/router.py @@ -0,0 +1,18 @@ +# Router for base api +from fastapi import APIRouter +from langflow.api.v1 import ( + chat_router, + endpoints_router, + validate_router, + flows_router, + flow_styles_router, +) + +router = APIRouter( + prefix="/api/v1", +) +router.include_router(chat_router) +router.include_router(endpoints_router) +router.include_router(validate_router) +router.include_router(flows_router) +router.include_router(flow_styles_router) diff --git a/src/backend/langflow/api/utils.py b/src/backend/langflow/api/utils.py new file mode 100644 index 000000000..bfd9e3da5 --- /dev/null +++ b/src/backend/langflow/api/utils.py @@ -0,0 +1,24 @@ +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) + ) + + +def remove_api_keys(flow: dict): + """Remove api keys from flow data.""" + if flow.get("data") and flow["data"].get("nodes"): + for node in flow["data"]["nodes"]: + 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") + ): + value["value"] = None + + return flow diff --git a/src/backend/langflow/api/v1/__init__.py b/src/backend/langflow/api/v1/__init__.py new file mode 100644 index 000000000..f18f90e42 --- /dev/null +++ b/src/backend/langflow/api/v1/__init__.py @@ -0,0 +1,13 @@ +from langflow.api.v1.endpoints import router as endpoints_router +from langflow.api.v1.validate import router as validate_router +from langflow.api.v1.chat import router as chat_router +from langflow.api.v1.flows import router as flows_router +from langflow.api.v1.flow_styles import router as flow_styles_router + +__all__ = [ + "chat_router", + "endpoints_router", + "validate_router", + "flows_router", + "flow_styles_router", +] diff --git a/src/backend/langflow/api/base.py b/src/backend/langflow/api/v1/base.py similarity index 96% rename from src/backend/langflow/api/base.py rename to src/backend/langflow/api/v1/base.py index 8cddc52e4..6941bedf3 100644 --- a/src/backend/langflow/api/base.py +++ b/src/backend/langflow/api/v1/base.py @@ -1,6 +1,6 @@ from pydantic import BaseModel, validator -from langflow.graph.utils import extract_input_variables_from_prompt +from langflow.interface.utils import extract_input_variables_from_prompt class CacheResponse(BaseModel): diff --git a/src/backend/langflow/api/callback.py b/src/backend/langflow/api/v1/callback.py similarity index 95% rename from src/backend/langflow/api/callback.py rename to src/backend/langflow/api/v1/callback.py index d63e107c4..b58393d7b 100644 --- a/src/backend/langflow/api/callback.py +++ b/src/backend/langflow/api/v1/callback.py @@ -3,7 +3,7 @@ from typing import Any from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler -from langflow.api.schemas import ChatResponse +from langflow.api.v1.schemas import ChatResponse # https://github.com/hwchase17/chat-langchain/blob/master/callback.py diff --git a/src/backend/langflow/api/v1/chat.py b/src/backend/langflow/api/v1/chat.py new file mode 100644 index 000000000..ed0caeba2 --- /dev/null +++ b/src/backend/langflow/api/v1/chat.py @@ -0,0 +1,128 @@ +import json +from fastapi import ( + APIRouter, + HTTPException, + WebSocket, + WebSocketException, + status, +) +from fastapi.responses import StreamingResponse +from langflow.api.v1.schemas import BuiltResponse, InitResponse + +from langflow.chat.manager import ChatManager +from langflow.graph.graph.base import Graph +from langflow.utils.logger import logger +from cachetools import LRUCache + +router = APIRouter(tags=["Chat"]) +chat_manager = ChatManager() +flow_data_store: LRUCache = LRUCache(maxsize=10) + + +@router.websocket("/chat/{client_id}") +async def chat(client_id: str, websocket: WebSocket): + """Websocket endpoint for chat.""" + try: + if client_id in chat_manager.in_memory_cache: + await chat_manager.handle_websocket(client_id, websocket) + else: + message = "Please, build the flow before sending messages" + await websocket.close(code=status.WS_1008_POLICY_VIOLATION, reason=message) + except WebSocketException as exc: + logger.error(exc) + await websocket.close(code=status.WS_1011_INTERNAL_ERROR, reason=str(exc)) + + +@router.post("/build/init", response_model=InitResponse, status_code=201) +async def init_build(graph_data: dict): + """Initialize the build by storing graph data and returning a unique session ID.""" + + try: + flow_id = graph_data.get("id") + if flow_id is None: + raise ValueError("No ID provided") + flow_data_store[flow_id] = graph_data + + return InitResponse(flowId=flow_id) + except Exception as exc: + logger.error(exc) + return HTTPException(status_code=500, detail=str(exc)) + + +@router.get("/build/{flow_id}/status", response_model=BuiltResponse) +async def build_status(flow_id: str): + """Check the flow_id is in the flow_data_store.""" + try: + built = flow_id in flow_data_store and not isinstance( + flow_data_store[flow_id], dict + ) + + return BuiltResponse( + built=built, + ) + + except Exception as exc: + logger.error(exc) + return HTTPException(status_code=500, detail=str(exc)) + + +@router.get("/build/stream/{flow_id}", response_class=StreamingResponse) +async def stream_build(flow_id: str): + """Stream the build process based on stored flow data.""" + + async def event_stream(flow_id): + final_response = json.dumps({"end_of_stream": True}) + try: + if flow_id not in flow_data_store: + error_message = "Invalid session ID" + yield f"data: {json.dumps({'error': error_message})}\n\n" + return + + graph_data = flow_data_store[flow_id].get("data") + + if not graph_data: + error_message = "No data provided" + yield f"data: {json.dumps({'error': error_message})}\n\n" + return + + logger.debug("Building langchain object") + graph = Graph.from_payload(graph_data) + number_of_nodes = len(graph.nodes) + for i, vertex in enumerate(graph.generator_build(), 1): + try: + log_dict = { + "log": f"Building node {vertex.vertex_type}", + "progress": round(i / number_of_nodes, 2), + } + yield f"data: {json.dumps(log_dict)}\n\n" + vertex.build() + params = vertex._built_object_repr() + valid = True + logger.debug( + f"Building node {params[:50]}{'...' if len(params) > 50 else ''}" + ) + except Exception as exc: + params = str(exc) + valid = False + + response = json.dumps( + { + "valid": valid, + "params": params, + "id": vertex.id, + } + ) + yield f"data: {response}\n\n" + + chat_manager.set_cache(flow_id, graph.build()) + except Exception as exc: + logger.error("Error while building the flow: %s", exc) + yield f"error: {json.dumps({'error': str(exc)})}\n\n" + finally: + yield f"data: {final_response}\n\n" + + try: + return StreamingResponse(event_stream(flow_id), media_type="text/event-stream") + except Exception as exc: + logger.error(exc) + raise HTTPException(status_code=500, detail=str(exc)) diff --git a/src/backend/langflow/api/v1/endpoints.py b/src/backend/langflow/api/v1/endpoints.py new file mode 100644 index 000000000..e822bdf31 --- /dev/null +++ b/src/backend/langflow/api/v1/endpoints.py @@ -0,0 +1,72 @@ +from langflow.database.models.flow import Flow +from langflow.processing.process import process_graph_cached, process_tweaks +from langflow.utils.logger import logger + +from fastapi import APIRouter, Depends, HTTPException, UploadFile + +from langflow.api.v1.schemas import ( + PredictRequest, + PredictResponse, +) + +from langflow.interface.types import build_langchain_types_dict +from langflow.database.base import get_session +from sqlmodel import Session + +# build router +router = APIRouter(tags=["Base"]) + + +@router.get("/all") +def get_all(): + return build_langchain_types_dict() + + +@router.post("/predict/{flow_id}", response_model=PredictResponse) +async def predict_flow( + predict_request: PredictRequest, + flow_id: str, + session: Session = Depends(get_session), +): + """ + Endpoint to process a message using the flow passed in the bearer token. + """ + + try: + flow = session.get(Flow, flow_id) + if flow is None: + raise ValueError(f"Flow {flow_id} not found") + + if flow.data is None: + raise ValueError(f"Flow {flow_id} has no data") + graph_data = flow.data + if predict_request.tweaks: + try: + graph_data = process_tweaks(graph_data, predict_request.tweaks) + except Exception as exc: + logger.error(f"Error processing tweaks: {exc}") + response = process_graph_cached(graph_data, predict_request.message) + return PredictResponse( + result=response.get("result", ""), + intermediate_steps=response.get("thought", ""), + ) + except Exception as e: + # Log stack trace + logger.exception(e) + raise HTTPException(status_code=500, detail=str(e)) from e + +@router.post("/upload/{client_id}") +async def create_upload_file(file: UploadFile, client_id: str): + # Cache file + file_path = save_uploaded_file(file.file, file_name=client_id) + + return {"file_path": file_path} + + +# get endpoint to return version of langflow +@router.get("/version") +def get_version(): + from langflow import __version__ + + return {"version": __version__} + diff --git a/src/backend/langflow/api/v1/flow_styles.py b/src/backend/langflow/api/v1/flow_styles.py new file mode 100644 index 000000000..40e292eb3 --- /dev/null +++ b/src/backend/langflow/api/v1/flow_styles.py @@ -0,0 +1,83 @@ +from uuid import UUID +from langflow.database.models.flow_style import ( + FlowStyle, + FlowStyleCreate, + FlowStyleRead, + FlowStyleUpdate, +) +from langflow.database.base import get_session +from sqlmodel import Session, select +from fastapi import APIRouter, Depends, HTTPException + + +# build router +router = APIRouter(prefix="/flow_styles", tags=["FlowStyles"]) + +# FlowStyleCreate: +# class FlowStyleBase(SQLModel): +# color: str = Field(index=True) +# emoji: str = Field(index=False) +# flow_id: UUID = Field(default=None, foreign_key="flow.id") + + +@router.post("/", response_model=FlowStyleRead) +def create_flow_style( + *, session: Session = Depends(get_session), flow_style: FlowStyleCreate +): + """Create a new flow_style.""" + db_flow_style = FlowStyle.from_orm(flow_style) + session.add(db_flow_style) + session.commit() + session.refresh(db_flow_style) + return db_flow_style + + +@router.get("/", response_model=list[FlowStyleRead]) +def read_flow_styles(*, session: Session = Depends(get_session)): + """Read all flows.""" + try: + flows = session.exec(select(FlowStyle)).all() + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) from e + return flows + + +@router.get("/{flow_styles_id}", response_model=FlowStyleRead) +def read_flow_style(*, session: Session = Depends(get_session), flow_styles_id: UUID): + """Read a flow_style.""" + if flow_style := session.get(FlowStyle, flow_styles_id): + return flow_style + else: + raise HTTPException(status_code=404, detail="FlowStyle not found") + + +@router.patch("/{flow_style_id}", response_model=FlowStyleRead) +def update_flow_style( + *, + session: Session = Depends(get_session), + flow_style_id: UUID, + flow_style: FlowStyleUpdate, +): + """Update a flow_style.""" + db_flow_style = session.get(FlowStyle, flow_style_id) + if not db_flow_style: + raise HTTPException(status_code=404, detail="FlowStyle not found") + flow_data = flow_style.dict(exclude_unset=True) + for key, value in flow_data.items(): + if hasattr(db_flow_style, key) and value is not None: + setattr(db_flow_style, key, value) + session.add(db_flow_style) + session.commit() + session.refresh(db_flow_style) + return db_flow_style + + +@router.delete("/{flow_id}") +def delete_flow_style(*, session: Session = Depends(get_session), flow_id: UUID): + """Delete a flow_style.""" + flow_style = session.get(FlowStyle, flow_id) + if not flow_style: + raise HTTPException(status_code=404, detail="FlowStyle not found") + session.delete(flow_style) + session.commit() + return {"message": "FlowStyle deleted successfully"} diff --git a/src/backend/langflow/api/v1/flows.py b/src/backend/langflow/api/v1/flows.py new file mode 100644 index 000000000..4e000a128 --- /dev/null +++ b/src/backend/langflow/api/v1/flows.py @@ -0,0 +1,120 @@ +from typing import List +from uuid import UUID +from langflow.settings import settings +from langflow.api.utils import remove_api_keys +from langflow.api.v1.schemas import FlowListCreate, FlowListRead +from langflow.database.models.flow import ( + Flow, + FlowCreate, + FlowRead, + FlowReadWithStyle, + FlowUpdate, +) +from langflow.database.base import get_session +from sqlmodel import Session, select +from fastapi import APIRouter, Depends, HTTPException +from fastapi.encoders import jsonable_encoder + +from fastapi import File, UploadFile +import json + +# build router +router = APIRouter(prefix="/flows", tags=["Flows"]) + + +@router.post("/", response_model=FlowRead, status_code=201) +def create_flow(*, session: Session = Depends(get_session), flow: FlowCreate): + """Create a new flow.""" + db_flow = Flow.from_orm(flow) + session.add(db_flow) + session.commit() + session.refresh(db_flow) + return db_flow + + +@router.get("/", response_model=list[FlowReadWithStyle], status_code=200) +def read_flows(*, session: Session = Depends(get_session)): + """Read all flows.""" + try: + flows = session.exec(select(Flow)).all() + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) from e + return [jsonable_encoder(flow) for flow in flows] + + +@router.get("/{flow_id}", response_model=FlowReadWithStyle, status_code=200) +def read_flow(*, session: Session = Depends(get_session), flow_id: UUID): + """Read a flow.""" + if flow := session.get(Flow, flow_id): + return flow + else: + raise HTTPException(status_code=404, detail="Flow not found") + + +@router.patch("/{flow_id}", response_model=FlowRead, status_code=200) +def update_flow( + *, session: Session = Depends(get_session), flow_id: UUID, flow: FlowUpdate +): + """Update a flow.""" + + db_flow = session.get(Flow, flow_id) + if not db_flow: + raise HTTPException(status_code=404, detail="Flow not found") + flow_data = flow.dict(exclude_unset=True) + if settings.remove_api_keys: + flow_data = remove_api_keys(flow_data) + for key, value in flow_data.items(): + setattr(db_flow, key, value) + session.add(db_flow) + session.commit() + session.refresh(db_flow) + return db_flow + + +@router.delete("/{flow_id}", status_code=200) +def delete_flow(*, session: Session = Depends(get_session), flow_id: UUID): + """Delete a flow.""" + flow = session.get(Flow, flow_id) + if not flow: + raise HTTPException(status_code=404, detail="Flow not found") + session.delete(flow) + session.commit() + return {"message": "Flow deleted successfully"} + + +# Define a new model to handle multiple flows + + +@router.post("/batch/", response_model=List[FlowRead], status_code=201) +def create_flows(*, session: Session = Depends(get_session), flow_list: FlowListCreate): + """Create multiple new flows.""" + db_flows = [] + for flow in flow_list.flows: + db_flow = Flow.from_orm(flow) + session.add(db_flow) + db_flows.append(db_flow) + session.commit() + for db_flow in db_flows: + session.refresh(db_flow) + return db_flows + + +@router.post("/upload/", response_model=List[FlowRead], status_code=201) +async def upload_file( + *, session: Session = Depends(get_session), file: UploadFile = File(...) +): + """Upload flows from a file.""" + contents = await file.read() + data = json.loads(contents) + if "flows" in data: + flow_list = FlowListCreate(**data) + else: + flow_list = FlowListCreate(flows=[FlowCreate(**flow) for flow in data]) + return create_flows(session=session, flow_list=flow_list) + + +@router.get("/download/", response_model=FlowListRead, status_code=200) +async def download_file(*, session: Session = Depends(get_session)): + """Download all flows as a file.""" + flows = read_flows(session=session) + return FlowListRead(flows=flows) diff --git a/src/backend/langflow/api/schemas.py b/src/backend/langflow/api/v1/schemas.py similarity index 56% rename from src/backend/langflow/api/schemas.py rename to src/backend/langflow/api/v1/schemas.py index f73b0642d..714f0df7f 100644 --- a/src/backend/langflow/api/schemas.py +++ b/src/backend/langflow/api/v1/schemas.py @@ -1,6 +1,6 @@ -from typing import Any, Dict, List, Union - -from pydantic import BaseModel, validator +from typing import Any, Dict, List, Optional, Union +from langflow.database.models.flow import FlowCreate, FlowRead +from pydantic import BaseModel, Field, validator class GraphData(BaseModel): @@ -23,13 +23,30 @@ class PredictRequest(BaseModel): """Predict request schema.""" message: str - exported_flow: ExportedFlow + tweaks: Optional[Dict[str, Dict[str, str]]] = Field(default_factory=dict) + + class Config: + schema_extra = { + "example": { + "message": "Hello, how are you?", + "tweaks": { + "dndnode_986363f0-4677-4035-9f38-74b94af5dd78": { + "name": "A tool name", + "description": "A tool description", + }, + "dndnode_986363f0-4677-4035-9f38-74b94af57378": { + "template": "A {template}", + }, + }, + } + } class PredictResponse(BaseModel): """Predict response schema.""" result: str + intermediate_steps: str = "" class ChatMessage(BaseModel): @@ -68,3 +85,19 @@ class FileResponse(ChatMessage): if v not in ["image", "csv"]: raise ValueError("data_type must be image or csv") return v + + +class FlowListCreate(BaseModel): + flows: List[FlowCreate] + + +class FlowListRead(BaseModel): + flows: List[FlowRead] + + +class InitResponse(BaseModel): + flowId: str + + +class BuiltResponse(BaseModel): + built: bool diff --git a/src/backend/langflow/api/validate.py b/src/backend/langflow/api/v1/validate.py similarity index 85% rename from src/backend/langflow/api/validate.py rename to src/backend/langflow/api/v1/validate.py index 4d397b8b4..d7ba8c15e 100644 --- a/src/backend/langflow/api/validate.py +++ b/src/backend/langflow/api/v1/validate.py @@ -2,20 +2,20 @@ import json from fastapi import APIRouter, HTTPException -from langflow.api.base import ( +from langflow.api.v1.base import ( Code, CodeValidationResponse, Prompt, PromptValidationResponse, validate_prompt, ) -from langflow.graph.nodes import VectorStoreNode -from langflow.interface.run import build_graph +from langflow.graph.vertex.types import VectorStoreVertex +from langflow.graph import Graph from langflow.utils.logger import logger from langflow.utils.validate import validate_code # build router -router = APIRouter(prefix="/validate", tags=["validate"]) +router = APIRouter(prefix="/validate", tags=["Validate"]) @router.post("/code", status_code=200, response_model=CodeValidationResponse) @@ -44,12 +44,12 @@ def post_validate_prompt(prompt: Prompt): def post_validate_node(node_id: str, data: dict): try: # build graph - graph = build_graph(data) + graph = Graph.from_payload(data) # validate node node = graph.get_node(node_id) if node is None: raise ValueError(f"Node {node_id} not found") - if not isinstance(node, VectorStoreNode): + if not isinstance(node, VectorStoreVertex): node.build() return json.dumps( { diff --git a/src/backend/langflow/cache/__init__.py b/src/backend/langflow/cache/__init__.py index 583d5ac6d..723aa9e18 100644 --- a/src/backend/langflow/cache/__init__.py +++ b/src/backend/langflow/cache/__init__.py @@ -1 +1,7 @@ -from langflow.cache.manager import cache_manager # noqa +from langflow.cache.manager import cache_manager +from langflow.cache.flow import InMemoryCache + +__all__ = [ + "cache_manager", + "InMemoryCache", +] diff --git a/src/backend/langflow/cache/base.py b/src/backend/langflow/cache/base.py index f5ef69bc4..a5cb8a797 100644 --- a/src/backend/langflow/cache/base.py +++ b/src/backend/langflow/cache/base.py @@ -1,167 +1,84 @@ -import base64 -import contextlib -import functools -import hashlib -import json -import os -import tempfile -from collections import OrderedDict -from pathlib import Path -from typing import Any, Dict - -import dill # type: ignore - -CACHE: Dict[str, Any] = {} +import abc -def create_cache_folder(func): - def wrapper(*args, **kwargs): - # Get the destination folder - cache_path = Path(tempfile.gettempdir()) / PREFIX - - # Create the destination folder if it doesn't exist - os.makedirs(cache_path, exist_ok=True) - - return func(*args, **kwargs) - - return wrapper - - -def memoize_dict(maxsize=128): - cache = OrderedDict() - - def decorator(func): - @functools.wraps(func) - def wrapper(*args, **kwargs): - hashed = compute_dict_hash(args[0]) - key = (func.