From da6b5eadbcf49e9080c64f4de272e8d57258bf24 Mon Sep 17 00:00:00 2001 From: Christophe Bornet Date: Tue, 8 Oct 2024 21:31:13 +0200 Subject: [PATCH] ref: Fix ruff rules in preview mode (#4039) Fix some ruff rules from preview mode --- src/backend/base/langflow/__main__.py | 8 +- src/backend/base/langflow/api/__init__.py | 2 +- .../base/langflow/api/health_check_router.py | 3 +- src/backend/base/langflow/api/log_router.py | 10 +- src/backend/base/langflow/api/utils.py | 2 +- src/backend/base/langflow/api/v1/__init__.py | 16 +- src/backend/base/langflow/api/v1/api_key.py | 18 +- src/backend/base/langflow/api/v1/endpoints.py | 13 +- src/backend/base/langflow/api/v1/files.py | 22 +- src/backend/base/langflow/api/v1/flows.py | 9 +- src/backend/base/langflow/api/v1/login.py | 12 +- src/backend/base/langflow/api/v1/monitor.py | 41 +- src/backend/base/langflow/api/v1/store.py | 26 +- src/backend/base/langflow/api/v1/users.py | 21 +- .../base/langflow/components/__init__.py | 8 +- .../langflow/components/agents/CrewAIAgent.py | 4 +- .../components/agents/SequentialTaskAgent.py | 2 +- .../assemblyai/AssemblyAIStartTranscript.py | 2 +- .../components/astra_assistants/__init__.py | 4 +- .../langflow/components/data/APIRequest.py | 2 +- .../base/langflow/components/data/File.py | 2 +- .../components/data/GoogleDriveSearch.py | 2 +- .../components/deactivated/ShouldRunNext.py | 4 +- .../components/deactivated/__init__.py | 6 +- .../components/embeddings/__init__.py | 2 +- .../helpers/DataConditionalRouter.py | 2 +- .../langflow/components/helpers/Memory.py | 5 +- .../langflow/components/helpers/__init__.py | 4 +- .../JSONDocumentBuilder.py | 2 +- .../langchain_utilities/SpiderTool.py | 10 +- .../langflow/components/models/__init__.py | 6 +- .../components/prompts/LangChainHubPrompt.py | 2 +- .../langflow/components/prompts/__init__.py | 2 +- .../components/prototypes/RunnableExecutor.py | 2 +- .../components/prototypes/__init__.py | 2 +- .../components/textsplitters/__init__.py | 2 +- .../langflow/components/toolkits/__init__.py | 2 +- .../langflow/components/tools/__init__.py | 6 +- .../components/vectorstores/Elasticsearch.py | 4 +- .../components/vectorstores/Qdrant.py | 6 +- src/backend/base/langflow/custom/__init__.py | 2 +- .../custom/code_parser/code_parser.py | 7 +- .../custom/custom_component/component.py | 6 +- .../directory_reader/directory_reader.py | 2 +- .../base/langflow/field_typing/__init__.py | 10 +- src/backend/base/langflow/graph/__init__.py | 2 +- .../base/langflow/graph/graph/ascii.py | 2 +- src/backend/base/langflow/graph/graph/base.py | 4 +- .../base/langflow/graph/graph/constants.py | 6 +- .../base/langflow/graph/state/model.py | 2 +- src/backend/base/langflow/graph/utils.py | 2 +- .../base/langflow/graph/vertex/base.py | 2 +- src/backend/base/langflow/helpers/__init__.py | 2 +- .../starter_projects/Agent Flow.json | 6 +- .../Basic Prompting (Hello, World).json | 2 +- .../starter_projects/Blog Writer.json | 2 +- .../starter_projects/Complex Agent.json | 16 +- .../starter_projects/Document QA.json | 4 +- .../starter_projects/Hierarchical Agent.json | 12 +- .../starter_projects/Memory Chatbot.json | 4 +- .../starter_projects/Sequential Agent.json | 10 +- .../Travel Planning Agents.json | 4 +- .../starter_projects/Vector Store RAG.json | 10 +- .../starter_projects/__init__.py | 12 +- src/backend/base/langflow/inputs/__init__.py | 12 +- src/backend/base/langflow/io/__init__.py | 12 +- src/backend/base/langflow/load/__init__.py | 2 +- src/backend/base/langflow/logging/__init__.py | 2 +- src/backend/base/langflow/schema/artifact.py | 6 +- src/backend/base/langflow/schema/schema.py | 6 +- .../base/langflow/services/__init__.py | 2 +- .../base/langflow/services/cache/__init__.py | 6 +- .../services/database/models/__init__.py | 2 +- .../database/models/api_key/__init__.py | 2 +- .../database/models/message/__init__.py | 2 +- src/backend/base/langflow/services/deps.py | 4 +- .../base/langflow/services/settings/base.py | 6 +- .../base/langflow/services/settings/utils.py | 2 +- .../langflow/services/store/exceptions.py | 2 +- .../langflow/services/tracing/langfuse.py | 4 +- .../base/langflow/template/__init__.py | 2 +- src/backend/base/langflow/utils/util.py | 6 +- src/backend/base/poetry.lock | 7255 ----------------- src/backend/base/pyproject.toml | 2 +- src/backend/tests/locust/locustfile.py | 2 +- uv.lock | 2 +- 86 files changed, 262 insertions(+), 7508 deletions(-) delete mode 100644 src/backend/base/poetry.lock diff --git a/src/backend/base/langflow/__main__.py b/src/backend/base/langflow/__main__.py index 42f327bb3..c3ebd26c4 100644 --- a/src/backend/base/langflow/__main__.py +++ b/src/backend/base/langflow/__main__.py @@ -68,7 +68,7 @@ def set_var_for_macos_issue(): # we need to set this var is we are running on MacOS # otherwise we get an error when running gunicorn - if platform.system() in ["Darwin"]: + if platform.system() == "Darwin": import os os.environ["OBJC_DISABLE_INITIALIZE_FORK_SAFETY"] = "YES" @@ -202,7 +202,7 @@ def run( return process: Process | None = None try: - if platform.system() in ["Windows"]: + if platform.system() == "Windows": # Run using uvicorn on MacOS and Windows # Windows doesn't support gunicorn # MacOS requires an env variable to be set to use gunicorn @@ -369,7 +369,7 @@ def run_langflow(host, port, log_level, options, app): Run Langflow server on localhost """ - if platform.system() in ["Windows"]: + if platform.system() == "Windows": # Run using uvicorn on MacOS and Windows # Windows doesn't support gunicorn # MacOS requires an env variable to be set to use gunicorn @@ -538,7 +538,7 @@ def api_key_banner(unmasked_api_key): f"[bold blue]{unmasked_api_key.api_key}[/bold blue]\n\n" "This is the only time the API key will be displayed. \n" "Make sure to store it in a secure location. \n\n" - f"The API key has been copied to your clipboard. [bold]{['Ctrl','Cmd'][is_mac]} + V[/bold] to paste it.", + f"The API key has been copied to your clipboard. [bold]{['Ctrl', 'Cmd'][is_mac]} + V[/bold] to paste it.", box=box.ROUNDED, border_style="blue", expand=False, diff --git a/src/backend/base/langflow/api/__init__.py b/src/backend/base/langflow/api/__init__.py index 202756b88..8150f0d9c 100644 --- a/src/backend/base/langflow/api/__init__.py +++ b/src/backend/base/langflow/api/__init__.py @@ -2,4 +2,4 @@ from langflow.api.health_check_router import health_check_router from langflow.api.log_router import log_router from langflow.api.router import router -__all__ = ["router", "health_check_router", "log_router"] +__all__ = ["health_check_router", "log_router", "router"] diff --git a/src/backend/base/langflow/api/health_check_router.py b/src/backend/base/langflow/api/health_check_router.py index be8c0eb78..d80e85823 100644 --- a/src/backend/base/langflow/api/health_check_router.py +++ b/src/backend/base/langflow/api/health_check_router.py @@ -1,4 +1,5 @@ import uuid +from typing import Annotated from fastapi import APIRouter, Depends, HTTPException, status from loguru import logger @@ -37,7 +38,7 @@ async def health(): # It's a reliable health check for a langflow instance @health_check_router.get("/health_check", response_model=HealthResponse) async def health_check( - session: Session = Depends(get_session), + session: Annotated[Session, Depends(get_session)], ): response = HealthResponse() # use a fixed valid UUId that UUID collision is very unlikely diff --git a/src/backend/base/langflow/api/log_router.py b/src/backend/base/langflow/api/log_router.py index 9a86f06bd..b968870b9 100644 --- a/src/backend/base/langflow/api/log_router.py +++ b/src/backend/base/langflow/api/log_router.py @@ -1,7 +1,7 @@ import asyncio import json from http import HTTPStatus -from typing import Any +from typing import Annotated, Any from fastapi import APIRouter, HTTPException, Query, Request from fastapi.responses import JSONResponse, StreamingResponse @@ -41,7 +41,7 @@ async def event_generator(request: Request): last_read_item = item if to_write: for ts, msg in to_write: - yield f"{json.dumps({ts:msg})}\n\n" + yield f"{json.dumps({ts: msg})}\n\n" else: current_not_sent += 1 if current_not_sent == NUMBER_OF_NOT_SENT_BEFORE_KEEPALIVE: @@ -72,9 +72,9 @@ async def stream_logs( @log_router.get("/logs") async def logs( - lines_before: int = Query(0, description="The number of logs before the timestamp or the last log"), - lines_after: int = Query(0, description="The number of logs after the timestamp"), - timestamp: int = Query(0, description="The timestamp to start getting logs from"), + lines_before: Annotated[int, Query(description="The number of logs before the timestamp or the last log")] = 0, + lines_after: Annotated[int, Query(description="The number of logs after the timestamp")] = 0, + timestamp: Annotated[int, Query(description="The timestamp to start getting logs from")] = 0, ): global log_buffer # noqa: PLW0602 if log_buffer.enabled() is False: diff --git a/src/backend/base/langflow/api/utils.py b/src/backend/base/langflow/api/utils.py index 4d3a6f392..2920927ed 100644 --- a/src/backend/base/langflow/api/utils.py +++ b/src/backend/base/langflow/api/utils.py @@ -45,7 +45,7 @@ def build_input_keys_response(langchain_object, artifacts): """Build the input keys response.""" input_keys_response = { - "input_keys": {key: "" for key in langchain_object.input_keys}, + "input_keys": dict.fromkeys(langchain_object.input_keys, ""), "memory_keys": [], "handle_keys": artifacts.get("handle_keys", []), } diff --git a/src/backend/base/langflow/api/v1/__init__.py b/src/backend/base/langflow/api/v1/__init__.py index e4a4b5fda..48383770a 100644 --- a/src/backend/base/langflow/api/v1/__init__.py +++ b/src/backend/base/langflow/api/v1/__init__.py @@ -13,17 +13,17 @@ from langflow.api.v1.validate import router as validate_router from langflow.api.v1.variable import router as variables_router __all__ = [ + "api_key_router", "chat_router", "endpoints_router", - "store_router", - "validate_router", - "flows_router", - "users_router", - "api_key_router", - "login_router", - "variables_router", - "monitor_router", "files_router", + "flows_router", "folders_router", + "login_router", + "monitor_router", "starter_projects_router", + "store_router", + "users_router", + "validate_router", + "variables_router", ] diff --git a/src/backend/base/langflow/api/v1/api_key.py b/src/backend/base/langflow/api/v1/api_key.py index fcf7f2786..297250ef3 100644 --- a/src/backend/base/langflow/api/v1/api_key.py +++ b/src/backend/base/langflow/api/v1/api_key.py @@ -1,4 +1,4 @@ -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, Annotated from uuid import UUID from fastapi import APIRouter, Depends, HTTPException, Response @@ -21,8 +21,8 @@ router = APIRouter(tags=["APIKey"], prefix="/api_key") @router.get("/", response_model=ApiKeysResponse) def get_api_keys_route( - db: Session = Depends(get_session), - current_user: User = Depends(auth_utils.get_current_active_user), + db: Annotated[Session, Depends(get_session)], + current_user: Annotated[User, Depends(auth_utils.get_current_active_user)], ): try: user_id = current_user.id @@ -36,8 +36,8 @@ def get_api_keys_route( @router.post("/", response_model=UnmaskedApiKeyRead) def create_api_key_route( req: ApiKeyCreate, - current_user: User = Depends(auth_utils.get_current_active_user), - db: Session = Depends(get_session), + current_user: Annotated[User, Depends(auth_utils.get_current_active_user)], + db: Annotated[Session, Depends(get_session)], ): try: user_id = current_user.id @@ -63,8 +63,8 @@ def delete_api_key_route( def save_store_api_key( api_key_request: ApiKeyCreateRequest, response: Response, - current_user: User = Depends(auth_utils.get_current_active_user), - db: Session = Depends(get_session), + current_user: Annotated[User, Depends(auth_utils.get_current_active_user)], + db: Annotated[Session, Depends(get_session)], settings_service=Depends(get_settings_service), ): auth_settings = settings_service.auth_settings @@ -95,8 +95,8 @@ def save_store_api_key( @router.delete("/store") def delete_store_api_key( - current_user: User = Depends(auth_utils.get_current_active_user), - db: Session = Depends(get_session), + current_user: Annotated[User, Depends(auth_utils.get_current_active_user)], + db: Annotated[Session, Depends(get_session)], ): try: current_user.store_api_key = None diff --git a/src/backend/base/langflow/api/v1/endpoints.py b/src/backend/base/langflow/api/v1/endpoints.py index bcde341f3..ce5fd1bba 100644 --- a/src/backend/base/langflow/api/v1/endpoints.py +++ b/src/backend/base/langflow/api/v1/endpoints.py @@ -172,7 +172,7 @@ async def simple_run_flow_task( logger.exception(f"Error running flow {flow.id} task") -@router.post("/run/{flow_id_or_name}", response_model=RunResponse, response_model_exclude_none=True) +@router.post("/run/{flow_id_or_name}", response_model=RunResponse, response_model_exclude_none=True) # noqa: RUF100, FAST003 async def simplified_run_flow( background_tasks: BackgroundTasks, flow: Annotated[FlowRead | None, Depends(get_flow_by_id_or_endpoint_name)], @@ -296,13 +296,13 @@ async def simplified_run_flow( raise APIException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, exception=exc, flow=flow) from exc -@router.post("/webhook/{flow_id_or_name}", response_model=dict, status_code=HTTPStatus.ACCEPTED) +@router.post("/webhook/{flow_id_or_name}", response_model=dict, status_code=HTTPStatus.ACCEPTED) # noqa: RUF100, FAST003 async def webhook_run_flow( flow: Annotated[Flow, Depends(get_flow_by_id_or_endpoint_name)], user: Annotated[User, Depends(get_user_by_flow_id_or_endpoint_name)], request: Request, background_tasks: BackgroundTasks, - telemetry_service: TelemetryService = Depends(get_telemetry_service), + telemetry_service: Annotated[TelemetryService, Depends(get_telemetry_service)], ): """ Run a flow using a webhook request. @@ -441,10 +441,9 @@ async def experimental_run_flow( if inputs is None: inputs = [InputValueRequest(components=[], input_value="")] - artifacts = {} if session_id: session_data = await session_service.load_session(session_id, flow_id=flow_id_str) - graph, artifacts = session_data if session_data else (None, None) + graph, _artifacts = session_data or (None, None) if graph is None: msg = f"Session {session_id} not found" raise ValueError(msg) @@ -585,7 +584,7 @@ def get_version(): @router.post("/custom_component", status_code=HTTPStatus.OK, response_model=CustomComponentResponse) async def custom_component( raw_code: CustomComponentRequest, - user: User = Depends(get_current_active_user), + user: Annotated[User, Depends(get_current_active_user)], ): component = Component(_code=raw_code.code) @@ -600,7 +599,7 @@ async def custom_component( @router.post("/custom_component/update", status_code=HTTPStatus.OK) async def custom_component_update( code_request: UpdateCustomComponentRequest, - user: User = Depends(get_current_active_user), + user: Annotated[User, Depends(get_current_active_user)], ): """ Update a custom component with the provided code request. diff --git a/src/backend/base/langflow/api/v1/files.py b/src/backend/base/langflow/api/v1/files.py index 14152cdf4..5839204c2 100644 --- a/src/backend/base/langflow/api/v1/files.py +++ b/src/backend/base/langflow/api/v1/files.py @@ -3,6 +3,7 @@ from datetime import datetime, timezone from http import HTTPStatus from io import BytesIO from pathlib import Path +from typing import Annotated from uuid import UUID from fastapi import APIRouter, Depends, HTTPException, UploadFile @@ -39,7 +40,7 @@ def get_flow_id( @router.post("/upload/{flow_id}", status_code=HTTPStatus.CREATED) async def upload_file( file: UploadFile, - flow_id: UUID = Depends(get_flow_id), + flow_id: Annotated[UUID, Depends(get_flow_id)], current_user=Depends(get_current_active_user), session=Depends(get_session), storage_service: StorageService = Depends(get_storage_service), @@ -68,7 +69,9 @@ async def upload_file( @router.get("/download/{flow_id}/{file_name}") -async def download_file(file_name: str, flow_id: UUID, storage_service: StorageService = Depends(get_storage_service)): +async def download_file( + file_name: str, flow_id: UUID, storage_service: Annotated[StorageService, Depends(get_storage_service)] +): try: flow_id_str = str(flow_id) extension = file_name.split(".")[-1] @@ -93,7 +96,9 @@ async def download_file(file_name: str, flow_id: UUID, storage_service: StorageS @router.get("/images/{flow_id}/{file_name}") -async def download_image(file_name: str, flow_id: UUID, storage_service: StorageService = Depends(get_storage_service)): +async def download_image( + file_name: str, flow_id: UUID, storage_service: Annotated[StorageService, Depends(get_storage_service)] +): try: extension = file_name.split(".")[-1] flow_id_str = str(flow_id) @@ -118,7 +123,7 @@ async def download_image(file_name: str, flow_id: UUID, storage_service: Storage async def download_profile_picture( folder_name: str, file_name: str, - storage_service: StorageService = Depends(get_storage_service), + storage_service: Annotated[StorageService, Depends(get_storage_service)], ): try: extension = file_name.split(".")[-1] @@ -134,7 +139,7 @@ async def download_profile_picture( @router.get("/profile_pictures/list") -async def list_profile_pictures(storage_service: StorageService = Depends(get_storage_service)): +async def list_profile_pictures(storage_service: Annotated[StorageService, Depends(get_storage_service)]): try: config_dir = get_storage_service().settings_service.settings.config_dir config_path = Path(config_dir) # type: ignore @@ -156,7 +161,8 @@ async def list_profile_pictures(storage_service: StorageService = Depends(get_st @router.get("/list/{flow_id}") async def list_files( - flow_id: UUID = Depends(get_flow_id), storage_service: StorageService = Depends(get_storage_service) + flow_id: Annotated[UUID, Depends(get_flow_id)], + storage_service: Annotated[StorageService, Depends(get_storage_service)], ): try: flow_id_str = str(flow_id) @@ -168,7 +174,9 @@ async def list_files( @router.delete("/delete/{flow_id}/{file_name}") async def delete_file( - file_name: str, flow_id: UUID = Depends(get_flow_id), storage_service: StorageService = Depends(get_storage_service) + file_name: str, + flow_id: Annotated[UUID, Depends(get_flow_id)], + storage_service: Annotated[StorageService, Depends(get_storage_service)], ): try: flow_id_str = str(flow_id) diff --git a/src/backend/base/langflow/api/v1/flows.py b/src/backend/base/langflow/api/v1/flows.py index e51ec3c97..a92a121be 100644 --- a/src/backend/base/langflow/api/v1/flows.py +++ b/src/backend/base/langflow/api/v1/flows.py @@ -5,6 +5,7 @@ import json import re import zipfile from datetime import datetime, timezone +from typing import Annotated from uuid import UUID import orjson @@ -326,7 +327,9 @@ async def upload_file( @router.delete("/") async def delete_multiple_flows( - flow_ids: list[UUID], user: User = Depends(get_current_active_user), db: Session = Depends(get_session) + flow_ids: list[UUID], + user: Annotated[User, Depends(get_current_active_user)], + db: Annotated[Session, Depends(get_session)], ): """ Delete multiple flows by their IDs. @@ -362,8 +365,8 @@ async def delete_multiple_flows( @router.post("/download/", status_code=200) async def download_multiple_file( flow_ids: list[UUID], - user: User = Depends(get_current_active_user), - db: Session = Depends(get_session), + user: Annotated[User, Depends(get_current_active_user)], + db: Annotated[Session, Depends(get_session)], ): """Download all flows as a zip file.""" flows = db.exec(select(Flow).where(and_(Flow.user_id == user.id, Flow.id.in_(flow_ids)))).all() # type: ignore diff --git a/src/backend/base/langflow/api/v1/login.py b/src/backend/base/langflow/api/v1/login.py index 1a53f352d..0015dd7dc 100644 --- a/src/backend/base/langflow/api/v1/login.py +++ b/src/backend/base/langflow/api/v1/login.py @@ -1,5 +1,7 @@ from __future__ import annotations +from typing import Annotated + from fastapi import APIRouter, Depends, HTTPException, Request, Response, status from fastapi.security import OAuth2PasswordRequestForm from sqlmodel import Session @@ -23,8 +25,8 @@ router = APIRouter(tags=["Login"]) @router.post("/login", response_model=Token) async def login_to_get_access_token( response: Response, - form_data: OAuth2PasswordRequestForm = Depends(), - db: Session = Depends(get_session), + form_data: Annotated[OAuth2PasswordRequestForm, Depends()], + db: Annotated[Session, Depends(get_session)], # _: Session = Depends(get_current_active_user) settings_service=Depends(get_settings_service), variable_service: VariableService = Depends(get_variable_service), @@ -82,7 +84,7 @@ async def login_to_get_access_token( @router.get("/auto_login") async def auto_login( - response: Response, db: Session = Depends(get_session), settings_service=Depends(get_settings_service) + response: Response, db: Annotated[Session, Depends(get_session)], settings_service=Depends(get_settings_service) ): auth_settings = settings_service.auth_settings @@ -129,8 +131,8 @@ async def auto_login( async def refresh_token( request: Request, response: Response, - settings_service: SettingsService = Depends(get_settings_service), - db: Session = Depends(get_session), + settings_service: Annotated[SettingsService, Depends(get_settings_service)], + db: Annotated[Session, Depends(get_session)], ): auth_settings = settings_service.auth_settings diff --git a/src/backend/base/langflow/api/v1/monitor.py b/src/backend/base/langflow/api/v1/monitor.py index de374d573..4d3e3c801 100644 --- a/src/backend/base/langflow/api/v1/monitor.py +++ b/src/backend/base/langflow/api/v1/monitor.py @@ -1,3 +1,4 @@ +from typing import Annotated from uuid import UUID from fastapi import APIRouter, Depends, HTTPException, Query @@ -22,8 +23,8 @@ router = APIRouter(prefix="/monitor", tags=["Monitor"]) @router.get("/builds", response_model=VertexBuildMapModel) async def get_vertex_builds( - flow_id: UUID = Query(), - session: Session = Depends(get_session), + flow_id: Annotated[UUID, Query()], + session: Annotated[Session, Depends(get_session)], ): try: vertex_builds = get_vertex_builds_by_flow_id(session, flow_id) @@ -34,8 +35,8 @@ async def get_vertex_builds( @router.delete("/builds", status_code=204) async def delete_vertex_builds( - flow_id: UUID = Query(), - session: Session = Depends(get_session), + flow_id: Annotated[UUID, Query()], + session: Annotated[Session, Depends(get_session)], ): try: delete_vertex_builds_by_flow_id(session, flow_id) @@ -45,12 +46,12 @@ async def delete_vertex_builds( @router.get("/messages", response_model=list[MessageResponse]) async def get_messages( - flow_id: str | None = Query(None), - session_id: str | None = Query(None), - sender: str | None = Query(None), - sender_name: str | None = Query(None), - order_by: str | None = Query("timestamp"), - session: Session = Depends(get_session), + session: Annotated[Session, Depends(get_session)], + flow_id: Annotated[str | None, Query()] = None, + session_id: Annotated[str | None, Query()] = None, + sender: Annotated[str | None, Query()] = None, + sender_name: Annotated[str | None, Query()] = None, + order_by: Annotated[str | None, Query()] = "timestamp", ): try: stmt = select(MessageTable) @@ -74,8 +75,8 @@ async def get_messages( @router.delete("/messages", status_code=204) async def delete_messages( message_ids: list[UUID], - session: Session = Depends(get_session), - current_user: User = Depends(get_current_active_user), + session: Annotated[Session, Depends(get_session)], + current_user: Annotated[User, Depends(get_current_active_user)], ): try: session.exec(delete(MessageTable).where(MessageTable.id.in_(message_ids))) # type: ignore @@ -88,8 +89,8 @@ async def delete_messages( async def update_message( message_id: UUID, message: MessageUpdate, - session: Session = Depends(get_session), - user: User = Depends(get_current_active_user), + session: Annotated[Session, Depends(get_session)], + user: Annotated[User, Depends(get_current_active_user)], ): try: db_message = session.get(MessageTable, message_id) @@ -110,9 +111,9 @@ async def update_message( @router.patch("/messages/session/{old_session_id}", response_model=list[MessageResponse]) async def update_session_id( old_session_id: str, - new_session_id: str = Query(..., description="The new session ID to update to"), - session: Session = Depends(get_session), - current_user: User = Depends(get_current_active_user), + new_session_id: Annotated[str, Query(..., description="The new session ID to update to")], + session: Annotated[Session, Depends(get_session)], + current_user: Annotated[User, Depends(get_current_active_user)], ): try: # Get all messages with the old session ID @@ -143,7 +144,7 @@ async def update_session_id( @router.delete("/messages/session/{session_id}", status_code=204) async def delete_messages_session( session_id: str, - session: Session = Depends(get_session), + session: Annotated[Session, Depends(get_session)], ): try: session.exec( # type: ignore @@ -159,8 +160,8 @@ async def delete_messages_session( @router.get("/transactions", response_model=list[TransactionReadResponse]) async def get_transactions( - flow_id: UUID = Query(), - session: Session = Depends(get_session), + flow_id: Annotated[UUID, Query()], + session: Annotated[Session, Depends(get_session)], ): try: transactions = get_transactions_by_flow_id(session, flow_id) diff --git a/src/backend/base/langflow/api/v1/store.py b/src/backend/base/langflow/api/v1/store.py index 447b958ac..7ae900302 100644 --- a/src/backend/base/langflow/api/v1/store.py +++ b/src/backend/base/langflow/api/v1/store.py @@ -58,8 +58,8 @@ def check_if_store_is_enabled( @router.get("/check/api_key") async def check_if_store_has_api_key( - api_key: str | None = Depends(get_optional_user_store_api_key), - store_service: StoreService = Depends(get_store_service), + api_key: Annotated[str | None, Depends(get_optional_user_store_api_key)], + store_service: Annotated[StoreService, Depends(get_store_service)], ): if api_key is None: return {"has_api_key": False, "is_valid": False} @@ -75,8 +75,8 @@ async def check_if_store_has_api_key( @router.post("/components/", response_model=CreateComponentResponse, status_code=201) async def share_component( component: StoreComponentCreate, - store_service: StoreService = Depends(get_store_service), - store_api_key: str = Depends(get_user_store_api_key), + store_service: Annotated[StoreService, Depends(get_store_service)], + store_api_key: Annotated[str, Depends(get_user_store_api_key)], ): try: await check_langflow_version(component) @@ -89,8 +89,8 @@ async def share_component( async def update_shared_component( component_id: UUID, component: StoreComponentCreate, - store_service: StoreService = Depends(get_store_service), - store_api_key: str = Depends(get_user_store_api_key), + store_service: Annotated[StoreService, Depends(get_store_service)], + store_api_key: Annotated[str, Depends(get_user_store_api_key)], ): try: await check_langflow_version(component) @@ -139,8 +139,8 @@ async def get_components( @router.get("/components/{component_id}", response_model=DownloadComponentResponse) async def download_component( component_id: UUID, - store_service: StoreService = Depends(get_store_service), - store_api_key: str = Depends(get_user_store_api_key), + store_service: Annotated[StoreService, Depends(get_store_service)], + store_api_key: Annotated[str, Depends(get_user_store_api_key)], ): try: component = await store_service.download(store_api_key, component_id) @@ -157,7 +157,7 @@ async def download_component( @router.get("/tags", response_model=list[TagResponse]) async def get_tags( - store_service: StoreService = Depends(get_store_service), + store_service: Annotated[StoreService, Depends(get_store_service)], ): try: return await store_service.get_tags() @@ -169,8 +169,8 @@ async def get_tags( @router.get("/users/likes", response_model=list[UsersLikesResponse]) async def get_list_of_components_liked_by_user( - store_service: StoreService = Depends(get_store_service), - store_api_key: str = Depends(get_user_store_api_key), + store_service: Annotated[StoreService, Depends(get_store_service)], + store_api_key: Annotated[str, Depends(get_user_store_api_key)], ): try: return await store_service.get_user_likes(store_api_key) @@ -183,8 +183,8 @@ async def get_list_of_components_liked_by_user( @router.post("/users/likes/{component_id}", response_model=UsersLikesResponse) async def like_component( component_id: UUID, - store_service: StoreService = Depends(get_store_service), - store_api_key: str = Depends(get_user_store_api_key), + store_service: Annotated[StoreService, Depends(get_store_service)], + store_api_key: Annotated[str, Depends(get_user_store_api_key)], ): try: result = await store_service.like_component(store_api_key, str(component_id)) diff --git a/src/backend/base/langflow/api/v1/users.py b/src/backend/base/langflow/api/v1/users.py index e1a1a4334..679211547 100644 --- a/src/backend/base/langflow/api/v1/users.py +++ b/src/backend/base/langflow/api/v1/users.py @@ -1,3 +1,4 @@ +from typing import Annotated from uuid import UUID from fastapi import APIRouter, Depends, HTTPException @@ -24,7 +25,7 @@ router = APIRouter(tags=["Users"], prefix="/users") @router.post("/", response_model=UserRead, status_code=201) def add_user( user: UserCreate, - session: Session = Depends(get_session), + session: Annotated[Session, Depends(get_session)], settings_service=Depends(get_settings_service), ) -> User: """ @@ -49,7 +50,7 @@ def add_user( @router.get("/whoami", response_model=UserRead) def read_current_user( - current_user: User = Depends(get_current_active_user), + current_user: Annotated[User, Depends(get_current_active_user)], ) -> User: """ Retrieve the current user's data. @@ -57,7 +58,7 @@ def read_current_user( return current_user -@router.get("/", response_model=UsersResponse) +@router.get("/") def read_all_users( skip: int = 0, limit: int = 10, @@ -83,8 +84,8 @@ def read_all_users( def patch_user( user_id: UUID, user_update: UserUpdate, - user: User = Depends(get_current_active_user), - session: Session = Depends(get_session), + user: Annotated[User, Depends(get_current_active_user)], + session: Annotated[Session, Depends(get_session)], ) -> User: """ Update an existing user's data. @@ -113,8 +114,8 @@ def patch_user( def reset_password( user_id: UUID, user_update: UserUpdate, - user: User = Depends(get_current_active_user), - session: Session = Depends(get_session), + user: Annotated[User, Depends(get_current_active_user)], + session: Annotated[Session, Depends(get_session)], ) -> User: """ Reset a user's password. @@ -134,11 +135,11 @@ def reset_password( return user -@router.delete("/{user_id}", response_model=dict) +@router.delete("/{user_id}") def delete_user( user_id: UUID, - current_user: User = Depends(get_current_active_superuser), - session: Session = Depends(get_session), + current_user: Annotated[User, Depends(get_current_active_superuser)], + session: Annotated[Session, Depends(get_session)], ) -> dict: """ Delete a user from the database. diff --git a/src/backend/base/langflow/components/__init__.py b/src/backend/base/langflow/components/__init__.py index 02459b49f..fbe764f24 100644 --- a/src/backend/base/langflow/components/__init__.py +++ b/src/backend/base/langflow/components/__init__.py @@ -23,15 +23,15 @@ __all__ = [ "chains", "documentloaders", "embeddings", - "prompts", - "prototypes", - "models", "helpers", "inputs", "link_extractors", "memories", - "outputs", + "models", "output_parsers", + "outputs", + "prompts", + "prototypes", "retrievers", "textsplitters", "toolkits", diff --git a/src/backend/base/langflow/components/agents/CrewAIAgent.py b/src/backend/base/langflow/components/agents/CrewAIAgent.py index c92ebc27e..2a9a11de9 100644 --- a/src/backend/base/langflow/components/agents/CrewAIAgent.py +++ b/src/backend/base/langflow/components/agents/CrewAIAgent.py @@ -68,7 +68,7 @@ class CrewAIAgentComponent(Component): ] def build_output(self) -> Agent: - kwargs = self.kwargs if self.kwargs else {} + kwargs = self.kwargs or {} agent = Agent( role=self.role, goal=self.goal, @@ -76,7 +76,7 @@ class CrewAIAgentComponent(Component): llm=self.llm, verbose=self.verbose, memory=self.memory, - tools=self.tools if self.tools else [], + tools=self.tools or [], allow_delegation=self.allow_delegation, allow_code_execution=self.allow_code_execution, **kwargs, diff --git a/src/backend/base/langflow/components/agents/SequentialTaskAgent.py b/src/backend/base/langflow/components/agents/SequentialTaskAgent.py index c2f3442a9..bb3e5c59c 100644 --- a/src/backend/base/langflow/components/agents/SequentialTaskAgent.py +++ b/src/backend/base/langflow/components/agents/SequentialTaskAgent.py @@ -114,7 +114,7 @@ class SequentialTaskAgentComponent(Component): llm=self.llm, verbose=self.verbose, memory=self.memory, - tools=self.tools if self.tools else [], + tools=self.tools or [], allow_delegation=self.allow_delegation, allow_code_execution=self.allow_code_execution, **agent_kwargs, diff --git a/src/backend/base/langflow/components/assemblyai/AssemblyAIStartTranscript.py b/src/backend/base/langflow/components/assemblyai/AssemblyAIStartTranscript.py index 88deb1e13..58a934cb8 100644 --- a/src/backend/base/langflow/components/assemblyai/AssemblyAIStartTranscript.py +++ b/src/backend/base/langflow/components/assemblyai/AssemblyAIStartTranscript.py @@ -143,7 +143,7 @@ class AssemblyAITranscriptionJobCreator(Component): self.status = "Error: Expected Number of Speakers must be a valid integer" return Data(data={"error": "Error: Expected Number of Speakers must be a valid integer"}) - language_code = self.language_code if self.language_code else None + language_code = self.language_code or None config = aai.TranscriptionConfig( speech_model=self.speech_model, diff --git a/src/backend/base/langflow/components/astra_assistants/__init__.py b/src/backend/base/langflow/components/astra_assistants/__init__.py index 909a5f88f..1de05f5b8 100644 --- a/src/backend/base/langflow/components/astra_assistants/__init__.py +++ b/src/backend/base/langflow/components/astra_assistants/__init__.py @@ -10,10 +10,10 @@ from .run import AssistantsRun __all__ = [ "AstraAssistantManager", "AssistantsCreateAssistant", + "AssistantsCreateThread", "AssistantsGetAssistantName", "AssistantsListAssistants", - "AssistantsCreateThread", "AssistantsRun", - "GetEnvVar", "Dotenv", + "GetEnvVar", ] diff --git a/src/backend/base/langflow/components/data/APIRequest.py b/src/backend/base/langflow/components/data/APIRequest.py index 9b2711445..20ebc8184 100644 --- a/src/backend/base/langflow/components/data/APIRequest.py +++ b/src/backend/base/langflow/components/data/APIRequest.py @@ -125,7 +125,7 @@ class APIRequestComponent(Component): body = None raise ValueError(msg) from e - data = body if body else None + data = body or None try: response = await client.request(method, url, headers=headers, json=data, timeout=timeout) diff --git a/src/backend/base/langflow/components/data/File.py b/src/backend/base/langflow/components/data/File.py index e11a49669..e142feb52 100644 --- a/src/backend/base/langflow/components/data/File.py +++ b/src/backend/base/langflow/components/data/File.py @@ -48,5 +48,5 @@ class FileComponent(Component): raise ValueError(msg) data = parse_text_file_to_data(resolved_path, silent_errors) - self.status = data if data else "No data" + self.status = data or "No data" return data or Data() diff --git a/src/backend/base/langflow/components/data/GoogleDriveSearch.py b/src/backend/base/langflow/components/data/GoogleDriveSearch.py index 033f7eb2c..3aa04e9e4 100644 --- a/src/backend/base/langflow/components/data/GoogleDriveSearch.py +++ b/src/backend/base/langflow/components/data/GoogleDriveSearch.py @@ -110,7 +110,7 @@ class GoogleDriveSearchComponent(Component): creds = Credentials.from_authorized_user_info(token_info) # Use the query string from the input (which might have been edited by the user) - query = self.query_string if self.query_string else self.generate_query_string() + query = self.query_string or self.generate_query_string() # Initialize the Google Drive API service service = build("drive", "v3", credentials=creds) diff --git a/src/backend/base/langflow/components/deactivated/ShouldRunNext.py b/src/backend/base/langflow/components/deactivated/ShouldRunNext.py index 183f66a73..ff1beb3c1 100644 --- a/src/backend/base/langflow/components/deactivated/ShouldRunNext.py +++ b/src/backend/base/langflow/components/deactivated/ShouldRunNext.py @@ -14,8 +14,8 @@ class ShouldRunNextComponent(CustomComponent): template = ( "Given the following question and the context below, answer with a yes or no.\n\n" "{error_message}\n\n" - "Question: {question}\n\n" - "Context: {context}\n\n" + "Question: {question}\n\n" # noqa: RUF100, RUF027 + "Context: {context}\n\n" # noqa: RUF100, RUF027 "Answer:" ) diff --git a/src/backend/base/langflow/components/deactivated/__init__.py b/src/backend/base/langflow/components/deactivated/__init__.py index 8be439d87..026cf6f40 100644 --- a/src/backend/base/langflow/components/deactivated/__init__.py +++ b/src/backend/base/langflow/components/deactivated/__init__.py @@ -8,14 +8,14 @@ __all__ = [ "ConditionalRouterComponent", "ExtractKeyFromDataComponent", "FlowToolComponent", - "ListenComponent", "ListFlowsComponent", + "ListenComponent", "MergeDataComponent", "NotifyComponent", "PythonFunctionComponent", "RunFlowComponent", - "SplitTextComponent", "SQLExecutorComponent", - "SubFlowComponent", "SelectivePassThroughComponent", + "SplitTextComponent", + "SubFlowComponent", ] diff --git a/src/backend/base/langflow/components/embeddings/__init__.py b/src/backend/base/langflow/components/embeddings/__init__.py index 4a7322195..cdc94fd2d 100644 --- a/src/backend/base/langflow/components/embeddings/__init__.py +++ b/src/backend/base/langflow/components/embeddings/__init__.py @@ -15,9 +15,9 @@ __all__ = [ "AstraVectorizeComponent", "AzureOpenAIEmbeddingsComponent", "CohereEmbeddingsComponent", + "GoogleGenerativeAIEmbeddingsComponent", "HuggingFaceInferenceAPIEmbeddingsComponent", "OllamaEmbeddingsComponent", "OpenAIEmbeddingsComponent", "VertexAIEmbeddingsComponent", - "GoogleGenerativeAIEmbeddingsComponent", ] diff --git a/src/backend/base/langflow/components/helpers/DataConditionalRouter.py b/src/backend/base/langflow/components/helpers/DataConditionalRouter.py index e77809c20..6a840479f 100644 --- a/src/backend/base/langflow/components/helpers/DataConditionalRouter.py +++ b/src/backend/base/langflow/components/helpers/DataConditionalRouter.py @@ -86,7 +86,7 @@ class DataConditionalRouterComponent(Component): else: false_output.append(item) self.stop("false_output" if true_output else "true_output") - return true_output if true_output else false_output + return true_output or false_output if not self.validate_input(self.data_input): return Data(data={"error": self.status}) result = self.process_single_data(self.data_input) diff --git a/src/backend/base/langflow/components/helpers/Memory.py b/src/backend/base/langflow/components/helpers/Memory.py index 6008f5860..988b92c16 100644 --- a/src/backend/base/langflow/components/helpers/Memory.py +++ b/src/backend/base/langflow/components/helpers/Memory.py @@ -116,8 +116,5 @@ class MemoryComponent(Component): return Message(text=stored_text) def build_lc_memory(self) -> BaseChatMemory: - if self.memory: - chat_memory = self.memory - else: - chat_memory = LCBuiltinChatMemory(flow_id=self.flow_id, session_id=self.session_id) + chat_memory = self.memory or LCBuiltinChatMemory(flow_id=self.flow_id, session_id=self.session_id) return ConversationBufferMemory(chat_memory=chat_memory) diff --git a/src/backend/base/langflow/components/helpers/__init__.py b/src/backend/base/langflow/components/helpers/__init__.py index b4ee09ef8..0bc52386e 100644 --- a/src/backend/base/langflow/components/helpers/__init__.py +++ b/src/backend/base/langflow/components/helpers/__init__.py @@ -10,15 +10,15 @@ from .SplitText import SplitTextComponent from .StoreMessage import StoreMessageComponent __all__ = [ - "CreateListComponent", "CombineTextComponent", + "CreateListComponent", "CustomComponent", "FilterDataComponent", "IDGeneratorComponent", + "ListComponent", "MemoryComponent", "MergeDataComponent", "ParseDataComponent", "SplitTextComponent", "StoreMessageComponent", - "ListComponent", ] diff --git a/src/backend/base/langflow/components/langchain_utilities/JSONDocumentBuilder.py b/src/backend/base/langflow/components/langchain_utilities/JSONDocumentBuilder.py index f4adb38e1..b6303c952 100644 --- a/src/backend/base/langflow/components/langchain_utilities/JSONDocumentBuilder.py +++ b/src/backend/base/langflow/components/langchain_utilities/JSONDocumentBuilder.py @@ -1,4 +1,4 @@ -### JSON Document Builder +# JSON Document Builder # Build a Document containing a JSON object using a key and another Document page content. diff --git a/src/backend/base/langflow/components/langchain_utilities/SpiderTool.py b/src/backend/base/langflow/components/langchain_utilities/SpiderTool.py index 8592fa908..478502423 100644 --- a/src/backend/base/langflow/components/langchain_utilities/SpiderTool.py +++ b/src/backend/base/langflow/components/langchain_utilities/SpiderTool.py @@ -92,12 +92,12 @@ class SpiderTool(Component): parameters = self.params["data"] else: parameters = { - "limit": self.limit if self.limit else None, - "depth": self.depth if self.depth else None, - "blacklist": self.blacklist if self.blacklist else None, - "whitelist": self.whitelist if self.whitelist else None, + "limit": self.limit or None, + "depth": self.depth or None, + "blacklist": self.blacklist or None, + "whitelist": self.whitelist or None, "readability": self.readability, - "request_timeout": self.request_timeout if self.request_timeout else None, + "request_timeout": self.request_timeout or None, "metadata": self.metadata, "return_format": "markdown", } diff --git a/src/backend/base/langflow/components/models/__init__.py b/src/backend/base/langflow/components/models/__init__.py index e89af0dc3..8881996c2 100644 --- a/src/backend/base/langflow/components/models/__init__.py +++ b/src/backend/base/langflow/components/models/__init__.py @@ -16,13 +16,13 @@ __all__ = [ "AmazonBedrockComponent", "AnthropicModelComponent", "AzureChatOpenAIComponent", - "QianfanChatEndpointComponent", + "ChatOllamaComponent", + "ChatVertexAIComponent", "CohereComponent", "GoogleGenerativeAIComponent", "HuggingFaceEndpointsComponent", - "ChatOllamaComponent", "OpenAIModelComponent", - "ChatVertexAIComponent", "PerplexityComponent", + "QianfanChatEndpointComponent", "base", ] diff --git a/src/backend/base/langflow/components/prompts/LangChainHubPrompt.py b/src/backend/base/langflow/components/prompts/LangChainHubPrompt.py index 6e8cae4ae..aa335e407 100644 --- a/src/backend/base/langflow/components/prompts/LangChainHubPrompt.py +++ b/src/backend/base/langflow/components/prompts/LangChainHubPrompt.py @@ -99,7 +99,7 @@ class LangChainHubPromptComponent(Component): template = self._fetch_langchain_hub_template() # Get the parameters from the attributes - original_params = {k[6:] if k.startswith("param_") else k: v for k, v in self._attributes.items()} + original_params = {k.removeprefix("param_"): v for k, v in self._attributes.items()} prompt_value = template.invoke(original_params) original_params["template"] = prompt_value.to_string() diff --git a/src/backend/base/langflow/components/prompts/__init__.py b/src/backend/base/langflow/components/prompts/__init__.py index 31e765624..3e8f6af1d 100644 --- a/src/backend/base/langflow/components/prompts/__init__.py +++ b/src/backend/base/langflow/components/prompts/__init__.py @@ -1,4 +1,4 @@ from .LangChainHubPrompt import LangChainHubPromptComponent from .Prompt import PromptComponent -__all__ = ["PromptComponent", "LangChainHubPromptComponent"] +__all__ = ["LangChainHubPromptComponent", "PromptComponent"] diff --git a/src/backend/base/langflow/components/prototypes/RunnableExecutor.py b/src/backend/base/langflow/components/prototypes/RunnableExecutor.py index 9d21f5e7b..0130ba6a6 100644 --- a/src/backend/base/langflow/components/prototypes/RunnableExecutor.py +++ b/src/backend/base/langflow/components/prototypes/RunnableExecutor.py @@ -111,7 +111,7 @@ class RunnableExecComponent(Component): if input_key in runnable.input_keys: input_dict[input_key] = input_value else: - input_dict = {k: input_value for k in runnable.input_keys} + input_dict = dict.fromkeys(runnable.input_keys, input_value) status = f"Warning: The input key is not '{input_key}'. The input key is '{runnable.input_keys}'." return input_dict, status diff --git a/src/backend/base/langflow/components/prototypes/__init__.py b/src/backend/base/langflow/components/prototypes/__init__.py index 4bccda481..7904ae750 100644 --- a/src/backend/base/langflow/components/prototypes/__init__.py +++ b/src/backend/base/langflow/components/prototypes/__init__.py @@ -13,6 +13,7 @@ from .UpdateData import UpdateDataComponent __all__ = [ "ConditionalRouterComponent", + "CreateDataComponent", "FlowToolComponent", "ListenComponent", "NotifyComponent", @@ -22,6 +23,5 @@ __all__ = [ "RunnableExecComponent", "SQLExecutorComponent", "SubFlowComponent", - "CreateDataComponent", "UpdateDataComponent", ] diff --git a/src/backend/base/langflow/components/textsplitters/__init__.py b/src/backend/base/langflow/components/textsplitters/__init__.py index 22fc5532f..7019b564c 100644 --- a/src/backend/base/langflow/components/textsplitters/__init__.py +++ b/src/backend/base/langflow/components/textsplitters/__init__.py @@ -6,6 +6,6 @@ from .RecursiveCharacterTextSplitter import RecursiveCharacterTextSplitterCompon __all__ = [ "CharacterTextSplitterComponent", "LanguageRecursiveTextSplitterComponent", - "RecursiveCharacterTextSplitterComponent", "NaturalLanguageTextSplitterComponent", + "RecursiveCharacterTextSplitterComponent", ] diff --git a/src/backend/base/langflow/components/toolkits/__init__.py b/src/backend/base/langflow/components/toolkits/__init__.py index 3750d2db5..24e41812a 100644 --- a/src/backend/base/langflow/components/toolkits/__init__.py +++ b/src/backend/base/langflow/components/toolkits/__init__.py @@ -3,7 +3,7 @@ from .Metaphor import MetaphorToolkit from .VectorStoreInfo import VectorStoreInfoComponent __all__ = [ + "ComposioAPIComponent", "MetaphorToolkit", "VectorStoreInfoComponent", - "ComposioAPIComponent", ] diff --git a/src/backend/base/langflow/components/tools/__init__.py b/src/backend/base/langflow/components/tools/__init__.py index 4156b522a..a344bfe7e 100644 --- a/src/backend/base/langflow/components/tools/__init__.py +++ b/src/backend/base/langflow/components/tools/__init__.py @@ -13,17 +13,17 @@ from .WikipediaAPI import WikipediaAPIComponent from .WolframAlphaAPI import WolframAlphaAPIComponent __all__ = [ - "RetrieverToolComponent", "BingSearchAPIComponent", + "CalculatorToolComponent", "GleanSearchAPIComponent", "GoogleSearchAPIComponent", "GoogleSerperAPIComponent", "PythonCodeStructuredTool", "PythonREPLToolComponent", - "SearchAPIComponent", + "RetrieverToolComponent", "SearXNGToolComponent", + "SearchAPIComponent", "SerpAPIComponent", "WikipediaAPIComponent", "WolframAlphaAPIComponent", - "CalculatorToolComponent", ] diff --git a/src/backend/base/langflow/components/vectorstores/Elasticsearch.py b/src/backend/base/langflow/components/vectorstores/Elasticsearch.py index d7c9e2ff5..dd2f07052 100644 --- a/src/backend/base/langflow/components/vectorstores/Elasticsearch.py +++ b/src/backend/base/langflow/components/vectorstores/Elasticsearch.py @@ -129,8 +129,8 @@ class ElasticsearchVectorStoreComponent(LCVectorStoreComponent): es_params = { "index_name": self.index_name, "embedding": self.embedding, - "es_user": self.username if self.username else None, - "es_password": self.password if self.password else None, + "es_user": self.username or None, + "es_password": self.password or None, } if self.cloud_id: diff --git a/src/backend/base/langflow/components/vectorstores/Qdrant.py b/src/backend/base/langflow/components/vectorstores/Qdrant.py index b616d6679..bc025f704 100644 --- a/src/backend/base/langflow/components/vectorstores/Qdrant.py +++ b/src/backend/base/langflow/components/vectorstores/Qdrant.py @@ -65,15 +65,15 @@ class QdrantVectorStoreComponent(LCVectorStoreComponent): } server_kwargs = { - "host": self.host if self.host else None, + "host": self.host or None, "port": int(self.port), # Ensure port is an integer "grpc_port": int(self.grpc_port), # Ensure grpc_port is an integer "api_key": self.api_key, "prefix": self.prefix, # Ensure timeout is an integer "timeout": int(self.timeout) if self.timeout else None, - "path": self.path if self.path else None, - "url": self.url if self.url else None, + "path": self.path or None, + "url": self.url or None, } server_kwargs = {k: v for k, v in server_kwargs.items() if v is not None} diff --git a/src/backend/base/langflow/custom/__init__.py b/src/backend/base/langflow/custom/__init__.py index 0ba9d831d..55a2b1973 100644 --- a/src/backend/base/langflow/custom/__init__.py +++ b/src/backend/base/langflow/custom/__init__.py @@ -1,4 +1,4 @@ from langflow.custom.custom_component.component import Component from langflow.custom.custom_component.custom_component import CustomComponent -__all__ = ["CustomComponent", "Component"] +__all__ = ["Component", "CustomComponent"] diff --git a/src/backend/base/langflow/custom/code_parser/code_parser.py b/src/backend/base/langflow/custom/code_parser/code_parser.py index 240353de2..ae9152ea4 100644 --- a/src/backend/base/langflow/custom/code_parser/code_parser.py +++ b/src/backend/base/langflow/custom/code_parser/code_parser.py @@ -2,6 +2,7 @@ import ast import contextlib import inspect import traceback +from itertools import starmap from pathlib import Path from typing import Any @@ -161,7 +162,7 @@ class CodeParser: if " as " in module: module, alias = module.split(" as ") if module in return_type_str or (alias and alias in return_type_str): - exec(f"import {module} as {alias if alias else module}", eval_env) + exec(f"import {module} as {alias or module}", eval_env) return eval_env def parse_callable_details(self, node: ast.FunctionDef) -> dict[str, Any]: @@ -218,7 +219,7 @@ class CodeParser: defaults = missing_defaults + default_values - return [self.parse_arg(arg, default) for arg, default in zip(node.args.args, defaults, strict=True)] + return list(starmap(self.parse_arg, zip(node.args.args, defaults, strict=True))) def parse_varargs(self, node: ast.FunctionDef) -> list[dict[str, Any]]: """ @@ -239,7 +240,7 @@ class CodeParser: ast.unparse(default) if default else None for default in node.args.kw_defaults ] - return [self.parse_arg(arg, default) for arg, default in zip(node.args.kwonlyargs, kw_defaults, strict=True)] + return list(starmap(self.parse_arg, zip(node.args.kwonlyargs, kw_defaults, strict=True))) def parse_kwargs(self, node: ast.FunctionDef) -> list[dict[str, Any]]: """ diff --git a/src/backend/base/langflow/custom/custom_component/component.py b/src/backend/base/langflow/custom/custom_component/component.py index a8dc759e7..8cc722b01 100644 --- a/src/backend/base/langflow/custom/custom_component/component.py +++ b/src/backend/base/langflow/custom/custom_component/component.py @@ -590,9 +590,9 @@ class Component(CustomComponent): return frontend_node def to_frontend_node(self): - #! This part here is clunky but we need it like this for - #! backwards compatibility. We can change how prompt component - #! works and then update this later + # ! This part here is clunky but we need it like this for + # ! backwards compatibility. We can change how prompt component + # ! works and then update this later field_config = self.get_template_config(self) frontend_node = ComponentFrontendNode.from_inputs(**field_config) for key in self._inputs: diff --git a/src/backend/base/langflow/custom/directory_reader/directory_reader.py b/src/backend/base/langflow/custom/directory_reader/directory_reader.py index 60b17889b..1d5795f4e 100644 --- a/src/backend/base/langflow/custom/directory_reader/directory_reader.py +++ b/src/backend/base/langflow/custom/directory_reader/directory_reader.py @@ -78,7 +78,7 @@ class DirectoryReader: component_tuple = (*build_component(component), component) components.append(component_tuple) except Exception as e: - logger.debug(f"Error while loading component { component['name']}") + logger.debug(f"Error while loading component {component['name']}") logger.debug(e) continue items.append({"name": menu["name"], "path": menu["path"], "components": components}) diff --git a/src/backend/base/langflow/field_typing/__init__.py b/src/backend/base/langflow/field_typing/__init__.py index 543043f77..5ac0502ad 100644 --- a/src/backend/base/langflow/field_typing/__init__.py +++ b/src/backend/base/langflow/field_typing/__init__.py @@ -60,8 +60,9 @@ def __getattr__(name: str) -> Any: __all__ = [ "AgentExecutor", "BaseChatMemory", - "BaseLanguageModel", + "BaseChatModel", "BaseLLM", + "BaseLanguageModel", "BaseLoader", "BaseMemory", "BaseOutputParser", @@ -75,15 +76,14 @@ __all__ = [ "Document", "Embeddings", "Input", + "LanguageModel", "NestedDict", "Object", "PromptTemplate", "RangeSpec", + "Retriever", + "Text", "TextSplitter", "Tool", "VectorStore", - "BaseChatModel", - "Retriever", - "Text", - "LanguageModel", ] diff --git a/src/backend/base/langflow/graph/__init__.py b/src/backend/base/langflow/graph/__init__.py index bb93f92cf..741f7468c 100644 --- a/src/backend/base/langflow/graph/__init__.py +++ b/src/backend/base/langflow/graph/__init__.py @@ -3,4 +3,4 @@ from langflow.graph.graph.base import Graph from langflow.graph.vertex.base import Vertex from langflow.graph.vertex.types import CustomComponentVertex, InterfaceVertex, StateVertex -__all__ = ["Edge", "Graph", "Vertex", "CustomComponentVertex", "InterfaceVertex", "StateVertex"] +__all__ = ["CustomComponentVertex", "Edge", "Graph", "InterfaceVertex", "StateVertex", "Vertex"] diff --git a/src/backend/base/langflow/graph/graph/ascii.py b/src/backend/base/langflow/graph/graph/ascii.py index d1fcdb131..e481eada0 100644 --- a/src/backend/base/langflow/graph/graph/ascii.py +++ b/src/backend/base/langflow/graph/graph/ascii.py @@ -118,7 +118,7 @@ def build_sugiyama_layout(vertexes, edges): for vertex in vertexes.values(): vertex.view = VertexViewer(vertex.data) - minw = min([v.view.w for v in vertexes.values()]) + minw = min(v.view.w for v in vertexes.values()) for edge in edges: edge.view = EdgeViewer() diff --git a/src/backend/base/langflow/graph/graph/base.py b/src/backend/base/langflow/graph/graph/base.py index 01330fdd1..a1e0aba7d 100644 --- a/src/backend/base/langflow/graph/graph/base.py +++ b/src/backend/base/langflow/graph/graph/base.py @@ -353,7 +353,7 @@ class Graph: raise ValueError(msg) if config is not None: self.