__name__, hashed, frozenset(kwargs.items())) - if key not in cache: - result = func(*args, **kwargs) - cache[key] = result - if len(cache) > maxsize: - cache.popitem(last=False) - else: - result = cache[key] - return result - - def clear_cache(): - cache.clear() - - wrapper.clear_cache = clear_cache # type: ignore - wrapper.cache = cache # type: ignore - return wrapper - - return decorator - - -PREFIX = "langflow_cache" - - -@create_cache_folder -def clear_old_cache_files(max_cache_size: int = 3): - cache_dir = Path(tempfile.gettempdir()) / PREFIX - 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 - ) - - for cache_file in cache_files_sorted_by_mtime[max_cache_size:]: - with contextlib.suppress(OSError): - os.remove(cache_file) - - -def compute_dict_hash(graph_data): - graph_data = filter_json(graph_data) - - cleaned_graph_json = json.dumps(graph_data, sort_keys=True) - return hashlib.sha256(cleaned_graph_json.encode("utf-8")).hexdigest() - - -def filter_json(json_data): - filtered_data = json_data.copy() - - # Remove 'viewport' and 'chatHistory' keys - if "viewport" in filtered_data: - del filtered_data["viewport"] - if "chatHistory" in filtered_data: - del filtered_data["chatHistory"] - - # Filter nodes - if "nodes" in filtered_data: - for node in filtered_data["nodes"]: - if "position" in node: - del node["position"] - if "positionAbsolute" in node: - del node["positionAbsolute"] - if "selected" in node: - del node["selected"] - if "dragging" in node: - del node["dragging"] - - return filtered_data - - -@create_cache_folder -def save_binary_file(content: str, file_name: str, accepted_types: list[str]) -> str: +class BaseCache(abc.ABC): """ - Save a binary file to the specified folder. - - Args: - content: The content of the file as a bytes object. - file_name: The name of the file, including its extension. - - Returns: - The path to the saved file. + Abstract base class for a cache. """ - if not any(file_name.endswith(suffix) for suffix in accepted_types): - raise ValueError(f"File {file_name} is not accepted") - # Get the destination folder - cache_path = Path(tempfile.gettempdir()) / PREFIX - if not content: - raise ValueError("Please, reload the file in the loader.") - data = content.split(",")[1] - decoded_bytes = base64.b64decode(data) + @abc.abstractmethod + def get(self, key): + """ + Retrieve an item from the cache. - # Create the full file path - file_path = os.path.join(cache_path, file_name) + Args: + key: The key of the item to retrieve. - # Save the binary content to the file - with open(file_path, "wb") as file: - file.write(decoded_bytes) + Returns: + The value associated with the key, or None if the key is not found. + """ - return file_path + @abc.abstractmethod + def set(self, key, value): + """ + Add an item to the cache. + + Args: + key: The key of the item. + value: The value to cache. + """ + + @abc.abstractmethod + def delete(self, key): + """ + Remove an item from the cache. + + Args: + key: The key of the item to remove. + """ + + @abc.abstractmethod + def clear(self): + """ + Clear all items from the cache. + """ + + @abc.abstractmethod + def __contains__(self, key): + """ + Check if the key is in the cache. + + Args: + key: The key of the item to check. + + Returns: + True if the key is in the cache, False otherwise. + """ + + @abc.abstractmethod + def __getitem__(self, key): + """ + Retrieve an item from the cache using the square bracket notation. + + Args: + key: The key of the item to retrieve. -@create_cache_folder -def save_uploaded_file(file, file_name): - cache_path = Path(tempfile.gettempdir()) / PREFIX - file_path = cache_path / file_name + @abc.abstractmethod + def __setitem__(self, key, value): + """ + Add an item to the cache using the square bracket notation. - with open(file_path, "wb") as new_file: - # Iterate over the uploaded file in small chunks to conserve memory - while chunk := file.read(8192): # Read 8KB at a time (adjust as needed) - new_file.write(chunk) + Args: + key: The key of the item. + value: The value to cache. + """ - return file_path + @abc.abstractmethod + def __delitem__(self, key): + """ + Remove an item from the cache using the square bracket notation. - -@create_cache_folder -def save_cache(hash_val: str, chat_data, clean_old_cache_files: bool): - cache_path = Path(tempfile.gettempdir()) / PREFIX / f"{hash_val}.dill" - with cache_path.open("wb") as cache_file: - dill.dump(chat_data, cache_file) - - if clean_old_cache_files: - clear_old_cache_files() - - -@create_cache_folder -def load_cache(hash_val): - cache_path = Path(tempfile.gettempdir()) / PREFIX / f"{hash_val}.dill" - if cache_path.exists(): - with cache_path.open("rb") as cache_file: - return dill.load(cache_file) - return None + Args: + key: The key of the item to remove. + """ diff --git a/src/backend/langflow/cache/flow.py b/src/backend/langflow/cache/flow.py new file mode 100644 index 000000000..6d8fee977 --- /dev/null +++ b/src/backend/langflow/cache/flow.py @@ -0,0 +1,146 @@ +import threading +import time +from collections import OrderedDict + +from langflow.cache.base import BaseCache + + +class InMemoryCache(BaseCache): + """ + A simple in-memory cache using an OrderedDict. + + This cache supports setting a maximum size and expiration time for cached items. + When the cache is full, it uses a Least Recently Used (LRU) eviction policy. + Thread-safe using a threading Lock. + + Attributes: + max_size (int, optional): Maximum number of items to store in the cache. + expiration_time (int, optional): Time in seconds after which a cached item expires. Default is 1 hour. + + Example: + + cache = InMemoryCache(max_size=3, expiration_time=5) + + # setting cache values + cache.set("a", 1) + cache.set("b", 2) + cache["c"] = 3 + + # getting cache values + a = cache.get("a") + b = cache["b"] + """ + + def __init__(self, max_size=None, expiration_time=60 * 60): + """ + Initialize a new InMemoryCache instance. + + Args: + max_size (int, optional): Maximum number of items to store in the cache. + expiration_time (int, optional): Time in seconds after which a cached item expires. Default is 1 hour. + """ + self._cache = OrderedDict() + self._lock = threading.Lock() + self.max_size = max_size + self.expiration_time = expiration_time + + def get(self, key): + """ + Retrieve an item from the cache. + + Args: + key: The key of the item to retrieve. + + Returns: + The value associated with the key, or None if the key is not found or the item has expired. + """ + with self._lock: + if key in self._cache: + item = self._cache.pop(key) + 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[key] = item + return item["value"] + else: + self.delete(key) + return None + + def set(self, key, value): + """ + Add an item to the cache. + + If the cache is full, the least recently used item is evicted. + + Args: + key: The key of the item. + value: The value to cache. + """ + with self._lock: + if key in self._cache: + # Remove existing key before re-inserting to update order + self.delete(key) + elif self.max_size and len(self._cache) >= self.max_size: + # Remove least recently used item + self._cache.popitem(last=False) + self._cache[key] = {"value": value, "time": time.time()} + + def get_or_set(self, key, value): + """ + Retrieve an item from the cache. If the item does not exist, set it with the provided value. + + Args: + key: The key of the item. + value: The value to cache if the item doesn't exist. + + Returns: + The cached value associated with the key. + """ + with self._lock: + if key in self._cache: + return self.get(key) + self.set(key, value) + return value + + def delete(self, key): + """ + Remove an item from the cache. + + Args: + key: The key of the item to remove. + """ + # with self._lock: + self._cache.pop(key, None) + + def clear(self): + """ + Clear all items from the cache. + """ + with self._lock: + self._cache.clear() + + def __contains__(self, key): + """Check if the key is in the cache.""" + return key in self._cache + + def __getitem__(self, key): + """Retrieve an item from the cache using the square bracket notation.""" + return self.get(key) + + def __setitem__(self, key, value): + """Add an item to the cache using the square bracket notation.""" + self.set(key, value) + + def __delitem__(self, key): + """Remove an item from the cache using the square bracket notation.""" + self.delete(key) + + def __len__(self): + """Return the number of items in the cache.""" + return len(self._cache) + + def __repr__(self): + """Return a string representation of the InMemoryCache instance.""" + return f"InMemoryCache(max_size={self.max_size}, expiration_time={self.expiration_time})" diff --git a/src/backend/langflow/cache/manager.py b/src/backend/langflow/cache/manager.py index 947f5ce21..13b281008 100644 --- a/src/backend/langflow/cache/manager.py +++ b/src/backend/langflow/cache/manager.py @@ -54,7 +54,7 @@ class CacheManager(Subject): def __init__(self): super().__init__() - self.CACHE = {} + self._cache = {} self.current_client_id = None self.current_cache = {} @@ -68,12 +68,12 @@ class CacheManager(Subject): """ previous_client_id = self.current_client_id self.current_client_id = client_id - self.current_cache = self.CACHE.setdefault(client_id, {}) + self.current_cache = self._cache.setdefault(client_id, {}) try: yield finally: self.current_client_id = previous_client_id - self.current_cache = self.CACHE.get(self.current_client_id, {}) + self.current_cache = self._cache.get(self.current_client_id, {}) def add(self, name: str, obj: Any, obj_type: str, extension: Optional[str] = None): """ diff --git a/src/backend/langflow/cache/utils.py b/src/backend/langflow/cache/utils.py new file mode 100644 index 000000000..9fe5a5c41 --- /dev/null +++ b/src/backend/langflow/cache/utils.py @@ -0,0 +1,167 @@ +import base64 +import contextlib +import functools +import hashlib +import json +import os +import tempfile +from collections import OrderedDict +from pathlib import Path +from typing import Any, Dict + + +CACHE: Dict[str, Any] = {} + + +def create_cache_folder(func): + def wrapper(*args, **kwargs): + # Get the destination folder + cache_path = Path(tempfile.gettempdir()) / PREFIX + + # Create the destination folder if it doesn't exist + os.makedirs(cache_path, exist_ok=True) + + return func(*args, **kwargs) + + return wrapper + + +def memoize_dict(maxsize=128): + cache = OrderedDict() + + def decorator(func): + @functools.wraps(func) + def wrapper(*args, **kwargs): + hashed = compute_dict_hash(args[0]) + key = (func.__name__, hashed, frozenset(kwargs.items())) + if key not in cache: + result = func(*args, **kwargs) + cache[key] = result + if len(cache) > maxsize: + cache.popitem(last=False) + else: + result = cache[key] + return result + + def clear_cache(): + cache.clear() + + wrapper.clear_cache = clear_cache # type: ignore + wrapper.cache = cache # type: ignore + return wrapper + + return decorator + + +PREFIX = "langflow_cache" + + +@create_cache_folder +def clear_old_cache_files(max_cache_size: int = 3): + cache_dir = Path(tempfile.gettempdir()) / PREFIX + 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 + ) + + for cache_file in cache_files_sorted_by_mtime[max_cache_size:]: + with contextlib.suppress(OSError): + os.remove(cache_file) + + +def compute_dict_hash(graph_data): + graph_data = filter_json(graph_data) + + cleaned_graph_json = json.dumps(graph_data, sort_keys=True) + return hashlib.sha256(cleaned_graph_json.encode("utf-8")).hexdigest() + + +def filter_json(json_data): + filtered_data = json_data.copy() + + # Remove 'viewport' and 'chatHistory' keys + if "viewport" in filtered_data: + del filtered_data["viewport"] + if "chatHistory" in filtered_data: + del filtered_data["chatHistory"] + + # Filter nodes + if "nodes" in filtered_data: + for node in filtered_data["nodes"]: + if "position" in node: + del node["position"] + if "positionAbsolute" in node: + del node["positionAbsolute"] + if "selected" in node: + del node["selected"] + if "dragging" in node: + del node["dragging"] + + return filtered_data + + +@create_cache_folder +def save_binary_file(content: str, file_name: str, accepted_types: list[str]) -> str: + """ + Save a binary file to the specified folder. + + Args: + content: The content of the file as a bytes object. + file_name: The name of the file, including its extension. + + Returns: + The path to the saved file. + """ + if not any(file_name.endswith(suffix) for suffix in accepted_types): + raise ValueError(f"File {file_name} is not accepted") + + # Get the destination folder + cache_path = Path(tempfile.gettempdir()) / PREFIX + if not content: + raise ValueError("Please, reload the file in the loader.") + data = content.split(",")[1] + decoded_bytes = base64.b64decode(data) + + # Create the full file path + file_path = os.path.join(cache_path, file_name) + + # Save the binary content to the file + with open(file_path, "wb") as file: + file.write(decoded_bytes) + + return file_path + + + +@create_cache_folder +def save_uploaded_file(file, file_name): + cache_path = Path(tempfile.gettempdir()) / PREFIX + file_path = cache_path / file_name + + with open(file_path, "wb") as new_file: + # Iterate over the uploaded file in small chunks to conserve memory + while chunk := file.read(8192): # Read 8KB at a time (adjust as needed) + new_file.write(chunk) + + return file_path + + +@create_cache_folder +def save_cache(hash_val: str, chat_data, clean_old_cache_files: bool): + cache_path = Path(tempfile.gettempdir()) / PREFIX / f"{hash_val}.dill" + with cache_path.open("wb") as cache_file: + dill.dump(chat_data, cache_file) + + if clean_old_cache_files: + clear_old_cache_files() + + +@create_cache_folder +def load_cache(hash_val): + cache_path = Path(tempfile.gettempdir()) / PREFIX / f"{hash_val}.dill" + if cache_path.exists(): + with cache_path.open("rb") as cache_file: + return dill.load(cache_file) + return None diff --git a/src/backend/langflow/chat/__init__.py b/src/backend/langflow/chat/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/api/chat_manager.py b/src/backend/langflow/chat/manager.py similarity index 76% rename from src/backend/langflow/api/chat_manager.py rename to src/backend/langflow/chat/manager.py index 8b1c7a621..7c3a08240 100644 --- a/src/backend/langflow/api/chat_manager.py +++ b/src/backend/langflow/chat/manager.py @@ -1,21 +1,20 @@ -import asyncio -import json from collections import defaultdict -from typing import Dict, List - from fastapi import WebSocket, status - -from langflow.api.schemas import ChatMessage, ChatResponse, FileResponse +from langflow.api.v1.schemas import ChatMessage, ChatResponse, FileResponse from langflow.cache import cache_manager from langflow.cache.manager import Subject -from langflow.interface.run import ( - get_result_and_steps, - load_or_build_langchain_object, -) -from langflow.interface.utils import pil_to_base64, try_setting_streaming_options +from langflow.chat.utils import process_graph +from langflow.interface.utils import pil_to_base64 from langflow.utils.logger import logger +import asyncio +import json +from typing import Any, Dict, List + +from langflow.cache.flow import InMemoryCache + + class ChatHistory(Subject): def __init__(self): super().__init__() @@ -49,6 +48,7 @@ class ChatManager: self.chat_history = ChatHistory() self.cache_manager = cache_manager self.cache_manager.attach(self.update) + self.in_memory_cache = InMemoryCache() def on_chat_history_update(self): """Send the last chat message to the client.""" @@ -102,24 +102,30 @@ class ChatManager: websocket = self.active_connections[client_id] await websocket.send_json(message.dict()) - async def process_message(self, client_id: str, payload: Dict): + async def close_connection(self, client_id: str, code: int, reason: str): + if websocket := self.active_connections[client_id]: + await websocket.close(code=code, reason=reason) + self.disconnect(client_id) + + async def process_message( + self, client_id: str, payload: Dict, langchain_object: Any + ): # Process the graph data and chat message chat_message = payload.pop("message", "") chat_message = ChatMessage(message=chat_message) self.chat_history.add_message(client_id, chat_message) - graph_data = payload + # graph_data = payload start_resp = ChatResponse(message=None, type="start", intermediate_steps="") await self.send_json(client_id, start_resp) - is_first_message = len(self.chat_history.get_history(client_id=client_id)) <= 1 + # is_first_message = len(self.chat_history.get_history(client_id=client_id)) <= 1 # Generate result and thought try: logger.debug("Generating result and thought") result, intermediate_steps = await process_graph( - graph_data=graph_data, - is_first_message=is_first_message, + langchain_object=langchain_object, chat_message=chat_message, websocket=self.active_connections[client_id], ) @@ -152,6 +158,14 @@ class ChatManager: await self.send_json(client_id, response) self.chat_history.add_message(client_id, response) + def set_cache(self, client_id: str, langchain_object: Any) -> bool: + """ + Set the cache for a client. + """ + + self.in_memory_cache.set(client_id, langchain_object) + return client_id in self.in_memory_cache + async def handle_websocket(self, client_id: str, websocket: WebSocket): await self.connect(client_id, websocket) @@ -172,52 +186,24 @@ class ChatManager: continue with self.cache_manager.set_client_id(client_id): - await self.process_message(client_id, payload) + langchain_object = self.in_memory_cache.get(client_id) + await self.process_message(client_id, payload, langchain_object) except Exception as e: # Handle any exceptions that might occur - logger.exception(e) - # send a message to the client - await self.active_connections[client_id].close( - code=status.WS_1011_INTERNAL_ERROR, reason=str(e)[:120] + logger.error(e) + await self.close_connection( + client_id=client_id, + code=status.WS_1011_INTERNAL_ERROR, + reason=str(e)[:120], ) - self.disconnect(client_id) finally: try: - connection = self.active_connections.get(client_id) - if connection: - await connection.close(code=1000, reason="Client disconnected") - self.disconnect(client_id) + await self.close_connection( + client_id=client_id, + code=status.WS_1000_NORMAL_CLOSURE, + reason="Client disconnected", + ) except Exception as e: - logger.exception(e) + logger.error(e) self.disconnect(client_id) - - -async def process_graph( - graph_data: Dict, - is_first_message: bool, - chat_message: ChatMessage, - websocket: WebSocket, -): - langchain_object = load_or_build_langchain_object(graph_data, is_first_message) - langchain_object = try_setting_streaming_options(langchain_object, websocket) - logger.debug("Loaded langchain object") - - if langchain_object is None: - # Raise user facing error - raise ValueError( - "There was an error loading the langchain_object. Please, check all the nodes and try again." - ) - - # Generate result and thought - try: - logger.debug("Generating result and thought") - result, intermediate_steps = await get_result_and_steps( - langchain_object, chat_message.message or "", websocket=websocket - ) - logger.debug("Generated result and intermediate_steps") - return result, intermediate_steps - except Exception as e: - # Log stack trace - logger.exception(e) - raise e diff --git a/src/backend/langflow/chat/utils.py b/src/backend/langflow/chat/utils.py new file mode 100644 index 000000000..2e2ee367f --- /dev/null +++ b/src/backend/langflow/chat/utils.py @@ -0,0 +1,33 @@ +from fastapi import WebSocket +from langflow.api.v1.schemas import ChatMessage +from langflow.processing.base import get_result_and_steps +from langflow.interface.utils import try_setting_streaming_options +from langflow.utils.logger import logger + + +async def process_graph( + langchain_object, + chat_message: ChatMessage, + websocket: WebSocket, +): + langchain_object = try_setting_streaming_options(langchain_object, websocket) + logger.debug("Loaded langchain object") + + if langchain_object is None: + # Raise user facing error + raise ValueError( + "There was an error loading the langchain_object. Please, check all the nodes and try again." + ) + + # Generate result and thought + try: + logger.debug("Generating result and thought") + result, intermediate_steps = await get_result_and_steps( + langchain_object, chat_message.message or "", websocket=websocket + ) + logger.debug("Generated result and intermediate_steps") + return result, intermediate_steps + except Exception as e: + # Log stack trace + logger.exception(e) + raise e diff --git a/src/backend/langflow/config.yaml b/src/backend/langflow/config.yaml index 5e6a5cbed..a5e952fc3 100644 --- a/src/backend/langflow/config.yaml +++ b/src/backend/langflow/config.yaml @@ -3,7 +3,7 @@ agents: - ZeroShotAgent - JsonAgent - CSVAgent - - initialize_agent + - AgentInitializer - VectorStoreAgent - VectorStoreRouterAgent - SQLAgent @@ -16,6 +16,10 @@ chains: - MidJourneyPromptChain - TimeTravelGuideChain - SQLDatabaseChain + - RetrievalQA + - RetrievalQAWithSourcesChain + - ConversationalRetrievalChain + - CombineDocsChain documentloaders: - AirbyteJSONLoader - CoNLLULoader @@ -51,10 +55,14 @@ embeddings: llms: - OpenAI # - AzureOpenAI + # - AzureChatOpenAI - ChatOpenAI - - LlamaCpp + - LlamaCpp - CTransformers - Cohere + - Anthropic + - ChatAnthropic + - HuggingFaceHub memories: - ConversationBufferMemory - ConversationSummaryMemory @@ -73,12 +81,14 @@ toolkits: - JsonToolkit - VectorStoreInfo - VectorStoreRouterToolkit + - VectorStoreToolkit tools: - Search - PAL-MATH - Calculator - Serper Search - Tool + - PythonFunctionTool - PythonFunction - JsonSpec - News API @@ -118,6 +128,7 @@ vectorstores: - Chroma - Qdrant - Weaviate + - FAISS wrappers: - RequestsWrapper # - ChatPromptTemplate diff --git a/src/backend/langflow/custom/customs.py b/src/backend/langflow/custom/customs.py index ee266b0ee..fb6c1da16 100644 --- a/src/backend/langflow/custom/customs.py +++ b/src/backend/langflow/custom/customs.py @@ -2,15 +2,18 @@ from langflow.template import frontend_node # These should always be instantiated CUSTOM_NODES = { - "prompts": {"ZeroShotPrompt": frontend_node.prompts.ZeroShotPromptNode()}, + "prompts": { + "ZeroShotPrompt": frontend_node.prompts.ZeroShotPromptNode(), + }, "tools": { + "PythonFunctionTool": frontend_node.tools.PythonFunctionToolNode(), "PythonFunction": frontend_node.tools.PythonFunctionNode(), "Tool": frontend_node.tools.ToolNode(), }, "agents": { "JsonAgent": frontend_node.agents.JsonAgentNode(), "CSVAgent": frontend_node.agents.CSVAgentNode(), - "initialize_agent": frontend_node.agents.InitializeAgentNode(), + "AgentInitializer": frontend_node.agents.InitializeAgentNode(), "VectorStoreAgent": frontend_node.agents.VectorStoreAgentNode(), "VectorStoreRouterAgent": frontend_node.agents.VectorStoreRouterAgentNode(), "SQLAgent": frontend_node.agents.SQLAgentNode(), @@ -22,6 +25,7 @@ CUSTOM_NODES = { "SeriesCharacterChain": frontend_node.chains.SeriesCharacterChainNode(), "TimeTravelGuideChain": frontend_node.chains.TimeTravelGuideChainNode(), "MidJourneyPromptChain": frontend_node.chains.MidJourneyPromptChainNode(), + "load_qa_chain": frontend_node.chains.CombineDocsChainNode(), }, } diff --git a/src/backend/langflow/database/__init__.py b/src/backend/langflow/database/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/database/base.py b/src/backend/langflow/database/base.py new file mode 100644 index 000000000..69f8b993b --- /dev/null +++ b/src/backend/langflow/database/base.py @@ -0,0 +1,18 @@ +from langflow.settings import settings +from sqlmodel import SQLModel, Session, create_engine + + +if settings.database_url.startswith("sqlite"): + connect_args = {"check_same_thread": False} +else: + connect_args = {} +engine = create_engine(settings.database_url, connect_args=connect_args) + + +def create_db_and_tables(): + SQLModel.metadata.create_all(engine) + + +def get_session(): + with Session(engine) as session: + yield session diff --git a/src/backend/langflow/database/models/__init__.py b/src/backend/langflow/database/models/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/database/models/base.py b/src/backend/langflow/database/models/base.py new file mode 100644 index 000000000..e20895b93 --- /dev/null +++ b/src/backend/langflow/database/models/base.py @@ -0,0 +1,14 @@ +from sqlmodel import SQLModel +import orjson + + +def orjson_dumps(v, *, default): + # orjson.dumps returns bytes, to match standard json.dumps we need to decode + return orjson.dumps(v, default=default).decode() + + +class SQLModelSerializable(SQLModel): + class Config: + orm_mode = True + json_loads = orjson.loads + json_dumps = orjson_dumps diff --git a/src/backend/langflow/database/models/flow.py b/src/backend/langflow/database/models/flow.py new file mode 100644 index 000000000..f9e3aa249 --- /dev/null +++ b/src/backend/langflow/database/models/flow.py @@ -0,0 +1,60 @@ +# Path: src/backend/langflow/database/models/flow.py + +from langflow.database.models.base import SQLModelSerializable +from pydantic import validator +from sqlmodel import Field, Relationship, JSON, Column +from uuid import UUID, uuid4 +from typing import Dict, Optional + +# if TYPE_CHECKING: +from langflow.database.models.flow_style import FlowStyle, FlowStyleRead + + +class FlowBase(SQLModelSerializable): + name: str = Field(index=True) + description: Optional[str] = Field(index=True) + data: Optional[Dict] = Field(default=None) + + @validator("data") + def validate_json(v): + # dict_keys(['description', 'name', 'id', 'data']) + if not v: + return v + if not isinstance(v, dict): + raise ValueError("Flow must be a valid JSON") + + # data must contain nodes and edges + if "nodes" not in v.keys(): + raise ValueError("Flow must have nodes") + if "edges" not in v.keys(): + raise ValueError("Flow must have edges") + + return v + + +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)) + style: Optional["FlowStyle"] = Relationship( + back_populates="flow", + # use "uselist=False" to make it a one-to-one relationship + sa_relationship_kwargs={"uselist": False}, + ) + + +class FlowCreate(FlowBase): + pass + + +class FlowRead(FlowBase): + id: UUID + + +class FlowReadWithStyle(FlowRead): + style: Optional["FlowStyleRead"] = None + + +class FlowUpdate(SQLModelSerializable): + name: Optional[str] = None + description: Optional[str] = None + data: Optional[Dict] = None diff --git a/src/backend/langflow/database/models/flow_style.py b/src/backend/langflow/database/models/flow_style.py new file mode 100644 index 000000000..fe53799fe --- /dev/null +++ b/src/backend/langflow/database/models/flow_style.py @@ -0,0 +1,33 @@ +# Path: src/backend/langflow/database/models/flowstyle.py + +from langflow.database.models.base import SQLModelSerializable +from sqlmodel import Field, Relationship +from uuid import UUID, uuid4 +from typing import TYPE_CHECKING, Optional + +if TYPE_CHECKING: + from langflow.database.models.flow import Flow + + +class FlowStyleBase(SQLModelSerializable): + color: str + emoji: str + flow_id: UUID = Field(default=None, foreign_key="flow.id") + + +class FlowStyle(FlowStyleBase, table=True): + id: UUID = Field(default_factory=uuid4, primary_key=True, unique=True) + flow: "Flow" = Relationship(back_populates="style") + + +class FlowStyleUpdate(SQLModelSerializable): + color: Optional[str] = None + emoji: Optional[str] = None + + +class FlowStyleCreate(FlowStyleBase): + pass + + +class FlowStyleRead(FlowStyleBase): + id: UUID diff --git a/src/backend/langflow/graph/__init__.py b/src/backend/langflow/graph/__init__.py index 097b7a695..a68e844ee 100644 --- a/src/backend/langflow/graph/__init__.py +++ b/src/backend/langflow/graph/__init__.py @@ -1,4 +1,35 @@ -from langflow.graph.base import Edge, Node -from langflow.graph.graph import Graph +from langflow.graph.edge.base import Edge +from langflow.graph.graph.base import Graph +from langflow.graph.vertex.base import Vertex +from langflow.graph.vertex.types import ( + AgentVertex, + ChainVertex, + DocumentLoaderVertex, + EmbeddingVertex, + LLMVertex, + MemoryVertex, + PromptVertex, + TextSplitterVertex, + ToolVertex, + ToolkitVertex, + VectorStoreVertex, + WrapperVertex, +) -__all__ = ["Graph", "Node", "Edge"] +__all__ = [ + "Graph", + "Vertex", + "Edge", + "AgentVertex", + "ChainVertex", + "DocumentLoaderVertex", + "EmbeddingVertex", + "LLMVertex", + "MemoryVertex", + "PromptVertex", + "TextSplitterVertex", + "ToolVertex", + "ToolkitVertex", + "VectorStoreVertex", + "WrapperVertex", +] diff --git a/src/backend/langflow/graph/edge/__init__.py b/src/backend/langflow/graph/edge/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/graph/edge/base.py b/src/backend/langflow/graph/edge/base.py new file mode 100644 index 000000000..08f084a5c --- /dev/null +++ b/src/backend/langflow/graph/edge/base.py @@ -0,0 +1,52 @@ +from langflow.utils.logger import logger +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from langflow.graph.vertex.base import Vertex + + +class Edge: + def __init__(self, source: "Vertex", target: "Vertex"): + self.source: "Vertex" = source + self.target: "Vertex" = target + self.validate_edge() + + def validate_edge(self) -> None: + # Validate that the outputs of the source node are valid inputs + # for the target node + self.source_types = self.source.output + self.target_reqs = self.target.required_inputs + self.target.optional_inputs + # 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 + ) + # Get what type of input the target node is expecting + + self.matched_type = next( + ( + output + for output in self.source_types + for target_req in self.target_reqs + if output in target_req + ), + None, + ) + no_matched_type = self.matched_type is None + if no_matched_type: + logger.debug(self.source_types) + logger.debug(self.target_reqs) + if no_matched_type: + raise ValueError( + f"Edge between {self.source.vertex_type} and {self.target.vertex_type} " + f"has no matched type" + ) + + def __repr__(self) -> str: + return ( + f"Edge(source={self.source.id}, target={self.target.id}, valid={self.valid}" + f", matched_type={self.matched_type})" + ) diff --git a/src/backend/langflow/graph/graph.py b/src/backend/langflow/graph/graph.py deleted file mode 100644 index b289d5c31..000000000 --- a/src/backend/langflow/graph/graph.py +++ /dev/null @@ -1,166 +0,0 @@ -from typing import Dict, List, Type, Union - -from langflow.graph.base import Edge, Node -from langflow.graph.nodes import ( - AgentNode, - ChainNode, - DocumentLoaderNode, - EmbeddingNode, - FileToolNode, - LLMNode, - MemoryNode, - PromptNode, - TextSplitterNode, - ToolkitNode, - ToolNode, - VectorStoreNode, - WrapperNode, -) -from langflow.interface.agents.base import agent_creator -from langflow.interface.chains.base import chain_creator -from langflow.interface.document_loaders.base import documentloader_creator -from langflow.interface.embeddings.base import embedding_creator -from langflow.interface.llms.base import llm_creator -from langflow.interface.memories.base import memory_creator -from langflow.interface.prompts.base import prompt_creator -from langflow.interface.text_splitters.base import textsplitter_creator -from langflow.interface.toolkits.base import toolkits_creator -from langflow.interface.tools.base import tool_creator -from langflow.interface.tools.constants import FILE_TOOLS -from langflow.interface.vector_store.base import vectorstore_creator -from langflow.interface.wrappers.base import wrapper_creator -from langflow.utils import payload - - -class Graph: - def __init__( - self, - nodes: List[Dict[str, Union[str, Dict[str, Union[str, List[str]]]]]], - edges: List[Dict[str, str]], - ) -> None: - self._nodes = nodes - self._edges = edges - self._build_graph() - - def _build_graph(self) -> None: - self.nodes = self._build_nodes() - self.edges = self._build_edges() - for edge in self.edges: - edge.source.add_edge(edge) - edge.target.add_edge(edge) - - # This is a hack to make sure that the LLM node is sent to - # the toolkit node - llm_node = None - for node in self.nodes: - node._build_params() - - if isinstance(node, LLMNode): - llm_node = node - - for node in self.nodes: - if isinstance(node, ToolkitNode): - node.params["llm"] = llm_node - # remove invalid nodes - self.nodes = [ - node - for node in self.nodes - if self._validate_node(node) - or (len(self.nodes) == 1 and len(self.edges) == 0) - ] - - def _validate_node(self, node: Node) -> bool: - # All nodes that do not have edges are invalid - return len(node.edges) > 0 - - def get_node(self, node_id: str) -> Union[None, Node]: - return next((node for node in self.nodes if node.id == node_id), None) - - def get_nodes_with_target(self, node: Node) -> List[Node]: - connected_nodes: List[Node] = [ - edge.source for edge in self.edges if edge.target == node - ] - return connected_nodes - - def build(self) -> List[Node]: - # Get root node - root_node = payload.get_root_node(self) - if root_node is None: - raise ValueError("No root node found") - return root_node.build() - - def get_node_neighbors(self, node: Node) -> Dict[Node, int]: - neighbors: Dict[Node, int] = {} - for edge in self.edges: - if edge.source == node: - neighbor = edge.target - if neighbor not in neighbors: - neighbors[neighbor] = 0 - neighbors[neighbor] += 1 - elif edge.target == node: - neighbor = edge.source - if neighbor not in neighbors: - neighbors[neighbor] = 0 - neighbors[neighbor] += 1 - return neighbors - - def _build_edges(self) -> List[Edge]: - # Edge takes two nodes as arguments, so we need to build the nodes first - # and then build the edges - # if we can't find a node, we raise an error - - edges: List[Edge] = [] - for edge in self._edges: - source = self.get_node(edge["source"]) - target = self.get_node(edge["target"]) - if source is None: - raise ValueError(f"Source node {edge['source']} not found") - if target is None: - raise ValueError(f"Target node {edge['target']} not found") - edges.append(Edge(source, target)) - return edges - - def _get_node_class(self, node_type: str, node_lc_type: str) -> Type[Node]: - node_type_map: Dict[str, Type[Node]] = { - **{t: PromptNode for t in prompt_creator.to_list()}, - **{t: AgentNode for t in agent_creator.to_list()}, - **{t: ChainNode for t in chain_creator.to_list()}, - **{t: ToolNode for t in tool_creator.to_list()}, - **{t: ToolkitNode for t in toolkits_creator.to_list()}, - **{t: WrapperNode for t in wrapper_creator.to_list()}, - **{t: LLMNode for t in llm_creator.to_list()}, - **{t: MemoryNode for t in memory_creator.to_list()}, - **{t: EmbeddingNode for t in embedding_creator.to_list()}, - **{t: VectorStoreNode for t in vectorstore_creator.to_list()}, - **{t: DocumentLoaderNode for t in documentloader_creator.to_list()}, - **{t: TextSplitterNode for t in textsplitter_creator.to_list()}, - } - - if node_type in FILE_TOOLS: - return FileToolNode - if node_type in node_type_map: - return node_type_map[node_type] - if node_lc_type in node_type_map: - return node_type_map[node_lc_type] - return Node - - def _build_nodes(self) -> List[Node]: - nodes: List[Node] = [] - for node in self._nodes: - node_data = node["data"] - node_type: str = node_data["type"] # type: ignore - node_lc_type: str = node_data["node"]["template"]["_type"] # type: ignore - - NodeClass = self._get_node_class(node_type, node_lc_type) - nodes.append(NodeClass(node)) - - return nodes - - def get_children_by_node_type(self, node: Node, node_type: str) -> List[Node]: - children = [] - node_types = [node.data["type"]] - if "node" in node.data: - node_types += node.data["node"]["base_classes"] - if node_type in node_types: - children.append(node) - return children diff --git a/src/backend/langflow/graph/graph/__init__.py b/src/backend/langflow/graph/graph/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/graph/graph/base.py b/src/backend/langflow/graph/graph/base.py new file mode 100644 index 000000000..4fa2f4d17 --- /dev/null +++ b/src/backend/langflow/graph/graph/base.py @@ -0,0 +1,216 @@ +from typing import Dict, Generator, List, Type, Union + +from langflow.graph.edge.base import Edge +from langflow.graph.graph.constants import VERTEX_TYPE_MAP +from langflow.graph.vertex.base import Vertex +from langflow.graph.vertex.types import ( + FileToolVertex, + LLMVertex, + ToolkitVertex, +) +from langflow.interface.tools.constants import FILE_TOOLS +from langflow.utils import payload +from langflow.utils.logger import logger +from langchain.chains.base import Chain + + +class Graph: + """A class representing a graph of nodes and edges.""" + + def __init__( + self, + nodes: List[Dict[str, Union[str, Dict[str, Union[str, List[str]]]]]], + edges: List[Dict[str, str]], + ) -> None: + self._nodes = nodes + self._edges = edges + self._build_graph() + + @classmethod + def from_payload(cls, payload: Dict) -> "Graph": + """ + Creates a graph from a payload. + + Args: + payload (Dict): The payload to create the graph from.˜` + + Returns: + Graph: The created graph. + """ + if "data" in payload: + payload = payload["data"] + try: + nodes = payload["nodes"] + edges = payload["edges"] + return cls(nodes, edges) + except KeyError as exc: + raise ValueError( + f"Invalid payload. Expected keys 'nodes' and 'edges'. Found {list(payload.keys())}" + ) from exc + + def _build_graph(self) -> None: + """Builds the graph from the nodes and edges.""" + self.nodes = self._build_vertices() + self.edges = self._build_edges() + for edge in self.edges: + edge.source.add_edge(edge) + edge.target.add_edge(edge) + + # This is a hack to make sure that the LLM node is sent to + # the toolkit node + self._build_node_params() + # remove invalid nodes + self._remove_invalid_nodes() + + def _build_node_params(self) -> None: + """Identifies and handles the LLM node within the graph.""" + llm_node = None + for node in self.nodes: + node._build_params() + if isinstance(node, LLMVertex): + llm_node = node + + if llm_node: + for node in self.nodes: + if isinstance(node, ToolkitVertex): + node.params["llm"] = llm_node + + def _remove_invalid_nodes(self) -> None: + """Removes invalid nodes from the graph.""" + self.nodes = [ + node + for node in self.nodes + if self._validate_node(node) + or (len(self.nodes) == 1 and len(self.edges) == 0) + ] + + def _validate_node(self, node: Vertex) -> bool: + """Validates a node.""" + # All nodes that do not have edges are invalid + return len(node.edges) > 0 + + def get_node(self, node_id: str) -> Union[None, Vertex]: + """Returns a node by id.""" + return next((node for node in self.nodes if node.id == node_id), None) + + 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 + ] + return connected_nodes + + def build(self) -> Chain: + """Builds the graph.""" + # Get root node + root_node = payload.get_root_node(self) + if root_node is None: + raise ValueError("No root node found") + return root_node.build() + + def topological_sort(self) -> List[Vertex]: + """ + Performs a topological sort of the vertices in the graph. + + Returns: + List[Vertex]: A list of vertices in topological order. + + Raises: + ValueError: If the graph contains a cycle. + """ + # States: 0 = unvisited, 1 = visiting, 2 = visited + state = {node: 0 for node in self.nodes} + sorted_vertices = [] + + def dfs(node): + if state[node] == 1: + # We have a cycle + raise ValueError( + "Graph contains a cycle, cannot perform topological sort" + ) + if state[node] == 0: + state[node] = 1 + for edge in node.edges: + if edge.source == node: + dfs(edge.target) + state[node] = 2 + sorted_vertices.append(node) + + # Visit each node + for node in self.nodes: + if state[node] == 0: + dfs(node) + + return list(reversed(sorted_vertices)) + + def generator_build(self) -> Generator: + """Builds each vertex in the graph and yields it.""" + sorted_vertices = self.topological_sort() + logger.info("Sorted vertices: %s", sorted_vertices) + yield from sorted_vertices + + def get_node_neighbors(self, node: Vertex) -> Dict[Vertex, int]: + """Returns the neighbors of a node.""" + neighbors: Dict[Vertex, int] = {} + for edge in self.edges: + if edge.source == node: + neighbor = edge.target + if neighbor not in neighbors: + neighbors[neighbor] = 0 + neighbors[neighbor] += 1 + elif edge.target == node: + neighbor = edge.source + if neighbor not in neighbors: + neighbors[neighbor] = 0 + neighbors[neighbor] += 1 + return neighbors + + def _build_edges(self) -> List[Edge]: + """Builds the edges of the graph.""" + # Edge takes two nodes as arguments, so we need to build the nodes first + # and then build the edges + # if we can't find a node, we raise an error + + edges: List[Edge] = [] + for edge in self._edges: + source = self.get_node(edge["source"]) + target = self.get_node(edge["target"]) + if source is None: + raise ValueError(f"Source node {edge['source']} not found") + if target is None: + raise ValueError(f"Target node {edge['target']} not found") + edges.append(Edge(source, target)) + return edges + + def _get_vertex_class(self, node_type: str, node_lc_type: str) -> Type[Vertex]: + """Returns the node class based on the node type.""" + if node_type in FILE_TOOLS: + return FileToolVertex + if node_type in VERTEX_TYPE_MAP: + return VERTEX_TYPE_MAP[node_type] + return ( + VERTEX_TYPE_MAP[node_lc_type] if node_lc_type in VERTEX_TYPE_MAP else Vertex + ) + + def _build_vertices(self) -> List[Vertex]: + """Builds the vertices of the graph.""" + nodes: List[Vertex] = [] + for node in self._nodes: + node_data = node["data"] + node_type: str = node_data["type"] # type: ignore + node_lc_type: str = node_data["node"]["template"]["_type"] # type: ignore + + VertexClass = self._get_vertex_class(node_type, node_lc_type) + nodes.append(VertexClass(node)) + + return nodes + + def get_children_by_node_type(self, node: Vertex, node_type: str) -> List[Vertex]: + """Returns the children of a node based on the node type.""" + children = [] + node_types = [node.data["type"]] + if "node" in node.data: + node_types += node.data["node"]["base_classes"] + if node_type in node_types: + children.append(node) + return children diff --git a/src/backend/langflow/graph/graph/constants.py b/src/backend/langflow/graph/graph/constants.py new file mode 100644 index 000000000..ff1317d39 --- /dev/null +++ b/src/backend/langflow/graph/graph/constants.py @@ -0,0 +1,49 @@ +from langflow.graph.vertex.base import Vertex +from langflow.graph.vertex.types import ( + AgentVertex, + ChainVertex, + DocumentLoaderVertex, + EmbeddingVertex, + LLMVertex, + MemoryVertex, + PromptVertex, + TextSplitterVertex, + ToolVertex, + ToolkitVertex, + VectorStoreVertex, + WrapperVertex, +) +from langflow.interface.agents.base import agent_creator +from langflow.interface.chains.base import chain_creator +from langflow.interface.document_loaders.base import documentloader_creator +from langflow.interface.embeddings.base import embedding_creator +from langflow.interface.llms.base import llm_creator +from langflow.interface.memories.base import memory_creator +from langflow.interface.prompts.base import prompt_creator +from langflow.interface.text_splitters.base import textsplitter_creator +from langflow.interface.toolkits.base import toolkits_creator +from langflow.interface.tools.base import tool_creator +from langflow.interface.vector_store.base import vectorstore_creator +from langflow.interface.wrappers.base import wrapper_creator + + +from typing import Dict, Type + + +DIRECT_TYPES = ["str", "bool", "code", "int", "float", "Any", "prompt"] + + +VERTEX_TYPE_MAP: Dict[str, Type[Vertex]] = { + **{t: PromptVertex for t in prompt_creator.to_list()}, + **{t: AgentVertex for t in agent_creator.to_list()}, + **{t: ChainVertex for t in chain_creator.to_list()}, + **{t: ToolVertex for t in tool_creator.to_list()}, + **{t: ToolkitVertex for t in toolkits_creator.to_list()}, + **{t: WrapperVertex for t in wrapper_creator.to_list()}, + **{t: LLMVertex for t in llm_creator.to_list()}, + **{t: MemoryVertex for t in memory_creator.to_list()}, + **{t: EmbeddingVertex for t in embedding_creator.to_list()}, + **{t: VectorStoreVertex for t in vectorstore_creator.to_list()}, + **{t: DocumentLoaderVertex for t in documentloader_creator.to_list()}, + **{t: TextSplitterVertex for t in textsplitter_creator.to_list()}, +} diff --git a/src/backend/langflow/graph/graph/utils.py b/src/backend/langflow/graph/graph/utils.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/graph/utils.py b/src/backend/langflow/graph/utils.py index 6d56e933e..b78b2f961 100644 --- a/src/backend/langflow/graph/utils.py +++ b/src/backend/langflow/graph/utils.py @@ -1,4 +1,6 @@ -import re +from typing import Any, Union + +from langflow.interface.utils import extract_input_variables_from_prompt def validate_prompt(prompt: str): @@ -14,6 +16,12 @@ def fix_prompt(prompt: str): return prompt + " {input}" -def extract_input_variables_from_prompt(prompt: str) -> list[str]: - """Extract input variables from prompt.""" - return re.findall(r"{(.