__apply_config(config) - #! Change this ASAP + # ! Change this ASAP nest_asyncio.apply() loop = asyncio.get_event_loop() async_gen = self.async_start(inputs, max_iterations, event_manager) @@ -1555,7 +1555,7 @@ class Graph: ValueError: If the graph contains a cycle. """ # States: 0 = unvisited, 1 = visiting, 2 = visited - state = {vertex: 0 for vertex in self.vertices} + state = dict.fromkeys(self.vertices, 0) sorted_vertices = [] def dfs(vertex): diff --git a/src/backend/base/langflow/graph/graph/constants.py b/src/backend/base/langflow/graph/graph/constants.py index 51da1182a..1ec2894f7 100644 --- a/src/backend/base/langflow/graph/graph/constants.py +++ b/src/backend/base/langflow/graph/graph/constants.py @@ -35,9 +35,9 @@ class VertexTypesDict(LazyLoadDictBase): def get_type_dict(self): types = self._types() return { - **{t: types.CustomComponentVertex for t in ["CustomComponent"]}, - **{t: types.ComponentVertex for t in ["Component"]}, - **{t: types.InterfaceVertex for t in CHAT_COMPONENTS}, + "CustomComponent": types.CustomComponentVertex, + "Component": types.ComponentVertex, + **dict.fromkeys(CHAT_COMPONENTS, types.InterfaceVertex), } def get_custom_component_vertex_type(self): diff --git a/src/backend/base/langflow/graph/state/model.py b/src/backend/base/langflow/graph/state/model.py index 44b0c38b3..6de79887f 100644 --- a/src/backend/base/langflow/graph/state/model.py +++ b/src/backend/base/langflow/graph/state/model.py @@ -215,7 +215,7 @@ def create_state_model(model_name: str = "State", validate: bool = True, **kwarg property_method = property(getter, setter) except ValueError as e: # If the method is not valid,assume it is already a getter - if "get_output_by_method" not in str(e) and "__self__" not in str(e) or validate: + if ("get_output_by_method" not in str(e) and "__self__" not in str(e)) or validate: raise e property_method = value fields[name] = computed_field(property_method) diff --git a/src/backend/base/langflow/graph/utils.py b/src/backend/base/langflow/graph/utils.py index c57917a77..0d236e8e4 100644 --- a/src/backend/base/langflow/graph/utils.py +++ b/src/backend/base/langflow/graph/utils.py @@ -106,7 +106,7 @@ def get_artifact_type(value, build_result) -> str: result = ArtifactType.MESSAGE if result == ArtifactType.UNKNOWN and ( - isinstance(build_result, Generator) or isinstance(value, Message) and isinstance(value.text, Generator) + isinstance(build_result, Generator) or (isinstance(value, Message) and isinstance(value.text, Generator)) ): result = ArtifactType.STREAM diff --git a/src/backend/base/langflow/graph/vertex/base.py b/src/backend/base/langflow/graph/vertex/base.py index c7fa8959c..935e6434e 100644 --- a/src/backend/base/langflow/graph/vertex/base.py +++ b/src/backend/base/langflow/graph/vertex/base.py @@ -758,7 +758,7 @@ class Vertex: logger.warning(message) elif isinstance(self._built_object, Iterator | AsyncIterator): - if self.display_name in ["Text Output"]: + if self.display_name == "Text Output": msg = f"You are trying to stream to a {self.display_name}. Try using a Chat Output instead." raise ValueError(msg) diff --git a/src/backend/base/langflow/helpers/__init__.py b/src/backend/base/langflow/helpers/__init__.py index 70c5733f6..02f9d73c6 100644 --- a/src/backend/base/langflow/helpers/__init__.py +++ b/src/backend/base/langflow/helpers/__init__.py @@ -1,3 +1,3 @@ from .data import data_to_text, docs_to_data, messages_to_text -__all__ = ["docs_to_data", "data_to_text", "messages_to_text"] +__all__ = ["data_to_text", "docs_to_data", "messages_to_text"] diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Agent Flow.json b/src/backend/base/langflow/initial_setup/starter_projects/Agent Flow.json index 9b49092c8..6bfab2132 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Agent Flow.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Agent Flow.json @@ -670,7 +670,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = LCModelComponent._base_inputs + [\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" + "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = [\n *LCModelComponent._base_inputs,\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" }, "input_value": { "_input_type": "MessageInput", @@ -1256,7 +1256,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import ast\nimport operator\n\nfrom langchain.tools import StructuredTool\nfrom pydantic import BaseModel, Field\n\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.field_typing import Tool\nfrom langflow.inputs import MessageTextInput\nfrom langflow.schema import Data\n\n\nclass CalculatorToolComponent(LCToolComponent):\n display_name = \"Calculator\"\n description = \"Perform basic arithmetic operations on a given expression.\"\n icon = \"calculator\"\n name = \"CalculatorTool\"\n\n inputs = [\n MessageTextInput(\n name=\"expression\",\n display_name=\"Expression\",\n info=\"The arithmetic expression to evaluate (e.g., '4*4*(33/22)+12-20').\",\n ),\n ]\n\n class CalculatorToolSchema(BaseModel):\n expression: str = Field(..., description=\"The arithmetic expression to evaluate.\")\n\n def run_model(self) -> list[Data]:\n return self._evaluate_expression(self.expression)\n\n def build_tool(self) -> Tool:\n return StructuredTool.from_function(\n name=\"calculator\",\n description=\"Evaluate basic arithmetic expressions. Input should be a string containing the expression.\",\n func=self._evaluate_expression,\n args_schema=self.CalculatorToolSchema,\n )\n\n def _evaluate_expression(self, expression: str) -> list[Data]:\n try:\n # Define the allowed operators\n operators = {\n ast.Add: operator.add,\n ast.Sub: operator.sub,\n ast.Mult: operator.mul,\n ast.Div: operator.truediv,\n ast.Pow: operator.pow,\n }\n\n def eval_expr(node):\n if isinstance(node, ast.Num):\n return node.n\n if isinstance(node, ast.BinOp):\n return operators[type(node.op)](eval_expr(node.left), eval_expr(node.right))\n if isinstance(node, ast.UnaryOp):\n return operators[type(node.op)](eval_expr(node.operand))\n raise TypeError(node)\n\n # Parse the expression and evaluate it\n tree = ast.parse(expression, mode=\"eval\")\n result = eval_expr(tree.body)\n\n # Format the result to a reasonable number of decimal places\n formatted_result = f\"{result:.6f}\".rstrip(\"0\").rstrip(\".\")\n\n self.status = formatted_result\n return [Data(data={\"result\": formatted_result})]\n\n except (SyntaxError, TypeError, KeyError) as e:\n error_message = f\"Invalid expression: {str(e)}\"\n self.status = error_message\n return [Data(data={\"error\": error_message})]\n except ZeroDivisionError:\n error_message = \"Error: Division by zero\"\n self.status = error_message\n return [Data(data={\"error\": error_message})]\n except Exception as e:\n error_message = f\"Error: {str(e)}\"\n self.status = error_message\n return [Data(data={\"error\": error_message})]\n" + "value": "import ast\nimport operator\n\nfrom langchain.tools import StructuredTool\nfrom pydantic import BaseModel, Field\n\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.field_typing import Tool\nfrom langflow.inputs import MessageTextInput\nfrom langflow.schema import Data\n\n\nclass CalculatorToolComponent(LCToolComponent):\n display_name = \"Calculator\"\n description = \"Perform basic arithmetic operations on a given expression.\"\n icon = \"calculator\"\n name = \"CalculatorTool\"\n\n inputs = [\n MessageTextInput(\n name=\"expression\",\n display_name=\"Expression\",\n info=\"The arithmetic expression to evaluate (e.g., '4*4*(33/22)+12-20').\",\n ),\n ]\n\n class CalculatorToolSchema(BaseModel):\n expression: str = Field(..., description=\"The arithmetic expression to evaluate.\")\n\n def run_model(self) -> list[Data]:\n return self._evaluate_expression(self.expression)\n\n def build_tool(self) -> Tool:\n return StructuredTool.from_function(\n name=\"calculator\",\n description=\"Evaluate basic arithmetic expressions. Input should be a string containing the expression.\",\n func=self._evaluate_expression,\n args_schema=self.CalculatorToolSchema,\n )\n\n def _evaluate_expression(self, expression: str) -> list[Data]:\n try:\n # Define the allowed operators\n operators = {\n ast.Add: operator.add,\n ast.Sub: operator.sub,\n ast.Mult: operator.mul,\n ast.Div: operator.truediv,\n ast.Pow: operator.pow,\n }\n\n def eval_expr(node):\n if isinstance(node, ast.Num):\n return node.n\n if isinstance(node, ast.BinOp):\n return operators[type(node.op)](eval_expr(node.left), eval_expr(node.right))\n if isinstance(node, ast.UnaryOp):\n return operators[type(node.op)](eval_expr(node.operand))\n raise TypeError(node)\n\n # Parse the expression and evaluate it\n tree = ast.parse(expression, mode=\"eval\")\n result = eval_expr(tree.body)\n\n # Format the result to a reasonable number of decimal places\n formatted_result = f\"{result:.6f}\".rstrip(\"0\").rstrip(\".\")\n\n self.status = formatted_result\n return [Data(data={\"result\": formatted_result})]\n\n except (SyntaxError, TypeError, KeyError) as e:\n error_message = f\"Invalid expression: {e}\"\n self.status = error_message\n return [Data(data={\"error\": error_message})]\n except ZeroDivisionError:\n error_message = \"Error: Division by zero\"\n self.status = error_message\n return [Data(data={\"error\": error_message})]\n except Exception as e:\n error_message = f\"Error: {e}\"\n self.status = error_message\n return [Data(data={\"error\": error_message})]\n" }, "expression": { "_input_type": "MessageTextInput", @@ -1379,7 +1379,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import importlib\n\nfrom langchain.tools import StructuredTool\nfrom langchain_experimental.utilities import PythonREPL\nfrom pydantic import BaseModel, Field\n\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.field_typing import Tool\nfrom langflow.inputs import StrInput\nfrom langflow.schema import Data\n\n\nclass PythonREPLToolComponent(LCToolComponent):\n display_name = \"Python REPL Tool\"\n description = \"A tool for running Python code in a REPL environment.\"\n name = \"PythonREPLTool\"\n\n inputs = [\n StrInput(\n name=\"name\",\n display_name=\"Tool Name\",\n info=\"The name of the tool.\",\n value=\"python_repl\",\n ),\n StrInput(\n name=\"description\",\n display_name=\"Tool Description\",\n info=\"A description of the tool.\",\n value=\"A Python shell. Use this to execute python commands. \"\n \"Input should be a valid python command. \"\n \"If you want to see the output of a value, you should print it out with `print(...)`.\",\n ),\n StrInput(\n name=\"global_imports\",\n display_name=\"Global Imports\",\n info=\"A comma-separated list of modules to import globally, e.g. 'math,numpy'.\",\n value=\"math\",\n ),\n StrInput(\n name=\"code\",\n display_name=\"Python Code\",\n info=\"The Python code to execute.\",\n value=\"print('Hello, World!')\",\n ),\n ]\n\n class PythonREPLSchema(BaseModel):\n code: str = Field(..., description=\"The Python code to execute.\")\n\n def get_globals(self, global_imports: str | list[str]) -> dict:\n global_dict = {}\n if isinstance(global_imports, str):\n modules = [module.strip() for module in global_imports.split(\",\")]\n elif isinstance(global_imports, list):\n modules = global_imports\n else:\n msg = \"global_imports must be either a string or a list\"\n raise ValueError(msg)\n\n for module in modules:\n try:\n imported_module = importlib.import_module(module)\n global_dict[imported_module.__name__] = imported_module\n except ImportError as e:\n msg = f\"Could not import module {module}\"\n raise ImportError(msg) from e\n return global_dict\n\n def build_tool(self) -> Tool:\n _globals = self.get_globals(self.global_imports)\n python_repl = PythonREPL(_globals=_globals)\n\n def run_python_code(code: str) -> str:\n try:\n return python_repl.run(code)\n except Exception as e:\n return f\"Error: {str(e)}\"\n\n tool = StructuredTool.from_function(\n name=self.name,\n description=self.description,\n func=run_python_code,\n args_schema=self.PythonREPLSchema,\n )\n\n self.status = f\"Python REPL Tool created with global imports: {self.global_imports}\"\n return tool\n\n def run_model(self) -> list[Data]:\n tool = self.build_tool()\n result = tool.run(self.code)\n return [Data(data={\"result\": result})]\n" + "value": "import importlib\n\nfrom langchain.tools import StructuredTool\nfrom langchain_experimental.utilities import PythonREPL\nfrom pydantic import BaseModel, Field\n\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.field_typing import Tool\nfrom langflow.inputs import StrInput\nfrom langflow.schema import Data\n\n\nclass PythonREPLToolComponent(LCToolComponent):\n display_name = \"Python REPL Tool\"\n description = \"A tool for running Python code in a REPL environment.\"\n name = \"PythonREPLTool\"\n\n inputs = [\n StrInput(\n name=\"name\",\n display_name=\"Tool Name\",\n info=\"The name of the tool.\",\n value=\"python_repl\",\n ),\n StrInput(\n name=\"description\",\n display_name=\"Tool Description\",\n info=\"A description of the tool.\",\n value=\"A Python shell. Use this to execute python commands. \"\n \"Input should be a valid python command. \"\n \"If you want to see the output of a value, you should print it out with `print(...)`.\",\n ),\n StrInput(\n name=\"global_imports\",\n display_name=\"Global Imports\",\n info=\"A comma-separated list of modules to import globally, e.g. 'math,numpy'.\",\n value=\"math\",\n ),\n StrInput(\n name=\"code\",\n display_name=\"Python Code\",\n info=\"The Python code to execute.\",\n value=\"print('Hello, World!')\",\n ),\n ]\n\n class PythonREPLSchema(BaseModel):\n code: str = Field(..., description=\"The Python code to execute.\")\n\n def get_globals(self, global_imports: str | list[str]) -> dict:\n global_dict = {}\n if isinstance(global_imports, str):\n modules = [module.strip() for module in global_imports.split(\",\")]\n elif isinstance(global_imports, list):\n modules = global_imports\n else:\n msg = \"global_imports must be either a string or a list\"\n raise ValueError(msg)\n\n for module in modules:\n try:\n imported_module = importlib.import_module(module)\n global_dict[imported_module.__name__] = imported_module\n except ImportError as e:\n msg = f\"Could not import module {module}\"\n raise ImportError(msg) from e\n return global_dict\n\n def build_tool(self) -> Tool:\n _globals = self.get_globals(self.global_imports)\n python_repl = PythonREPL(_globals=_globals)\n\n def run_python_code(code: str) -> str:\n try:\n return python_repl.run(code)\n except Exception as e:\n return f\"Error: {e}\"\n\n tool = StructuredTool.from_function(\n name=self.name,\n description=self.description,\n func=run_python_code,\n args_schema=self.PythonREPLSchema,\n )\n\n self.status = f\"Python REPL Tool created with global imports: {self.global_imports}\"\n return tool\n\n def run_model(self) -> list[Data]:\n tool = self.build_tool()\n result = tool.run(self.code)\n return [Data(data={\"result\": result})]\n" }, "description": { "_input_type": "StrInput", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, World).json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, World).json index 95bc1fe69..aba6ffe30 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, World).json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, World).json @@ -727,7 +727,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = LCModelComponent._base_inputs + [\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" + "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = [\n *LCModelComponent._base_inputs,\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" }, "input_value": { "advanced": false, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json b/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json index c2b5426cb..bd7d44678 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json @@ -949,7 +949,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = LCModelComponent._base_inputs + [\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" + "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = [\n *LCModelComponent._base_inputs,\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" }, "input_value": { "advanced": false, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Complex Agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Complex Agent.json index bcaa088ae..84af95b5d 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Complex Agent.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Complex Agent.json @@ -682,7 +682,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from crewai import Crew, Process # type: ignore\n\nfrom langflow.base.agents.crewai.crew import BaseCrewComponent\nfrom langflow.io import HandleInput\n\n\nclass HierarchicalCrewComponent(BaseCrewComponent):\n display_name: str = \"Hierarchical Crew\"\n description: str = (\n \"Represents a group of agents, defining how they should collaborate and the tasks they should perform.\"\n )\n documentation: str = \"https://docs.crewai.com/how-to/Hierarchical/\"\n icon = \"CrewAI\"\n\n inputs = BaseCrewComponent._base_inputs + [\n HandleInput(name=\"agents\", display_name=\"Agents\", input_types=[\"Agent\"], is_list=True),\n HandleInput(name=\"tasks\", display_name=\"Tasks\", input_types=[\"HierarchicalTask\"], is_list=True),\n HandleInput(name=\"manager_llm\", display_name=\"Manager LLM\", input_types=[\"LanguageModel\"], required=False),\n HandleInput(name=\"manager_agent\", display_name=\"Manager Agent\", input_types=[\"Agent\"], required=False),\n ]\n\n def build_crew(self) -> Crew:\n tasks, agents = self.get_tasks_and_agents()\n return Crew(\n agents=agents,\n tasks=tasks,\n process=Process.hierarchical,\n verbose=self.verbose,\n memory=self.memory,\n cache=self.use_cache,\n max_rpm=self.max_rpm,\n share_crew=self.share_crew,\n function_calling_llm=self.function_calling_llm,\n manager_agent=self.manager_agent,\n manager_llm=self.manager_llm,\n step_callback=self.get_step_callback(),\n task_callback=self.get_task_callback(),\n )\n" + "value": "from crewai import Crew, Process # type: ignore\n\nfrom langflow.base.agents.crewai.crew import BaseCrewComponent\nfrom langflow.io import HandleInput\n\n\nclass HierarchicalCrewComponent(BaseCrewComponent):\n display_name: str = \"Hierarchical Crew\"\n description: str = (\n \"Represents a group of agents, defining how they should collaborate and the tasks they should perform.\"\n )\n documentation: str = \"https://docs.crewai.com/how-to/Hierarchical/\"\n icon = \"CrewAI\"\n\n inputs = [\n *BaseCrewComponent._base_inputs,\n HandleInput(name=\"agents\", display_name=\"Agents\", input_types=[\"Agent\"], is_list=True),\n HandleInput(name=\"tasks\", display_name=\"Tasks\", input_types=[\"HierarchicalTask\"], is_list=True),\n HandleInput(name=\"manager_llm\", display_name=\"Manager LLM\", input_types=[\"LanguageModel\"], required=False),\n HandleInput(name=\"manager_agent\", display_name=\"Manager Agent\", input_types=[\"Agent\"], required=False),\n ]\n\n def build_crew(self) -> Crew:\n tasks, agents = self.get_tasks_and_agents()\n return Crew(\n agents=agents,\n tasks=tasks,\n process=Process.hierarchical,\n verbose=self.verbose,\n memory=self.memory,\n cache=self.use_cache,\n max_rpm=self.max_rpm,\n share_crew=self.share_crew,\n function_calling_llm=self.function_calling_llm,\n manager_agent=self.manager_agent,\n manager_llm=self.manager_llm,\n step_callback=self.get_step_callback(),\n task_callback=self.get_task_callback(),\n )\n" }, "function_calling_llm": { "advanced": true, @@ -956,7 +956,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = LCModelComponent._base_inputs + [\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" + "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = [\n *LCModelComponent._base_inputs,\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" }, "input_value": { "advanced": false, @@ -1613,7 +1613,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from crewai import Agent # type: ignore\n\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, DictInput, HandleInput, MultilineInput, Output\n\n\nclass CrewAIAgentComponent(Component):\n display_name = \"CrewAI Agent\"\n description = \"Represents an agent of CrewAI.\"\n documentation: str = \"https://docs.crewai.com/how-to/LLM-Connections/\"\n icon = \"CrewAI\"\n\n inputs = [\n MultilineInput(name=\"role\", display_name=\"Role\", info=\"The role of the agent.\"),\n MultilineInput(name=\"goal\", display_name=\"Goal\", info=\"The objective of the agent.\"),\n MultilineInput(name=\"backstory\", display_name=\"Backstory\", info=\"The backstory of the agent.\"),\n HandleInput(\n name=\"tools\",\n display_name=\"Tools\",\n input_types=[\"Tool\"],\n is_list=True,\n info=\"Tools at agents disposal\",\n value=[],\n ),\n HandleInput(\n name=\"llm\",\n display_name=\"Language Model\",\n info=\"Language model that will run the agent.\",\n input_types=[\"LanguageModel\"],\n ),\n BoolInput(\n name=\"memory\",\n display_name=\"Memory\",\n info=\"Whether the agent should have memory or not\",\n advanced=True,\n value=True,\n ),\n BoolInput(\n name=\"verbose\",\n display_name=\"Verbose\",\n advanced=True,\n value=False,\n ),\n BoolInput(\n name=\"allow_delegation\",\n display_name=\"Allow Delegation\",\n info=\"Whether the agent is allowed to delegate tasks to other agents.\",\n value=True,\n ),\n BoolInput(\n name=\"allow_code_execution\",\n display_name=\"Allow Code Execution\",\n info=\"Whether the agent is allowed to execute code.\",\n value=False,\n advanced=True,\n ),\n DictInput(\n name=\"kwargs\",\n display_name=\"kwargs\",\n info=\"kwargs of agent.\",\n is_list=True,\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Agent\", name=\"output\", method=\"build_output\"),\n ]\n\n def build_output(self) -> Agent:\n kwargs = self.kwargs if self.kwargs else {}\n agent = Agent(\n role=self.role,\n goal=self.goal,\n backstory=self.backstory,\n llm=self.llm,\n verbose=self.verbose,\n memory=self.memory,\n tools=self.tools if self.tools else [],\n allow_delegation=self.allow_delegation,\n allow_code_execution=self.allow_code_execution,\n **kwargs,\n )\n self.status = repr(agent)\n return agent\n" + "value": "from crewai import Agent # type: ignore\n\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, DictInput, HandleInput, MultilineInput, Output\n\n\nclass CrewAIAgentComponent(Component):\n display_name = \"CrewAI Agent\"\n description = \"Represents an agent of CrewAI.\"\n documentation: str = \"https://docs.crewai.com/how-to/LLM-Connections/\"\n icon = \"CrewAI\"\n\n inputs = [\n MultilineInput(name=\"role\", display_name=\"Role\", info=\"The role of the agent.\"),\n MultilineInput(name=\"goal\", display_name=\"Goal\", info=\"The objective of the agent.\"),\n MultilineInput(name=\"backstory\", display_name=\"Backstory\", info=\"The backstory of the agent.\"),\n HandleInput(\n name=\"tools\",\n display_name=\"Tools\",\n input_types=[\"Tool\"],\n is_list=True,\n info=\"Tools at agents disposal\",\n value=[],\n ),\n HandleInput(\n name=\"llm\",\n display_name=\"Language Model\",\n info=\"Language model that will run the agent.\",\n input_types=[\"LanguageModel\"],\n ),\n BoolInput(\n name=\"memory\",\n display_name=\"Memory\",\n info=\"Whether the agent should have memory or not\",\n advanced=True,\n value=True,\n ),\n BoolInput(\n name=\"verbose\",\n display_name=\"Verbose\",\n advanced=True,\n value=False,\n ),\n BoolInput(\n name=\"allow_delegation\",\n display_name=\"Allow Delegation\",\n info=\"Whether the agent is allowed to delegate tasks to other agents.\",\n value=True,\n ),\n BoolInput(\n name=\"allow_code_execution\",\n display_name=\"Allow Code Execution\",\n info=\"Whether the agent is allowed to execute code.\",\n value=False,\n advanced=True,\n ),\n DictInput(\n name=\"kwargs\",\n display_name=\"kwargs\",\n info=\"kwargs of agent.\",\n is_list=True,\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Agent\", name=\"output\", method=\"build_output\"),\n ]\n\n def build_output(self) -> Agent:\n kwargs = self.kwargs or {}\n agent = Agent(\n role=self.role,\n goal=self.goal,\n backstory=self.backstory,\n llm=self.llm,\n verbose=self.verbose,\n memory=self.memory,\n tools=self.tools or [],\n allow_delegation=self.allow_delegation,\n allow_code_execution=self.allow_code_execution,\n **kwargs,\n )\n self.status = repr(agent)\n return agent\n" }, "goal": { "advanced": false, @@ -1868,7 +1868,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from crewai import Agent # type: ignore\n\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, DictInput, HandleInput, MultilineInput, Output\n\n\nclass CrewAIAgentComponent(Component):\n display_name = \"CrewAI Agent\"\n description = \"Represents an agent of CrewAI.\"\n documentation: str = \"https://docs.crewai.com/how-to/LLM-Connections/\"\n icon = \"CrewAI\"\n\n inputs = [\n MultilineInput(name=\"role\", display_name=\"Role\", info=\"The role of the agent.\"),\n MultilineInput(name=\"goal\", display_name=\"Goal\", info=\"The objective of the agent.\"),\n MultilineInput(name=\"backstory\", display_name=\"Backstory\", info=\"The backstory of the agent.\"),\n HandleInput(\n name=\"tools\",\n display_name=\"Tools\",\n input_types=[\"Tool\"],\n is_list=True,\n info=\"Tools at agents disposal\",\n value=[],\n ),\n HandleInput(\n name=\"llm\",\n display_name=\"Language Model\",\n info=\"Language model that will run the agent.\",\n input_types=[\"LanguageModel\"],\n ),\n BoolInput(\n name=\"memory\",\n display_name=\"Memory\",\n info=\"Whether the agent should have memory or not\",\n advanced=True,\n value=True,\n ),\n BoolInput(\n name=\"verbose\",\n display_name=\"Verbose\",\n advanced=True,\n value=False,\n ),\n BoolInput(\n name=\"allow_delegation\",\n display_name=\"Allow Delegation\",\n info=\"Whether the agent is allowed to delegate tasks to other agents.\",\n value=True,\n ),\n BoolInput(\n name=\"allow_code_execution\",\n display_name=\"Allow Code Execution\",\n info=\"Whether the agent is allowed to execute code.\",\n value=False,\n advanced=True,\n ),\n DictInput(\n name=\"kwargs\",\n display_name=\"kwargs\",\n info=\"kwargs of agent.\",\n is_list=True,\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Agent\", name=\"output\", method=\"build_output\"),\n ]\n\n def build_output(self) -> Agent:\n kwargs = self.kwargs if self.kwargs else {}\n agent = Agent(\n role=self.role,\n goal=self.goal,\n backstory=self.backstory,\n llm=self.llm,\n verbose=self.verbose,\n memory=self.memory,\n tools=self.tools if self.tools else [],\n allow_delegation=self.allow_delegation,\n allow_code_execution=self.allow_code_execution,\n **kwargs,\n )\n self.status = repr(agent)\n return agent\n" + "value": "from crewai import Agent # type: ignore\n\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, DictInput, HandleInput, MultilineInput, Output\n\n\nclass CrewAIAgentComponent(Component):\n display_name = \"CrewAI Agent\"\n description = \"Represents an agent of CrewAI.\"\n documentation: str = \"https://docs.crewai.com/how-to/LLM-Connections/\"\n icon = \"CrewAI\"\n\n inputs = [\n MultilineInput(name=\"role\", display_name=\"Role\", info=\"The role of the agent.\"),\n MultilineInput(name=\"goal\", display_name=\"Goal\", info=\"The objective of the agent.\"),\n MultilineInput(name=\"backstory\", display_name=\"Backstory\", info=\"The backstory of the agent.\"),\n HandleInput(\n name=\"tools\",\n display_name=\"Tools\",\n input_types=[\"Tool\"],\n is_list=True,\n info=\"Tools at agents disposal\",\n value=[],\n ),\n HandleInput(\n name=\"llm\",\n display_name=\"Language Model\",\n info=\"Language model that will run the agent.\",\n input_types=[\"LanguageModel\"],\n ),\n BoolInput(\n name=\"memory\",\n display_name=\"Memory\",\n info=\"Whether the agent should have memory or not\",\n advanced=True,\n value=True,\n ),\n BoolInput(\n name=\"verbose\",\n display_name=\"Verbose\",\n advanced=True,\n value=False,\n ),\n BoolInput(\n name=\"allow_delegation\",\n display_name=\"Allow Delegation\",\n info=\"Whether the agent is allowed to delegate tasks to other agents.\",\n value=True,\n ),\n BoolInput(\n name=\"allow_code_execution\",\n display_name=\"Allow Code Execution\",\n info=\"Whether the agent is allowed to execute code.\",\n value=False,\n advanced=True,\n ),\n DictInput(\n name=\"kwargs\",\n display_name=\"kwargs\",\n info=\"kwargs of agent.\",\n is_list=True,\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Agent\", name=\"output\", method=\"build_output\"),\n ]\n\n def build_output(self) -> Agent:\n kwargs = self.kwargs or {}\n agent = Agent(\n role=self.role,\n goal=self.goal,\n backstory=self.backstory,\n llm=self.llm,\n verbose=self.verbose,\n memory=self.memory,\n tools=self.tools or [],\n allow_delegation=self.allow_delegation,\n allow_code_execution=self.allow_code_execution,\n **kwargs,\n )\n self.status = repr(agent)\n return agent\n" }, "goal": { "advanced": false, @@ -2123,7 +2123,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = LCModelComponent._base_inputs + [\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" + "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = [\n *LCModelComponent._base_inputs,\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" }, "input_value": { "advanced": false, @@ -2894,7 +2894,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = LCModelComponent._base_inputs + [\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" + "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = [\n *LCModelComponent._base_inputs,\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" }, "input_value": { "advanced": false, @@ -3344,7 +3344,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = LCModelComponent._base_inputs + [\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" + "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = [\n *LCModelComponent._base_inputs,\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" }, "input_value": { "advanced": false, @@ -3818,7 +3818,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = LCModelComponent._base_inputs + [\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" + "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = [\n *LCModelComponent._base_inputs,\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" }, "input_value": { "advanced": false, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Document QA.json b/src/backend/base/langflow/initial_setup/starter_projects/Document QA.json index 1934f6ebe..06653b068 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Document QA.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Document QA.json @@ -805,7 +805,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = LCModelComponent._base_inputs + [\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" + "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = [\n *LCModelComponent._base_inputs,\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" }, "input_value": { "advanced": false, @@ -1215,7 +1215,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from pathlib import Path\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_data\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, FileInput, Output\nfrom langflow.schema import Data\n\n\nclass FileComponent(Component):\n display_name = \"File\"\n description = \"A generic file loader.\"\n icon = \"file-text\"\n name = \"File\"\n\n inputs = [\n FileInput(\n name=\"path\",\n display_name=\"Path\",\n file_types=TEXT_FILE_TYPES,\n info=f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n ),\n BoolInput(\n name=\"silent_errors\",\n display_name=\"Silent Errors\",\n advanced=True,\n info=\"If true, errors will not raise an exception.\",\n ),\n ]\n\n outputs = [\n Output(display_name=\"Data\", name=\"data\", method=\"load_file\"),\n ]\n\n def load_file(self) -> Data:\n if not self.path:\n msg = \"Please, upload a file to use this component.\"\n raise ValueError(msg)\n resolved_path = self.resolve_path(self.path)\n silent_errors = self.silent_errors\n\n extension = Path(resolved_path).suffix[1:].lower()\n\n if extension == \"doc\":\n msg = \"doc files are not supported. Please save as .docx\"\n raise ValueError(msg)\n if extension not in TEXT_FILE_TYPES:\n msg = f\"Unsupported file type: {extension}\"\n raise ValueError(msg)\n\n data = parse_text_file_to_data(resolved_path, silent_errors)\n self.status = data if data else \"No data\"\n return data or Data()\n" + "value": "from pathlib import Path\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_data\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, FileInput, Output\nfrom langflow.schema import Data\n\n\nclass FileComponent(Component):\n display_name = \"File\"\n description = \"A generic file loader.\"\n icon = \"file-text\"\n name = \"File\"\n\n inputs = [\n FileInput(\n name=\"path\",\n display_name=\"Path\",\n file_types=TEXT_FILE_TYPES,\n info=f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n ),\n BoolInput(\n name=\"silent_errors\",\n display_name=\"Silent Errors\",\n advanced=True,\n info=\"If true, errors will not raise an exception.\",\n ),\n ]\n\n outputs = [\n Output(display_name=\"Data\", name=\"data\", method=\"load_file\"),\n ]\n\n def load_file(self) -> Data:\n if not self.path:\n msg = \"Please, upload a file to use this component.\"\n raise ValueError(msg)\n resolved_path = self.resolve_path(self.path)\n silent_errors = self.silent_errors\n\n extension = Path(resolved_path).suffix[1:].lower()\n\n if extension == \"doc\":\n msg = \"doc files are not supported. Please save as .docx\"\n raise ValueError(msg)\n if extension not in TEXT_FILE_TYPES:\n msg = f\"Unsupported file type: {extension}\"\n raise ValueError(msg)\n\n data = parse_text_file_to_data(resolved_path, silent_errors)\n self.status = data or \"No data\"\n return data or Data()\n" }, "path": { "advanced": false, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Hierarchical Agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Hierarchical Agent.json index 09c68ff49..358ae3373 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Hierarchical Agent.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Hierarchical Agent.json @@ -379,7 +379,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from crewai import Crew, Process # type: ignore\n\nfrom langflow.base.agents.crewai.crew import BaseCrewComponent\nfrom langflow.io import HandleInput\n\n\nclass HierarchicalCrewComponent(BaseCrewComponent):\n display_name: str = \"Hierarchical Crew\"\n description: str = (\n \"Represents a group of agents, defining how they should collaborate and the tasks they should perform.\"\n )\n documentation: str = \"https://docs.crewai.com/how-to/Hierarchical/\"\n icon = \"CrewAI\"\n\n inputs = BaseCrewComponent._base_inputs + [\n HandleInput(name=\"agents\", display_name=\"Agents\", input_types=[\"Agent\"], is_list=True),\n HandleInput(name=\"tasks\", display_name=\"Tasks\", input_types=[\"HierarchicalTask\"], is_list=True),\n HandleInput(name=\"manager_llm\", display_name=\"Manager LLM\", input_types=[\"LanguageModel\"], required=False),\n HandleInput(name=\"manager_agent\", display_name=\"Manager Agent\", input_types=[\"Agent\"], required=False),\n ]\n\n def build_crew(self) -> Crew:\n tasks, agents = self.get_tasks_and_agents()\n return Crew(\n agents=agents,\n tasks=tasks,\n process=Process.hierarchical,\n verbose=self.verbose,\n memory=self.memory,\n cache=self.use_cache,\n max_rpm=self.max_rpm,\n share_crew=self.share_crew,\n function_calling_llm=self.function_calling_llm,\n manager_agent=self.manager_agent,\n manager_llm=self.manager_llm,\n step_callback=self.get_step_callback(),\n task_callback=self.get_task_callback(),\n )\n" + "value": "from crewai import Crew, Process # type: ignore\n\nfrom langflow.base.agents.crewai.crew import BaseCrewComponent\nfrom langflow.io import HandleInput\n\n\nclass HierarchicalCrewComponent(BaseCrewComponent):\n display_name: str = \"Hierarchical Crew\"\n description: str = (\n \"Represents a group of agents, defining how they should collaborate and the tasks they should perform.\"\n )\n documentation: str = \"https://docs.crewai.com/how-to/Hierarchical/\"\n icon = \"CrewAI\"\n\n inputs = [\n *BaseCrewComponent._base_inputs,\n HandleInput(name=\"agents\", display_name=\"Agents\", input_types=[\"Agent\"], is_list=True),\n HandleInput(name=\"tasks\", display_name=\"Tasks\", input_types=[\"HierarchicalTask\"], is_list=True),\n HandleInput(name=\"manager_llm\", display_name=\"Manager LLM\", input_types=[\"LanguageModel\"], required=False),\n HandleInput(name=\"manager_agent\", display_name=\"Manager Agent\", input_types=[\"Agent\"], required=False),\n ]\n\n def build_crew(self) -> Crew:\n tasks, agents = self.get_tasks_and_agents()\n return Crew(\n agents=agents,\n tasks=tasks,\n process=Process.hierarchical,\n verbose=self.verbose,\n memory=self.memory,\n cache=self.use_cache,\n max_rpm=self.max_rpm,\n share_crew=self.share_crew,\n function_calling_llm=self.function_calling_llm,\n manager_agent=self.manager_agent,\n manager_llm=self.manager_llm,\n step_callback=self.get_step_callback(),\n task_callback=self.get_task_callback(),\n )\n" }, "function_calling_llm": { "advanced": true, @@ -655,7 +655,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = LCModelComponent._base_inputs + [\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" + "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = [\n *LCModelComponent._base_inputs,\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" }, "input_value": { "advanced": false, @@ -1327,7 +1327,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from crewai import Agent # type: ignore\n\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, DictInput, HandleInput, MultilineInput, Output\n\n\nclass CrewAIAgentComponent(Component):\n display_name = \"CrewAI Agent\"\n description = \"Represents an agent of CrewAI.\"\n documentation: str = \"https://docs.crewai.com/how-to/LLM-Connections/\"\n icon = \"CrewAI\"\n\n inputs = [\n MultilineInput(name=\"role\", display_name=\"Role\", info=\"The role of the agent.\"),\n MultilineInput(name=\"goal\", display_name=\"Goal\", info=\"The objective of the agent.\"),\n MultilineInput(name=\"backstory\", display_name=\"Backstory\", info=\"The backstory of the agent.\"),\n HandleInput(\n name=\"tools\",\n display_name=\"Tools\",\n input_types=[\"Tool\"],\n is_list=True,\n info=\"Tools at agents disposal\",\n value=[],\n ),\n HandleInput(\n name=\"llm\",\n display_name=\"Language Model\",\n info=\"Language model that will run the agent.\",\n input_types=[\"LanguageModel\"],\n ),\n BoolInput(\n name=\"memory\",\n display_name=\"Memory\",\n info=\"Whether the agent should have memory or not\",\n advanced=True,\n value=True,\n ),\n BoolInput(\n name=\"verbose\",\n display_name=\"Verbose\",\n advanced=True,\n value=False,\n ),\n BoolInput(\n name=\"allow_delegation\",\n display_name=\"Allow Delegation\",\n info=\"Whether the agent is allowed to delegate tasks to other agents.\",\n value=True,\n ),\n BoolInput(\n name=\"allow_code_execution\",\n display_name=\"Allow Code Execution\",\n info=\"Whether the agent is allowed to execute code.\",\n value=False,\n advanced=True,\n ),\n DictInput(\n name=\"kwargs\",\n display_name=\"kwargs\",\n info=\"kwargs of agent.\",\n is_list=True,\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Agent\", name=\"output\", method=\"build_output\"),\n ]\n\n def build_output(self) -> Agent:\n kwargs = self.kwargs if self.kwargs else {}\n agent = Agent(\n role=self.role,\n goal=self.goal,\n backstory=self.backstory,\n llm=self.llm,\n verbose=self.verbose,\n memory=self.memory,\n tools=self.tools if self.tools else [],\n allow_delegation=self.allow_delegation,\n allow_code_execution=self.allow_code_execution,\n **kwargs,\n )\n self.status = repr(agent)\n return agent\n" + "value": "from crewai import Agent # type: ignore\n\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, DictInput, HandleInput, MultilineInput, Output\n\n\nclass CrewAIAgentComponent(Component):\n display_name = \"CrewAI Agent\"\n description = \"Represents an agent of CrewAI.\"\n documentation: str = \"https://docs.crewai.com/how-to/LLM-Connections/\"\n icon = \"CrewAI\"\n\n inputs = [\n MultilineInput(name=\"role\", display_name=\"Role\", info=\"The role of the agent.\"),\n MultilineInput(name=\"goal\", display_name=\"Goal\", info=\"The objective of the agent.\"),\n MultilineInput(name=\"backstory\", display_name=\"Backstory\", info=\"The backstory of the agent.\"),\n HandleInput(\n name=\"tools\",\n display_name=\"Tools\",\n input_types=[\"Tool\"],\n is_list=True,\n info=\"Tools at agents disposal\",\n value=[],\n ),\n HandleInput(\n name=\"llm\",\n display_name=\"Language Model\",\n info=\"Language model that will run the agent.\",\n input_types=[\"LanguageModel\"],\n ),\n BoolInput(\n name=\"memory\",\n display_name=\"Memory\",\n info=\"Whether the agent should have memory or not\",\n advanced=True,\n value=True,\n ),\n BoolInput(\n name=\"verbose\",\n display_name=\"Verbose\",\n advanced=True,\n value=False,\n ),\n BoolInput(\n name=\"allow_delegation\",\n display_name=\"Allow Delegation\",\n info=\"Whether the agent is allowed to delegate tasks to other agents.\",\n value=True,\n ),\n BoolInput(\n name=\"allow_code_execution\",\n display_name=\"Allow Code Execution\",\n info=\"Whether the agent is allowed to execute code.\",\n value=False,\n advanced=True,\n ),\n DictInput(\n name=\"kwargs\",\n display_name=\"kwargs\",\n info=\"kwargs of agent.\",\n is_list=True,\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Agent\", name=\"output\", method=\"build_output\"),\n ]\n\n def build_output(self) -> Agent:\n kwargs = self.kwargs or {}\n agent = Agent(\n role=self.role,\n goal=self.goal,\n backstory=self.backstory,\n llm=self.llm,\n verbose=self.verbose,\n memory=self.memory,\n tools=self.tools or [],\n allow_delegation=self.allow_delegation,\n allow_code_execution=self.allow_code_execution,\n **kwargs,\n )\n self.status = repr(agent)\n return agent\n" }, "goal": { "advanced": false, @@ -1586,7 +1586,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from crewai import Agent # type: ignore\n\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, DictInput, HandleInput, MultilineInput, Output\n\n\nclass CrewAIAgentComponent(Component):\n display_name = \"CrewAI Agent\"\n description = \"Represents an agent of CrewAI.\"\n documentation: str = \"https://docs.crewai.com/how-to/LLM-Connections/\"\n icon = \"CrewAI\"\n\n inputs = [\n MultilineInput(name=\"role\", display_name=\"Role\", info=\"The role of the agent.\"),\n MultilineInput(name=\"goal\", display_name=\"Goal\", info=\"The objective of the agent.\"),\n MultilineInput(name=\"backstory\", display_name=\"Backstory\", info=\"The backstory of the agent.\"),\n HandleInput(\n name=\"tools\",\n display_name=\"Tools\",\n input_types=[\"Tool\"],\n is_list=True,\n info=\"Tools at agents disposal\",\n value=[],\n ),\n HandleInput(\n name=\"llm\",\n display_name=\"Language Model\",\n info=\"Language model that will run the agent.\",\n input_types=[\"LanguageModel\"],\n ),\n BoolInput(\n name=\"memory\",\n display_name=\"Memory\",\n info=\"Whether the agent should have memory or not\",\n advanced=True,\n value=True,\n ),\n BoolInput(\n name=\"verbose\",\n display_name=\"Verbose\",\n advanced=True,\n value=False,\n ),\n BoolInput(\n name=\"allow_delegation\",\n display_name=\"Allow Delegation\",\n info=\"Whether the agent is allowed to delegate tasks to other agents.\",\n value=True,\n ),\n BoolInput(\n name=\"allow_code_execution\",\n display_name=\"Allow Code Execution\",\n info=\"Whether the agent is allowed to execute code.\",\n value=False,\n advanced=True,\n ),\n DictInput(\n name=\"kwargs\",\n display_name=\"kwargs\",\n info=\"kwargs of agent.\",\n is_list=True,\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Agent\", name=\"output\", method=\"build_output\"),\n ]\n\n def build_output(self) -> Agent:\n kwargs = self.kwargs if self.kwargs else {}\n agent = Agent(\n role=self.role,\n goal=self.goal,\n backstory=self.backstory,\n llm=self.llm,\n verbose=self.verbose,\n memory=self.memory,\n tools=self.tools if self.tools else [],\n allow_delegation=self.allow_delegation,\n allow_code_execution=self.allow_code_execution,\n **kwargs,\n )\n self.status = repr(agent)\n return agent\n" + "value": "from crewai import Agent # type: ignore\n\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, DictInput, HandleInput, MultilineInput, Output\n\n\nclass CrewAIAgentComponent(Component):\n display_name = \"CrewAI Agent\"\n description = \"Represents an agent of CrewAI.\"\n documentation: str = \"https://docs.crewai.com/how-to/LLM-Connections/\"\n icon = \"CrewAI\"\n\n inputs = [\n MultilineInput(name=\"role\", display_name=\"Role\", info=\"The role of the agent.\"),\n MultilineInput(name=\"goal\", display_name=\"Goal\", info=\"The objective of the agent.\"),\n MultilineInput(name=\"backstory\", display_name=\"Backstory\", info=\"The backstory of the agent.\"),\n HandleInput(\n name=\"tools\",\n display_name=\"Tools\",\n input_types=[\"Tool\"],\n is_list=True,\n info=\"Tools at agents disposal\",\n value=[],\n ),\n HandleInput(\n name=\"llm\",\n display_name=\"Language Model\",\n info=\"Language model that will run the agent.\",\n input_types=[\"LanguageModel\"],\n ),\n BoolInput(\n name=\"memory\",\n display_name=\"Memory\",\n info=\"Whether the agent should have memory or not\",\n advanced=True,\n value=True,\n ),\n BoolInput(\n name=\"verbose\",\n display_name=\"Verbose\",\n advanced=True,\n value=False,\n ),\n BoolInput(\n name=\"allow_delegation\",\n display_name=\"Allow Delegation\",\n info=\"Whether the agent is allowed to delegate tasks to other agents.\",\n value=True,\n ),\n BoolInput(\n name=\"allow_code_execution\",\n display_name=\"Allow Code Execution\",\n info=\"Whether the agent is allowed to execute code.\",\n value=False,\n advanced=True,\n ),\n DictInput(\n name=\"kwargs\",\n display_name=\"kwargs\",\n info=\"kwargs of agent.\",\n is_list=True,\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Agent\", name=\"output\", method=\"build_output\"),\n ]\n\n def build_output(self) -> Agent:\n kwargs = self.kwargs or {}\n agent = Agent(\n role=self.role,\n goal=self.goal,\n backstory=self.backstory,\n llm=self.llm,\n verbose=self.verbose,\n memory=self.memory,\n tools=self.tools or [],\n allow_delegation=self.allow_delegation,\n allow_code_execution=self.allow_code_execution,\n **kwargs,\n )\n self.status = repr(agent)\n return agent\n" }, "goal": { "advanced": false, @@ -1842,7 +1842,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = LCModelComponent._base_inputs + [\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" + "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = [\n *LCModelComponent._base_inputs,\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" }, "input_value": { "advanced": false, @@ -2521,7 +2521,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from crewai import Agent # type: ignore\n\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, DictInput, HandleInput, MultilineInput, Output\n\n\nclass CrewAIAgentComponent(Component):\n display_name = \"CrewAI Agent\"\n description = \"Represents an agent of CrewAI.\"\n documentation: str = \"https://docs.crewai.com/how-to/LLM-Connections/\"\n icon = \"CrewAI\"\n\n inputs = [\n MultilineInput(name=\"role\", display_name=\"Role\", info=\"The role of the agent.\"),\n MultilineInput(name=\"goal\", display_name=\"Goal\", info=\"The objective of the agent.\"),\n MultilineInput(name=\"backstory\", display_name=\"Backstory\", info=\"The backstory of the agent.\"),\n HandleInput(\n name=\"tools\",\n display_name=\"Tools\",\n input_types=[\"Tool\"],\n is_list=True,\n info=\"Tools at agents disposal\",\n value=[],\n ),\n HandleInput(\n name=\"llm\",\n display_name=\"Language Model\",\n info=\"Language model that will run the agent.\",\n input_types=[\"LanguageModel\"],\n ),\n BoolInput(\n name=\"memory\",\n display_name=\"Memory\",\n info=\"Whether the agent should have memory or not\",\n advanced=True,\n value=True,\n ),\n BoolInput(\n name=\"verbose\",\n display_name=\"Verbose\",\n advanced=True,\n value=False,\n ),\n BoolInput(\n name=\"allow_delegation\",\n display_name=\"Allow Delegation\",\n info=\"Whether the agent is allowed to delegate tasks to other agents.