*?)}", prompt) +def flatten_list(list_of_lists: list[Union[list, Any]]) -> list: + """Flatten list of lists.""" + new_list = [] + for item in list_of_lists: + if isinstance(item, list): + new_list.extend(item) + else: + new_list.append(item) + return new_list diff --git a/src/backend/langflow/graph/vertex/__init__.py b/src/backend/langflow/graph/vertex/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/graph/base.py b/src/backend/langflow/graph/vertex/base.py similarity index 74% rename from src/backend/langflow/graph/base.py rename to src/backend/langflow/graph/vertex/base.py index 846580a05..d7bd82fa7 100644 --- a/src/backend/langflow/graph/base.py +++ b/src/backend/langflow/graph/vertex/base.py @@ -1,26 +1,27 @@ -# Description: Graph class for building a graph of nodes and edges -# Insights: -# - Defer prompts building to the last moment or when they have all the tools -# - Build each inner agent first, then build the outer agent - -import contextlib -import inspect -import types -import warnings -from typing import Any, Dict, List, Optional - -from langflow.graph.constants import DIRECT_TYPES +from langflow.cache import utils as cache_utils +from langflow.graph.vertex.constants import DIRECT_TYPES from langflow.interface import loading from langflow.interface.listing import ALL_TYPES_DICT from langflow.utils.logger import logger from langflow.utils.util import sync_to_async -class Node: +import contextlib +import inspect +import types +import warnings +from typing import Any, Dict, List, Optional +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from langflow.graph.edge.base import Edge + + +class Vertex: def __init__(self, data: Dict, base_type: Optional[str] = None) -> None: self.id: str = data["id"] self._data = data - self.edges: List[Edge] = [] + self.edges: List["Edge"] = [] self.base_type: Optional[str] = base_type self._parse_data() self._built_object = None @@ -47,12 +48,15 @@ class Node: ] template_dict = self.data["node"]["template"] - self.node_type = ( - self.data["type"] if "Tool" not in self.output else template_dict["_type"] + self.vertex_type = ( + self.data["type"] + if "Tool" not in self.output or template_dict["_type"].islower() + else template_dict["_type"] ) + if self.base_type is None: for base_type, value in ALL_TYPES_DICT.items(): - if self.node_type in value: + if self.vertex_type in value: self.base_type = base_type break @@ -107,7 +111,7 @@ class Node: if value["required"] and not edges: # If a required parameter is not found, raise an error raise ValueError( - f"Required input {key} for module {self.node_type} not found" + f"Required input {key} for module {self.vertex_type} not found" ) elif value["list"]: # If this is a list parameter, append all sources to a list @@ -122,7 +126,7 @@ class Node: # so we need to check if value has value new_value = value.get("value") if new_value is None: - warnings.warn(f"Value for {key} in {self.node_type} is None. ") + warnings.warn(f"Value for {key} in {self.vertex_type} is None. ") if value.get("type") == "int": with contextlib.suppress(TypeError, ValueError): new_value = int(new_value) # type: ignore @@ -142,12 +146,12 @@ class Node: # and continue # Another aspect is that the node_type is the class that we need to import # and instantiate with these built params - logger.debug(f"Building {self.node_type}") + logger.debug(f"Building {self.vertex_type}") # Build each node in the params dict for key, value in self.params.copy().items(): # Check if Node or list of Nodes and not self # to avoid recursion - if isinstance(value, Node): + if isinstance(value, Vertex): if value == self: del self.params[key] continue @@ -168,10 +172,16 @@ class Node: # turn result which is a function into a coroutine # so that it can be awaited self.params["coroutine"] = sync_to_async(result) + if isinstance(result, list): + # If the result is a list, then we need to extend the list + # with the result but first check if the key exists + # if it doesn't, then we need to create a new list + if isinstance(self.params[key], list): + self.params[key].extend(result) self.params[key] = result elif isinstance(value, list) and all( - isinstance(node, Node) for node in value + isinstance(node, Vertex) for node in value ): self.params[key] = [] for node in value: @@ -187,17 +197,17 @@ class Node: try: self._built_object = loading.instantiate_class( - node_type=self.node_type, + node_type=self.vertex_type, base_type=self.base_type, params=self.params, ) except Exception as exc: raise ValueError( - f"Error building node {self.node_type}: {str(exc)}" + f"Error building node {self.vertex_type}: {str(exc)}" ) from exc if self._built_object is None: - raise ValueError(f"Node type {self.node_type} not found") + raise ValueError(f"Node type {self.vertex_type} not found") self._built = True @@ -214,57 +224,10 @@ class Node: return f"Node(id={self.id}, data={self.data})" def __eq__(self, __o: object) -> bool: - return self.id == __o.id if isinstance(__o, Node) else False + return self.id == __o.id if isinstance(__o, Vertex) else False def __hash__(self) -> int: return id(self) def _built_object_repr(self): return repr(self._built_object) - - -class Edge: - def __init__(self, source: "Node", target: "Node"): - self.source: "Node" = source - self.target: "Node" = target - self.validate_edge() - - def validate_edge(self) -> None: - # Validate that the outputs of the source node are valid inputs - # for the target node - self.source_types = self.source.output - self.target_reqs = self.target.required_inputs + self.target.optional_inputs - # 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 - ) - # Get what type of input the target node is expecting - - self.matched_type = next( - ( - output - for output in self.source_types - for target_req in self.target_reqs - if output in target_req - ), - None, - ) - no_matched_type = self.matched_type is None - if no_matched_type: - logger.debug(self.source_types) - logger.debug(self.target_reqs) - if no_matched_type: - raise ValueError( - f"Edge between {self.source.node_type} and {self.target.node_type} " - f"has no matched type" - ) - - def __repr__(self) -> str: - return ( - f"Edge(source={self.source.id}, target={self.target.id}, valid={self.valid}" - f", matched_type={self.matched_type})" - ) diff --git a/src/backend/langflow/graph/constants.py b/src/backend/langflow/graph/vertex/constants.py similarity index 100% rename from src/backend/langflow/graph/constants.py rename to src/backend/langflow/graph/vertex/constants.py diff --git a/src/backend/langflow/graph/nodes.py b/src/backend/langflow/graph/vertex/types.py similarity index 72% rename from src/backend/langflow/graph/nodes.py rename to src/backend/langflow/graph/vertex/types.py index 189e40b5c..4eb20f416 100644 --- a/src/backend/langflow/graph/nodes.py +++ b/src/backend/langflow/graph/vertex/types.py @@ -1,22 +1,23 @@ from typing import Any, Dict, List, Optional, Union -from langflow.graph.base import Node -from langflow.graph.utils import extract_input_variables_from_prompt +from langflow.graph.vertex.base import Vertex +from langflow.graph.utils import flatten_list +from langflow.interface.utils import extract_input_variables_from_prompt -class AgentNode(Node): +class AgentVertex(Vertex): def __init__(self, data: Dict): super().__init__(data, base_type="agents") - self.tools: List[ToolNode] = [] - self.chains: List[ChainNode] = [] + self.tools: List[Union[ToolkitVertex, ToolVertex]] = [] + self.chains: List[ChainVertex] = [] def _set_tools_and_chains(self) -> None: for edge in self.edges: source_node = edge.source - if isinstance(source_node, ToolNode): + if isinstance(source_node, (ToolVertex, ToolkitVertex)): self.tools.append(source_node) - elif isinstance(source_node, ChainNode): + elif isinstance(source_node, ChainVertex): self.chains.append(source_node) def build(self, force: bool = False) -> Any: @@ -32,25 +33,130 @@ class AgentNode(Node): self._build() - #! Cannot deepcopy VectorStore, VectorStoreRouter, or SQL agents - if self.node_type in ["VectorStoreAgent", "VectorStoreRouterAgent", "SQLAgent"]: - return self._built_object return self._built_object -class ToolNode(Node): +class ToolVertex(Vertex): def __init__(self, data: Dict): super().__init__(data, base_type="tools") -class PromptNode(Node): +class LLMVertex(Vertex): + built_node_type = None + class_built_object = None + + def __init__(self, data: Dict): + super().__init__(data, base_type="llms") + + def build(self, force: bool = False) -> Any: + # LLM is different because some models might take up too much memory + # or time to load. So we only load them when we need them.ß + if self.vertex_type == self.built_node_type: + return self.class_built_object + if not self._built or force: + self._build() + self.built_node_type = self.vertex_type + self.class_built_object = self._built_object + # Avoid deepcopying the LLM + # that are loaded from a file + return self._built_object + + +class ToolkitVertex(Vertex): + def __init__(self, data: Dict): + super().__init__(data, base_type="toolkits") + + +class FileToolVertex(ToolVertex): + def __init__(self, data: Dict): + super().__init__(data) + + +class WrapperVertex(Vertex): + def __init__(self, data: Dict): + super().__init__(data, base_type="wrappers") + + def build(self, force: bool = False) -> Any: + if not self._built or force: + if "headers" in self.params: + self.params["headers"] = eval(self.params["headers"]) + self._build() + return self._built_object + + +class DocumentLoaderVertex(Vertex): + def __init__(self, data: Dict): + super().__init__(data, base_type="documentloaders") + + def _built_object_repr(self): + # This built_object is a list of documents. Maybe we should + # show how many documents are in the list? + if self._built_object: + return f"""{self.vertex_type}({len(self._built_object)} documents) + Documents: {self._built_object[:3]}...""" + return f"{self.vertex_type}()" + + +class EmbeddingVertex(Vertex): + def __init__(self, data: Dict): + super().__init__(data, base_type="embeddings") + + +class VectorStoreVertex(Vertex): + def __init__(self, data: Dict): + super().__init__(data, base_type="vectorstores") + + def _built_object_repr(self): + return "Vector stores can take time to build. It will build on the first query." + + +class MemoryVertex(Vertex): + def __init__(self, data: Dict): + super().__init__(data, base_type="memory") + + +class TextSplitterVertex(Vertex): + def __init__(self, data: Dict): + super().__init__(data, base_type="textsplitters") + + def _built_object_repr(self): + # This built_object is a list of documents. Maybe we should + # show how many documents are in the list? + if self._built_object: + return f"""{self.vertex_type}({len(self._built_object)} documents) + \nDocuments: {self._built_object[:3]}...""" + return f"{self.vertex_type}()" + + +class ChainVertex(Vertex): + def __init__(self, data: Dict): + super().__init__(data, base_type="chains") + + def build( + self, + force: bool = False, + tools: Optional[List[Union[ToolkitVertex, ToolVertex]]] = None, + ) -> Any: + if not self._built or force: + # Check if the chain requires a PromptVertex + for key, value in self.params.items(): + if isinstance(value, PromptVertex): + # Build the PromptVertex, passing the tools if available + self.params[key] = value.build(tools=tools, force=force) + + self._build() + + return self._built_object + + +class PromptVertex(Vertex): def __init__(self, data: Dict): super().__init__(data, base_type="prompts") def build( self, force: bool = False, - tools: Optional[Union[List[Node], List[ToolNode]]] = None, + tools: Optional[List[Union[ToolkitVertex, ToolVertex]]] = None, ) -> Any: if not self._built or force: if ( @@ -59,12 +165,16 @@ class PromptNode(Node): ): self.params["input_variables"] = [] # Check if it is a ZeroShotPrompt and needs a tool - if "ShotPrompt" in self.node_type: + if "ShotPrompt" in self.vertex_type: tools = ( [tool_node.build() 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 @@ -81,113 +191,3 @@ class PromptNode(Node): self._build() return self._built_object - - -class ChainNode(Node): - def __init__(self, data: Dict): - super().__init__(data, base_type="chains") - - def build( - self, - force: bool = False, - tools: Optional[Union[List[Node], List[ToolNode]]] = None, - ) -> Any: - if not self._built or force: - # Check if the chain requires a PromptNode - for key, value in self.params.items(): - if isinstance(value, PromptNode): - # Build the PromptNode, passing the tools if available - self.params[key] = value.build(tools=tools, force=force) - - self._build() - - #! Cannot deepcopy SQLDatabaseChain - if self.node_type in ["SQLDatabaseChain"]: - return self._built_object - return self._built_object - - -class LLMNode(Node): - built_node_type = None - class_built_object = None - - def __init__(self, data: Dict): - super().__init__(data, base_type="llms") - - def build(self, force: bool = False) -> Any: - # LLM is different because some models might take up too much memory - # or time to load. So we only load them when we need them.ß - if self.node_type == self.built_node_type: - return self.class_built_object - if not self._built or force: - self._build() - self.built_node_type = self.node_type - self.class_built_object = self._built_object - # Avoid deepcopying the LLM - # that are loaded from a file - return self._built_object - - -class ToolkitNode(Node): - def __init__(self, data: Dict): - super().__init__(data, base_type="toolkits") - - -class FileToolNode(ToolNode): - def __init__(self, data: Dict): - super().__init__(data) - - -class WrapperNode(Node): - def __init__(self, data: Dict): - super().__init__(data, base_type="wrappers") - - def build(self, force: bool = False) -> Any: - if not self._built or force: - if "headers" in self.params: - self.params["headers"] = eval(self.params["headers"]) - self._build() - return self._built_object - - -class DocumentLoaderNode(Node): - def __init__(self, data: Dict): - super().__init__(data, base_type="documentloaders") - - def _built_object_repr(self): - # This built_object is a list of documents. Maybe we should - # show how many documents are in the list? - if self._built_object: - return f"""{self.node_type}({len(self._built_object)} documents) - Documents: {self._built_object[:3]}...""" - return f"{self.node_type}()" - - -class EmbeddingNode(Node): - def __init__(self, data: Dict): - super().__init__(data, base_type="embeddings") - - -class VectorStoreNode(Node): - def __init__(self, data: Dict): - super().__init__(data, base_type="vectorstores") - - def _built_object_repr(self): - return "Vector stores can take time to build. It will build on the first query." - - -class MemoryNode(Node): - def __init__(self, data: Dict): - super().__init__(data, base_type="memory") - - -class TextSplitterNode(Node): - def __init__(self, data: Dict): - super().__init__(data, base_type="textsplitters") - - def _built_object_repr(self): - # This built_object is a list of documents. Maybe we should - # show how many documents are in the list? - if self._built_object: - return f"""{self.node_type}({len(self._built_object)} documents)\nDocuments: {self._built_object[:3]}...""" - return f"{self.node_type}()" diff --git a/src/backend/langflow/interface/agents/custom.py b/src/backend/langflow/interface/agents/custom.py index 4654ef7cb..b0c794c2d 100644 --- a/src/backend/langflow/interface/agents/custom.py +++ b/src/backend/langflow/interface/agents/custom.py @@ -1,4 +1,3 @@ -from abc import ABC from typing import Any, List, Optional from langchain import LLMChain @@ -33,24 +32,7 @@ from langchain.memory.chat_memory import BaseChatMemory from langchain.sql_database import SQLDatabase from langchain.tools.python.tool import PythonAstREPLTool from langchain.tools.sql_database.prompt import QUERY_CHECKER - - -class CustomAgentExecutor(AgentExecutor, ABC): - """Custom agent executor""" - - @staticmethod - def function_name(): - return "CustomAgentExecutor" - - @classmethod - def initialize(cls, *args, **kwargs): - pass - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - def run(self, *args, **kwargs): - return super().run(*args, **kwargs) +from langflow.interface.base import CustomAgentExecutor class JsonAgent(CustomAgentExecutor): @@ -69,7 +51,7 @@ class JsonAgent(CustomAgentExecutor): @classmethod def from_toolkit_and_llm(cls, toolkit: JsonToolkit, llm: BaseLanguageModel): - tools = toolkit.get_tools() + tools = toolkit if isinstance(toolkit, list) else toolkit.get_tools() tool_names = {tool.name for tool in tools} prompt = ZeroShotAgent.create_prompt( tools, @@ -82,7 +64,9 @@ class JsonAgent(CustomAgentExecutor): llm=llm, prompt=prompt, ) - agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names) # type: ignore + agent = ZeroShotAgent( + llm_chain=llm_chain, allowed_tools=tool_names # type: ignore + ) return cls.from_agent_and_tools(agent=agent, tools=tools, verbose=True) def run(self, *args, **kwargs): @@ -129,7 +113,9 @@ class CSVAgent(CustomAgentExecutor): prompt=partial_prompt, ) tool_names = {tool.name for tool in tools} - agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs) # type: ignore + agent = ZeroShotAgent( + llm_chain=llm_chain, allowed_tools=tool_names, **kwargs # type: ignore + ) return cls.from_agent_and_tools(agent=agent, tools=tools, verbose=True) @@ -138,7 +124,7 @@ class CSVAgent(CustomAgentExecutor): class VectorStoreAgent(CustomAgentExecutor): - """Vector Store agent""" + """Vector store agent""" @staticmethod def function_name(): @@ -166,7 +152,9 @@ class VectorStoreAgent(CustomAgentExecutor): prompt=prompt, ) tool_names = {tool.name for tool in tools} - agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs) # type: ignore + 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 ) @@ -193,7 +181,7 @@ class SQLAgent(CustomAgentExecutor): def from_toolkit_and_llm( cls, llm: BaseLanguageModel, database_uri: str, **kwargs: Any ): - """Construct a sql agent from an LLM and tools.""" + """Construct an SQL agent from an LLM and tools.""" db = SQLDatabase.from_uri(database_uri) toolkit = SQLDatabaseToolkit(db=db, llm=llm) @@ -234,7 +222,9 @@ class SQLAgent(CustomAgentExecutor): prompt=prompt, ) tool_names = {tool.name for tool in tools} # type: ignore - agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs) # type: ignore + agent = ZeroShotAgent( + llm_chain=llm_chain, allowed_tools=tool_names, **kwargs # type: ignore + ) return AgentExecutor.from_agent_and_tools( agent=agent, tools=tools, # type: ignore @@ -270,14 +260,20 @@ class VectorStoreRouterAgent(CustomAgentExecutor): ): """Construct a vector store router agent from an LLM and tools.""" - tools = vectorstoreroutertoolkit.get_tools() + tools = ( + vectorstoreroutertoolkit + if isinstance(vectorstoreroutertoolkit, list) + else vectorstoreroutertoolkit.get_tools() + ) prompt = ZeroShotAgent.create_prompt(tools, prefix=VECTORSTORE_ROUTER_PREFIX) llm_chain = LLMChain( llm=llm, prompt=prompt, ) tool_names = {tool.name for tool in tools} - agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs) # type: ignore + 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 ) @@ -287,11 +283,11 @@ class VectorStoreRouterAgent(CustomAgentExecutor): class InitializeAgent(CustomAgentExecutor): - """Implementation of initialize_agent function""" + """Implementation of AgentInitializer function""" @staticmethod def function_name(): - return "initialize_agent" + return "AgentInitializer" @classmethod def initialize( @@ -320,7 +316,7 @@ class InitializeAgent(CustomAgentExecutor): CUSTOM_AGENTS = { "JsonAgent": JsonAgent, "CSVAgent": CSVAgent, - "initialize_agent": InitializeAgent, + "AgentInitializer": InitializeAgent, "VectorStoreAgent": VectorStoreAgent, "VectorStoreRouterAgent": VectorStoreRouterAgent, "SQLAgent": SQLAgent, diff --git a/src/backend/langflow/interface/base.py b/src/backend/langflow/interface/base.py index a1fe3c994..df0c2c50c 100644 --- a/src/backend/langflow/interface/base.py +++ b/src/backend/langflow/interface/base.py @@ -1,6 +1,7 @@ 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 pydantic import BaseModel from langflow.template.field.base import TemplateField @@ -81,5 +82,42 @@ class LangChainTypeCreator(BaseModel, ABC): ) signature.add_extra_fields() + signature.add_extra_base_classes() return signature + + +class CustomChain(Chain, ABC): + """Custom chain""" + + @staticmethod + def function_name(): + return "CustomChain" + + @classmethod + def initialize(cls, *args, **kwargs): + pass + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def run(self, *args, **kwargs): + return super().run(*args, **kwargs) + + +class CustomAgentExecutor(AgentExecutor, ABC): + """Custom chain""" + + @staticmethod + def function_name(): + return "CustomChain" + + @classmethod + def initialize(cls, *args, **kwargs): + pass + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def run(self, *args, **kwargs): + return super().run(*args, **kwargs) diff --git a/src/backend/langflow/interface/chains/base.py b/src/backend/langflow/interface/chains/base.py index ad66f07b2..ff7e1ee33 100644 --- a/src/backend/langflow/interface/chains/base.py +++ b/src/backend/langflow/interface/chains/base.py @@ -1,12 +1,13 @@ -from typing import Dict, List, Optional, Type +from typing import Any, Dict, List, Optional, Type from langflow.custom.customs import get_custom_nodes from langflow.interface.base import LangChainTypeCreator -from langflow.interface.custom_lists import chain_type_to_cls_dict +from langflow.interface.importing.utils import import_class from langflow.settings import settings from langflow.template.frontend_node.chains import ChainFrontendNode from langflow.utils.logger import logger -from langflow.utils.util import build_template_from_class +from langflow.utils.util import build_template_from_class, build_template_from_method +from langchain import chains # Assuming necessary imports for Field, Template, and FrontendNode classes @@ -18,10 +19,19 @@ class ChainCreator(LangChainTypeCreator): def frontend_node_class(self) -> Type[ChainFrontendNode]: return ChainFrontendNode + #! We need to find a better solution for this + from_method_nodes = { + "ConversationalRetrievalChain": "from_llm", + "LLMCheckerChain": "from_llm", + } + @property def type_to_loader_dict(self) -> Dict: if self.type_dict is None: - self.type_dict = chain_type_to_cls_dict + self.type_dict: dict[str, Any] = { + chain_name: import_class(f"langchain.chains.