\",\n value=True,\n ),\n BoolInput(\n name=\"allow_code_execution\",\n display_name=\"Allow Code Execution\",\n info=\"Whether the agent is allowed to execute code.\",\n value=False,\n advanced=True,\n ),\n DictInput(\n name=\"kwargs\",\n display_name=\"kwargs\",\n info=\"kwargs of agent.\",\n is_list=True,\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Agent\", name=\"output\", method=\"build_output\"),\n ]\n\n def build_output(self) -> Agent:\n kwargs = self.kwargs if self.kwargs else {}\n agent = Agent(\n role=self.role,\n goal=self.goal,\n backstory=self.backstory,\n llm=self.llm,\n verbose=self.verbose,\n memory=self.memory,\n tools=self.tools if self.tools else [],\n allow_delegation=self.allow_delegation,\n allow_code_execution=self.allow_code_execution,\n **kwargs,\n )\n self.status = repr(agent)\n return agent\n" + "value": "from crewai import Agent # type: ignore\n\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, DictInput, HandleInput, MultilineInput, Output\n\n\nclass CrewAIAgentComponent(Component):\n display_name = \"CrewAI Agent\"\n description = \"Represents an agent of CrewAI.\"\n documentation: str = \"https://docs.crewai.com/how-to/LLM-Connections/\"\n icon = \"CrewAI\"\n\n inputs = [\n MultilineInput(name=\"role\", display_name=\"Role\", info=\"The role of the agent.\"),\n MultilineInput(name=\"goal\", display_name=\"Goal\", info=\"The objective of the agent.\"),\n MultilineInput(name=\"backstory\", display_name=\"Backstory\", info=\"The backstory of the agent.\"),\n HandleInput(\n name=\"tools\",\n display_name=\"Tools\",\n input_types=[\"Tool\"],\n is_list=True,\n info=\"Tools at agents disposal\",\n value=[],\n ),\n HandleInput(\n name=\"llm\",\n display_name=\"Language Model\",\n info=\"Language model that will run the agent.\",\n input_types=[\"LanguageModel\"],\n ),\n BoolInput(\n name=\"memory\",\n display_name=\"Memory\",\n info=\"Whether the agent should have memory or not\",\n advanced=True,\n value=True,\n ),\n BoolInput(\n name=\"verbose\",\n display_name=\"Verbose\",\n advanced=True,\n value=False,\n ),\n BoolInput(\n name=\"allow_delegation\",\n display_name=\"Allow Delegation\",\n info=\"Whether the agent is allowed to delegate tasks to other agents.\",\n value=True,\n ),\n BoolInput(\n name=\"allow_code_execution\",\n display_name=\"Allow Code Execution\",\n info=\"Whether the agent is allowed to execute code.\",\n value=False,\n advanced=True,\n ),\n DictInput(\n name=\"kwargs\",\n display_name=\"kwargs\",\n info=\"kwargs of agent.\",\n is_list=True,\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Agent\", name=\"output\", method=\"build_output\"),\n ]\n\n def build_output(self) -> Agent:\n kwargs = self.kwargs or {}\n agent = Agent(\n role=self.role,\n goal=self.goal,\n backstory=self.backstory,\n llm=self.llm,\n verbose=self.verbose,\n memory=self.memory,\n tools=self.tools or [],\n allow_delegation=self.allow_delegation,\n allow_code_execution=self.allow_code_execution,\n **kwargs,\n )\n self.status = repr(agent)\n return agent\n" }, "goal": { "advanced": false, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json b/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json index dfac20b34..a7481e1fb 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json @@ -584,7 +584,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = LCModelComponent._base_inputs + [\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" + "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = [\n *LCModelComponent._base_inputs,\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" }, "input_value": { "advanced": false, @@ -1079,7 +1079,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from langchain.memory import ConversationBufferMemory\n\nfrom langflow.custom import Component\nfrom langflow.field_typing import BaseChatMemory\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs import HandleInput\nfrom langflow.io import DropdownInput, IntInput, MessageTextInput, MultilineInput, Output\nfrom langflow.memory import LCBuiltinChatMemory, get_messages\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n display_name = \"Chat Memory\"\n description = \"Retrieves stored chat messages from Langflow tables or an external memory.\"\n icon = \"message-square-more\"\n name = \"Memory\"\n\n inputs = [\n HandleInput(\n name=\"memory\",\n display_name=\"External Memory\",\n input_types=[\"BaseChatMessageHistory\"],\n info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n value=\"Machine and User\",\n info=\"Filter by sender type.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Filter by sender name.\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Messages\",\n value=100,\n info=\"Number of messages to retrieve.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"order\",\n display_name=\"Order\",\n options=[\"Ascending\", \"Descending\"],\n value=\"Ascending\",\n info=\"Order of the messages.\",\n advanced=True,\n ),\n MultilineInput(\n name=\"template\",\n display_name=\"Template\",\n info=\"The template to use for formatting the data. \"\n \"It can contain the keys {text}, {sender} or any other key in the message data.\",\n value=\"{sender_name}: {text}\",\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Messages (Data)\", name=\"messages\", method=\"retrieve_messages\"),\n Output(display_name=\"Messages (Text)\", name=\"messages_text\", method=\"retrieve_messages_as_text\"),\n Output(display_name=\"Memory\", name=\"lc_memory\", method=\"build_lc_memory\"),\n ]\n\n def retrieve_messages(self) -> Data:\n sender = self.sender\n sender_name = self.sender_name\n session_id = self.session_id\n n_messages = self.n_messages\n order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n if sender == \"Machine and User\":\n sender = None\n\n if self.memory:\n # override session_id\n self.memory.session_id = session_id\n\n stored = self.memory.messages\n # langchain memories are supposed to return messages in ascending order\n if order == \"DESC\":\n stored = stored[::-1]\n if n_messages:\n stored = stored[:n_messages]\n stored = [Message.from_lc_message(m) for m in stored]\n if sender:\n expected_type = MESSAGE_SENDER_AI if sender == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n stored = [m for m in stored if m.type == expected_type]\n else:\n stored = get_messages(\n sender=sender,\n sender_name=sender_name,\n session_id=session_id,\n limit=n_messages,\n order=order,\n )\n self.status = stored\n return stored\n\n def retrieve_messages_as_text(self) -> Message:\n stored_text = data_to_text(self.template, self.retrieve_messages())\n self.status = stored_text\n return Message(text=stored_text)\n\n def build_lc_memory(self) -> BaseChatMemory:\n if self.memory:\n chat_memory = self.memory\n else:\n chat_memory = LCBuiltinChatMemory(flow_id=self.flow_id, session_id=self.session_id)\n return ConversationBufferMemory(chat_memory=chat_memory)\n" + "value": "from langchain.memory import ConversationBufferMemory\n\nfrom langflow.custom import Component\nfrom langflow.field_typing import BaseChatMemory\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs import HandleInput\nfrom langflow.io import DropdownInput, IntInput, MessageTextInput, MultilineInput, Output\nfrom langflow.memory import LCBuiltinChatMemory, get_messages\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n display_name = \"Chat Memory\"\n description = \"Retrieves stored chat messages from Langflow tables or an external memory.\"\n icon = \"message-square-more\"\n name = \"Memory\"\n\n inputs = [\n HandleInput(\n name=\"memory\",\n display_name=\"External Memory\",\n input_types=[\"BaseChatMessageHistory\"],\n info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n value=\"Machine and User\",\n info=\"Filter by sender type.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Filter by sender name.\",\n advanced=True,\n ),\n IntInput(\n name=\"n_messages\",\n display_name=\"Number of Messages\",\n value=100,\n info=\"Number of messages to retrieve.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"order\",\n display_name=\"Order\",\n options=[\"Ascending\", \"Descending\"],\n value=\"Ascending\",\n info=\"Order of the messages.\",\n advanced=True,\n ),\n MultilineInput(\n name=\"template\",\n display_name=\"Template\",\n info=\"The template to use for formatting the data. \"\n \"It can contain the keys {text}, {sender} or any other key in the message data.\",\n value=\"{sender_name}: {text}\",\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Messages (Data)\", name=\"messages\", method=\"retrieve_messages\"),\n Output(display_name=\"Messages (Text)\", name=\"messages_text\", method=\"retrieve_messages_as_text\"),\n Output(display_name=\"Memory\", name=\"lc_memory\", method=\"build_lc_memory\"),\n ]\n\n def retrieve_messages(self) -> Data:\n sender = self.sender\n sender_name = self.sender_name\n session_id = self.session_id\n n_messages = self.n_messages\n order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n if sender == \"Machine and User\":\n sender = None\n\n if self.memory:\n # override session_id\n self.memory.session_id = session_id\n\n stored = self.memory.messages\n # langchain memories are supposed to return messages in ascending order\n if order == \"DESC\":\n stored = stored[::-1]\n if n_messages:\n stored = stored[:n_messages]\n stored = [Message.from_lc_message(m) for m in stored]\n if sender:\n expected_type = MESSAGE_SENDER_AI if sender == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n stored = [m for m in stored if m.type == expected_type]\n else:\n stored = get_messages(\n sender=sender,\n sender_name=sender_name,\n session_id=session_id,\n limit=n_messages,\n order=order,\n )\n self.status = stored\n return stored\n\n def retrieve_messages_as_text(self) -> Message:\n stored_text = data_to_text(self.template, self.retrieve_messages())\n self.status = stored_text\n return Message(text=stored_text)\n\n def build_lc_memory(self) -> BaseChatMemory:\n chat_memory = self.memory or LCBuiltinChatMemory(flow_id=self.flow_id, session_id=self.session_id)\n return ConversationBufferMemory(chat_memory=chat_memory)\n" }, "memory": { "advanced": false, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Sequential Agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Sequential Agent.json index e09ae8b97..483b8e8e8 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Sequential Agent.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Sequential Agent.json @@ -425,7 +425,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from crewai import Agent, Crew, Process, Task # type: ignore\n\nfrom langflow.base.agents.crewai.crew import BaseCrewComponent\nfrom langflow.io import HandleInput\nfrom langflow.schema.message import Message\n\n\nclass SequentialCrewComponent(BaseCrewComponent):\n display_name: str = \"Sequential Crew\"\n description: str = \"Represents a group of agents with tasks that are executed sequentially.\"\n documentation: str = \"https://docs.crewai.com/how-to/Sequential/\"\n icon = \"CrewAI\"\n\n inputs = BaseCrewComponent._base_inputs + [\n HandleInput(name=\"tasks\", display_name=\"Tasks\", input_types=[\"SequentialTask\"], is_list=True),\n ]\n\n def get_tasks_and_agents(self) -> tuple[list[Task], list[Agent]]:\n return self.tasks, [task.agent for task in self.tasks]\n\n def build_crew(self) -> Message:\n tasks, agents = self.get_tasks_and_agents()\n return Crew(\n agents=agents,\n tasks=tasks,\n process=Process.sequential,\n verbose=self.verbose,\n memory=self.memory,\n cache=self.use_cache,\n max_rpm=self.max_rpm,\n share_crew=self.share_crew,\n function_calling_llm=self.function_calling_llm,\n step_callback=self.get_step_callback(),\n task_callback=self.get_task_callback(),\n )\n" + "value": "from crewai import Agent, Crew, Process, Task # type: ignore\n\nfrom langflow.base.agents.crewai.crew import BaseCrewComponent\nfrom langflow.io import HandleInput\nfrom langflow.schema.message import Message\n\n\nclass SequentialCrewComponent(BaseCrewComponent):\n display_name: str = \"Sequential Crew\"\n description: str = \"Represents a group of agents with tasks that are executed sequentially.\"\n documentation: str = \"https://docs.crewai.com/how-to/Sequential/\"\n icon = \"CrewAI\"\n\n inputs = [\n *BaseCrewComponent._base_inputs,\n HandleInput(name=\"tasks\", display_name=\"Tasks\", input_types=[\"SequentialTask\"], is_list=True),\n ]\n\n def get_tasks_and_agents(self) -> tuple[list[Task], list[Agent]]:\n return self.tasks, [task.agent for task in self.tasks]\n\n def build_crew(self) -> Message:\n tasks, agents = self.get_tasks_and_agents()\n return Crew(\n agents=agents,\n tasks=tasks,\n process=Process.sequential,\n verbose=self.verbose,\n memory=self.memory,\n cache=self.use_cache,\n max_rpm=self.max_rpm,\n share_crew=self.share_crew,\n function_calling_llm=self.function_calling_llm,\n step_callback=self.get_step_callback(),\n task_callback=self.get_task_callback(),\n )\n" }, "function_calling_llm": { "advanced": true, @@ -669,7 +669,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = LCModelComponent._base_inputs + [\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" + "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = [\n *LCModelComponent._base_inputs,\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" }, "input_value": { "advanced": false, @@ -1690,7 +1690,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from crewai import Agent, Task\n\nfrom langflow.base.agents.crewai.tasks import SequentialTask\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, DictInput, HandleInput, MultilineInput, Output\n\n\nclass SequentialTaskAgentComponent(Component):\n display_name = \"Sequential Task Agent\"\n description = \"Creates a CrewAI Task and its associated Agent.\"\n documentation = \"https://docs.crewai.com/how-to/LLM-Connections/\"\n icon = \"CrewAI\"\n\n inputs = [\n # Agent inputs\n MultilineInput(name=\"role\", display_name=\"Role\", info=\"The role of the agent.\"),\n MultilineInput(name=\"goal\", display_name=\"Goal\", info=\"The objective of the agent.\"),\n MultilineInput(\n name=\"backstory\",\n display_name=\"Backstory\",\n info=\"The backstory of the agent.\",\n ),\n HandleInput(\n name=\"tools\",\n display_name=\"Tools\",\n input_types=[\"Tool\"],\n is_list=True,\n info=\"Tools at agent's disposal\",\n value=[],\n ),\n HandleInput(\n name=\"llm\",\n display_name=\"Language Model\",\n info=\"Language model that will run the agent.\",\n input_types=[\"LanguageModel\"],\n ),\n BoolInput(\n name=\"memory\",\n display_name=\"Memory\",\n info=\"Whether the agent should have memory or not\",\n advanced=True,\n value=True,\n ),\n BoolInput(\n name=\"verbose\",\n display_name=\"Verbose\",\n advanced=True,\n value=True,\n ),\n BoolInput(\n name=\"allow_delegation\",\n display_name=\"Allow Delegation\",\n info=\"Whether the agent is allowed to delegate tasks to other agents.\",\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"allow_code_execution\",\n display_name=\"Allow Code Execution\",\n info=\"Whether the agent is allowed to execute code.\",\n value=False,\n advanced=True,\n ),\n DictInput(\n name=\"agent_kwargs\",\n display_name=\"Agent kwargs\",\n info=\"Additional kwargs for the agent.\",\n is_list=True,\n advanced=True,\n ),\n # Task inputs\n MultilineInput(\n name=\"task_description\",\n display_name=\"Task Description\",\n info=\"Descriptive text detailing task's purpose and execution.\",\n ),\n MultilineInput(\n name=\"expected_output\",\n display_name=\"Expected Task Output\",\n info=\"Clear definition of expected task outcome.\",\n ),\n BoolInput(\n name=\"async_execution\",\n display_name=\"Async Execution\",\n value=False,\n advanced=True,\n info=\"Boolean flag indicating asynchronous task execution.\",\n ),\n # Chaining input\n HandleInput(\n name=\"previous_task\",\n display_name=\"Previous Task\",\n input_types=[\"SequentialTask\"],\n info=\"The previous task in the sequence (for chaining).\",\n required=False,\n ),\n ]\n\n outputs = [\n Output(\n display_name=\"Sequential Task\",\n name=\"task_output\",\n method=\"build_agent_and_task\",\n ),\n ]\n\n def build_agent_and_task(self) -> list[SequentialTask]:\n # Build the agent\n agent_kwargs = self.agent_kwargs or {}\n agent = Agent(\n role=self.role,\n goal=self.goal,\n backstory=self.backstory,\n llm=self.llm,\n verbose=self.verbose,\n memory=self.memory,\n tools=self.tools if self.tools else [],\n allow_delegation=self.allow_delegation,\n allow_code_execution=self.allow_code_execution,\n **agent_kwargs,\n )\n\n # Build the task\n task = Task(\n description=self.task_description,\n expected_output=self.expected_output,\n agent=agent,\n async_execution=self.async_execution,\n )\n\n # If there's a previous task, create a list of tasks\n if self.previous_task:\n tasks = self.previous_task + [task] if isinstance(self.previous_task, list) else [self.previous_task, task]\n else:\n tasks = [task]\n\n self.status = f\"Agent: {repr(agent)}\\nTask: {repr(task)}\"\n return tasks\n" + "value": "from crewai import Agent, Task\n\nfrom langflow.base.agents.crewai.tasks import SequentialTask\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, DictInput, HandleInput, MultilineInput, Output\n\n\nclass SequentialTaskAgentComponent(Component):\n display_name = \"Sequential Task Agent\"\n description = \"Creates a CrewAI Task and its associated Agent.\"\n documentation = \"https://docs.crewai.com/how-to/LLM-Connections/\"\n icon = \"CrewAI\"\n\n inputs = [\n # Agent inputs\n MultilineInput(name=\"role\", display_name=\"Role\", info=\"The role of the agent.\"),\n MultilineInput(name=\"goal\", display_name=\"Goal\", info=\"The objective of the agent.\"),\n MultilineInput(\n name=\"backstory\",\n display_name=\"Backstory\",\n info=\"The backstory of the agent.\",\n ),\n HandleInput(\n name=\"tools\",\n display_name=\"Tools\",\n input_types=[\"Tool\"],\n is_list=True,\n info=\"Tools at agent's disposal\",\n value=[],\n ),\n HandleInput(\n name=\"llm\",\n display_name=\"Language Model\",\n info=\"Language model that will run the agent.\",\n input_types=[\"LanguageModel\"],\n ),\n BoolInput(\n name=\"memory\",\n display_name=\"Memory\",\n info=\"Whether the agent should have memory or not\",\n advanced=True,\n value=True,\n ),\n BoolInput(\n name=\"verbose\",\n display_name=\"Verbose\",\n advanced=True,\n value=True,\n ),\n BoolInput(\n name=\"allow_delegation\",\n display_name=\"Allow Delegation\",\n info=\"Whether the agent is allowed to delegate tasks to other agents.\",\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"allow_code_execution\",\n display_name=\"Allow Code Execution\",\n info=\"Whether the agent is allowed to execute code.\",\n value=False,\n advanced=True,\n ),\n DictInput(\n name=\"agent_kwargs\",\n display_name=\"Agent kwargs\",\n info=\"Additional kwargs for the agent.\",\n is_list=True,\n advanced=True,\n ),\n # Task inputs\n MultilineInput(\n name=\"task_description\",\n display_name=\"Task Description\",\n info=\"Descriptive text detailing task's purpose and execution.\",\n ),\n MultilineInput(\n name=\"expected_output\",\n display_name=\"Expected Task Output\",\n info=\"Clear definition of expected task outcome.\",\n ),\n BoolInput(\n name=\"async_execution\",\n display_name=\"Async Execution\",\n value=False,\n advanced=True,\n info=\"Boolean flag indicating asynchronous task execution.\",\n ),\n # Chaining input\n HandleInput(\n name=\"previous_task\",\n display_name=\"Previous Task\",\n input_types=[\"SequentialTask\"],\n info=\"The previous task in the sequence (for chaining).\",\n required=False,\n ),\n ]\n\n outputs = [\n Output(\n display_name=\"Sequential Task\",\n name=\"task_output\",\n method=\"build_agent_and_task\",\n ),\n ]\n\n def build_agent_and_task(self) -> list[SequentialTask]:\n # Build the agent\n agent_kwargs = self.agent_kwargs or {}\n agent = Agent(\n role=self.role,\n goal=self.goal,\n backstory=self.backstory,\n llm=self.llm,\n verbose=self.verbose,\n memory=self.memory,\n tools=self.tools or [],\n allow_delegation=self.allow_delegation,\n allow_code_execution=self.allow_code_execution,\n **agent_kwargs,\n )\n\n # Build the task\n task = Task(\n description=self.task_description,\n expected_output=self.expected_output,\n agent=agent,\n async_execution=self.async_execution,\n )\n\n # If there's a previous task, create a list of tasks\n if self.previous_task:\n tasks = [*self.previous_task, task] if isinstance(self.previous_task, list) else [self.previous_task, task]\n else:\n tasks = [task]\n\n self.status = f\"Agent: {agent!r}\\nTask: {task!r}\"\n return tasks\n" }, "expected_output": { "_input_type": "MultilineInput", @@ -2041,7 +2041,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from crewai import Agent, Task\n\nfrom langflow.base.agents.crewai.tasks import SequentialTask\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, DictInput, HandleInput, MultilineInput, Output\n\n\nclass SequentialTaskAgentComponent(Component):\n display_name = \"Sequential Task Agent\"\n description = \"Creates a CrewAI Task and its associated Agent.\"\n documentation = \"https://docs.crewai.com/how-to/LLM-Connections/\"\n icon = \"CrewAI\"\n\n inputs = [\n # Agent inputs\n MultilineInput(name=\"role\", display_name=\"Role\", info=\"The role of the agent.\"),\n MultilineInput(name=\"goal\", display_name=\"Goal\", info=\"The objective of the agent.\"),\n MultilineInput(\n name=\"backstory\",\n display_name=\"Backstory\",\n info=\"The backstory of the agent.\",\n ),\n HandleInput(\n name=\"tools\",\n display_name=\"Tools\",\n input_types=[\"Tool\"],\n is_list=True,\n info=\"Tools at agent's disposal\",\n value=[],\n ),\n HandleInput(\n name=\"llm\",\n display_name=\"Language Model\",\n info=\"Language model that will run the agent.\",\n input_types=[\"LanguageModel\"],\n ),\n BoolInput(\n name=\"memory\",\n display_name=\"Memory\",\n info=\"Whether the agent should have memory or not\",\n advanced=True,\n value=True,\n ),\n BoolInput(\n name=\"verbose\",\n display_name=\"Verbose\",\n advanced=True,\n value=True,\n ),\n BoolInput(\n name=\"allow_delegation\",\n display_name=\"Allow Delegation\",\n info=\"Whether the agent is allowed to delegate tasks to other agents.\",\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"allow_code_execution\",\n display_name=\"Allow Code Execution\",\n info=\"Whether the agent is allowed to execute code.\",\n value=False,\n advanced=True,\n ),\n DictInput(\n name=\"agent_kwargs\",\n display_name=\"Agent kwargs\",\n info=\"Additional kwargs for the agent.\",\n is_list=True,\n advanced=True,\n ),\n # Task inputs\n MultilineInput(\n name=\"task_description\",\n display_name=\"Task Description\",\n info=\"Descriptive text detailing task's purpose and execution.\",\n ),\n MultilineInput(\n name=\"expected_output\",\n display_name=\"Expected Task Output\",\n info=\"Clear definition of expected task outcome.\",\n ),\n BoolInput(\n name=\"async_execution\",\n display_name=\"Async Execution\",\n value=False,\n advanced=True,\n info=\"Boolean flag indicating asynchronous task execution.\",\n ),\n # Chaining input\n HandleInput(\n name=\"previous_task\",\n display_name=\"Previous Task\",\n input_types=[\"SequentialTask\"],\n info=\"The previous task in the sequence (for chaining).\",\n required=False,\n ),\n ]\n\n outputs = [\n Output(\n display_name=\"Sequential Task\",\n name=\"task_output\",\n method=\"build_agent_and_task\",\n ),\n ]\n\n def build_agent_and_task(self) -> list[SequentialTask]:\n # Build the agent\n agent_kwargs = self.agent_kwargs or {}\n agent = Agent(\n role=self.role,\n goal=self.goal,\n backstory=self.backstory,\n llm=self.llm,\n verbose=self.verbose,\n memory=self.memory,\n tools=self.tools if self.tools else [],\n allow_delegation=self.allow_delegation,\n allow_code_execution=self.allow_code_execution,\n **agent_kwargs,\n )\n\n # Build the task\n task = Task(\n description=self.task_description,\n expected_output=self.expected_output,\n agent=agent,\n async_execution=self.async_execution,\n )\n\n # If there's a previous task, create a list of tasks\n if self.previous_task:\n tasks = self.previous_task + [task] if isinstance(self.previous_task, list) else [self.previous_task, task]\n else:\n tasks = [task]\n\n self.status = f\"Agent: {repr(agent)}\\nTask: {repr(task)}\"\n return tasks\n" + "value": "from crewai import Agent, Task\n\nfrom langflow.base.agents.crewai.tasks import SequentialTask\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, DictInput, HandleInput, MultilineInput, Output\n\n\nclass SequentialTaskAgentComponent(Component):\n display_name = \"Sequential Task Agent\"\n description = \"Creates a CrewAI Task and its associated Agent.\"\n documentation = \"https://docs.crewai.com/how-to/LLM-Connections/\"\n icon = \"CrewAI\"\n\n inputs = [\n # Agent inputs\n MultilineInput(name=\"role\", display_name=\"Role\", info=\"The role of the agent.\"),\n MultilineInput(name=\"goal\", display_name=\"Goal\", info=\"The objective of the agent.\"),\n MultilineInput(\n name=\"backstory\",\n display_name=\"Backstory\",\n info=\"The backstory of the agent.\",\n ),\n HandleInput(\n name=\"tools\",\n display_name=\"Tools\",\n input_types=[\"Tool\"],\n is_list=True,\n info=\"Tools at agent's disposal\",\n value=[],\n ),\n HandleInput(\n name=\"llm\",\n display_name=\"Language Model\",\n info=\"Language model that will run the agent.\",\n input_types=[\"LanguageModel\"],\n ),\n BoolInput(\n name=\"memory\",\n display_name=\"Memory\",\n info=\"Whether the agent should have memory or not\",\n advanced=True,\n value=True,\n ),\n BoolInput(\n name=\"verbose\",\n display_name=\"Verbose\",\n advanced=True,\n value=True,\n ),\n BoolInput(\n name=\"allow_delegation\",\n display_name=\"Allow Delegation\",\n info=\"Whether the agent is allowed to delegate tasks to other agents.\",\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"allow_code_execution\",\n display_name=\"Allow Code Execution\",\n info=\"Whether the agent is allowed to execute code.\",\n value=False,\n advanced=True,\n ),\n DictInput(\n name=\"agent_kwargs\",\n display_name=\"Agent kwargs\",\n info=\"Additional kwargs for the agent.\",\n is_list=True,\n advanced=True,\n ),\n # Task inputs\n MultilineInput(\n name=\"task_description\",\n display_name=\"Task Description\",\n info=\"Descriptive text detailing task's purpose and execution.\",\n ),\n MultilineInput(\n name=\"expected_output\",\n display_name=\"Expected Task Output\",\n info=\"Clear definition of expected task outcome.\",\n ),\n BoolInput(\n name=\"async_execution\",\n display_name=\"Async Execution\",\n value=False,\n advanced=True,\n info=\"Boolean flag indicating asynchronous task execution.\",\n ),\n # Chaining input\n HandleInput(\n name=\"previous_task\",\n display_name=\"Previous Task\",\n input_types=[\"SequentialTask\"],\n info=\"The previous task in the sequence (for chaining).\",\n required=False,\n ),\n ]\n\n outputs = [\n Output(\n display_name=\"Sequential Task\",\n name=\"task_output\",\n method=\"build_agent_and_task\",\n ),\n ]\n\n def build_agent_and_task(self) -> list[SequentialTask]:\n # Build the agent\n agent_kwargs = self.agent_kwargs or {}\n agent = Agent(\n role=self.role,\n goal=self.goal,\n backstory=self.backstory,\n llm=self.llm,\n verbose=self.verbose,\n memory=self.memory,\n tools=self.tools or [],\n allow_delegation=self.allow_delegation,\n allow_code_execution=self.allow_code_execution,\n **agent_kwargs,\n )\n\n # Build the task\n task = Task(\n description=self.task_description,\n expected_output=self.expected_output,\n agent=agent,\n async_execution=self.async_execution,\n )\n\n # If there's a previous task, create a list of tasks\n if self.previous_task:\n tasks = [*self.previous_task, task] if isinstance(self.previous_task, list) else [self.previous_task, task]\n else:\n tasks = [task]\n\n self.status = f\"Agent: {agent!r}\\nTask: {task!r}\"\n return tasks\n" }, "expected_output": { "_input_type": "MultilineInput", @@ -2392,7 +2392,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from crewai import Agent, Task\n\nfrom langflow.base.agents.crewai.tasks import SequentialTask\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, DictInput, HandleInput, MultilineInput, Output\n\n\nclass SequentialTaskAgentComponent(Component):\n display_name = \"Sequential Task Agent\"\n description = \"Creates a CrewAI Task and its associated Agent.\"\n documentation = \"https://docs.crewai.com/how-to/LLM-Connections/\"\n icon = \"CrewAI\"\n\n inputs = [\n # Agent inputs\n MultilineInput(name=\"role\", display_name=\"Role\", info=\"The role of the agent.\"),\n MultilineInput(name=\"goal\", display_name=\"Goal\", info=\"The objective of the agent.\"),\n MultilineInput(\n name=\"backstory\",\n display_name=\"Backstory\",\n info=\"The backstory of the agent.\",\n ),\n HandleInput(\n name=\"tools\",\n display_name=\"Tools\",\n input_types=[\"Tool\"],\n is_list=True,\n info=\"Tools at agent's disposal\",\n value=[],\n ),\n HandleInput(\n name=\"llm\",\n display_name=\"Language Model\",\n info=\"Language model that will run the agent.\",\n input_types=[\"LanguageModel\"],\n ),\n BoolInput(\n name=\"memory\",\n display_name=\"Memory\",\n info=\"Whether the agent should have memory or not\",\n advanced=True,\n value=True,\n ),\n BoolInput(\n name=\"verbose\",\n display_name=\"Verbose\",\n advanced=True,\n value=True,\n ),\n BoolInput(\n name=\"allow_delegation\",\n display_name=\"Allow Delegation\",\n info=\"Whether the agent is allowed to delegate tasks to other agents.\",\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"allow_code_execution\",\n display_name=\"Allow Code Execution\",\n info=\"Whether the agent is allowed to execute code.\",\n value=False,\n advanced=True,\n ),\n DictInput(\n name=\"agent_kwargs\",\n display_name=\"Agent kwargs\",\n info=\"Additional kwargs for the agent.\",\n is_list=True,\n advanced=True,\n ),\n # Task inputs\n MultilineInput(\n name=\"task_description\",\n display_name=\"Task Description\",\n info=\"Descriptive text detailing task's purpose and execution.\",\n ),\n MultilineInput(\n name=\"expected_output\",\n display_name=\"Expected Task Output\",\n info=\"Clear definition of expected task outcome.\",\n ),\n BoolInput(\n name=\"async_execution\",\n display_name=\"Async Execution\",\n value=False,\n advanced=True,\n info=\"Boolean flag indicating asynchronous task execution.\",\n ),\n # Chaining input\n HandleInput(\n name=\"previous_task\",\n display_name=\"Previous Task\",\n input_types=[\"SequentialTask\"],\n info=\"The previous task in the sequence (for chaining).\",\n required=False,\n ),\n ]\n\n outputs = [\n Output(\n display_name=\"Sequential Task\",\n name=\"task_output\",\n method=\"build_agent_and_task\",\n ),\n ]\n\n def build_agent_and_task(self) -> list[SequentialTask]:\n # Build the agent\n agent_kwargs = self.agent_kwargs or {}\n agent = Agent(\n role=self.role,\n goal=self.goal,\n backstory=self.backstory,\n llm=self.llm,\n verbose=self.verbose,\n memory=self.memory,\n tools=self.tools if self.tools else [],\n allow_delegation=self.allow_delegation,\n allow_code_execution=self.allow_code_execution,\n **agent_kwargs,\n )\n\n # Build the task\n task = Task(\n description=self.task_description,\n expected_output=self.expected_output,\n agent=agent,\n async_execution=self.async_execution,\n )\n\n # If there's a previous task, create a list of tasks\n if self.previous_task:\n tasks = self.previous_task + [task] if isinstance(self.previous_task, list) else [self.previous_task, task]\n else:\n tasks = [task]\n\n self.status = f\"Agent: {repr(agent)}\\nTask: {repr(task)}\"\n return tasks\n" + "value": "from crewai import Agent, Task\n\nfrom langflow.base.agents.crewai.tasks import SequentialTask\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, DictInput, HandleInput, MultilineInput, Output\n\n\nclass SequentialTaskAgentComponent(Component):\n display_name = \"Sequential Task Agent\"\n description = \"Creates a CrewAI Task and its associated Agent.\"\n documentation = \"https://docs.crewai.com/how-to/LLM-Connections/\"\n icon = \"CrewAI\"\n\n inputs = [\n # Agent inputs\n MultilineInput(name=\"role\", display_name=\"Role\", info=\"The role of the agent.\"),\n MultilineInput(name=\"goal\", display_name=\"Goal\", info=\"The objective of the agent.\"),\n MultilineInput(\n name=\"backstory\",\n display_name=\"Backstory\",\n info=\"The backstory of the agent.\",\n ),\n HandleInput(\n name=\"tools\",\n display_name=\"Tools\",\n input_types=[\"Tool\"],\n is_list=True,\n info=\"Tools at agent's disposal\",\n value=[],\n ),\n HandleInput(\n name=\"llm\",\n display_name=\"Language Model\",\n info=\"Language model that will run the agent.\",\n input_types=[\"LanguageModel\"],\n ),\n BoolInput(\n name=\"memory\",\n display_name=\"Memory\",\n info=\"Whether the agent should have memory or not\",\n advanced=True,\n value=True,\n ),\n BoolInput(\n name=\"verbose\",\n display_name=\"Verbose\",\n advanced=True,\n value=True,\n ),\n BoolInput(\n name=\"allow_delegation\",\n display_name=\"Allow Delegation\",\n info=\"Whether the agent is allowed to delegate tasks to other agents.\",\n value=False,\n advanced=True,\n ),\n BoolInput(\n name=\"allow_code_execution\",\n display_name=\"Allow Code Execution\",\n info=\"Whether the agent is allowed to execute code.\",\n value=False,\n advanced=True,\n ),\n DictInput(\n name=\"agent_kwargs\",\n display_name=\"Agent kwargs\",\n info=\"Additional kwargs for the agent.\",\n is_list=True,\n advanced=True,\n ),\n # Task inputs\n MultilineInput(\n name=\"task_description\",\n display_name=\"Task Description\",\n info=\"Descriptive text detailing task's purpose and execution.\",\n ),\n MultilineInput(\n name=\"expected_output\",\n display_name=\"Expected Task Output\",\n info=\"Clear definition of expected task outcome.\",\n ),\n BoolInput(\n name=\"async_execution\",\n display_name=\"Async Execution\",\n value=False,\n advanced=True,\n info=\"Boolean flag indicating asynchronous task execution.\",\n ),\n # Chaining input\n HandleInput(\n name=\"previous_task\",\n display_name=\"Previous Task\",\n input_types=[\"SequentialTask\"],\n info=\"The previous task in the sequence (for chaining).\",\n required=False,\n ),\n ]\n\n outputs = [\n Output(\n display_name=\"Sequential Task\",\n name=\"task_output\",\n method=\"build_agent_and_task\",\n ),\n ]\n\n def build_agent_and_task(self) -> list[SequentialTask]:\n # Build the agent\n agent_kwargs = self.agent_kwargs or {}\n agent = Agent(\n role=self.role,\n goal=self.goal,\n backstory=self.backstory,\n llm=self.llm,\n verbose=self.verbose,\n memory=self.memory,\n tools=self.tools or [],\n allow_delegation=self.allow_delegation,\n allow_code_execution=self.allow_code_execution,\n **agent_kwargs,\n )\n\n # Build the task\n task = Task(\n description=self.task_description,\n expected_output=self.expected_output,\n agent=agent,\n async_execution=self.async_execution,\n )\n\n # If there's a previous task, create a list of tasks\n if self.previous_task:\n tasks = [*self.previous_task, task] if isinstance(self.previous_task, list) else [self.previous_task, task]\n else:\n tasks = [task]\n\n self.status = f\"Agent: {agent!r}\\nTask: {task!r}\"\n return tasks\n" }, "expected_output": { "_input_type": "MultilineInput", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json b/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json index 14e5982a6..09010729d 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json @@ -909,7 +909,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = LCModelComponent._base_inputs + [\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" + "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = [\n *LCModelComponent._base_inputs,\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" }, "input_value": { "_input_type": "MessageInput", @@ -2372,7 +2372,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import ast\nimport operator\n\nfrom langchain.tools import StructuredTool\nfrom pydantic import BaseModel, Field\n\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.field_typing import Tool\nfrom langflow.inputs import MessageTextInput\nfrom langflow.schema import Data\n\n\nclass CalculatorToolComponent(LCToolComponent):\n display_name = \"Calculator\"\n description = \"Perform basic arithmetic operations on a given expression.\"\n icon = \"calculator\"\n name = \"CalculatorTool\"\n\n inputs = [\n MessageTextInput(\n name=\"expression\",\n display_name=\"Expression\",\n info=\"The arithmetic expression to evaluate (e.g., '4*4*(33/22)+12-20').\",\n ),\n ]\n\n class CalculatorToolSchema(BaseModel):\n expression: str = Field(..., description=\"The arithmetic expression to evaluate.\")\n\n def run_model(self) -> list[Data]:\n return self._evaluate_expression(self.expression)\n\n def build_tool(self) -> Tool:\n return StructuredTool.from_function(\n name=\"calculator\",\n description=\"Evaluate basic arithmetic expressions. Input should be a string containing the expression.\",\n func=self._evaluate_expression,\n args_schema=self.CalculatorToolSchema,\n )\n\n def _evaluate_expression(self, expression: str) -> list[Data]:\n try:\n # Define the allowed operators\n operators = {\n ast.Add: operator.add,\n ast.Sub: operator.sub,\n ast.Mult: operator.mul,\n ast.Div: operator.truediv,\n ast.Pow: operator.pow,\n }\n\n def eval_expr(node):\n if isinstance(node, ast.Num):\n return node.n\n if isinstance(node, ast.BinOp):\n return operators[type(node.op)](eval_expr(node.left), eval_expr(node.right))\n if isinstance(node, ast.UnaryOp):\n return operators[type(node.op)](eval_expr(node.operand))\n raise TypeError(node)\n\n # Parse the expression and evaluate it\n tree = ast.parse(expression, mode=\"eval\")\n result = eval_expr(tree.body)\n\n # Format the result to a reasonable number of decimal places\n formatted_result = f\"{result:.6f}\".rstrip(\"0\").rstrip(\".\")\n\n self.status = formatted_result\n return [Data(data={\"result\": formatted_result})]\n\n except (SyntaxError, TypeError, KeyError) as e:\n error_message = f\"Invalid expression: {str(e)}\"\n self.status = error_message\n return [Data(data={\"error\": error_message})]\n except ZeroDivisionError:\n error_message = \"Error: Division by zero\"\n self.status = error_message\n return [Data(data={\"error\": error_message})]\n except Exception as e:\n error_message = f\"Error: {str(e)}\"\n self.status = error_message\n return [Data(data={\"error\": error_message})]\n" + "value": "import ast\nimport operator\n\nfrom langchain.tools import StructuredTool\nfrom pydantic import BaseModel, Field\n\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.field_typing import Tool\nfrom langflow.inputs import MessageTextInput\nfrom langflow.schema import Data\n\n\nclass CalculatorToolComponent(LCToolComponent):\n display_name = \"Calculator\"\n description = \"Perform basic arithmetic operations on a given expression.\"\n icon = \"calculator\"\n name = \"CalculatorTool\"\n\n inputs = [\n MessageTextInput(\n name=\"expression\",\n display_name=\"Expression\",\n info=\"The arithmetic expression to evaluate (e.g., '4*4*(33/22)+12-20').\",\n ),\n ]\n\n class CalculatorToolSchema(BaseModel):\n expression: str = Field(..., description=\"The arithmetic expression to evaluate.\")\n\n def run_model(self) -> list[Data]:\n return self._evaluate_expression(self.expression)\n\n def build_tool(self) -> Tool:\n return StructuredTool.from_function(\n name=\"calculator\",\n description=\"Evaluate basic arithmetic expressions. Input should be a string containing the expression.\",\n func=self._evaluate_expression,\n args_schema=self.CalculatorToolSchema,\n )\n\n def _evaluate_expression(self, expression: str) -> list[Data]:\n try:\n # Define the allowed operators\n operators = {\n ast.Add: operator.add,\n ast.Sub: operator.sub,\n ast.Mult: operator.mul,\n ast.Div: operator.truediv,\n ast.Pow: operator.pow,\n }\n\n def eval_expr(node):\n if isinstance(node, ast.Num):\n return node.n\n if isinstance(node, ast.BinOp):\n return operators[type(node.op)](eval_expr(node.left), eval_expr(node.right))\n if isinstance(node, ast.UnaryOp):\n return operators[type(node.op)](eval_expr(node.operand))\n raise TypeError(node)\n\n # Parse the expression and evaluate it\n tree = ast.parse(expression, mode=\"eval\")\n result = eval_expr(tree.body)\n\n # Format the result to a reasonable number of decimal places\n formatted_result = f\"{result:.6f}\".rstrip(\"0\").rstrip(\".\")\n\n self.status = formatted_result\n return [Data(data={\"result\": formatted_result})]\n\n except (SyntaxError, TypeError, KeyError) as e:\n error_message = f\"Invalid expression: {e}\"\n self.status = error_message\n return [Data(data={\"error\": error_message})]\n except ZeroDivisionError:\n error_message = \"Error: Division by zero\"\n self.status = error_message\n return [Data(data={\"error\": error_message})]\n except Exception as e:\n error_message = f\"Error: {e}\"\n self.status = error_message\n return [Data(data={\"error\": error_message})]\n" }, "expression": { "_input_type": "MessageTextInput", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json b/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json index 8e868c185..d7777096f 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json @@ -683,7 +683,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import os\n\nfrom astrapy.admin import parse_api_endpoint\nfrom loguru import logger\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom langflow.helpers import docs_to_data\nfrom langflow.inputs import DictInput, FloatInput, MessageTextInput\nfrom langflow.io import (\n BoolInput,\n DataInput,\n DropdownInput,\n HandleInput,\n IntInput,\n MultilineInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.schema import Data\n\n\nclass AstraVectorStoreComponent(LCVectorStoreComponent):\n display_name: str = \"Astra DB\"\n description: str = \"Implementation of Vector Store using Astra DB with search capabilities\"\n documentation: str = \"https://docs.langflow.org/starter-projects-vector-store-rag\"\n name = \"AstraDB\"\n icon: str = \"AstraDB\"\n\n VECTORIZE_PROVIDERS_MAPPING = {\n \"Azure OpenAI\": [\"azureOpenAI\", [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"]],\n \"Hugging Face - Dedicated\": [\"huggingfaceDedicated\", [\"endpoint-defined-model\"]],\n \"Hugging Face - Serverless\": [\n \"huggingface\",\n [\n \"sentence-transformers/all-MiniLM-L6-v2\",\n \"intfloat/multilingual-e5-large\",\n \"intfloat/multilingual-e5-large-instruct\",\n \"BAAI/bge-small-en-v1.5\",\n \"BAAI/bge-base-en-v1.5\",\n \"BAAI/bge-large-en-v1.5\",\n ],\n ],\n \"Jina AI\": [\n \"jinaAI\",\n [\n \"jina-embeddings-v2-base-en\",\n \"jina-embeddings-v2-base-de\",\n \"jina-embeddings-v2-base-es\",\n \"jina-embeddings-v2-base-code\",\n \"jina-embeddings-v2-base-zh\",\n ],\n ],\n \"Mistral AI\": [\"mistral\", [\"mistral-embed\"]],\n \"NVIDIA\": [\"nvidia\", [\"NV-Embed-QA\"]],\n \"OpenAI\": [\"openai\", [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"]],\n \"Upstage\": [\"upstageAI\", [\"solar-embedding-1-large\"]],\n \"Voyage AI\": [\n \"voyageAI\",\n [\"voyage-large-2-instruct\", \"voyage-law-2\", \"voyage-code-2\", \"voyage-large-2\", \"voyage-2\"],\n ],\n }\n\n inputs = [\n SecretStrInput(\n name=\"token\",\n display_name=\"Astra DB Application Token\",\n info=\"Authentication token for accessing Astra DB.\",\n value=\"ASTRA_DB_APPLICATION_TOKEN\",\n required=True,\n advanced=os.getenv(\"ASTRA_ENHANCED\", \"false\").lower() == \"true\",\n ),\n SecretStrInput(\n name=\"api_endpoint\",\n display_name=\"Database\" if os.getenv(\"ASTRA_ENHANCED\", \"false\").lower() == \"true\" else \"API Endpoint\",\n info=\"API endpoint URL for the Astra DB service.\",\n value=\"ASTRA_DB_API_ENDPOINT\",\n required=True,\n ),\n StrInput(\n name=\"collection_name\",\n display_name=\"Collection Name\",\n info=\"The name of the collection within Astra DB where the vectors will be stored.\",\n required=True,\n ),\n MultilineInput(\n name=\"search_input\",\n display_name=\"Search Input\",\n ),\n DataInput(\n name=\"ingest_data\",\n display_name=\"Ingest Data\",\n is_list=True,\n ),\n StrInput(\n name=\"namespace\",\n display_name=\"Namespace\",\n info=\"Optional namespace within Astra DB to use for the collection.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"embedding_service\",\n display_name=\"Embedding Model or Astra Vectorize\",\n info=\"Determines whether to use Astra Vectorize for the collection.\",\n options=[\"Embedding Model\", \"Astra Vectorize\"],\n real_time_refresh=True,\n value=\"Embedding Model\",\n ),\n HandleInput(\n name=\"embedding\",\n display_name=\"Embedding Model\",\n input_types=[\"Embeddings\"],\n info=\"Allows an embedding model configuration.\",\n ),\n DropdownInput(\n name=\"metric\",\n display_name=\"Metric\",\n info=\"Optional distance metric for vector comparisons in the vector store.\",\n options=[\"cosine\", \"dot_product\", \"euclidean\"],\n advanced=True,\n ),\n IntInput(\n name=\"batch_size\",\n display_name=\"Batch Size\",\n info=\"Optional number of data to process in a single batch.\",\n advanced=True,\n ),\n IntInput(\n name=\"bulk_insert_batch_concurrency\",\n display_name=\"Bulk Insert Batch Concurrency\",\n info=\"Optional concurrency level for bulk insert operations.\",\n advanced=True,\n ),\n IntInput(\n name=\"bulk_insert_overwrite_concurrency\",\n display_name=\"Bulk Insert Overwrite Concurrency\",\n info=\"Optional concurrency level for bulk insert operations that overwrite existing data.\",\n advanced=True,\n ),\n IntInput(\n name=\"bulk_delete_concurrency\",\n display_name=\"Bulk Delete Concurrency\",\n info=\"Optional concurrency level for bulk delete operations.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"setup_mode\",\n display_name=\"Setup Mode\",\n info=\"Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.\",\n options=[\"Sync\", \"Async\", \"Off\"],\n advanced=True,\n value=\"Sync\",\n ),\n BoolInput(\n name=\"pre_delete_collection\",\n display_name=\"Pre Delete Collection\",\n info=\"Boolean flag to determine whether to delete the collection before creating a new one.\",\n advanced=True,\n ),\n StrInput(\n name=\"metadata_indexing_include\",\n display_name=\"Metadata Indexing Include\",\n info=\"Optional list of metadata fields to include in the indexing.\",\n advanced=True,\n ),\n StrInput(\n name=\"metadata_indexing_exclude\",\n display_name=\"Metadata Indexing Exclude\",\n info=\"Optional list of metadata fields to exclude from the indexing.\",\n advanced=True,\n ),\n StrInput(\n name=\"collection_indexing_policy\",\n display_name=\"Collection Indexing Policy\",\n info=\"Optional dictionary defining the indexing policy for the collection.\",\n advanced=True,\n ),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Number of Results\",\n info=\"Number of results to return.\",\n advanced=True,\n value=4,\n ),\n DropdownInput(\n name=\"search_type\",\n display_name=\"Search Type\",\n info=\"Search type to use\",\n options=[\"Similarity\", \"Similarity with score threshold\", \"MMR (Max Marginal Relevance)\"],\n value=\"Similarity\",\n advanced=True,\n ),\n FloatInput(\n name=\"search_score_threshold\",\n display_name=\"Search Score Threshold\",\n info=\"Minimum similarity score threshold for search results. \"\n \"(when using 'Similarity with score threshold')\",\n value=0,\n advanced=True,\n ),\n DictInput(\n name=\"search_filter\",\n display_name=\"Search Metadata Filter\",\n info=\"Optional dictionary of filters to apply to the search query.\",\n advanced=True,\n is_list=True,\n ),\n ]\n\n def insert_in_dict(self, build_config, field_name, new_parameters):\n # Insert the new key-value pair after the found key\n for new_field_name, new_parameter in new_parameters.items():\n # Get all the items as a list of tuples (key, value)\n items = list(build_config.items())\n\n # Find the index of the key to insert after\n idx = len(items)\n for i, (key, _value) in enumerate(items):\n if key == field_name:\n idx = i + 1\n break\n\n items.insert(idx, (new_field_name, new_parameter))\n\n # Clear the original dictionary and update with the modified items\n build_config.clear()\n build_config.update(items)\n\n return build_config\n\n def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n if field_name == \"embedding_service\":\n if field_value == \"Astra Vectorize\":\n for field in [\"embedding\"]:\n if field in build_config:\n del build_config[field]\n\n new_parameter = DropdownInput(\n name=\"provider\",\n display_name=\"Vectorize Provider\",\n options=self.VECTORIZE_PROVIDERS_MAPPING.keys(),\n value=\"\",\n required=True,\n real_time_refresh=True,\n ).to_dict()\n\n self.insert_in_dict(build_config, \"embedding_service\", {\"provider\": new_parameter})\n else:\n for field in [\n \"provider\",\n \"z_00_model_name\",\n \"z_01_model_parameters\",\n \"z_02_api_key_name\",\n \"z_03_provider_api_key\",\n \"z_04_authentication\",\n ]:\n if field in build_config:\n del build_config[field]\n\n new_parameter = HandleInput(\n name=\"embedding\",\n display_name=\"Embedding Model\",\n input_types=[\"Embeddings\"],\n info=\"Allows an embedding model configuration.\",\n ).to_dict()\n\n self.insert_in_dict(build_config, \"embedding_service\", {\"embedding\": new_parameter})\n\n elif field_name == \"provider\":\n for field in [\n \"z_00_model_name\",\n \"z_01_model_parameters\",\n \"z_02_api_key_name\",\n \"z_03_provider_api_key\",\n \"z_04_authentication\",\n ]:\n if field in build_config:\n del build_config[field]\n\n model_options = self.VECTORIZE_PROVIDERS_MAPPING[field_value][1]\n\n new_parameter_0 = DropdownInput(\n name=\"z_00_model_name\",\n display_name=\"Model Name\",\n info=\"The embedding model to use for the selected provider. Each provider has a different set of \"\n \"models available (full list at \"\n \"https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html):\\n\\n\"\n f\"{', '.join(model_options)}\",\n options=model_options,\n required=True,\n ).to_dict()\n\n new_parameter_1 = DictInput(\n name=\"z_01_model_parameters\",\n display_name=\"Model Parameters\",\n is_list=True,\n ).to_dict()\n\n new_parameter_2 = MessageTextInput(\n name=\"z_02_api_key_name\",\n display_name=\"API Key name\",\n info=\"The name of the embeddings provider API key stored on Astra. \"\n \"If set, it will override the 'ProviderKey' in the authentication parameters.\",\n ).to_dict()\n\n new_parameter_3 = SecretStrInput(\n name=\"z_03_provider_api_key\",\n display_name=\"Provider API Key\",\n info=\"An alternative to the Astra Authentication that passes an API key for the provider \"\n \"with each request to Astra DB. \"\n \"This may be used when Vectorize is configured for the collection, \"\n \"but no corresponding provider secret is stored within Astra's key management system.\",\n ).to_dict()\n\n new_parameter_4 = DictInput(\n name=\"z_04_authentication\",\n display_name=\"Authentication parameters\",\n is_list=True,\n ).to_dict()\n\n self.insert_in_dict(\n build_config,\n \"provider\",\n {\n \"z_00_model_name\": new_parameter_0,\n \"z_01_model_parameters\": new_parameter_1,\n \"z_02_api_key_name\": new_parameter_2,\n \"z_03_provider_api_key\": new_parameter_3,\n \"z_04_authentication\": new_parameter_4,\n },\n )\n\n return build_config\n\n def build_vectorize_options(self, **kwargs):\n for attribute in [\n \"provider\",\n \"z_00_model_name\",\n \"z_01_model_parameters\",\n \"z_02_api_key_name\",\n \"z_03_provider_api_key\",\n \"z_04_authentication\",\n ]:\n if not hasattr(self, attribute):\n setattr(self, attribute, None)\n\n # Fetch values from kwargs if any self.