{chain_name}") + for chain_name in chains.__all__ + } from langflow.interface.chains.custom import CUSTOM_CHAINS self.type_dict.update(CUSTOM_CHAINS) @@ -37,20 +47,32 @@ class ChainCreator(LangChainTypeCreator): try: if name in get_custom_nodes(self.type_name).keys(): return get_custom_nodes(self.type_name)[name] + elif name in self.from_method_nodes.keys(): + return build_template_from_method( + name, + type_to_cls_dict=self.type_to_loader_dict, + method_name=self.from_method_nodes[name], + add_function=True, + ) return build_template_from_class( name, self.type_to_loader_dict, add_function=True ) except ValueError as exc: - raise ValueError("Chain not found") from exc + raise ValueError(f"Chain {name} not found: {exc}") from exc except AttributeError as exc: logger.error(f"Chain {name} not loaded: {exc}") return None def to_list(self) -> List[str]: - custom_chains = list(get_custom_nodes("chains").keys()) - default_chains = list(self.type_to_loader_dict.keys()) - - return default_chains + custom_chains + names = [] + for _, chain in self.type_to_loader_dict.items(): + chain_name = ( + chain.function_name() + if hasattr(chain, "function_name") + else chain.__name__ + ) + names.append(chain_name) + return names chain_creator = ChainCreator() diff --git a/src/backend/langflow/interface/chains/custom.py b/src/backend/langflow/interface/chains/custom.py index cb76a53c8..01dd9bab0 100644 --- a/src/backend/langflow/interface/chains/custom.py +++ b/src/backend/langflow/interface/chains/custom.py @@ -1,11 +1,13 @@ -from typing import Dict, Optional, Type +from typing import Dict, Optional, Type, Union from langchain.chains import ConversationChain from langchain.memory.buffer import ConversationBufferMemory from langchain.schema import BaseMemory +from langflow.interface.base import CustomChain from pydantic import Field, root_validator - -from langflow.graph.utils import extract_input_variables_from_prompt +from langchain.chains.question_answering import load_qa_chain +from langflow.interface.utils import extract_input_variables_from_prompt +from langchain.base_language import BaseLanguageModel DEFAULT_SUFFIX = """" Current conversation: @@ -14,7 +16,7 @@ Human: {input} {ai_prefix}""" -class BaseCustomChain(ConversationChain): +class BaseCustomConversationChain(ConversationChain): """BaseCustomChain is a chain you can use to have a conversation with a custom character.""" template: Optional[str] @@ -47,7 +49,7 @@ class BaseCustomChain(ConversationChain): return values -class SeriesCharacterChain(BaseCustomChain): +class SeriesCharacterChain(BaseCustomConversationChain): """SeriesCharacterChain is a chain you can use to have a conversation with a character from a series.""" character: str @@ -66,7 +68,7 @@ Human: {input} """Default memory store.""" -class MidJourneyPromptChain(BaseCustomChain): +class MidJourneyPromptChain(BaseCustomConversationChain): """MidJourneyPromptChain is a chain you can use to generate new MidJourney prompts.""" template: Optional[ @@ -84,7 +86,7 @@ class MidJourneyPromptChain(BaseCustomChain): AI:""" # noqa: E501 -class TimeTravelGuideChain(BaseCustomChain): +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. @@ -94,7 +96,26 @@ class TimeTravelGuideChain(BaseCustomChain): AI:""" # noqa: E501 -CUSTOM_CHAINS: Dict[str, Type[ConversationChain]] = { +class CombineDocsChain(CustomChain): + """Implementation of load_qa_chain function""" + + @staticmethod + def function_name(): + return "load_qa_chain" + + @classmethod + def initialize(cls, llm: BaseLanguageModel, chain_type: str): + return load_qa_chain(llm=llm, chain_type=chain_type) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def run(self, *args, **kwargs): + return super().run(*args, **kwargs) + + +CUSTOM_CHAINS: Dict[str, Type[Union[ConversationChain, CustomChain]]] = { + "CombineDocsChain": CombineDocsChain, "SeriesCharacterChain": SeriesCharacterChain, "MidJourneyPromptChain": MidJourneyPromptChain, "TimeTravelGuideChain": TimeTravelGuideChain, diff --git a/src/backend/langflow/interface/custom_lists.py b/src/backend/langflow/interface/custom_lists.py index 0fea838b6..2f7f69f14 100644 --- a/src/backend/langflow/interface/custom_lists.py +++ b/src/backend/langflow/interface/custom_lists.py @@ -2,7 +2,6 @@ import inspect from typing import Any from langchain import ( - chains, document_loaders, embeddings, llms, @@ -11,21 +10,21 @@ from langchain import ( text_splitter, ) from langchain.agents import agent_toolkits -from langchain.chat_models import ChatOpenAI +from langchain.chat_models import AzureChatOpenAI, ChatOpenAI +from langchain.chat_models import ChatAnthropic from langflow.interface.importing.utils import import_class +from langflow.interface.agents.custom import CUSTOM_AGENTS +from langflow.interface.chains.custom import CUSTOM_CHAINS -## LLMs +# LLMs llm_type_to_cls_dict = llms.type_to_cls_dict +llm_type_to_cls_dict["anthropic-chat"] = ChatAnthropic # type: ignore +llm_type_to_cls_dict["azure-chat"] = AzureChatOpenAI # type: ignore llm_type_to_cls_dict["openai-chat"] = ChatOpenAI # type: ignore -## Chains -chain_type_to_cls_dict: dict[str, Any] = { - chain_name: import_class(f"langchain.chains.{chain_name}") - for chain_name in chains.__all__ -} -## Toolkits +# Toolkits toolkit_type_to_loader_dict: dict[str, Any] = { toolkit_name: import_class(f"langchain.agents.agent_toolkits.{toolkit_name}") # if toolkit_name is lower case it is a loader @@ -40,25 +39,25 @@ toolkit_type_to_cls_dict: dict[str, Any] = { if not toolkit_name.islower() } -## Memories +# Memories memory_type_to_cls_dict: dict[str, Any] = { memory_name: import_class(f"langchain.memory.{memory_name}") for memory_name in memory.__all__ } -## Wrappers +# Wrappers wrapper_type_to_cls_dict: dict[str, Any] = { wrapper.__name__: wrapper for wrapper in [requests.RequestsWrapper] } -## Embeddings +# Embeddings embedding_type_to_cls_dict: dict[str, Any] = { embedding_name: import_class(f"langchain.embeddings.{embedding_name}") for embedding_name in embeddings.__all__ } -## Document Loaders +# Document Loaders documentloaders_type_to_cls_dict: dict[str, Any] = { documentloader_name: import_class( f"langchain.document_loaders.{documentloader_name}" @@ -66,7 +65,10 @@ documentloaders_type_to_cls_dict: dict[str, Any] = { for documentloader_name in document_loaders.__all__ } -## Text Splitters +# Text Splitters 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 diff --git a/src/backend/langflow/interface/document_loaders/base.py b/src/backend/langflow/interface/document_loaders/base.py index cb02ebe28..5219fbd13 100644 --- a/src/backend/langflow/interface/document_loaders/base.py +++ b/src/backend/langflow/interface/document_loaders/base.py @@ -1,30 +1,20 @@ -from typing import Dict, List, Optional +from typing import Dict, List, Optional, Type from langflow.interface.base import LangChainTypeCreator +from langflow.template.frontend_node.documentloaders import DocumentLoaderFrontNode from langflow.interface.custom_lists import documentloaders_type_to_cls_dict from langflow.settings import settings from langflow.utils.logger import logger from langflow.utils.util import build_template_from_class -def build_file_path_template( - suffixes: list, fileTypes: list, name: str = "file_path" -) -> Dict: - """Build a file path template for a document loader.""" - return { - "type": "file", - "required": True, - "show": True, - "name": name, - "value": "", - "suffixes": suffixes, - "fileTypes": fileTypes, - } - - class DocumentLoaderCreator(LangChainTypeCreator): type_name: str = "documentloaders" + @property + def frontend_node_class(self) -> Type[DocumentLoaderFrontNode]: + return DocumentLoaderFrontNode + @property def type_to_loader_dict(self) -> Dict: return documentloaders_type_to_cls_dict @@ -32,106 +22,7 @@ class DocumentLoaderCreator(LangChainTypeCreator): def get_signature(self, name: str) -> Optional[Dict]: """Get the signature of a document loader.""" try: - signature = build_template_from_class( - name, documentloaders_type_to_cls_dict - ) - - file_path_templates = { - "AirbyteJSONLoader": build_file_path_template( - suffixes=[".json"], fileTypes=["json"] - ), - "CoNLLULoader": build_file_path_template( - suffixes=[".csv"], fileTypes=["csv"] - ), - "CSVLoader": build_file_path_template( - suffixes=[".csv"], fileTypes=["csv"] - ), - "UnstructuredEmailLoader": build_file_path_template( - suffixes=[".eml"], fileTypes=["eml"] - ), - "EverNoteLoader": build_file_path_template( - suffixes=[".xml"], fileTypes=["xml"] - ), - "FacebookChatLoader": build_file_path_template( - suffixes=[".json"], fileTypes=["json"] - ), - "GutenbergLoader": build_file_path_template( - suffixes=[".txt"], fileTypes=["txt"] - ), - "BSHTMLLoader": build_file_path_template( - suffixes=[".html"], fileTypes=["html"] - ), - "UnstructuredHTMLLoader": build_file_path_template( - suffixes=[".html"], fileTypes=["html"] - ), - "UnstructuredImageLoader": build_file_path_template( - suffixes=[".jpg", ".jpeg", ".png", ".gif", ".bmp"], - fileTypes=["jpg", "jpeg", "png", "gif", "bmp"], - ), - "UnstructuredMarkdownLoader": build_file_path_template( - suffixes=[".md"], fileTypes=["md"] - ), - "PyPDFLoader": build_file_path_template( - suffixes=[".pdf"], fileTypes=["pdf"] - ), - "UnstructuredPowerPointLoader": build_file_path_template( - suffixes=[".pptx", ".ppt"], fileTypes=["pptx", "ppt"] - ), - "SRTLoader": build_file_path_template( - suffixes=[".srt"], fileTypes=["srt"] - ), - "TelegramChatLoader": build_file_path_template( - suffixes=[".json"], fileTypes=["json"] - ), - "TextLoader": build_file_path_template( - suffixes=[".txt"], fileTypes=["txt"] - ), - "UnstructuredWordDocumentLoader": build_file_path_template( - suffixes=[".docx", ".doc"], fileTypes=["docx", "doc"] - ), - "SlackDirectoryLoader": build_file_path_template( - suffixes=[".zip"], fileTypes=["zip"] - ), - } - - if name in file_path_templates: - signature["template"]["file_path"] = file_path_templates[name] - elif name in { - "WebBaseLoader", - "AZLyricsLoader", - "CollegeConfidentialLoader", - "HNLoader", - "IFixitLoader", - "IMSDbLoader", - }: - signature["template"]["web_path"] = { - "type": "str", - "required": True, - "show": True, - "name": "web_path", - "value": "", - "display_name": "Web Page", - } - elif name in {"GitbookLoader"}: - signature["template"]["web_page"] = { - "type": "str", - "required": True, - "show": True, - "name": "web_page", - "value": "", - "display_name": "Web Page", - } - elif name in {"ReadTheDocsLoader", "NotionDirectoryLoader"}: - signature["template"]["path"] = { - "type": "str", - "required": True, - "show": True, - "name": "path", - "value": "", - "display_name": "Web Page", - } - - return signature + return build_template_from_class(name, documentloaders_type_to_cls_dict) except ValueError as exc: raise ValueError(f"Documment Loader {name} not found") from exc except AttributeError as exc: diff --git a/src/backend/langflow/interface/importing/utils.py b/src/backend/langflow/interface/importing/utils.py index d08e52999..f65376d48 100644 --- a/src/backend/langflow/interface/importing/utils.py +++ b/src/backend/langflow/interface/importing/utils.py @@ -9,6 +9,7 @@ from langchain.base_language import BaseLanguageModel from langchain.chains.base import Chain from langchain.chat_models.base import BaseChatModel from langchain.tools import BaseTool +from langflow.utils import validate def import_module(module_path: str) -> Any: @@ -147,3 +148,10 @@ def import_utility(utility: str) -> Any: if utility == "SQLDatabase": return import_class(f"langchain.sql_database.{utility}") return import_class(f"langchain.utilities.{utility}") + + +def get_function(code): + """Get the function""" + function_name = validate.extract_function_name(code) + + return validate.create_function(code, function_name) diff --git a/src/backend/langflow/interface/loading.py b/src/backend/langflow/interface/loading.py index 1ced1a667..af1b51678 100644 --- a/src/backend/langflow/interface/loading.py +++ b/src/backend/langflow/interface/loading.py @@ -20,10 +20,10 @@ from langchain.chains.loading import load_chain_from_config from langchain.llms.loading import load_llm_from_config from pydantic import ValidationError -from langflow.interface.agents.custom import CUSTOM_AGENTS -from langflow.interface.importing.utils import import_by_type -from langflow.interface.run import fix_memory_inputs +from langflow.interface.custom_lists import CUSTOM_NODES +from langflow.interface.importing.utils import get_function, import_by_type from langflow.interface.toolkits.base import toolkits_creator +from langflow.interface.chains.base import chain_creator from langflow.interface.types import get_type_list from langflow.interface.utils import load_file_into_dict from langflow.utils import util, validate @@ -33,10 +33,11 @@ def instantiate_class(node_type: str, base_type: str, params: Dict) -> Any: """Instantiate class from module type and key, and params""" params = convert_params_to_sets(params) params = convert_kwargs(params) - if node_type in CUSTOM_AGENTS: - custom_agent = CUSTOM_AGENTS.get(node_type) - if custom_agent: - return custom_agent.initialize(**params) + if node_type in CUSTOM_NODES: + if custom_node := CUSTOM_NODES.get(node_type): + if hasattr(custom_node, "initialize"): + return custom_node.initialize(**params) + return custom_node(**params) class_object = import_by_type(_type=base_type, name=node_type) return instantiate_based_on_type(class_object, base_type, node_type, params) @@ -80,10 +81,24 @@ def instantiate_based_on_type(class_object, base_type, node_type, params): return instantiate_textsplitter(class_object, params) elif base_type == "utilities": return instantiate_utility(node_type, class_object, params) + elif base_type == "chains": + return instantiate_chains(node_type, class_object, params) else: return class_object(**params) +def instantiate_chains(node_type, class_object, params): + 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: + method = chain_creator.from_method_nodes[node_type] + if class_method := getattr(class_object, method, None): + return class_method(**params) + raise ValueError(f"Method {method} not found in {class_object}") + + return class_object(**params) + + def instantiate_agent(class_object, params): return load_agent_executor(class_object, params) @@ -100,6 +115,10 @@ def instantiate_tool(node_type, class_object, params): if node_type == "JsonSpec": params["dict_"] = load_file_into_dict(params.pop("path")) return class_object(**params) + elif node_type == "PythonFunctionTool": + params["func"] = get_function(params.get("code")) + return class_object(**params) + # For backward compatibility elif node_type == "PythonFunction": function_string = params["code"] if isinstance(function_string, str): @@ -112,8 +131,11 @@ def instantiate_tool(node_type, class_object, params): def instantiate_toolkit(node_type, class_object, params): loaded_toolkit = class_object(**params) - if toolkits_creator.has_create_function(node_type): - return load_toolkits_executor(node_type, loaded_toolkit, params) + # Commenting this out for now to use toolkits as normal tools + # if toolkits_creator.has_create_function(node_type): + # return load_toolkits_executor(node_type, loaded_toolkit, params) + if isinstance(loaded_toolkit, BaseToolkit): + return loaded_toolkit.get_tools() return loaded_toolkit @@ -137,6 +159,14 @@ def instantiate_vectorstore(class_object, params): "The source you provided did not load correctly or was empty." "This may cause an error in the vectorstore." ) + # Chroma requires all metadata values to not be None + if class_object.__name__ == "Chroma": + for doc in params["documents"]: + if doc.metadata is None: + doc.metadata = {} + for key, value in doc.metadata.items(): + if value is None: + doc.metadata[key] = "" return class_object.from_documents(**params) @@ -168,38 +198,6 @@ def instantiate_utility(node_type, class_object, params): return class_object(**params) -def load_flow_from_json(path: str, build=True): - """Load flow from json file""" - # This is done to avoid circular imports - from langflow.graph import Graph - - with open(path, "r", encoding="utf-8") as f: - flow_graph = json.load(f) - data_graph = flow_graph["data"] - nodes = data_graph["nodes"] - # Substitute ZeroShotPrompt with PromptTemplate - # nodes = replace_zero_shot_prompt_with_prompt_template(nodes) - # Add input variables - # nodes = payload.extract_input_variables(nodes) - - # Nodes, edges and root node - edges = data_graph["edges"] - graph = Graph(nodes, edges) - if build: - langchain_object = graph.build() - if hasattr(langchain_object, "verbose"): - langchain_object.verbose = True - - if hasattr(langchain_object, "return_intermediate_steps"): - # https://github.com/hwchase17/langchain/issues/2068 - # Deactivating until we have a frontend solution - # to display intermediate steps - langchain_object.return_intermediate_steps = False - fix_memory_inputs(langchain_object) - return langchain_object - return graph - - def replace_zero_shot_prompt_with_prompt_template(nodes): """Replace ZeroShotPrompt with PromptTemplate""" for node in nodes: diff --git a/src/backend/langflow/interface/prompts/custom.py b/src/backend/langflow/interface/prompts/custom.py index b1dbef370..ef16f1474 100644 --- a/src/backend/langflow/interface/prompts/custom.py +++ b/src/backend/langflow/interface/prompts/custom.py @@ -3,7 +3,7 @@ from typing import Dict, List, Optional, Type from langchain.prompts import PromptTemplate from pydantic import root_validator -from langflow.graph.utils import extract_input_variables_from_prompt +from langflow.interface.utils import extract_input_variables_from_prompt # Steps to create a BaseCustomPrompt: # 1. Create a prompt template that endes with: @@ -71,7 +71,3 @@ Human: {input} CUSTOM_PROMPTS: Dict[str, Type[BaseCustomPrompt]] = { "SeriesCharacterPrompt": SeriesCharacterPrompt } - -if __name__ == "__main__": - prompt = SeriesCharacterPrompt(character="Harry Potter", series="Harry Potter") - print(prompt.template) diff --git a/src/backend/langflow/interface/run.py b/src/backend/langflow/interface/run.py index d24b6a0dc..a3efe2b0c 100644 --- a/src/backend/langflow/interface/run.py +++ b/src/backend/langflow/interface/run.py @@ -1,38 +1,8 @@ -import contextlib -import io -from typing import Any, Dict, List, Tuple - -from langchain.schema import AgentAction - -from langflow.api.callback import AsyncStreamingLLMCallbackHandler, StreamingLLMCallbackHandler # type: ignore -from langflow.cache.base import compute_dict_hash, load_cache, memoize_dict -from langflow.graph.graph import Graph +from langflow.cache.utils import memoize_dict +from langflow.graph import Graph from langflow.utils.logger import logger -def load_langchain_object(data_graph, is_first_message=False): - """ - Load langchain object from cache if it exists, otherwise build it. - """ - computed_hash = compute_dict_hash(data_graph) - if is_first_message: - langchain_object = build_langchain_object(data_graph) - else: - logger.debug("Loading langchain object from cache") - langchain_object = load_cache(computed_hash) - - return computed_hash, langchain_object - - -def load_or_build_langchain_object(data_graph, is_first_message=False): - """ - Load langchain object from cache if it exists, otherwise build it. - """ - if is_first_message: - build_langchain_object_with_caching.clear_cache() - return build_langchain_object_with_caching(data_graph) - - @memoize_dict(maxsize=10) def build_langchain_object_with_caching(data_graph): """ @@ -40,16 +10,10 @@ def build_langchain_object_with_caching(data_graph): """ logger.debug("Building langchain object") - graph = build_graph(data_graph) + graph = Graph.from_payload(data_graph) return graph.build() -def build_graph(data_graph): - nodes = data_graph["nodes"] - edges = data_graph["edges"] - return Graph(nodes, edges) - - def build_langchain_object(data_graph): """ Build langchain object from data_graph. @@ -66,29 +30,6 @@ def build_langchain_object(data_graph): return graph.build() -def process_graph_cached(data_graph: Dict[str, Any], message: str): - """ - Process graph by extracting input variables and replacing ZeroShotPrompt - with PromptTemplate,then run the graph and return the result and thought. - """ - # Load langchain object - is_first_message = len(data_graph.get("chatHistory", [])) == 0 - langchain_object = load_or_build_langchain_object(data_graph, is_first_message) - logger.debug("Loaded