* attributes are None\n provider_value = self.VECTORIZE_PROVIDERS_MAPPING.get(self.provider, [None])[0] or kwargs.get(\"provider\")\n authentication = {**(self.z_04_authentication or kwargs.get(\"z_04_authentication\", {}))}\n\n api_key_name = self.z_02_api_key_name or kwargs.get(\"z_02_api_key_name\")\n provider_key = self.z_03_provider_api_key or kwargs.get(\"z_03_provider_api_key\")\n if api_key_name:\n authentication[\"providerKey\"] = api_key_name\n\n return {\n # must match astrapy.info.CollectionVectorServiceOptions\n \"collection_vector_service_options\": {\n \"provider\": provider_value,\n \"modelName\": self.z_00_model_name or kwargs.get(\"z_00_model_name\"),\n \"authentication\": authentication,\n \"parameters\": self.z_01_model_parameters or kwargs.get(\"z_01_model_parameters\", {}),\n },\n \"collection_embedding_api_key\": provider_key,\n }\n\n @check_cached_vector_store\n def build_vector_store(self, vectorize_options=None):\n try:\n from langchain_astradb import AstraDBVectorStore\n from langchain_astradb.utils.astradb import SetupMode\n except ImportError as e:\n msg = (\n \"Could not import langchain Astra DB integration package. \"\n \"Please install it with `pip install langchain-astradb`.\"\n )\n raise ImportError(msg) from e\n\n try:\n if not self.setup_mode:\n self.setup_mode = self._inputs[\"setup_mode\"].options[0]\n\n setup_mode_value = SetupMode[self.setup_mode.upper()]\n except KeyError as e:\n msg = f\"Invalid setup mode: {self.setup_mode}\"\n raise ValueError(msg) from e\n\n if self.embedding:\n embedding_dict = {\"embedding\": self.embedding}\n else:\n from astrapy.info import CollectionVectorServiceOptions\n\n dict_options = vectorize_options or self.build_vectorize_options()\n dict_options[\"authentication\"] = {\n k: v for k, v in dict_options.get(\"authentication\", {}).items() if k and v\n }\n dict_options[\"parameters\"] = {k: v for k, v in dict_options.get(\"parameters\", {}).items() if k and v}\n\n embedding_dict = {\n \"collection_vector_service_options\": CollectionVectorServiceOptions.from_dict(\n dict_options.get(\"collection_vector_service_options\", {})\n ),\n }\n\n vector_store_kwargs = {\n **embedding_dict,\n \"collection_name\": self.collection_name,\n \"token\": self.token,\n \"api_endpoint\": self.api_endpoint,\n \"namespace\": self.namespace or None,\n \"environment\": parse_api_endpoint(self.api_endpoint).environment,\n \"metric\": self.metric or None,\n \"batch_size\": self.batch_size or None,\n \"bulk_insert_batch_concurrency\": self.bulk_insert_batch_concurrency or None,\n \"bulk_insert_overwrite_concurrency\": self.bulk_insert_overwrite_concurrency or None,\n \"bulk_delete_concurrency\": self.bulk_delete_concurrency or None,\n \"setup_mode\": setup_mode_value,\n \"pre_delete_collection\": self.pre_delete_collection or False,\n }\n\n if self.metadata_indexing_include:\n vector_store_kwargs[\"metadata_indexing_include\"] = self.metadata_indexing_include\n elif self.metadata_indexing_exclude:\n vector_store_kwargs[\"metadata_indexing_exclude\"] = self.metadata_indexing_exclude\n elif self.collection_indexing_policy:\n vector_store_kwargs[\"collection_indexing_policy\"] = self.collection_indexing_policy\n\n try:\n vector_store = AstraDBVectorStore(**vector_store_kwargs)\n except Exception as e:\n msg = f\"Error initializing AstraDBVectorStore: {str(e)}\"\n raise ValueError(msg) from e\n\n self._add_documents_to_vector_store(vector_store)\n\n return vector_store\n\n def _add_documents_to_vector_store(self, vector_store):\n documents = []\n for _input in self.ingest_data or []:\n if isinstance(_input, Data):\n documents.append(_input.to_lc_document())\n else:\n msg = \"Vector Store Inputs must be Data objects.\"\n raise ValueError(msg)\n\n if documents:\n logger.debug(f\"Adding {len(documents)} documents to the Vector Store.\")\n try:\n vector_store.add_documents(documents)\n except Exception as e:\n msg = f\"Error adding documents to AstraDBVectorStore: {str(e)}\"\n raise ValueError(msg) from e\n else:\n logger.debug(\"No documents to add to the Vector Store.\")\n\n def _map_search_type(self):\n if self.search_type == \"Similarity with score threshold\":\n return \"similarity_score_threshold\"\n if self.search_type == \"MMR (Max Marginal Relevance)\":\n return \"mmr\"\n return \"similarity\"\n\n def _build_search_args(self):\n args = {\n \"k\": self.number_of_results,\n \"score_threshold\": self.search_score_threshold,\n }\n\n if self.search_filter:\n clean_filter = {k: v for k, v in self.search_filter.items() if k and v}\n if len(clean_filter) > 0:\n args[\"filter\"] = clean_filter\n return args\n\n def search_documents(self, vector_store=None) -> list[Data]:\n if not vector_store:\n vector_store = self.build_vector_store()\n\n logger.debug(f\"Search input: {self.search_input}\")\n logger.debug(f\"Search type: {self.search_type}\")\n logger.debug(f\"Number of results: {self.number_of_results}\")\n\n if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():\n try:\n search_type = self._map_search_type()\n search_args = self._build_search_args()\n\n docs = vector_store.search(query=self.search_input, search_type=search_type, **search_args)\n except Exception as e:\n msg = f\"Error performing search in AstraDBVectorStore: {str(e)}\"\n raise ValueError(msg) from e\n\n logger.debug(f\"Retrieved documents: {len(docs)}\")\n\n data = docs_to_data(docs)\n logger.debug(f\"Converted documents to data: {len(data)}\")\n self.status = data\n return data\n logger.debug(\"No search input provided. Skipping search.\")\n return []\n\n def get_retriever_kwargs(self):\n search_args = self._build_search_args()\n return {\n \"search_type\": self._map_search_type(),\n \"search_kwargs\": search_args,\n }\n" + "value": "import os\n\nfrom astrapy.admin import parse_api_endpoint\nfrom loguru import logger\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom langflow.helpers import docs_to_data\nfrom langflow.inputs import DictInput, FloatInput, MessageTextInput\nfrom langflow.io import (\n BoolInput,\n DataInput,\n DropdownInput,\n HandleInput,\n IntInput,\n MultilineInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.schema import Data\n\n\nclass AstraVectorStoreComponent(LCVectorStoreComponent):\n display_name: str = \"Astra DB\"\n description: str = \"Implementation of Vector Store using Astra DB with search capabilities\"\n documentation: str = \"https://docs.langflow.org/starter-projects-vector-store-rag\"\n name = \"AstraDB\"\n icon: str = \"AstraDB\"\n\n VECTORIZE_PROVIDERS_MAPPING = {\n \"Azure OpenAI\": [\"azureOpenAI\", [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"]],\n \"Hugging Face - Dedicated\": [\"huggingfaceDedicated\", [\"endpoint-defined-model\"]],\n \"Hugging Face - Serverless\": [\n \"huggingface\",\n [\n \"sentence-transformers/all-MiniLM-L6-v2\",\n \"intfloat/multilingual-e5-large\",\n \"intfloat/multilingual-e5-large-instruct\",\n \"BAAI/bge-small-en-v1.5\",\n \"BAAI/bge-base-en-v1.5\",\n \"BAAI/bge-large-en-v1.5\",\n ],\n ],\n \"Jina AI\": [\n \"jinaAI\",\n [\n \"jina-embeddings-v2-base-en\",\n \"jina-embeddings-v2-base-de\",\n \"jina-embeddings-v2-base-es\",\n \"jina-embeddings-v2-base-code\",\n \"jina-embeddings-v2-base-zh\",\n ],\n ],\n \"Mistral AI\": [\"mistral\", [\"mistral-embed\"]],\n \"NVIDIA\": [\"nvidia\", [\"NV-Embed-QA\"]],\n \"OpenAI\": [\"openai\", [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"]],\n \"Upstage\": [\"upstageAI\", [\"solar-embedding-1-large\"]],\n \"Voyage AI\": [\n \"voyageAI\",\n [\"voyage-large-2-instruct\", \"voyage-law-2\", \"voyage-code-2\", \"voyage-large-2\", \"voyage-2\"],\n ],\n }\n\n inputs = [\n SecretStrInput(\n name=\"token\",\n display_name=\"Astra DB Application Token\",\n info=\"Authentication token for accessing Astra DB.\",\n value=\"ASTRA_DB_APPLICATION_TOKEN\",\n required=True,\n advanced=os.getenv(\"ASTRA_ENHANCED\", \"false\").lower() == \"true\",\n ),\n SecretStrInput(\n name=\"api_endpoint\",\n display_name=\"Database\" if os.getenv(\"ASTRA_ENHANCED\", \"false\").lower() == \"true\" else \"API Endpoint\",\n info=\"API endpoint URL for the Astra DB service.\",\n value=\"ASTRA_DB_API_ENDPOINT\",\n required=True,\n ),\n StrInput(\n name=\"collection_name\",\n display_name=\"Collection Name\",\n info=\"The name of the collection within Astra DB where the vectors will be stored.\",\n required=True,\n ),\n MultilineInput(\n name=\"search_input\",\n display_name=\"Search Input\",\n ),\n DataInput(\n name=\"ingest_data\",\n display_name=\"Ingest Data\",\n is_list=True,\n ),\n StrInput(\n name=\"namespace\",\n display_name=\"Namespace\",\n info=\"Optional namespace within Astra DB to use for the collection.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"embedding_service\",\n display_name=\"Embedding Model or Astra Vectorize\",\n info=\"Determines whether to use Astra Vectorize for the collection.\",\n options=[\"Embedding Model\", \"Astra Vectorize\"],\n real_time_refresh=True,\n value=\"Embedding Model\",\n ),\n HandleInput(\n name=\"embedding\",\n display_name=\"Embedding Model\",\n input_types=[\"Embeddings\"],\n info=\"Allows an embedding model configuration.\",\n ),\n DropdownInput(\n name=\"metric\",\n display_name=\"Metric\",\n info=\"Optional distance metric for vector comparisons in the vector store.\",\n options=[\"cosine\", \"dot_product\", \"euclidean\"],\n advanced=True,\n ),\n IntInput(\n name=\"batch_size\",\n display_name=\"Batch Size\",\n info=\"Optional number of data to process in a single batch.\",\n advanced=True,\n ),\n IntInput(\n name=\"bulk_insert_batch_concurrency\",\n display_name=\"Bulk Insert Batch Concurrency\",\n info=\"Optional concurrency level for bulk insert operations.\",\n advanced=True,\n ),\n IntInput(\n name=\"bulk_insert_overwrite_concurrency\",\n display_name=\"Bulk Insert Overwrite Concurrency\",\n info=\"Optional concurrency level for bulk insert operations that overwrite existing data.\",\n advanced=True,\n ),\n IntInput(\n name=\"bulk_delete_concurrency\",\n display_name=\"Bulk Delete Concurrency\",\n info=\"Optional concurrency level for bulk delete operations.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"setup_mode\",\n display_name=\"Setup Mode\",\n info=\"Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.\",\n options=[\"Sync\", \"Async\", \"Off\"],\n advanced=True,\n value=\"Sync\",\n ),\n BoolInput(\n name=\"pre_delete_collection\",\n display_name=\"Pre Delete Collection\",\n info=\"Boolean flag to determine whether to delete the collection before creating a new one.\",\n advanced=True,\n ),\n StrInput(\n name=\"metadata_indexing_include\",\n display_name=\"Metadata Indexing Include\",\n info=\"Optional list of metadata fields to include in the indexing.\",\n advanced=True,\n ),\n StrInput(\n name=\"metadata_indexing_exclude\",\n display_name=\"Metadata Indexing Exclude\",\n info=\"Optional list of metadata fields to exclude from the indexing.\",\n advanced=True,\n ),\n StrInput(\n name=\"collection_indexing_policy\",\n display_name=\"Collection Indexing Policy\",\n info=\"Optional dictionary defining the indexing policy for the collection.\",\n advanced=True,\n ),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Number of Results\",\n info=\"Number of results to return.\",\n advanced=True,\n value=4,\n ),\n DropdownInput(\n name=\"search_type\",\n display_name=\"Search Type\",\n info=\"Search type to use\",\n options=[\"Similarity\", \"Similarity with score threshold\", \"MMR (Max Marginal Relevance)\"],\n value=\"Similarity\",\n advanced=True,\n ),\n FloatInput(\n name=\"search_score_threshold\",\n display_name=\"Search Score Threshold\",\n info=\"Minimum similarity score threshold for search results. \"\n \"(when using 'Similarity with score threshold')\",\n value=0,\n advanced=True,\n ),\n DictInput(\n name=\"search_filter\",\n display_name=\"Search Metadata Filter\",\n info=\"Optional dictionary of filters to apply to the search query.\",\n advanced=True,\n is_list=True,\n ),\n ]\n\n def insert_in_dict(self, build_config, field_name, new_parameters):\n # Insert the new key-value pair after the found key\n for new_field_name, new_parameter in new_parameters.items():\n # Get all the items as a list of tuples (key, value)\n items = list(build_config.items())\n\n # Find the index of the key to insert after\n idx = len(items)\n for i, (key, _value) in enumerate(items):\n if key == field_name:\n idx = i + 1\n break\n\n items.insert(idx, (new_field_name, new_parameter))\n\n # Clear the original dictionary and update with the modified items\n build_config.clear()\n build_config.update(items)\n\n return build_config\n\n def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n if field_name == \"embedding_service\":\n if field_value == \"Astra Vectorize\":\n for field in [\"embedding\"]:\n if field in build_config:\n del build_config[field]\n\n new_parameter = DropdownInput(\n name=\"provider\",\n display_name=\"Vectorize Provider\",\n options=self.VECTORIZE_PROVIDERS_MAPPING.keys(),\n value=\"\",\n required=True,\n real_time_refresh=True,\n ).to_dict()\n\n self.insert_in_dict(build_config, \"embedding_service\", {\"provider\": new_parameter})\n else:\n for field in [\n \"provider\",\n \"z_00_model_name\",\n \"z_01_model_parameters\",\n \"z_02_api_key_name\",\n \"z_03_provider_api_key\",\n \"z_04_authentication\",\n ]:\n if field in build_config:\n del build_config[field]\n\n new_parameter = HandleInput(\n name=\"embedding\",\n display_name=\"Embedding Model\",\n input_types=[\"Embeddings\"],\n info=\"Allows an embedding model configuration.\",\n ).to_dict()\n\n self.insert_in_dict(build_config, \"embedding_service\", {\"embedding\": new_parameter})\n\n elif field_name == \"provider\":\n for field in [\n \"z_00_model_name\",\n \"z_01_model_parameters\",\n \"z_02_api_key_name\",\n \"z_03_provider_api_key\",\n \"z_04_authentication\",\n ]:\n if field in build_config:\n del build_config[field]\n\n model_options = self.VECTORIZE_PROVIDERS_MAPPING[field_value][1]\n\n new_parameter_0 = DropdownInput(\n name=\"z_00_model_name\",\n display_name=\"Model Name\",\n info=\"The embedding model to use for the selected provider. Each provider has a different set of \"\n \"models available (full list at \"\n \"https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html):\\n\\n\"\n f\"{', '.join(model_options)}\",\n options=model_options,\n required=True,\n ).to_dict()\n\n new_parameter_1 = DictInput(\n name=\"z_01_model_parameters\",\n display_name=\"Model Parameters\",\n is_list=True,\n ).to_dict()\n\n new_parameter_2 = MessageTextInput(\n name=\"z_02_api_key_name\",\n display_name=\"API Key name\",\n info=\"The name of the embeddings provider API key stored on Astra. \"\n \"If set, it will override the 'ProviderKey' in the authentication parameters.\",\n ).to_dict()\n\n new_parameter_3 = SecretStrInput(\n name=\"z_03_provider_api_key\",\n display_name=\"Provider API Key\",\n info=\"An alternative to the Astra Authentication that passes an API key for the provider \"\n \"with each request to Astra DB. \"\n \"This may be used when Vectorize is configured for the collection, \"\n \"but no corresponding provider secret is stored within Astra's key management system.\",\n ).to_dict()\n\n new_parameter_4 = DictInput(\n name=\"z_04_authentication\",\n display_name=\"Authentication parameters\",\n is_list=True,\n ).to_dict()\n\n self.insert_in_dict(\n build_config,\n \"provider\",\n {\n \"z_00_model_name\": new_parameter_0,\n \"z_01_model_parameters\": new_parameter_1,\n \"z_02_api_key_name\": new_parameter_2,\n \"z_03_provider_api_key\": new_parameter_3,\n \"z_04_authentication\": new_parameter_4,\n },\n )\n\n return build_config\n\n def build_vectorize_options(self, **kwargs):\n for attribute in [\n \"provider\",\n \"z_00_model_name\",\n \"z_01_model_parameters\",\n \"z_02_api_key_name\",\n \"z_03_provider_api_key\",\n \"z_04_authentication\",\n ]:\n if not hasattr(self, attribute):\n setattr(self, attribute, None)\n\n # Fetch values from kwargs if any self.* attributes are None\n provider_value = self.VECTORIZE_PROVIDERS_MAPPING.get(self.provider, [None])[0] or kwargs.get(\"provider\")\n authentication = {**(self.z_04_authentication or kwargs.get(\"z_04_authentication\", {}))}\n\n api_key_name = self.z_02_api_key_name or kwargs.get(\"z_02_api_key_name\")\n provider_key = self.z_03_provider_api_key or kwargs.get(\"z_03_provider_api_key\")\n if api_key_name:\n authentication[\"providerKey\"] = api_key_name\n\n return {\n # must match astrapy.info.CollectionVectorServiceOptions\n \"collection_vector_service_options\": {\n \"provider\": provider_value,\n \"modelName\": self.z_00_model_name or kwargs.get(\"z_00_model_name\"),\n \"authentication\": authentication,\n \"parameters\": self.z_01_model_parameters or kwargs.get(\"z_01_model_parameters\", {}),\n },\n \"collection_embedding_api_key\": provider_key,\n }\n\n @check_cached_vector_store\n def build_vector_store(self, vectorize_options=None):\n try:\n from langchain_astradb import AstraDBVectorStore\n from langchain_astradb.utils.astradb import SetupMode\n except ImportError as e:\n msg = (\n \"Could not import langchain Astra DB integration package. \"\n \"Please install it with `pip install langchain-astradb`.\"\n )\n raise ImportError(msg) from e\n\n try:\n if not self.setup_mode:\n self.setup_mode = self._inputs[\"setup_mode\"].options[0]\n\n setup_mode_value = SetupMode[self.setup_mode.upper()]\n except KeyError as e:\n msg = f\"Invalid setup mode: {self.setup_mode}\"\n raise ValueError(msg) from e\n\n if self.embedding:\n embedding_dict = {\"embedding\": self.embedding}\n else:\n from astrapy.info import CollectionVectorServiceOptions\n\n dict_options = vectorize_options or self.build_vectorize_options()\n dict_options[\"authentication\"] = {\n k: v for k, v in dict_options.get(\"authentication\", {}).items() if k and v\n }\n dict_options[\"parameters\"] = {k: v for k, v in dict_options.get(\"parameters\", {}).items() if k and v}\n\n embedding_dict = {\n \"collection_vector_service_options\": CollectionVectorServiceOptions.from_dict(\n dict_options.get(\"collection_vector_service_options\", {})\n ),\n }\n\n vector_store_kwargs = {\n **embedding_dict,\n \"collection_name\": self.collection_name,\n \"token\": self.token,\n \"api_endpoint\": self.api_endpoint,\n \"namespace\": self.namespace or None,\n \"environment\": parse_api_endpoint(self.api_endpoint).environment,\n \"metric\": self.metric or None,\n \"batch_size\": self.batch_size or None,\n \"bulk_insert_batch_concurrency\": self.bulk_insert_batch_concurrency or None,\n \"bulk_insert_overwrite_concurrency\": self.bulk_insert_overwrite_concurrency or None,\n \"bulk_delete_concurrency\": self.bulk_delete_concurrency or None,\n \"setup_mode\": setup_mode_value,\n \"pre_delete_collection\": self.pre_delete_collection or False,\n }\n\n if self.metadata_indexing_include:\n vector_store_kwargs[\"metadata_indexing_include\"] = self.metadata_indexing_include\n elif self.metadata_indexing_exclude:\n vector_store_kwargs[\"metadata_indexing_exclude\"] = self.metadata_indexing_exclude\n elif self.collection_indexing_policy:\n vector_store_kwargs[\"collection_indexing_policy\"] = self.collection_indexing_policy\n\n try:\n vector_store = AstraDBVectorStore(**vector_store_kwargs)\n except Exception as e:\n msg = f\"Error initializing AstraDBVectorStore: {e}\"\n raise ValueError(msg) from e\n\n self._add_documents_to_vector_store(vector_store)\n\n return vector_store\n\n def _add_documents_to_vector_store(self, vector_store):\n documents = []\n for _input in self.ingest_data or []:\n if isinstance(_input, Data):\n documents.append(_input.to_lc_document())\n else:\n msg = \"Vector Store Inputs must be Data objects.\"\n raise ValueError(msg)\n\n if documents:\n logger.debug(f\"Adding {len(documents)} documents to the Vector Store.\")\n try:\n vector_store.add_documents(documents)\n except Exception as e:\n msg = f\"Error adding documents to AstraDBVectorStore: {e}\"\n raise ValueError(msg) from e\n else:\n logger.debug(\"No documents to add to the Vector Store.\")\n\n def _map_search_type(self):\n if self.search_type == \"Similarity with score threshold\":\n return \"similarity_score_threshold\"\n if self.search_type == \"MMR (Max Marginal Relevance)\":\n return \"mmr\"\n return \"similarity\"\n\n def _build_search_args(self):\n args = {\n \"k\": self.number_of_results,\n \"score_threshold\": self.search_score_threshold,\n }\n\n if self.search_filter:\n clean_filter = {k: v for k, v in self.search_filter.items() if k and v}\n if len(clean_filter) > 0:\n args[\"filter\"] = clean_filter\n return args\n\n def search_documents(self, vector_store=None) -> list[Data]:\n if not vector_store:\n vector_store = self.build_vector_store()\n\n logger.debug(f\"Search input: {self.search_input}\")\n logger.debug(f\"Search type: {self.search_type}\")\n logger.debug(f\"Number of results: {self.number_of_results}\")\n\n if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():\n try:\n search_type = self._map_search_type()\n search_args = self._build_search_args()\n\n docs = vector_store.search(query=self.search_input, search_type=search_type, **search_args)\n except Exception as e:\n msg = f\"Error performing search in AstraDBVectorStore: {e}\"\n raise ValueError(msg) from e\n\n logger.debug(f\"Retrieved documents: {len(docs)}\")\n\n data = docs_to_data(docs)\n logger.debug(f\"Converted documents to data: {len(data)}\")\n self.status = data\n return data\n logger.debug(\"No search input provided. Skipping search.\")\n return []\n\n def get_retriever_kwargs(self):\n search_args = self._build_search_args()\n return {\n \"search_type\": self._map_search_type(),\n \"search_kwargs\": search_args,\n }\n" }, "collection_indexing_policy": { "advanced": true, @@ -1559,7 +1559,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from langchain_text_splitters import CharacterTextSplitter\n\nfrom langflow.custom import Component\nfrom langflow.io import HandleInput, IntInput, MessageTextInput, Output\nfrom langflow.schema import Data\nfrom langflow.utils.util import unescape_string\n\n\nclass SplitTextComponent(Component):\n display_name: str = \"Split Text\"\n description: str = \"Split text into chunks based on specified criteria.\"\n icon = \"scissors-line-dashed\"\n name = \"SplitText\"\n\n inputs = [\n HandleInput(\n name=\"data_inputs\",\n display_name=\"Data Inputs\",\n info=\"The data to split.\",\n input_types=[\"Data\"],\n is_list=True,\n ),\n IntInput(\n name=\"chunk_overlap\",\n display_name=\"Chunk Overlap\",\n info=\"Number of characters to overlap between chunks.\",\n value=200,\n ),\n IntInput(\n name=\"chunk_size\",\n display_name=\"Chunk Size\",\n info=\"The maximum number of characters in each chunk.\",\n value=1000,\n ),\n MessageTextInput(\n name=\"separator\",\n display_name=\"Separator\",\n info=\"The character to split on. Defaults to newline.\",\n value=\"\\n\",\n ),\n ]\n\n outputs = [\n Output(display_name=\"Chunks\", name=\"chunks\", method=\"split_text\"),\n ]\n\n def _docs_to_data(self, docs):\n data = []\n for doc in docs:\n data.append(Data(text=doc.page_content, data=doc.metadata))\n return data\n\n def split_text(self) -> list[Data]:\n separator = unescape_string(self.separator)\n\n documents = []\n for _input in self.data_inputs:\n if isinstance(_input, Data):\n documents.append(_input.to_lc_document())\n\n splitter = CharacterTextSplitter(\n chunk_overlap=self.chunk_overlap,\n chunk_size=self.chunk_size,\n separator=separator,\n )\n docs = splitter.split_documents(documents)\n data = self._docs_to_data(docs)\n self.status = data\n return data\n" + "value": "from langchain_text_splitters import CharacterTextSplitter\n\nfrom langflow.custom import Component\nfrom langflow.io import HandleInput, IntInput, MessageTextInput, Output\nfrom langflow.schema import Data\nfrom langflow.utils.util import unescape_string\n\n\nclass SplitTextComponent(Component):\n display_name: str = \"Split Text\"\n description: str = \"Split text into chunks based on specified criteria.\"\n icon = \"scissors-line-dashed\"\n name = \"SplitText\"\n\n inputs = [\n HandleInput(\n name=\"data_inputs\",\n display_name=\"Data Inputs\",\n info=\"The data to split.\",\n input_types=[\"Data\"],\n is_list=True,\n ),\n IntInput(\n name=\"chunk_overlap\",\n display_name=\"Chunk Overlap\",\n info=\"Number of characters to overlap between chunks.\",\n value=200,\n ),\n IntInput(\n name=\"chunk_size\",\n display_name=\"Chunk Size\",\n info=\"The maximum number of characters in each chunk.\",\n value=1000,\n ),\n MessageTextInput(\n name=\"separator\",\n display_name=\"Separator\",\n info=\"The character to split on. Defaults to newline.\",\n value=\"\\n\",\n ),\n ]\n\n outputs = [\n Output(display_name=\"Chunks\", name=\"chunks\", method=\"split_text\"),\n ]\n\n def _docs_to_data(self, docs):\n return [Data(text=doc.page_content, data=doc.metadata) for doc in docs]\n\n def split_text(self) -> list[Data]:\n separator = unescape_string(self.separator)\n\n documents = [_input.to_lc_document() for _input in self.data_inputs if isinstance(_input, Data)]\n\n splitter = CharacterTextSplitter(\n chunk_overlap=self.chunk_overlap,\n chunk_size=self.chunk_size,\n separator=separator,\n )\n docs = splitter.split_documents(documents)\n data = self._docs_to_data(docs)\n self.status = data\n return data\n" }, "data_inputs": { "advanced": false, @@ -1674,7 +1674,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from pathlib import Path\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_data\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, FileInput, Output\nfrom langflow.schema import Data\n\n\nclass FileComponent(Component):\n display_name = \"File\"\n description = \"A generic file loader.