langchain object") - - if langchain_object is None: - # Raise user facing error - raise ValueError( - "There was an error loading the langchain_object. Please, check all the nodes and try again." - ) - - # Generate result and thought - logger.debug("Generating result and thought") - result, thought = get_result_and_thought(langchain_object, message) - logger.debug("Generated result and thought") - return {"result": str(result), "thought": thought.strip()} - - def get_memory_key(langchain_object): """ Given a LangChain object, this function retrieves the current memory key from the object's memory attribute. @@ -124,147 +65,3 @@ def update_memory_keys(langchain_object, possible_new_mem_key): langchain_object.memory.input_key = input_key langchain_object.memory.output_key = output_key langchain_object.memory.memory_key = possible_new_mem_key - - -def fix_memory_inputs(langchain_object): - """ - Given a LangChain object, this function checks if it has a memory attribute and if that memory key exists in the - object's input variables. If so, it does nothing. Otherwise, it gets a possible new memory key using the - get_memory_key function and updates the memory keys using the update_memory_keys function. - """ - if hasattr(langchain_object, "memory") and langchain_object.memory is not None: - try: - if langchain_object.memory.memory_key in langchain_object.input_variables: - return - except AttributeError: - input_variables = ( - langchain_object.prompt.input_variables - if hasattr(langchain_object, "prompt") - else langchain_object.input_keys - ) - if langchain_object.memory.memory_key in input_variables: - return - - possible_new_mem_key = get_memory_key(langchain_object) - if possible_new_mem_key is not None: - update_memory_keys(langchain_object, possible_new_mem_key) - - -async def get_result_and_steps(langchain_object, message: str, **kwargs): - """Get result and thought from extracted json""" - - try: - if hasattr(langchain_object, "verbose"): - langchain_object.verbose = True - chat_input = None - memory_key = "" - if hasattr(langchain_object, "memory") and langchain_object.memory is not None: - memory_key = langchain_object.memory.memory_key - - if hasattr(langchain_object, "input_keys"): - for key in langchain_object.input_keys: - if key not in [memory_key, "chat_history"]: - chat_input = {key: message} - else: - chat_input = message # type: ignore - - if hasattr(langchain_object, "return_intermediate_steps"): - # https://github.com/hwchase17/langchain/issues/2068 - # Deactivating until we have a frontend solution - # to display intermediate steps - langchain_object.return_intermediate_steps = True - - fix_memory_inputs(langchain_object) - try: - async_callbacks = [AsyncStreamingLLMCallbackHandler(**kwargs)] - output = await langchain_object.acall(chat_input, callbacks=async_callbacks) - except Exception as exc: - # make the error message more informative - logger.debug(f"Error: {str(exc)}") - sync_callbacks = [StreamingLLMCallbackHandler(**kwargs)] - output = langchain_object(chat_input, callbacks=sync_callbacks) - - 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 - ) - thought = format_actions(intermediate_steps) if intermediate_steps else "" - except Exception as exc: - raise ValueError(f"Error: {str(exc)}") from exc - return result, thought - - -def get_result_and_thought(langchain_object, message: str): - """Get result and thought from extracted json""" - try: - if hasattr(langchain_object, "verbose"): - langchain_object.verbose = True - chat_input = None - memory_key = "" - if hasattr(langchain_object, "memory") and langchain_object.memory is not None: - memory_key = langchain_object.memory.memory_key - - if hasattr(langchain_object, "input_keys"): - for key in langchain_object.input_keys: - if key not in [memory_key, "chat_history"]: - chat_input = {key: message} - else: - chat_input = message # type: ignore - - if hasattr(langchain_object, "return_intermediate_steps"): - # https://github.com/hwchase17/langchain/issues/2068 - # Deactivating until we have a frontend solution - # to display intermediate steps - langchain_object.return_intermediate_steps = False - - fix_memory_inputs(langchain_object) - - with io.StringIO() as output_buffer, contextlib.redirect_stdout(output_buffer): - try: - # if hasattr(langchain_object, "acall"): - # output = await langchain_object.acall(chat_input) - # else: - output = langchain_object(chat_input) - except ValueError as exc: - # make the error message more informative - logger.debug(f"Error: {str(exc)}") - output = langchain_object.run(chat_input) - - 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 - ) - if intermediate_steps: - thought = format_actions(intermediate_steps) - else: - thought = output_buffer.getvalue() - - except Exception as exc: - raise ValueError(f"Error: {str(exc)}") from exc - return result, thought - - -def format_actions(actions: List[Tuple[AgentAction, str]]) -> str: - """Format a list of (AgentAction, answer) tuples into a string.""" - output = [] - for action, answer in actions: - log = action.log - tool = action.tool - tool_input = action.tool_input - output.append(f"Log: {log}") - if "Action" not in log and "Action Input" not in log: - output.append(f"Tool: {tool}") - output.append(f"Tool Input: {tool_input}") - output.append(f"Answer: {answer}") - output.append("") # Add a blank line - return "\n".join(output) diff --git a/src/backend/langflow/interface/text_splitters/base.py b/src/backend/langflow/interface/text_splitters/base.py index 58468a593..203f30086 100644 --- a/src/backend/langflow/interface/text_splitters/base.py +++ b/src/backend/langflow/interface/text_splitters/base.py @@ -1,6 +1,7 @@ -from typing import Dict, List, Optional +from typing import Dict, List, Optional, Type from langflow.interface.base import LangChainTypeCreator +from langflow.template.frontend_node.textsplitters import TextSplittersFrontendNode from langflow.interface.custom_lists import textsplitter_type_to_cls_dict from langflow.settings import settings from langflow.utils.logger import logger @@ -10,6 +11,10 @@ from langflow.utils.util import build_template_from_class class TextSplitterCreator(LangChainTypeCreator): type_name: str = "textsplitters" + @property + def frontend_node_class(self) -> Type[TextSplittersFrontendNode]: + return TextSplittersFrontendNode + @property def type_to_loader_dict(self) -> Dict: return textsplitter_type_to_cls_dict @@ -17,47 +22,7 @@ class TextSplitterCreator(LangChainTypeCreator): def get_signature(self, name: str) -> Optional[Dict]: """Get the signature of a text splitter.""" try: - signature = build_template_from_class(name, textsplitter_type_to_cls_dict) - - signature["template"]["documents"] = { - "type": "BaseLoader", - "required": True, - "show": True, - "name": "documents", - } - if name == "RecursiveCharacterTextSplitter": - separator_name = "separators" - else: - separator_name = "separator" - - signature["template"][separator_name] = { - "type": "str", - "required": True, - "show": True, - "value": ".", - "name": separator_name, - "display_name": separator_name.title(), - } - - signature["template"]["chunk_size"] = { - "type": "int", - "required": True, - "show": True, - "value": 1000, - "name": "chunk_size", - "display_name": "Chunk Size", - } - - signature["template"]["chunk_overlap"] = { - "type": "int", - "required": True, - "show": True, - "value": 200, - "name": "chunk_overlap", - "display_name": "Chunk Overlap", - } - - return signature + return build_template_from_class(name, textsplitter_type_to_cls_dict) except ValueError as exc: raise ValueError(f"Text Splitter {name} not found") from exc except AttributeError as exc: diff --git a/src/backend/langflow/interface/toolkits/base.py b/src/backend/langflow/interface/toolkits/base.py index cbe625f0d..be2345c02 100644 --- a/src/backend/langflow/interface/toolkits/base.py +++ b/src/backend/langflow/interface/toolkits/base.py @@ -42,24 +42,27 @@ class ToolkitCreator(LangChainTypeCreator): def get_signature(self, name: str) -> Optional[Dict]: try: - return build_template_from_class(name, self.type_to_loader_dict) + template = build_template_from_class(name, self.type_to_loader_dict) + # add Tool to base_classes + if "toolkit" in name.lower() and template: + template["base_classes"].append("Tool") + return template except ValueError as exc: - raise ValueError("Prompt not found") from exc + raise ValueError("Toolkit not found") from exc except AttributeError as exc: - logger.error(f"Prompt {name} not loaded: {exc}") + logger.error(f"Toolkit {name} not loaded: {exc}") return None def to_list(self) -> List[str]: return list(self.type_to_loader_dict.keys()) def get_create_function(self, name: str) -> Callable: - if loader_name := self.create_functions.get(name, None): - # import loader + if loader_name := self.create_functions.get(name): return import_module( f"from langchain.agents.agent_toolkits import {loader_name[0]}" ) else: - raise ValueError("Loader not found") + raise ValueError("Toolkit not found") def has_create_function(self, name: str) -> bool: # check if the function list is not empty diff --git a/src/backend/langflow/interface/tools/base.py b/src/backend/langflow/interface/tools/base.py index a8e7045c0..d6b114e4c 100644 --- a/src/backend/langflow/interface/tools/base.py +++ b/src/backend/langflow/interface/tools/base.py @@ -71,7 +71,8 @@ class ToolCreator(LangChainTypeCreator): for tool, tool_fcn in ALL_TOOLS_NAMES.items(): tool_params = get_tool_params(tool_fcn) - tool_name = tool_params.get("name", tool) + + tool_name = tool_params.get("name") or tool if tool_name in settings.tools or settings.dev: if tool_name == "JsonSpec": diff --git a/src/backend/langflow/interface/tools/constants.py b/src/backend/langflow/interface/tools/constants.py index f939d55ad..fea3c5237 100644 --- a/src/backend/langflow/interface/tools/constants.py +++ b/src/backend/langflow/interface/tools/constants.py @@ -9,10 +9,14 @@ from langchain.agents.load_tools import ( from langchain.tools.json.tool import JsonSpec from langflow.interface.importing.utils import import_class -from langflow.interface.tools.custom import PythonFunction +from langflow.interface.tools.custom import PythonFunctionTool, PythonFunction FILE_TOOLS = {"JsonSpec": JsonSpec} -CUSTOM_TOOLS = {"Tool": Tool, "PythonFunction": PythonFunction} +CUSTOM_TOOLS = { + "Tool": Tool, + "PythonFunctionTool": PythonFunctionTool, + "PythonFunction": PythonFunction, +} OTHER_TOOLS = {tool: import_class(f"langchain.tools.{tool}") for tool in tools.__all__} diff --git a/src/backend/langflow/interface/tools/custom.py b/src/backend/langflow/interface/tools/custom.py index 4c641f388..0e2e5ff57 100644 --- a/src/backend/langflow/interface/tools/custom.py +++ b/src/backend/langflow/interface/tools/custom.py @@ -1,8 +1,10 @@ from typing import Callable, Optional +from langflow.interface.importing.utils import get_function from pydantic import BaseModel, validator from langflow.utils import validate +from langchain.agents.tools import Tool class Function(BaseModel): @@ -31,6 +33,21 @@ class Function(BaseModel): return validate.create_function(self.code, function_name) +class PythonFunctionTool(Function, Tool): + """Python function""" + + name: str = "Custom Tool" + description: str + code: str + + def ___init__(self, name: str, description: str, code: str): + self.name = name + self.description = description + self.code = code + self.func = get_function(self.code) + super().__init__(name=name, description=description, func=self.func) + + class PythonFunction(Function): """Python function""" diff --git a/src/backend/langflow/interface/utils.py b/src/backend/langflow/interface/utils.py index 2b7c5acd1..2777025ab 100644 --- a/src/backend/langflow/interface/utils.py +++ b/src/backend/langflow/interface/utils.py @@ -2,6 +2,7 @@ import base64 import json import os from io import BytesIO +import re import yaml from langchain.base_language import BaseLanguageModel @@ -44,7 +45,16 @@ def try_setting_streaming_options(langchain_object, websocket): langchain_object.llm_chain, "llm" ): llm = langchain_object.llm_chain.llm - if isinstance(llm, BaseLanguageModel) and hasattr(llm, "streaming"): - llm.streaming = True + + if isinstance(llm, BaseLanguageModel): + if hasattr(llm, "streaming") and isinstance(llm.streaming, bool): + llm.streaming = True + elif hasattr(llm, "stream") and isinstance(llm.stream, bool): + llm.stream = True return langchain_object + + +def extract_input_variables_from_prompt(prompt: str) -> list[str]: + """Extract input variables from prompt.""" + return re.findall(r"{(.*?)}", prompt) diff --git a/src/backend/langflow/main.py b/src/backend/langflow/main.py index 56cc32e46..ad3217eb5 100644 --- a/src/backend/langflow/main.py +++ b/src/backend/langflow/main.py @@ -1,19 +1,23 @@ from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware -from langflow.api.chat import router as chat_router -from langflow.api.endpoints import router as endpoints_router -from langflow.api.validate import router as validate_router +from langflow.api import router +from langflow.database.base import create_db_and_tables def create_app(): """Create the FastAPI app and include the router.""" + app = FastAPI() origins = [ "*", ] + @app.get("/health") + def get_health(): + return {"status": "OK"} + app.add_middleware( CORSMiddleware, allow_origins=origins, @@ -22,9 +26,8 @@ def create_app(): allow_headers=["*"], ) - app.include_router(endpoints_router) - app.include_router(validate_router) - app.include_router(chat_router) + app.include_router(router) + app.on_event("startup")(create_db_and_tables) return app diff --git a/src/backend/langflow/processing/__init__.py b/src/backend/langflow/processing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/processing/base.py b/src/backend/langflow/processing/base.py new file mode 100644 index 000000000..97b0d5be0 --- /dev/null +++ b/src/backend/langflow/processing/base.py @@ -0,0 +1,55 @@ +from langflow.api.v1.callback import ( + AsyncStreamingLLMCallbackHandler, + StreamingLLMCallbackHandler, +) +from langflow.processing.process import fix_memory_inputs, format_actions +from langflow.utils.logger import logger + + +async def get_result_and_steps(langchain_object, message: str, **kwargs): + """Get result and thought from extracted json""" + + try: + if hasattr(langchain_object, "verbose"): + langchain_object.verbose = True + chat_input = None + memory_key = "" + if hasattr(langchain_object, "memory") and langchain_object.memory is not None: + memory_key = langchain_object.memory.memory_key + + if hasattr(langchain_object, "input_keys"): + for key in langchain_object.input_keys: + if key not in [memory_key, "chat_history"]: + chat_input = {key: message} + else: + chat_input = message # type: ignore + + if hasattr(langchain_object, "return_intermediate_steps"): + # https://github.com/hwchase17/langchain/issues/2068 + # Deactivating until we have a frontend solution + # to display intermediate steps + langchain_object.return_intermediate_steps = True + + fix_memory_inputs(langchain_object) + try: + async_callbacks = [AsyncStreamingLLMCallbackHandler(**kwargs)] + output = await langchain_object.acall(chat_input, callbacks=async_callbacks) + except Exception as exc: + # make the error message more informative + logger.debug(f"Error: {str(exc)}") + sync_callbacks = [StreamingLLMCallbackHandler(**kwargs)] + output = langchain_object(chat_input, callbacks=sync_callbacks) + + 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 + ) + thought = format_actions(intermediate_steps) if intermediate_steps else "" + except Exception as exc: + raise ValueError(f"Error: {str(exc)}") from exc + return result, thought diff --git a/src/backend/langflow/processing/process.py b/src/backend/langflow/processing/process.py new file mode 100644 index 000000000..4f5295c60 --- /dev/null +++ b/src/backend/langflow/processing/process.py @@ -0,0 +1,239 @@ +import contextlib +import io +from pathlib import Path +from langchain.schema import AgentAction +import json +from langflow.interface.run import ( + build_langchain_object_with_caching, + get_memory_key, + update_memory_keys, +) +from langflow.utils.logger import logger +from langflow.graph import Graph + +from typing import Any, Dict, List, Optional, Tuple, Union + + +def fix_memory_inputs(langchain_object): + """ + Given a LangChain object, this function checks if it has a memory attribute and if that memory key exists in the + object's input variables. If so, it does nothing. Otherwise, it gets a possible new memory key using the + get_memory_key function and updates the memory keys using the update_memory_keys function. + """ + if not hasattr(langchain_object, "memory") or langchain_object.memory is None: + return + try: + if langchain_object.memory.memory_key in langchain_object.input_variables: + return + except AttributeError: + input_variables = ( + langchain_object.prompt.input_variables + if hasattr(langchain_object, "prompt") + else langchain_object.input_keys + ) + if langchain_object.memory.memory_key in input_variables: + return + + possible_new_mem_key = get_memory_key(langchain_object) + if possible_new_mem_key is not None: + update_memory_keys(langchain_object, possible_new_mem_key) + + +def format_actions(actions: List[Tuple[AgentAction, str]]) -> str: + """Format a list of (AgentAction, answer) tuples into a string.""" + output = [] + for action, answer in actions: + log = action.log + tool = action.tool + tool_input = action.tool_input + output.append(f"Log: {log}") + if "Action" not in log and "Action Input" not in log: + output.append(f"Tool: {tool}") + output.append(f"Tool Input: {tool_input}") + output.append(f"Answer: {answer}") + output.append("") # Add a blank line + return "\n".join(output) + + +def get_result_and_thought(langchain_object, message: str): + """Get result and thought from extracted json""" + try: + if hasattr(langchain_object, "verbose"): + langchain_object.verbose = True + chat_input = None + memory_key = "" + if hasattr(langchain_object, "memory") and langchain_object.memory is not None: + memory_key = langchain_object.memory.memory_key + + if hasattr(langchain_object, "input_keys"): + for key in langchain_object.input_keys: + if key not in [memory_key, "chat_history"]: + chat_input = {key: message} + else: + chat_input = message # type: ignore + + if hasattr(langchain_object, "return_intermediate_steps"): + # https://github.com/hwchase17/langchain/issues/2068 + # Deactivating until we have a frontend solution + # to display intermediate steps + langchain_object.return_intermediate_steps = False + + fix_memory_inputs(langchain_object) + + with io.StringIO() as output_buffer, contextlib.redirect_stdout(output_buffer): + try: + # if hasattr(langchain_object, "acall"): + # output = await langchain_object.acall(chat_input) + # else: + output = langchain_object(chat_input) + except ValueError as exc: + # make the error message more informative + logger.debug(f"Error: {str(exc)}") + output = langchain_object.run(chat_input) + + 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 + ) + if intermediate_steps: + thought = format_actions(intermediate_steps) + else: + thought = output_buffer.getvalue() + + except Exception as exc: + raise ValueError(f"Error: {str(exc)}") from exc + return result, thought + + +def process_graph_cached(data_graph: Dict[str, Any], message: str): + """ + Process graph by extracting input variables and replacing ZeroShotPrompt + with PromptTemplate,then run the graph and return the result and thought. + """ + # Load langchain object + langchain_object = build_langchain_object_with_caching(data_graph) + logger.debug("Loaded langchain object") + + if langchain_object is None: + # Raise user facing error + raise ValueError( + "There was an error loading the langchain_object. Please, check all the nodes and try again." + ) + + # Generate result and thought + logger.debug("Generating result and thought") + result, thought = get_result_and_thought(langchain_object, message) + logger.debug("Generated result and thought") + return {"result": str(result), "thought": thought.strip()} + + +def load_flow_from_json( + input: Union[Path, str, dict], tweaks: Optional[dict] = None, build=True +): + """ + Load flow from a JSON file or a JSON object. + + :param input: JSON file path or JSON object + :param tweaks: Optional tweaks to be processed + :param build: If True, build the graph, otherwise return the graph object + :return: Langchain object or Graph object depending on the build parameter + """ + # If input is a file path, load JSON from the file + if isinstance(input, (str, Path)): + with open(input, "r", encoding="utf-8") as f: + flow_graph = json.load(f) + # If input is a dictionary, assume it's a JSON object + elif isinstance(input, dict): + flow_graph = input + else: + 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: + graph_data = process_tweaks(graph_data, tweaks) + nodes = graph_data["nodes"] + edges = graph_data["edges"] + graph = Graph(nodes, edges) + + if build: + langchain_object = graph.build() + + if hasattr(langchain_object, "verbose"): + langchain_object.verbose = True + + if hasattr(langchain_object, "return_intermediate_steps"): + # Deactivating until we have a frontend solution + # to display intermediate steps + langchain_object.return_intermediate_steps = False + + fix_memory_inputs(langchain_object) + return langchain_object + + return graph + + +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" + ) + + return nodes + + +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" + ) + return + + for tweak_name, tweak_value in node_tweaks.items(): + if tweak_name and tweak_value and tweak_name in template_data: + template_data[tweak_name]["value"] = tweak_value + + +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. + + :param graph_data: The dictionary containing the graph data. It must contain a 'data' key with + 'nodes' as its child or directly contain 'nodes' key. Each node should have an 'id' and 'data'. + :param tweaks: A dictionary where the key is the node id and the value is a dictionary of the tweaks. + The inner dictionary contains the name of a certain parameter as the key and the value to be tweaked. + + :return: The modified graph_data dictionary. + + :raises ValueError: If the input is not in the expected format. + """ + nodes = validate_input(graph_data, tweaks) + + for node in nodes: + if isinstance(node, dict) and isinstance(node.get("id"), str): + node_id = node["id"] + 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" + ) + + return graph_data diff --git a/src/backend/langflow/settings.py b/src/backend/langflow/settings.py index 5ef61ab7b..9d6ac3fa9 100644 --- a/src/backend/langflow/settings.py +++ b/src/backend/langflow/settings.py @@ -20,10 +20,13 @@ class Settings(BaseSettings): textsplitters: List[str] = [] utilities: List[str] = [] dev: bool = False + database_url: str = "sqlite:///./langflow.db" + remove_api_keys: bool = False class Config: validate_assignment = True extra = "ignore" + env_prefix = "LANGFLOW_" @root_validator(allow_reuse=True) def validate_lists(cls, values): @@ -46,6 +49,11 @@ class Settings(BaseSettings): self.utilities = new_settings.utilities or [] self.dev = dev + def update_settings(self, **kwargs): + for key, value in kwargs.items(): + if hasattr(self, key): + setattr(self, key, value) + def save_settings_to_yaml(settings: Settings, file_path: str): with open(file_path, "w") as f: diff --git a/src/backend/langflow/template/frontend_node/__init__.py b/src/backend/langflow/template/frontend_node/__init__.py index 1aa946d41..c36234364 100644 --- a/src/backend/langflow/template/frontend_node/__init__.py +++ b/src/backend/langflow/template/frontend_node/__init__.py @@ -7,6 +7,8 @@ from langflow.template.frontend_node import ( prompts, tools, vectorstores, + documentloaders, + textsplitters, ) __all__ = [ @@ -18,4 +20,6 @@ __all__ = [ "llms", "prompts", "vectorstores", + "documentloaders", + "textsplitters", ] diff --git a/src/backend/langflow/template/frontend_node/agents.py b/src/backend/langflow/template/frontend_node/agents.py index e4fe40187..f692a7d6c 100644 --- a/src/backend/langflow/template/frontend_node/agents.py +++ b/src/backend/langflow/template/frontend_node/agents.py @@ -37,7 +37,7 @@ class SQLAgentNode(FrontendNode): ), ], ) - description: str = """Construct a sql agent from an LLM and tools.""" + description: str = """Construct an SQL agent from an LLM and tools.""" base_classes: list[str] = ["AgentExecutor"] def to_dict(self): @@ -146,7 +146,7 @@ class CSVAgentNode(FrontendNode): ), ], ) - description: str = """Construct a json agent from a CSV and tools.""" + description: str = """Construct a CSV agent from a CSV and tools.""" base_classes: list[str] = ["AgentExecutor"] def to_dict(self): @@ -154,9 +154,10 @@ class CSVAgentNode(FrontendNode): class InitializeAgentNode(FrontendNode): - name: str = "initialize_agent" + name: str = "AgentInitializer" + display_name: str = "AgentInitializer" template: Template = Template( - type_name="initailize_agent", + type_name="initialize_agent", fields=[ TemplateField( field_type="str", @@ -194,7 +195,7 @@ class InitializeAgentNode(FrontendNode): ), ], ) - description: str = """Construct a json agent from an LLM and tools.""" + description: str = """Construct a zero shot agent from an LLM and tools.""" base_classes: list[str] = ["AgentExecutor", "function"] def to_dict(self): diff --git a/src/backend/langflow/template/frontend_node/base.py b/src/backend/langflow/template/frontend_node/base.py index a64195813..4801da086 100644 --- a/src/backend/langflow/template/frontend_node/base.py +++ b/src/backend/langflow/template/frontend_node/base.py @@ -14,6 +14,7 @@ class FrontendNode(BaseModel): description: str base_classes: List[str] name: str = "" + display_name: str = "" def to_dict(self) -> dict: return { @@ -21,12 +22,16 @@ class FrontendNode(BaseModel): "template": self.template.to_dict(self.format_field), "description": self.description, "base_classes": self.base_classes, - } + "display_name": self.display_name or self.name, + }, } def add_extra_fields(self) -> None: pass + def add_extra_base_classes(self) -> None: + pass + @staticmethod def format_field(field: TemplateField, name: Optional[str] = None) -> None: """Formats a given field based on its attributes and value.""" @@ -117,14 +122,30 @@ class FrontendNode(BaseModel): ) -> None: """Handles specific field values for certain fields.""" if key == "headers": - field.value = """{'Authorization': - 'Bearer '}""" - if name == "OpenAI" and key == "model_name": - field.options = constants.OPENAI_MODELS - field.is_list = True - elif name == "ChatOpenAI" and key == "model_name": - field.options = constants.CHAT_OPENAI_MODELS + field.value = """{'Authorization': 'Bearer '}""" + FrontendNode._handle_model_specific_field_values(field, key, name) + FrontendNode._handle_api_key_specific_field_values(field, key, name) + + @staticmethod + def _handle_model_specific_field_values( + field: TemplateField, key: str, name: Optional[str] = None + ) -> None: + """Handles specific field values related to models.""" + model_dict = { + "OpenAI": constants.OPENAI_MODELS, + "ChatOpenAI": constants.CHAT_OPENAI_MODELS, + "Anthropic": constants.ANTHROPIC_MODELS, + "ChatAnthropic": constants.ANTHROPIC_MODELS, + } + if name in model_dict and key == "model_name": + field.options = model_dict[name] field.is_list = True + + @staticmethod + def _handle_api_key_specific_field_values( + field: TemplateField, key: str, name: Optional[str] = None + ) -> None: + """Handles specific field values related to API keys.""" if "api_key" in key and "OpenAI" in str(name): field.display_name = "OpenAI API Key" field.required = False diff --git a/src/backend/langflow/template/frontend_node/chains.py b/src/backend/langflow/template/frontend_node/chains.py index cfcda2ef4..1a1753df3 100644 --- a/src/backend/langflow/template/frontend_node/chains.py +++ b/src/backend/langflow/template/frontend_node/chains.py @@ -2,10 +2,24 @@ from typing import Optional from langflow.template.field.base import TemplateField from langflow.template.frontend_node.base import FrontendNode +from langflow.template.frontend_node.constants import QA_CHAIN_TYPES from langflow.template.template.base import Template class ChainFrontendNode(FrontendNode): + def add_extra_fields(self) -> None: + if self.template.type_name == "ConversationalRetrievalChain": + # add memory + self.template.add_field( + TemplateField( + field_type="BaseChatMemory", + required=False, + show=True, + name="memory", + advanced=False, + ) + ) + @staticmethod def format_field(field: TemplateField, name: Optional[str] = None) -> None: FrontendNode.format_field(field, name) @@ -19,6 +33,14 @@ class ChainFrontendNode(FrontendNode): field.show = True field.advanced = True + # We should think of a way to deal with this later + # if field.field_type == "PromptTemplate": + # field.field_type = "str" + # field.multiline = True + # field.show = True + # field.advanced = False + # field.value = field.value.template + # Separated for possible future changes if field.name == "prompt" and field.value is None: field.required = True @@ -112,7 +134,7 @@ class TimeTravelGuideChainNode(FrontendNode): ), ], ) - description: str = "Time travel guide chain to be used in the flow." + description: str = "Time travel guide chain." base_classes: list[str] = [ "LLMChain", "BaseCustomChain", @@ -155,3 +177,41 @@ class MidJourneyPromptChainNode(FrontendNode): "ConversationChain", "MidJourneyPromptChain", ] + + +class CombineDocsChainNode(FrontendNode): + name: str = "CombineDocsChain" + template: Template = Template( + type_name="load_qa_chain", + fields=[ + TemplateField( + field_type="str", + required=True, + is_list=True, + show=True, + multiline=False, + options=QA_CHAIN_TYPES, + value=QA_CHAIN_TYPES[0], + name="chain_type", + advanced=False, + ), + TemplateField( + field_type="BaseLanguageModel", + required=True, + show=True, + name="llm", + display_name="LLM", + advanced=False, + ), + ], + ) + description: str = """Load question answering chain.""" + base_classes: list[str] = ["BaseCombineDocumentsChain", "function"] + + def to_dict(self): + return super().to_dict() + + @staticmethod + def format_field(field: TemplateField, name: Optional[str] = None) -> None: + # do nothing and don't return anything + pass diff --git a/src/backend/langflow/template/frontend_node/constants.py b/src/backend/langflow/template/frontend_node/constants.py index ae08d3691..20b8a0c61 100644 --- a/src/backend/langflow/template/frontend_node/constants.py +++ b/src/backend/langflow/template/frontend_node/constants.py @@ -30,3 +30,5 @@ You are a good listener and you can talk about anything. """ HUMAN_PROMPT = "{input}" + +QA_CHAIN_TYPES = ["stuff", "map_reduce", "map_rerank", "refine"] diff --git a/src/backend/langflow/template/frontend_node/documentloaders.py b/src/backend/langflow/template/frontend_node/documentloaders.py new file mode 100644 index 000000000..b8f26a9ca --- /dev/null +++ b/src/backend/langflow/template/frontend_node/documentloaders.py @@ -0,0 +1,79 @@ +from langflow.template.field.base import TemplateField +from langflow.template.frontend_node.base import FrontendNode + + +def build_template( + suffixes: list, fileTypes: list, name: str = "file_path" +) -> TemplateField: + """Build a template field for a document loader.""" + return TemplateField( + field_type="file", + required=True, + show=True, + name=name, + value="", + suffixes=suffixes, + fileTypes=fileTypes, + ) + + +class DocumentLoaderFrontNode(FrontendNode): + file_path_templates = { + "AirbyteJSONLoader": build_template(suffixes=[".json"], fileTypes=["json"]), + "CoNLLULoader": build_template(suffixes=[".csv"], fileTypes=["csv"]), + "CSVLoader": build_template(suffixes=[".csv"], fileTypes=["csv"]), + "UnstructuredEmailLoader": build_template(suffixes=[".eml"], fileTypes=["eml"]), + "EverNoteLoader": build_template(suffixes=[".xml"], fileTypes=["xml"]), + "FacebookChatLoader": build_template(suffixes=[".json"], fileTypes=["json"]), + "GutenbergLoader": build_template(suffixes=[".txt"], fileTypes=["txt"]), + "BSHTMLLoader": build_template(suffixes=[".html"], fileTypes=["html"]), + "UnstructuredHTMLLoader": build_template( + suffixes=[".html"], fileTypes=["html"] + ), + "UnstructuredImageLoader": build_template( + suffixes=[".jpg", ".jpeg", ".png", ".gif", ".bmp"], + fileTypes=["jpg", "jpeg", "png", "gif", "bmp"], + ), + "UnstructuredMarkdownLoader": build_template( + suffixes=[".md"], fileTypes=["md"] + ), + "PyPDFLoader": build_template(suffixes=[".pdf"], fileTypes=["pdf"]), + "UnstructuredPowerPointLoader": build_template( + suffixes=[".pptx", ".ppt"], fileTypes=["pptx", "ppt"] + ), + "SRTLoader": build_template(suffixes=[".srt"], fileTypes=["srt"]), + "TelegramChatLoader": build_template(suffixes=[".json"], fileTypes=["json"]), + "TextLoader": build_template(suffixes=[".txt"], fileTypes=["txt"]), + "UnstructuredWordDocumentLoader": build_template( + suffixes=[".docx", ".doc"], fileTypes=["docx", "doc"] + ), + } + + def add_extra_fields(self) -> None: + name = None + if self.template.type_name in self.file_path_templates: + self.template.add_field(self.file_path_templates[self.template.type_name]) + elif self.template.type_name in { + "WebBaseLoader", + "AZLyricsLoader", + "CollegeConfidentialLoader", + "HNLoader", + "IFixitLoader", + "IMSDbLoader", + }: + name = "web_path" + elif self.template.type_name in {"GitbookLoader"}: + name = "web_page" + elif self.template.type_name in {"ReadTheDocsLoader"}: + name = "path" + if name: + self.template.add_field( + TemplateField( + field_type="str", + required=True, + show=True, + name=name, + value="", + display_name="Web Page", + ) + ) diff --git a/src/backend/langflow/template/frontend_node/llms.py b/src/backend/langflow/template/frontend_node/llms.py index 272770e2e..272e42c7f 100644 --- a/src/backend/langflow/template/frontend_node/llms.py +++ b/src/backend/langflow/template/frontend_node/llms.py @@ -12,17 +12,44 @@ class LLMFrontendNode(FrontendNode): field.name.title().replace("Openai", "OpenAI").replace("_", " ") ).replace("Api", "API") + if "key" not in field.name.lower() and "token" not in field.name.lower(): + field.password = False + + @staticmethod + def format_azure_field(field: TemplateField): + if field.name == "model_name": + field.show = False # Azure uses deployment_name instead of model_name. + elif field.name == "openai_api_type": + field.show = False + field.password = False + field.value = "azure" + elif field.name == "openai_api_version": + field.password = False + + @staticmethod + def format_llama_field(field: TemplateField): + field.show = True + field.advanced = not field.required + @staticmethod def format_field(field: TemplateField, name: Optional[str] = None) -> None: display_names_dict = { "huggingfacehub_api_token": "HuggingFace Hub API Token", } FrontendNode.format_field(field, name) + LLMFrontendNode.format_openai_field(field) + if name and "azure" in name.lower(): + LLMFrontendNode.format_azure_field(field) + if name and "llama" in name.lower(): + LLMFrontendNode.format_llama_field(field) SHOW_FIELDS = ["repo_id"] if field.name in SHOW_FIELDS: field.show = True - if "api" in field.name and ("key" in field.name or "token" in field.name): + if "api" in field.name and ( + "key" in field.name + or ("token" in field.name and "tokens" not in field.name) + ): field.password = True field.show = True # Required should be False to support @@ -34,7 +61,8 @@ class LLMFrontendNode(FrontendNode): field.required = True field.show = True field.is_list = True - field.options = ["text-generation", "text2text-generation"] + field.options = ["text-generation", "text2text-generation", "summarization"] + field.value = field.options[0] field.advanced = True if display_name := display_names_dict.get(field.name): @@ -43,8 +71,12 @@ class LLMFrontendNode(FrontendNode): field.field_type = "code" field.advanced = True field.show = True - elif field.name in ["model_name", "temperature", "model_file", "model_type"]: + elif field.name in [ + "model_name", + "temperature", + "model_file", + "model_type", + "deployment_name", + ]: field.advanced = False field.show = True - - LLMFrontendNode.format_openai_field(field) diff --git a/src/backend/langflow/template/frontend_node/memories.py b/src/backend/langflow/template/frontend_node/memories.py index 91d892627..20c3c9272 100644 --- a/src/backend/langflow/template/frontend_node/memories.py +++ b/src/backend/langflow/template/frontend_node/memories.py @@ -5,6 +5,20 @@ from langflow.template.frontend_node.base import FrontendNode class MemoryFrontendNode(FrontendNode): + #! Needs testing + def add_extra_fields(self) -> None: + # add return_messages field + self.template.add_field( + TemplateField( + field_type="bool", + required=False, + show=True, + name="return_messages", + advanced=False, + value=False, + ) + ) + @staticmethod def format_field(field: TemplateField, name: Optional[str] = None) -> None: FrontendNode.format_field(field, name) @@ -18,3 +32,7 @@ class MemoryFrontendNode(FrontendNode): field.value = 10 field.display_name = "Memory Size" field.password = False + if field.name == "return_messages": + field.required = False + field.show = True + field.advanced = False diff --git a/src/backend/langflow/template/frontend_node/prompts.py b/src/backend/langflow/template/frontend_node/prompts.py index 8738f1795..da5d2a300 100644 --- a/src/backend/langflow/template/frontend_node/prompts.py +++ b/src/backend/langflow/template/frontend_node/prompts.py @@ -74,7 +74,7 @@ class BasePromptFrontendNode(FrontendNode): class ZeroShotPromptNode(BasePromptFrontendNode): name: str = "ZeroShotPrompt" template: Template = Template( - type_name="zero_shot", + type_name="ZeroShotPrompt", fields=[ TemplateField( field_type="str", diff --git a/src/backend/langflow/template/frontend_node/textsplitters.py b/src/backend/langflow/template/frontend_node/textsplitters.py new file mode 100644 index 000000000..03880379d --- /dev/null +++ b/src/backend/langflow/template/frontend_node/textsplitters.py @@ -0,0 +1,49 @@ +from langflow.template.field.base import TemplateField +from langflow.template.frontend_node.base import FrontendNode + + +class TextSplittersFrontendNode(FrontendNode): + def add_extra_fields(self) -> None: + self.template.add_field( + TemplateField( + field_type="BaseLoader", + required=True, + show=True, + name="documents", + ) + ) + name = "separator" + if self.template.type_name == "CharacterTextSplitter": + name = "separator" + elif self.template.type_name == "RecursiveCharacterTextSplitter": + name = "separators" + self.template.add_field( + TemplateField( + field_type="str", + required=True, + show=True, + value=".", + name=name, + display_name="Separator", + ) + ) + self.template.add_field( + TemplateField( + field_type="int", + required=True, + show=True, + value=1000, + name="chunk_size", + display_name="Chunk Size", + ) + ) + self.template.add_field( + TemplateField( + field_type="int", + required=True, + show=True, + value=200, + name="chunk_overlap", + display_name="Chunk Overlap", + ) + ) diff --git a/src/backend/langflow/template/frontend_node/tools.py b/src/backend/langflow/template/frontend_node/tools.py index 2819be4d9..52fd31ecf 100644 --- a/src/backend/langflow/template/frontend_node/tools.py +++ b/src/backend/langflow/template/frontend_node/tools.py @@ -52,7 +52,53 @@ class ToolNode(FrontendNode): ), ], ) - description: str = "Tool to be used in the flow." + description: str = "Converts a chain, agent or function into a tool." + base_classes: list[str] = ["Tool"] + + def to_dict(self): + return super().to_dict() + + +class PythonFunctionToolNode(FrontendNode): + name: str = "PythonFunctionTool" + template: Template = Template( + type_name="PythonFunctionTool", + fields=[ + TemplateField( + field_type="str", + required=True, + placeholder="", + is_list=False, + show=True, + multiline=False, + value="", + name="name", + advanced=False, + ), + TemplateField( + field_type="str", + required=True, + placeholder="", + is_list=False, + show=True, + multiline=False, + value="", + name="description", + advanced=False, + ), + TemplateField( + field_type="code", + required=True, + placeholder="", + is_list=False, + show=True, + value=DEFAULT_PYTHON_FUNCTION, + name="code", + advanced=False, + ), + ], + ) + description: str = "Python function to be executed." base_classes: list[str] = ["Tool"] def to_dict(self): @@ -62,7 +108,7 @@ class ToolNode(FrontendNode): class PythonFunctionNode(FrontendNode): name: str = "PythonFunction" template: Template = Template( - type_name="python_function", + type_name="PythonFunction", fields=[ TemplateField( field_type="code", diff --git a/src/backend/langflow/template/frontend_node/vectorstores.py b/src/backend/langflow/template/frontend_node/vectorstores.py index 1aefaf10c..d04936a8b 100644 --- a/src/backend/langflow/template/frontend_node/vectorstores.py +++ b/src/backend/langflow/template/frontend_node/vectorstores.py @@ -20,6 +20,9 @@ class VectorStoreFrontendNode(FrontendNode): self.template.add_field(extra_field) + def add_extra_base_classes(self) -> None: + self.base_classes.append("BaseRetriever") + @staticmethod def format_field(field: TemplateField, name: Optional[str] = None) -> None: FrontendNode.format_field(field, name) diff --git a/src/backend/langflow/utils/constants.py b/src/backend/langflow/utils/constants.py index 2d101ab98..bf59c6cc4 100644 --- a/src/backend/langflow/utils/constants.py +++ b/src/backend/langflow/utils/constants.py @@ -5,8 +5,31 @@ OPENAI_MODELS = [ "text-babbage-001", "text-ada-001", ] -CHAT_OPENAI_MODELS = ["gpt-3.5-turbo", "gpt-4", "gpt-4-32k"] +CHAT_OPENAI_MODELS = [ + "gpt-3.5-turbo-0613", + "gpt-3.5-turbo", + "gpt-3.5-turbo-16k-0613", + "gpt-3.5-turbo-16k", + "gpt-4-0613", + "gpt-4-32k-0613", + "gpt-4", + "gpt-4-32k", +] +ANTHROPIC_MODELS = [ + "claude-v1", # largest model, ideal for a wide range of more complex tasks. + "claude-v1-100k", # An enhanced version of claude-v1 with a 100,000 token (roughly 75,000 word) context window. + "claude-instant-v1", # A smaller model with far lower latency, sampling at roughly 40 words/sec! + "claude-instant-v1-100k", # Like claude-instant-v1 with a 100,000 token context window but retains its performance. + # Specific sub-versions of the above models: + "claude-v1.3", # Vs claude-v1.2: better instruction-following, code, and non-English dialogue and writing. + "claude-v1.3-100k", # An enhanced version of claude-v1.3 with a 100,000 token (roughly 75,000 word) context window. + "claude-v1.2", # Vs claude-v1.1: small adv in general helpfulness, instruction following, coding, and other tasks. + "claude-v1.0", # An earlier version of claude-v1. + "claude-instant-v1.1", # Latest version of claude-instant-v1. Better than claude-instant-v1.0 at most tasks. + "claude-instant-v1.1-100k", # Version of claude-instant-v1.1 with a 100K token context window. + "claude-instant-v1.0", # An earlier version of claude-instant-v1. +] DEFAULT_PYTHON_FUNCTION = """ def python_function(text: str) -> str: diff --git a/src/backend/langflow/utils/util.py b/src/backend/langflow/utils/util.py index 293d31154..f4e4927d8 100644 --- a/src/backend/langflow/utils/util.py +++ b/src/backend/langflow/utils/util.py @@ -302,7 +302,9 @@ def format_dict(d, name: Optional[str] = None): elif name == "ChatOpenAI" and key == "model_name": value["options"] = constants.CHAT_OPENAI_MODELS value["list"] = True - + elif (name == "Anthropic" or name == "ChatAnthropic") and key == "model_name": + value["options"] = constants.ANTHROPIC_MODELS + value["list"] = True return d diff --git a/src/backend/langflow/utils/validate.py b/src/backend/langflow/utils/validate.py index d1353bd77..905b9dd44 100644 --- a/src/backend/langflow/utils/validate.py +++ b/src/backend/langflow/utils/validate.py @@ -1,4 +1,5 @@ import ast +import contextlib import importlib import types from typing import Dict @@ -147,11 +148,8 @@ def create_function(code, function_name): code_obj = compile( ast.Module(body=[function_code], type_ignores=[]), "", "exec" ) - try: + with contextlib.suppress(Exception): exec(code_obj, exec_globals, locals()) - except Exception: - pass - exec_globals[function_name] = locals()[function_name] # Return a function that imports necessary modules and calls the target function diff --git a/src/frontend/package-lock.json b/src/frontend/package-lock.json index 374236168..68f198dd6 100644 --- a/src/frontend/package-lock.json +++ b/src/frontend/package-lock.json @@ -1,7641 +1,9816 @@ { - "name": "langflow", - "version": "0.1.2", - "lockfileVersion": 3, - "requires": true, - "packages": { - "": { - "name": "langflow", - "version": "0.1.2", - "dependencies": { - "@emotion/react": "^11.10.5", - 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"ansi-to-html": "^0.7.2", - "axios": "^1.3.2", - "base64-js": "^1.5.1", - "lodash": "^4.17.21", - "react": "^18.2.0", - "react-ace": "^10.1.0", - "react-cookie": "^4.1.1", - "react-dom": "^18.2.0", - "react-error-boundary": "^4.0.2", - "react-icons": "^4.8.0", - "react-laag": "^2.0.5", - "react-markdown": "^8.0.7", - "react-router-dom": "^6.8.1", - "react-syntax-highlighter": "^15.5.0", - "react-tabs": "^6.0.0", - "react-tooltip": "^5.13.1", - "reactflow": "^11.5.5", - "rehype-mathjax": "^4.0.2", - "remark-gfm": "^3.0.1", - "remark-math": "^5.1.1", - "uuid": "^9.0.0", - "vite-plugin-svgr": "^3.2.0", - "web-vitals": "^2.1.4" - }, - "scripts": { - "dev:docker": "vite --host 0.0.0.0", - "start": "vite", - "build": "vite build", - "serve": "vite preview", - "format": "npx prettier --write \"src/**/*.{js,jsx,ts,tsx,json,md}\"" - }, - "eslintConfig": { - "extends": [ - "react-app", - "react-app/jest" - ] - }, - "browserslist": { - "production": [ - ">0.2%", - "not dead", - "not op_mini all" - ], - "development": [ - "last 1 chrome version", - "last 1 firefox version", - "last 1 safari version" - ] - }, - "proxy": "http://127.0.0.1:7860", - "devDependencies": { - "@tailwindcss/typography": "^0.5.9", - "@testing-library/jest-dom": "^5.16.5", - "@testing-library/react": "^13.4.0", - "@testing-library/user-event": "^13.5.0", - "@types/jest": "^27.5.2", - "@types/lodash": "^4.14.194", - "@types/node": "^16.18.12", - "@types/react": "^18.0.28", - "@types/react-dom": "^18.0.11", - "@types/uuid": "^9.0.1", - "@vitejs/plugin-react-swc": "^3.0.0", - "autoprefixer": "^10.4.14", - "postcss": "^8.4.23", - "tailwindcss": "^3.3.2", - "typescript": "^5.0.2", - "vite": "^4.3.5" - } + "name": "langflow", + "version": "0.1.2", + "private": true, + "dependencies": { + "@emotion/react": "^11.10.5", + "@emotion/styled": "^11.10.5", + "@headlessui/react": "^1.7.10", + "@heroicons/react": "^2.0.15", + "@mui/material": "^5.11.9", + "@radix-ui/react-dropdown-menu": "^2.0.5", + "@radix-ui/react-menubar": "^1.0.3", + "@radix-ui/react-separator": "^1.0.3", + "@radix-ui/react-slot": "^1.0.2", + "@radix-ui/react-tabs": "^1.0.4", + "@radix-ui/react-checkbox": "^1.0.4", + "@radix-ui/react-dialog": "^1.0.4", + "@radix-ui/react-label": "^2.0.2", + "@radix-ui/react-switch": "^1.0.3", + "@radix-ui/react-tooltip": "^1.0.6", + "@tabler/icons-react": "^2.18.0", + "@tailwindcss/forms": "^0.5.3", + "@tailwindcss/line-clamp": "^0.4.4", + "ace-builds": "^1.16.0", + "add": "^2.0.6", + "ansi-to-html": "^0.7.2", + "axios": "^1.3.2", + "base64-js": "^1.5.1", + "class-variance-authority": "^0.6.0", + "clsx": "^1.2.1", + "esbuild": "^0.17.18", + "lodash": "^4.17.21", + "lucide-react": "^0.233.0", + "react": "^18.2.0", + "react-ace": "^10.1.0", + "react-cookie": "^4.1.1", + "react-dom": "^18.2.0", + "react-error-boundary": "^4.0.2", + "react-icons": "^4.8.0", + "react-laag": "^2.0.5", + "react-markdown": "^8.0.7", + "react-router-dom": "^6.8.1", + "react-syntax-highlighter": "^15.5.0", + "react-tabs": "^6.0.0", + "react-tooltip": "^5.13.1", + "reactflow": "^11.5.5", + "rehype-mathjax": "^4.0.2", + "remark-gfm": "^3.0.1", + "remark-math": "^5.1.1", + "shadcn-ui": "^0.1.3", + "short-unique-id": "^4.4.4", + "switch": "^0.0.0", + "table": "^6.8.1", + "tailwind-merge": "^1.13.0", + "tailwindcss-animate": "^1.0.5", + "uuid": "^9.0.0", + "vite-plugin-svgr": "^3.2.0", + "web-vitals": "^2.1.4" + }, + "scripts": { + "dev:docker": "vite --host 0.0.0.0", + "start": "vite", + "build": "vite build", + "serve": "vite preview", + "format": "npx prettier --write \"**/*.{js,jsx,ts,tsx,json,md}\"" + }, + "eslintConfig": { + "extends": [ + "react-app", + "react-app/jest" + ] + }, + "browserslist": { + "production": [ + ">0.2%", + "not dead", + "not op_mini all" + ], + "development": [ + "last 1 chrome version", + "last 1 firefox version", + "last 1 safari version" + ] + }, + "proxy": "http://127.0.0.1:7860", + "devDependencies": { + "@swc/cli": "^0.1.62", + "@swc/core": "^1.3.62", + "@tailwindcss/typography": "^0.5.9", + "@testing-library/jest-dom": "^5.16.5", + "@testing-library/react": "^13.4.0", + "@testing-library/user-event": "^13.5.0", + "@types/jest": "^27.5.2", + "@types/lodash": "^4.14.194", + "@types/node": "^16.18.12", + "@types/react": "^18.0.28", + "@types/react-dom": "^18.0.11", + "@types/uuid": "^9.0.1", + "@vitejs/plugin-react-swc": "^3.0.0", + "autoprefixer": "^10.4.14", + "postcss": "^8.4.23", + "tailwindcss": "^3.3.2", + "typescript": "^5.0.2", + "vite": "^4.3.9" + } } diff --git a/src/frontend/postcss.config.js b/src/frontend/postcss.config.js index 33ad091d2..12a703d90 100644 --- a/src/frontend/postcss.config.js +++ b/src/frontend/postcss.config.js @@ -3,4 +3,4 @@ module.exports = { tailwindcss: {}, autoprefixer: {}, }, -} +}; diff --git a/src/frontend/src/App.tsx b/src/frontend/src/App.tsx index e00019822..259c40fad 100644 --- a/src/frontend/src/App.tsx +++ b/src/frontend/src/App.tsx @@ -7,13 +7,14 @@ import _ from "lodash"; import ErrorAlert from "./alerts/error"; import NoticeAlert from "./alerts/notice"; import SuccessAlert from "./alerts/success"; -import ExtraSidebar from "./components/ExtraSidebarComponent"; import { alertContext } from "./contexts/alertContext"; import { locationContext } from "./contexts/locationContext"; -import TabsManagerComponent from "./pages/FlowPage/components/tabsManagerComponent"; import { ErrorBoundary } from "react-error-boundary"; import CrashErrorComponent from "./components/CrashErrorComponent"; import { TabsContext } from "./contexts/tabsContext"; +import { getVersion } from "./controllers/API"; +import Router from "./routes"; +import Header from "./components/headerComponent"; export default function App() { let { setCurrent, setShowSideBar, setIsStackedOpen } = @@ -46,15 +47,6 @@ export default function App() { }> >([]); - // Initialize state variable for the version - const [version, setVersion] = useState(""); - useEffect(() => { - fetch("/version") - .then((res) => res.json()) - .then((data) => { - setVersion(data.version); - }); - }, []); // Use effect hook to update alertsList when a new alert is added useEffect(() => { // If there is an error alert open with data, add it to the alertsList @@ -112,7 +104,6 @@ export default function App() { return ( //need parent component with width and height
-
{ window.localStorage.removeItem("tabsData"); @@ -122,19 +113,14 @@ export default function App() { }} FallbackComponent={CrashErrorComponent} > -
- - {/* Main area */} -
- {/* Primary column */} -
- -
-
-
+
+
-
+
{alertsList.map((alert) => (
{alert.type === "error" ? ( @@ -164,14 +150,6 @@ export default function App() {
))}
- - {version &&
⛓️ LangFlow v{version}
} -
Created by Logspace
-
); } diff --git a/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx b/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx index 782c22f6e..725abb9c3 100644 --- a/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx +++ b/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx @@ -1,9 +1,11 @@ import { Handle, Position, useUpdateNodeInternals } from "reactflow"; -import Tooltip from "../../../../components/TooltipComponent"; -import { classNames, isValidConnection } from "../../../../utils"; +import { + classNames, + groupByFamily, + isValidConnection, +} from "../../../../utils"; import { useContext, useEffect, useRef, useState } from "react"; import InputComponent from "../../../../components/inputComponent"; -import ToggleComponent from "../../../../components/toggleComponent"; import InputListComponent from "../../../../components/inputListComponent"; import TextAreaComponent from "../../../../components/textAreaComponent"; import { typesContext } from "../../../../contexts/typesContext"; @@ -15,6 +17,12 @@ import InputFileComponent from "../../../../components/inputFileComponent"; import { TabsContext } from "../../../../contexts/tabsContext"; import IntComponent from "../../../../components/intComponent"; import PromptAreaComponent from "../../../../components/promptComponent"; +import { nodeNames, nodeIcons } from "../../../../utils"; +import React from "react"; +import { nodeColors } from "../../../../utils"; +import ShadTooltip from "../../../../components/ShadTooltipComponent"; +import { PopUpContext } from "../../../../contexts/popUpContext"; +import ToggleShadComponent from "../../../../components/toggleShadComponent"; export default function ParameterComponent({ left, @@ -28,14 +36,18 @@ export default function ParameterComponent({ required = false, }: ParameterComponentType) { const ref = useRef(null); + const refHtml = useRef(null); const updateNodeInternals = useUpdateNodeInternals(); const [position, setPosition] = useState(0); + const { closePopUp } = useContext(PopUpContext); + const { setTabsState, tabId } = useContext(TabsContext); + useEffect(() => { if (ref.current && ref.current.offsetTop && ref.current.clientHeight) { setPosition(ref.current.offsetTop + ref.current.clientHeight / 2); updateNodeInternals(data.id); } - }, [data.id, ref, updateNodeInternals]); + }, [data.id, ref, ref.current, ref.current?.offsetTop, updateNodeInternals]); useEffect(() => { updateNodeInternals(data.id); @@ -44,15 +56,72 @@ export default function ParameterComponent({ const [enabled, setEnabled] = useState( data.node.template[name]?.value ?? false ); + + useEffect(() => {}, [closePopUp, data.node.template]); + const { reactFlowInstance } = useContext(typesContext); let disabled = reactFlowInstance?.getEdges().some((e) => e.targetHandle === id) ?? false; - const { save } = useContext(TabsContext); + const [myData, setMyData] = useState(useContext(typesContext).data); + + const handleOnNewValue = (newValue: any) => { + data.node.template[name].value = newValue; + // Set state to pending + setTabsState((prev) => { + return { + ...prev, + [tabId]: { + isPending: true, + }, + }; + }); + }; + + useEffect(() => { + const groupedObj = groupByFamily(myData, tooltipTitle); + + refHtml.current = groupedObj.map((item, i) => ( + 0 ? "items-center flex mt-3" : "items-center flex" + )} + > +
+ {React.createElement(nodeIcons[item.family])} +
+ + {nodeNames[item.family] ?? ""}{" "} + + {" "} + -  + {item.type.split(", ").length > 2 + ? item.type.split(", ").map((el, i) => ( + <> + + {i == item.type.split(", ").length - 1 + ? el + : (el += `, `)} + + {i % 2 == 0 && i > 0 &&

} + + )) + : item.type} + + + + )); + }, [tooltipTitle]); return (
<>
@@ -69,7 +138,12 @@ export default function ParameterComponent({ type === "int") ? ( <> ) : ( - + 0} + > - + )} {left === true && @@ -102,19 +176,13 @@ export default function ParameterComponent({ ? [""] : data.node.template[name].value } - onChange={(t: string[]) => { - data.node.template[name].value = t; - save(); - }} + onChange={handleOnNewValue} /> ) : data.node.template[name].multiline ? ( { - data.node.template[name].value = t; - save(); - }} + onChange={handleOnNewValue} /> ) : ( { - data.node.template[name].value = t; - save(); - }} + onChange={handleOnNewValue} /> )}
) : left === true && type === "bool" ? (
- { - data.node.template[name].value = t; + handleOnNewValue(t); setEnabled(t); - save(); }} + size="large" />
) : left === true && type === "float" ? ( - { - data.node.template[name].value = t; - save(); - }} - /> +
+ +
) : left === true && type === "str" && data.node.template[name].options ? ( - (data.node.template[name].value = newValue)} - value={data.node.template[name].value ?? "Choose an option"} - > +
+ +
) : left === true && type === "code" ? ( { - data.node.template[name].value = t; - save(); - }} + onChange={handleOnNewValue} /> ) : left === true && type === "file" ? ( { - data.node.template[name].value = t; - }} + onChange={handleOnNewValue} fileTypes={data.node.template[name].fileTypes} suffixes={data.node.template[name].suffixes} onFileChange={(t: string) => { @@ -183,23 +244,19 @@ export default function ParameterComponent({ }} > ) : left === true && type === "int" ? ( - { - data.node.template[name].value = t; - save(); - }} - /> +
+ +
) : left === true && type === "prompt" ? ( { - data.node.template[name].value = t; - save(); - }} + onChange={handleOnNewValue} /> ) : ( <> diff --git a/src/frontend/src/CustomNodes/GenericNode/index.tsx b/src/frontend/src/CustomNodes/GenericNode/index.tsx index 724dfdac4..fb1ed5f59 100644 --- a/src/frontend/src/CustomNodes/GenericNode/index.tsx +++ b/src/frontend/src/CustomNodes/GenericNode/index.tsx @@ -1,35 +1,18 @@ -import { - BugAntIcon, - Cog6ToothIcon, - InformationCircleIcon, - TrashIcon, -} from "@heroicons/react/24/outline"; - -import { - CheckCircleIcon, - EllipsisHorizontalCircleIcon, - ExclamationCircleIcon, -} from "@heroicons/react/24/solid"; - -import { - classNames, - nodeColors, - nodeIcons, - toNormalCase, - toTitleCase, -} from "../../utils"; +import { classNames, nodeColors, nodeIcons, toTitleCase } from "../../utils"; import ParameterComponent from "./components/parameterComponent"; import { typesContext } from "../../contexts/typesContext"; -import { useContext, useState, useEffect, useRef, Fragment } from "react"; +import { useContext, useState, useEffect, useRef } from "react"; import { NodeDataType } from "../../types/flow"; import { alertContext } from "../../contexts/alertContext"; import { PopUpContext } from "../../contexts/popUpContext"; import NodeModal from "../../modals/NodeModal"; -import { useCallback } from "react"; -import { TabsContext } from "../../contexts/tabsContext"; -import { debounce } from "../../utils"; -import TooltipReact from "../../components/ReactTooltipComponent"; import Tooltip from "../../components/TooltipComponent"; +import { NodeToolbar } from "reactflow"; +import NodeToolbarComponent from "../../pages/FlowPage/components/nodeToolbarComponent"; + +import ShadTooltip from "../../components/ShadTooltipComponent"; +import { useSSE } from "../../contexts/SSEContext"; + export default function GenericNode({ data, selected, @@ -40,50 +23,30 @@ export default function GenericNode({ const { setErrorData } = useContext(alertContext); const showError = useRef(true); const { types, deleteNode } = useContext(typesContext); - const { openPopUp } = useContext(PopUpContext); + + const { closePopUp, openPopUp } = useContext(PopUpContext); const Icon = nodeIcons[data.type] || nodeIcons[types[data.type]]; const [validationStatus, setValidationStatus] = useState(null); // State for outline color - const [isValid, setIsValid] = useState(false); - const { save } = useContext(TabsContext); - const { reactFlowInstance } = useContext(typesContext); - const [params, setParams] = useState([]); + const { sseData, isBuilding } = useSSE(); + // useEffect(() => { + // if (reactFlowInstance) { + // setParams(Object.values(reactFlowInstance.toObject())); + // } + // }, [save]); + + // New useEffect to watch for changes in sseData and update validation status useEffect(() => { - if (reactFlowInstance) { - setParams(Object.values(reactFlowInstance.toObject())); + const relevantData = sseData[data.id]; + if (relevantData) { + // Extract validation information from relevantData and update the validationStatus state + setValidationStatus(relevantData); + } else { + setValidationStatus(null); } - }, [save]); - - const validateNode = useCallback( - debounce(async () => { - try { - const response = await fetch(`/validate/node/${data.id}`, { - method: "POST", - headers: { - "Content-Type": "application/json", - }, - body: JSON.stringify(reactFlowInstance.toObject()), - }); - - if (response.status === 200) { - let jsonResponse = await response.json(); - let jsonResponseParsed = await JSON.parse(jsonResponse); - setValidationStatus(jsonResponseParsed); - } - } catch (error) { - // console.error("Error validating node:", error); - setValidationStatus("error"); - } - }, 1000), // Adjust the debounce delay (500ms) as needed - [reactFlowInstance, data.id] - ); - useEffect(() => { - if (params.length > 0) { - validateNode(); - } - }, [params, validateNode]); + }, [sseData, data.id]); if (!Icon) { if (showError.current) { @@ -97,199 +60,174 @@ export default function GenericNode({ deleteNode(data.id); return; } - // console.log(data); + + useEffect(() => {}, [closePopUp, data.node.template]); return ( -
-
-
- -
- -
{data.type}
- -
-
-
- +
+
+
+ + {validationStatus.params || + "" + .split("\n") + .map((line, index) =>
{line}
)} +
+ ) + } + > +
+ + + +
+ +
+
+
+ +
+
+ {data.node.description} +
+ + <> + {Object.keys(data.node.template) + .filter((t) => t.charAt(0) !== "_") + .map((t: string, idx) => ( +
+ {/* {idx === 0 ? ( +
+ !key.startsWith("_") && + data.node.template[key].show && + !data.node.template[key].advanced + ).length === 0 + ? "hidden" + : "" + )} + > + Inputs +
+ ) : ( + <> + )} */} + {data.node.template[t].show && + !data.node.template[t].advanced ? ( + + ) : ( + <> + )} +
+ ))} +
- data.node.template[t].advanced && data.node.template[t].show - ) - ? "" - : "hidden", - "w-5 h-5 dark:text-gray-300" + Object.keys(data.node.template).length < 1 ? "hidden" : "", + "flex w-full justify-center" )} - > - - - - -
- - {validationStatus.params.split("\n").map((line, index) => ( -
{line}
- ))} -
- ) - } > -
- - - -
- -
+ {" "} +
+ {/*
+ Output +
*/} + +
- -
-
- {data.node.description} -
- - <> - {Object.keys(data.node.template) - .filter((t) => t.charAt(0) !== "_") - .map((t: string, idx) => ( -
- {/* {idx === 0 ? ( -
- !key.startsWith("_") && - data.node.template[key].show && - !data.node.template[key].advanced - ).length === 0 - ? "hidden" - : "" - )} - > - Inputs -
- ) : ( - <> - )} */} - {data.node.template[t].show && - !data.node.template[t].advanced ? ( - - ) : ( - <> - )} -
- ))} -
- {" "} -
- {/*
- Output -
*/} - - -
- + ); } diff --git a/src/frontend/src/alerts/alertDropDown/components/singleAlertComponent/index.tsx b/src/frontend/src/alerts/alertDropDown/components/singleAlertComponent/index.tsx index ace9b5a0b..ad505e23e 100644 --- a/src/frontend/src/alerts/alertDropDown/components/singleAlertComponent/index.tsx +++ b/src/frontend/src/alerts/alertDropDown/components/singleAlertComponent/index.tsx @@ -94,7 +94,7 @@ export default function SingleAlert({ {dropItem.link ? ( Details diff --git a/src/frontend/src/alerts/notice/index.tsx b/src/frontend/src/alerts/notice/index.tsx index 1b2fb1514..81dce8ca0 100644 --- a/src/frontend/src/alerts/notice/index.tsx +++ b/src/frontend/src/alerts/notice/index.tsx @@ -51,7 +51,7 @@ export default function NoticeAlert({ {link !== "" ? ( Details diff --git a/src/frontend/src/components/EditFlowSettingsComponent/index.tsx b/src/frontend/src/components/EditFlowSettingsComponent/index.tsx new file mode 100644 index 000000000..90e8f90a6 --- /dev/null +++ b/src/frontend/src/components/EditFlowSettingsComponent/index.tsx @@ -0,0 +1,82 @@ +import React, { useState, ChangeEvent } from "react"; +import { Textarea } from "../../components/ui/textarea"; +import { Label } from "../../components/ui/label"; +import { Input } from "../../components/ui/input"; + +type InputProps = { + name: string | null; + description: string | null; + maxLength?: number; + flows: Array<{ id: string; name: string }>; + tabId: string; + setName: (name: string) => void; + setDescription: (description: string) => void; + updateFlow: (flow: { id: string; name: string }) => void; +}; + +export const EditFlowSettings: React.FC = ({ + name, + description, + maxLength = 50, + flows, + tabId, + setName, + setDescription, + updateFlow, +}) => { + const [isMaxLength, setIsMaxLength] = useState(false); + + const handleNameChange = (event: ChangeEvent) => { + const { value } = event.target; + if (value.length >= maxLength) { + setIsMaxLength(true); + } else { + setIsMaxLength(false); + } + + setName(value); + }; + + const handleDescriptionChange = (event: ChangeEvent) => { + setDescription(event.target.value); + }; + + return ( + <> + +