\"\n icon = \"file-text\"\n name = \"File\"\n\n inputs = [\n FileInput(\n name=\"path\",\n display_name=\"Path\",\n file_types=TEXT_FILE_TYPES,\n info=f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n ),\n BoolInput(\n name=\"silent_errors\",\n display_name=\"Silent Errors\",\n advanced=True,\n info=\"If true, errors will not raise an exception.\",\n ),\n ]\n\n outputs = [\n Output(display_name=\"Data\", name=\"data\", method=\"load_file\"),\n ]\n\n def load_file(self) -> Data:\n if not self.path:\n msg = \"Please, upload a file to use this component.\"\n raise ValueError(msg)\n resolved_path = self.resolve_path(self.path)\n silent_errors = self.silent_errors\n\n extension = Path(resolved_path).suffix[1:].lower()\n\n if extension == \"doc\":\n msg = \"doc files are not supported. Please save as .docx\"\n raise ValueError(msg)\n if extension not in TEXT_FILE_TYPES:\n msg = f\"Unsupported file type: {extension}\"\n raise ValueError(msg)\n\n data = parse_text_file_to_data(resolved_path, silent_errors)\n self.status = data if data else \"No data\"\n return data or Data()\n" + "value": "from pathlib import Path\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_data\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, FileInput, Output\nfrom langflow.schema import Data\n\n\nclass FileComponent(Component):\n display_name = \"File\"\n description = \"A generic file loader.\"\n icon = \"file-text\"\n name = \"File\"\n\n inputs = [\n FileInput(\n name=\"path\",\n display_name=\"Path\",\n file_types=TEXT_FILE_TYPES,\n info=f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n ),\n BoolInput(\n name=\"silent_errors\",\n display_name=\"Silent Errors\",\n advanced=True,\n info=\"If true, errors will not raise an exception.\",\n ),\n ]\n\n outputs = [\n Output(display_name=\"Data\", name=\"data\", method=\"load_file\"),\n ]\n\n def load_file(self) -> Data:\n if not self.path:\n msg = \"Please, upload a file to use this component.\"\n raise ValueError(msg)\n resolved_path = self.resolve_path(self.path)\n silent_errors = self.silent_errors\n\n extension = Path(resolved_path).suffix[1:].lower()\n\n if extension == \"doc\":\n msg = \"doc files are not supported. Please save as .docx\"\n raise ValueError(msg)\n if extension not in TEXT_FILE_TYPES:\n msg = f\"Unsupported file type: {extension}\"\n raise ValueError(msg)\n\n data = parse_text_file_to_data(resolved_path, silent_errors)\n self.status = data or \"No data\"\n return data or Data()\n" }, "path": { "advanced": false, @@ -1948,7 +1948,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import os\n\nfrom astrapy.admin import parse_api_endpoint\nfrom loguru import logger\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom langflow.helpers import docs_to_data\nfrom langflow.inputs import DictInput, FloatInput, MessageTextInput\nfrom langflow.io import (\n BoolInput,\n DataInput,\n DropdownInput,\n HandleInput,\n IntInput,\n MultilineInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.schema import Data\n\n\nclass AstraVectorStoreComponent(LCVectorStoreComponent):\n display_name: str = \"Astra DB\"\n description: str = \"Implementation of Vector Store using Astra DB with search capabilities\"\n documentation: str = \"https://docs.langflow.org/starter-projects-vector-store-rag\"\n name = \"AstraDB\"\n icon: str = \"AstraDB\"\n\n VECTORIZE_PROVIDERS_MAPPING = {\n \"Azure OpenAI\": [\"azureOpenAI\", [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"]],\n \"Hugging Face - Dedicated\": [\"huggingfaceDedicated\", [\"endpoint-defined-model\"]],\n \"Hugging Face - Serverless\": [\n \"huggingface\",\n [\n \"sentence-transformers/all-MiniLM-L6-v2\",\n \"intfloat/multilingual-e5-large\",\n \"intfloat/multilingual-e5-large-instruct\",\n \"BAAI/bge-small-en-v1.5\",\n \"BAAI/bge-base-en-v1.5\",\n \"BAAI/bge-large-en-v1.5\",\n ],\n ],\n \"Jina AI\": [\n \"jinaAI\",\n [\n \"jina-embeddings-v2-base-en\",\n \"jina-embeddings-v2-base-de\",\n \"jina-embeddings-v2-base-es\",\n \"jina-embeddings-v2-base-code\",\n \"jina-embeddings-v2-base-zh\",\n ],\n ],\n \"Mistral AI\": [\"mistral\", [\"mistral-embed\"]],\n \"NVIDIA\": [\"nvidia\", [\"NV-Embed-QA\"]],\n \"OpenAI\": [\"openai\", [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"]],\n \"Upstage\": [\"upstageAI\", [\"solar-embedding-1-large\"]],\n \"Voyage AI\": [\n \"voyageAI\",\n [\"voyage-large-2-instruct\", \"voyage-law-2\", \"voyage-code-2\", \"voyage-large-2\", \"voyage-2\"],\n ],\n }\n\n inputs = [\n SecretStrInput(\n name=\"token\",\n display_name=\"Astra DB Application Token\",\n info=\"Authentication token for accessing Astra DB.\",\n value=\"ASTRA_DB_APPLICATION_TOKEN\",\n required=True,\n advanced=os.getenv(\"ASTRA_ENHANCED\", \"false\").lower() == \"true\",\n ),\n SecretStrInput(\n name=\"api_endpoint\",\n display_name=\"Database\" if os.getenv(\"ASTRA_ENHANCED\", \"false\").lower() == \"true\" else \"API Endpoint\",\n info=\"API endpoint URL for the Astra DB service.\",\n value=\"ASTRA_DB_API_ENDPOINT\",\n required=True,\n ),\n StrInput(\n name=\"collection_name\",\n display_name=\"Collection Name\",\n info=\"The name of the collection within Astra DB where the vectors will be stored.\",\n required=True,\n ),\n MultilineInput(\n name=\"search_input\",\n display_name=\"Search Input\",\n ),\n DataInput(\n name=\"ingest_data\",\n display_name=\"Ingest Data\",\n is_list=True,\n ),\n StrInput(\n name=\"namespace\",\n display_name=\"Namespace\",\n info=\"Optional namespace within Astra DB to use for the collection.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"embedding_service\",\n display_name=\"Embedding Model or Astra Vectorize\",\n info=\"Determines whether to use Astra Vectorize for the collection.\",\n options=[\"Embedding Model\", \"Astra Vectorize\"],\n real_time_refresh=True,\n value=\"Embedding Model\",\n ),\n HandleInput(\n name=\"embedding\",\n display_name=\"Embedding Model\",\n input_types=[\"Embeddings\"],\n info=\"Allows an embedding model configuration.\",\n ),\n DropdownInput(\n name=\"metric\",\n display_name=\"Metric\",\n info=\"Optional distance metric for vector comparisons in the vector store.\",\n options=[\"cosine\", \"dot_product\", \"euclidean\"],\n advanced=True,\n ),\n IntInput(\n name=\"batch_size\",\n display_name=\"Batch Size\",\n info=\"Optional number of data to process in a single batch.\",\n advanced=True,\n ),\n IntInput(\n name=\"bulk_insert_batch_concurrency\",\n display_name=\"Bulk Insert Batch Concurrency\",\n info=\"Optional concurrency level for bulk insert operations.\",\n advanced=True,\n ),\n IntInput(\n name=\"bulk_insert_overwrite_concurrency\",\n display_name=\"Bulk Insert Overwrite Concurrency\",\n info=\"Optional concurrency level for bulk insert operations that overwrite existing data.\",\n advanced=True,\n ),\n IntInput(\n name=\"bulk_delete_concurrency\",\n display_name=\"Bulk Delete Concurrency\",\n info=\"Optional concurrency level for bulk delete operations.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"setup_mode\",\n display_name=\"Setup Mode\",\n info=\"Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.\",\n options=[\"Sync\", \"Async\", \"Off\"],\n advanced=True,\n value=\"Sync\",\n ),\n BoolInput(\n name=\"pre_delete_collection\",\n display_name=\"Pre Delete Collection\",\n info=\"Boolean flag to determine whether to delete the collection before creating a new one.\",\n advanced=True,\n ),\n StrInput(\n name=\"metadata_indexing_include\",\n display_name=\"Metadata Indexing Include\",\n info=\"Optional list of metadata fields to include in the indexing.\",\n advanced=True,\n ),\n StrInput(\n name=\"metadata_indexing_exclude\",\n display_name=\"Metadata Indexing Exclude\",\n info=\"Optional list of metadata fields to exclude from the indexing.\",\n advanced=True,\n ),\n StrInput(\n name=\"collection_indexing_policy\",\n display_name=\"Collection Indexing Policy\",\n info=\"Optional dictionary defining the indexing policy for the collection.\",\n advanced=True,\n ),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Number of Results\",\n info=\"Number of results to return.\",\n advanced=True,\n value=4,\n ),\n DropdownInput(\n name=\"search_type\",\n display_name=\"Search Type\",\n info=\"Search type to use\",\n options=[\"Similarity\", \"Similarity with score threshold\", \"MMR (Max Marginal Relevance)\"],\n value=\"Similarity\",\n advanced=True,\n ),\n FloatInput(\n name=\"search_score_threshold\",\n display_name=\"Search Score Threshold\",\n info=\"Minimum similarity score threshold for search results. \"\n \"(when using 'Similarity with score threshold')\",\n value=0,\n advanced=True,\n ),\n DictInput(\n name=\"search_filter\",\n display_name=\"Search Metadata Filter\",\n info=\"Optional dictionary of filters to apply to the search query.\",\n advanced=True,\n is_list=True,\n ),\n ]\n\n def insert_in_dict(self, build_config, field_name, new_parameters):\n # Insert the new key-value pair after the found key\n for new_field_name, new_parameter in new_parameters.items():\n # Get all the items as a list of tuples (key, value)\n items = list(build_config.items())\n\n # Find the index of the key to insert after\n idx = len(items)\n for i, (key, _value) in enumerate(items):\n if key == field_name:\n idx = i + 1\n break\n\n items.insert(idx, (new_field_name, new_parameter))\n\n # Clear the original dictionary and update with the modified items\n build_config.clear()\n build_config.update(items)\n\n return build_config\n\n def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n if field_name == \"embedding_service\":\n if field_value == \"Astra Vectorize\":\n for field in [\"embedding\"]:\n if field in build_config:\n del build_config[field]\n\n new_parameter = DropdownInput(\n name=\"provider\",\n display_name=\"Vectorize Provider\",\n options=self.VECTORIZE_PROVIDERS_MAPPING.keys(),\n value=\"\",\n required=True,\n real_time_refresh=True,\n ).to_dict()\n\n self.insert_in_dict(build_config, \"embedding_service\", {\"provider\": new_parameter})\n else:\n for field in [\n \"provider\",\n \"z_00_model_name\",\n \"z_01_model_parameters\",\n \"z_02_api_key_name\",\n \"z_03_provider_api_key\",\n \"z_04_authentication\",\n ]:\n if field in build_config:\n del build_config[field]\n\n new_parameter = HandleInput(\n name=\"embedding\",\n display_name=\"Embedding Model\",\n input_types=[\"Embeddings\"],\n info=\"Allows an embedding model configuration.\",\n ).to_dict()\n\n self.insert_in_dict(build_config, \"embedding_service\", {\"embedding\": new_parameter})\n\n elif field_name == \"provider\":\n for field in [\n \"z_00_model_name\",\n \"z_01_model_parameters\",\n \"z_02_api_key_name\",\n \"z_03_provider_api_key\",\n \"z_04_authentication\",\n ]:\n if field in build_config:\n del build_config[field]\n\n model_options = self.VECTORIZE_PROVIDERS_MAPPING[field_value][1]\n\n new_parameter_0 = DropdownInput(\n name=\"z_00_model_name\",\n display_name=\"Model Name\",\n info=\"The embedding model to use for the selected provider. Each provider has a different set of \"\n \"models available (full list at \"\n \"https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html):\\n\\n\"\n f\"{', '.join(model_options)}\",\n options=model_options,\n required=True,\n ).to_dict()\n\n new_parameter_1 = DictInput(\n name=\"z_01_model_parameters\",\n display_name=\"Model Parameters\",\n is_list=True,\n ).to_dict()\n\n new_parameter_2 = MessageTextInput(\n name=\"z_02_api_key_name\",\n display_name=\"API Key name\",\n info=\"The name of the embeddings provider API key stored on Astra. \"\n \"If set, it will override the 'ProviderKey' in the authentication parameters.\",\n ).to_dict()\n\n new_parameter_3 = SecretStrInput(\n name=\"z_03_provider_api_key\",\n display_name=\"Provider API Key\",\n info=\"An alternative to the Astra Authentication that passes an API key for the provider \"\n \"with each request to Astra DB. \"\n \"This may be used when Vectorize is configured for the collection, \"\n \"but no corresponding provider secret is stored within Astra's key management system.\",\n ).to_dict()\n\n new_parameter_4 = DictInput(\n name=\"z_04_authentication\",\n display_name=\"Authentication parameters\",\n is_list=True,\n ).to_dict()\n\n self.insert_in_dict(\n build_config,\n \"provider\",\n {\n \"z_00_model_name\": new_parameter_0,\n \"z_01_model_parameters\": new_parameter_1,\n \"z_02_api_key_name\": new_parameter_2,\n \"z_03_provider_api_key\": new_parameter_3,\n \"z_04_authentication\": new_parameter_4,\n },\n )\n\n return build_config\n\n def build_vectorize_options(self, **kwargs):\n for attribute in [\n \"provider\",\n \"z_00_model_name\",\n \"z_01_model_parameters\",\n \"z_02_api_key_name\",\n \"z_03_provider_api_key\",\n \"z_04_authentication\",\n ]:\n if not hasattr(self, attribute):\n setattr(self, attribute, None)\n\n # Fetch values from kwargs if any self.* attributes are None\n provider_value = self.VECTORIZE_PROVIDERS_MAPPING.get(self.provider, [None])[0] or kwargs.get(\"provider\")\n authentication = {**(self.z_04_authentication or kwargs.get(\"z_04_authentication\", {}))}\n\n api_key_name = self.z_02_api_key_name or kwargs.get(\"z_02_api_key_name\")\n provider_key = self.z_03_provider_api_key or kwargs.get(\"z_03_provider_api_key\")\n if api_key_name:\n authentication[\"providerKey\"] = api_key_name\n\n return {\n # must match astrapy.info.CollectionVectorServiceOptions\n \"collection_vector_service_options\": {\n \"provider\": provider_value,\n \"modelName\": self.z_00_model_name or kwargs.get(\"z_00_model_name\"),\n \"authentication\": authentication,\n \"parameters\": self.z_01_model_parameters or kwargs.get(\"z_01_model_parameters\", {}),\n },\n \"collection_embedding_api_key\": provider_key,\n }\n\n @check_cached_vector_store\n def build_vector_store(self, vectorize_options=None):\n try:\n from langchain_astradb import AstraDBVectorStore\n from langchain_astradb.utils.astradb import SetupMode\n except ImportError as e:\n msg = (\n \"Could not import langchain Astra DB integration package. \"\n \"Please install it with `pip install langchain-astradb`.\"\n )\n raise ImportError(msg) from e\n\n try:\n if not self.setup_mode:\n self.setup_mode = self._inputs[\"setup_mode\"].options[0]\n\n setup_mode_value = SetupMode[self.setup_mode.upper()]\n except KeyError as e:\n msg = f\"Invalid setup mode: {self.setup_mode}\"\n raise ValueError(msg) from e\n\n if self.embedding:\n embedding_dict = {\"embedding\": self.embedding}\n else:\n from astrapy.info import CollectionVectorServiceOptions\n\n dict_options = vectorize_options or self.build_vectorize_options()\n dict_options[\"authentication\"] = {\n k: v for k, v in dict_options.get(\"authentication\", {}).items() if k and v\n }\n dict_options[\"parameters\"] = {k: v for k, v in dict_options.get(\"parameters\", {}).items() if k and v}\n\n embedding_dict = {\n \"collection_vector_service_options\": CollectionVectorServiceOptions.from_dict(\n dict_options.get(\"collection_vector_service_options\", {})\n ),\n }\n\n vector_store_kwargs = {\n **embedding_dict,\n \"collection_name\": self.collection_name,\n \"token\": self.token,\n \"api_endpoint\": self.api_endpoint,\n \"namespace\": self.namespace or None,\n \"environment\": parse_api_endpoint(self.api_endpoint).environment,\n \"metric\": self.metric or None,\n \"batch_size\": self.batch_size or None,\n \"bulk_insert_batch_concurrency\": self.bulk_insert_batch_concurrency or None,\n \"bulk_insert_overwrite_concurrency\": self.bulk_insert_overwrite_concurrency or None,\n \"bulk_delete_concurrency\": self.bulk_delete_concurrency or None,\n \"setup_mode\": setup_mode_value,\n \"pre_delete_collection\": self.pre_delete_collection or False,\n }\n\n if self.metadata_indexing_include:\n vector_store_kwargs[\"metadata_indexing_include\"] = self.metadata_indexing_include\n elif self.metadata_indexing_exclude:\n vector_store_kwargs[\"metadata_indexing_exclude\"] = self.metadata_indexing_exclude\n elif self.collection_indexing_policy:\n vector_store_kwargs[\"collection_indexing_policy\"] = self.collection_indexing_policy\n\n try:\n vector_store = AstraDBVectorStore(**vector_store_kwargs)\n except Exception as e:\n msg = f\"Error initializing AstraDBVectorStore: {str(e)}\"\n raise ValueError(msg) from e\n\n self._add_documents_to_vector_store(vector_store)\n\n return vector_store\n\n def _add_documents_to_vector_store(self, vector_store):\n documents = []\n for _input in self.ingest_data or []:\n if isinstance(_input, Data):\n documents.append(_input.to_lc_document())\n else:\n msg = \"Vector Store Inputs must be Data objects.\"\n raise ValueError(msg)\n\n if documents:\n logger.debug(f\"Adding {len(documents)} documents to the Vector Store.\")\n try:\n vector_store.add_documents(documents)\n except Exception as e:\n msg = f\"Error adding documents to AstraDBVectorStore: {str(e)}\"\n raise ValueError(msg) from e\n else:\n logger.debug(\"No documents to add to the Vector Store.\")\n\n def _map_search_type(self):\n if self.search_type == \"Similarity with score threshold\":\n return \"similarity_score_threshold\"\n if self.search_type == \"MMR (Max Marginal Relevance)\":\n return \"mmr\"\n return \"similarity\"\n\n def _build_search_args(self):\n args = {\n \"k\": self.number_of_results,\n \"score_threshold\": self.search_score_threshold,\n }\n\n if self.search_filter:\n clean_filter = {k: v for k, v in self.search_filter.items() if k and v}\n if len(clean_filter) > 0:\n args[\"filter\"] = clean_filter\n return args\n\n def search_documents(self, vector_store=None) -> list[Data]:\n if not vector_store:\n vector_store = self.build_vector_store()\n\n logger.debug(f\"Search input: {self.search_input}\")\n logger.debug(f\"Search type: {self.search_type}\")\n logger.debug(f\"Number of results: {self.number_of_results}\")\n\n if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():\n try:\n search_type = self._map_search_type()\n search_args = self._build_search_args()\n\n docs = vector_store.search(query=self.search_input, search_type=search_type, **search_args)\n except Exception as e:\n msg = f\"Error performing search in AstraDBVectorStore: {str(e)}\"\n raise ValueError(msg) from e\n\n logger.debug(f\"Retrieved documents: {len(docs)}\")\n\n data = docs_to_data(docs)\n logger.debug(f\"Converted documents to data: {len(data)}\")\n self.status = data\n return data\n logger.debug(\"No search input provided. Skipping search.\")\n return []\n\n def get_retriever_kwargs(self):\n search_args = self._build_search_args()\n return {\n \"search_type\": self._map_search_type(),\n \"search_kwargs\": search_args,\n }\n" + "value": "import os\n\nfrom astrapy.admin import parse_api_endpoint\nfrom loguru import logger\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom langflow.helpers import docs_to_data\nfrom langflow.inputs import DictInput, FloatInput, MessageTextInput\nfrom langflow.io import (\n BoolInput,\n DataInput,\n DropdownInput,\n HandleInput,\n IntInput,\n MultilineInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.schema import Data\n\n\nclass AstraVectorStoreComponent(LCVectorStoreComponent):\n display_name: str = \"Astra DB\"\n description: str = \"Implementation of Vector Store using Astra DB with search capabilities\"\n documentation: str = \"https://docs.langflow.org/starter-projects-vector-store-rag\"\n name = \"AstraDB\"\n icon: str = \"AstraDB\"\n\n VECTORIZE_PROVIDERS_MAPPING = {\n \"Azure OpenAI\": [\"azureOpenAI\", [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"]],\n \"Hugging Face - Dedicated\": [\"huggingfaceDedicated\", [\"endpoint-defined-model\"]],\n \"Hugging Face - Serverless\": [\n \"huggingface\",\n [\n \"sentence-transformers/all-MiniLM-L6-v2\",\n \"intfloat/multilingual-e5-large\",\n \"intfloat/multilingual-e5-large-instruct\",\n \"BAAI/bge-small-en-v1.5\",\n \"BAAI/bge-base-en-v1.5\",\n \"BAAI/bge-large-en-v1.5\",\n ],\n ],\n \"Jina AI\": [\n \"jinaAI\",\n [\n \"jina-embeddings-v2-base-en\",\n \"jina-embeddings-v2-base-de\",\n \"jina-embeddings-v2-base-es\",\n \"jina-embeddings-v2-base-code\",\n \"jina-embeddings-v2-base-zh\",\n ],\n ],\n \"Mistral AI\": [\"mistral\", [\"mistral-embed\"]],\n \"NVIDIA\": [\"nvidia\", [\"NV-Embed-QA\"]],\n \"OpenAI\": [\"openai\", [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"]],\n \"Upstage\": [\"upstageAI\", [\"solar-embedding-1-large\"]],\n \"Voyage AI\": [\n \"voyageAI\",\n [\"voyage-large-2-instruct\", \"voyage-law-2\", \"voyage-code-2\", \"voyage-large-2\", \"voyage-2\"],\n ],\n }\n\n inputs = [\n SecretStrInput(\n name=\"token\",\n display_name=\"Astra DB Application Token\",\n info=\"Authentication token for accessing Astra DB.\",\n value=\"ASTRA_DB_APPLICATION_TOKEN\",\n required=True,\n advanced=os.getenv(\"ASTRA_ENHANCED\", \"false\").lower() == \"true\",\n ),\n SecretStrInput(\n name=\"api_endpoint\",\n display_name=\"Database\" if os.getenv(\"ASTRA_ENHANCED\", \"false\").lower() == \"true\" else \"API Endpoint\",\n info=\"API endpoint URL for the Astra DB service.\",\n value=\"ASTRA_DB_API_ENDPOINT\",\n required=True,\n ),\n StrInput(\n name=\"collection_name\",\n display_name=\"Collection Name\",\n info=\"The name of the collection within Astra DB where the vectors will be stored.\",\n required=True,\n ),\n MultilineInput(\n name=\"search_input\",\n display_name=\"Search Input\",\n ),\n DataInput(\n name=\"ingest_data\",\n display_name=\"Ingest Data\",\n is_list=True,\n ),\n StrInput(\n name=\"namespace\",\n display_name=\"Namespace\",\n info=\"Optional namespace within Astra DB to use for the collection.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"embedding_service\",\n display_name=\"Embedding Model or Astra Vectorize\",\n info=\"Determines whether to use Astra Vectorize for the collection.\",\n options=[\"Embedding Model\", \"Astra Vectorize\"],\n real_time_refresh=True,\n value=\"Embedding Model\",\n ),\n HandleInput(\n name=\"embedding\",\n display_name=\"Embedding Model\",\n input_types=[\"Embeddings\"],\n info=\"Allows an embedding model configuration.\",\n ),\n DropdownInput(\n name=\"metric\",\n display_name=\"Metric\",\n info=\"Optional distance metric for vector comparisons in the vector store.\",\n options=[\"cosine\", \"dot_product\", \"euclidean\"],\n advanced=True,\n ),\n IntInput(\n name=\"batch_size\",\n display_name=\"Batch Size\",\n info=\"Optional number of data to process in a single batch.\",\n advanced=True,\n ),\n IntInput(\n name=\"bulk_insert_batch_concurrency\",\n display_name=\"Bulk Insert Batch Concurrency\",\n info=\"Optional concurrency level for bulk insert operations.\",\n advanced=True,\n ),\n IntInput(\n name=\"bulk_insert_overwrite_concurrency\",\n display_name=\"Bulk Insert Overwrite Concurrency\",\n info=\"Optional concurrency level for bulk insert operations that overwrite existing data.\",\n advanced=True,\n ),\n IntInput(\n name=\"bulk_delete_concurrency\",\n display_name=\"Bulk Delete Concurrency\",\n info=\"Optional concurrency level for bulk delete operations.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"setup_mode\",\n display_name=\"Setup Mode\",\n info=\"Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.\",\n options=[\"Sync\", \"Async\", \"Off\"],\n advanced=True,\n value=\"Sync\",\n ),\n BoolInput(\n name=\"pre_delete_collection\",\n display_name=\"Pre Delete Collection\",\n info=\"Boolean flag to determine whether to delete the collection before creating a new one.\",\n advanced=True,\n ),\n StrInput(\n name=\"metadata_indexing_include\",\n display_name=\"Metadata Indexing Include\",\n info=\"Optional list of metadata fields to include in the indexing.\",\n advanced=True,\n ),\n StrInput(\n name=\"metadata_indexing_exclude\",\n display_name=\"Metadata Indexing Exclude\",\n info=\"Optional list of metadata fields to exclude from the indexing.\",\n advanced=True,\n ),\n StrInput(\n name=\"collection_indexing_policy\",\n display_name=\"Collection Indexing Policy\",\n info=\"Optional dictionary defining the indexing policy for the collection.\",\n advanced=True,\n ),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Number of Results\",\n info=\"Number of results to return.\",\n advanced=True,\n value=4,\n ),\n DropdownInput(\n name=\"search_type\",\n display_name=\"Search Type\",\n info=\"Search type to use\",\n options=[\"Similarity\", \"Similarity with score threshold\", \"MMR (Max Marginal Relevance)\"],\n value=\"Similarity\",\n advanced=True,\n ),\n FloatInput(\n name=\"search_score_threshold\",\n display_name=\"Search Score Threshold\",\n info=\"Minimum similarity score threshold for search results. \"\n \"(when using 'Similarity with score threshold')\",\n value=0,\n advanced=True,\n ),\n DictInput(\n name=\"search_filter\",\n display_name=\"Search Metadata Filter\",\n info=\"Optional dictionary of filters to apply to the search query.\",\n advanced=True,\n is_list=True,\n ),\n ]\n\n def insert_in_dict(self, build_config, field_name, new_parameters):\n # Insert the new key-value pair after the found key\n for new_field_name, new_parameter in new_parameters.items():\n # Get all the items as a list of tuples (key, value)\n items = list(build_config.items())\n\n # Find the index of the key to insert after\n idx = len(items)\n for i, (key, _value) in enumerate(items):\n if key == field_name:\n idx = i + 1\n break\n\n items.insert(idx, (new_field_name, new_parameter))\n\n # Clear the original dictionary and update with the modified items\n build_config.clear()\n build_config.update(items)\n\n return build_config\n\n def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n if field_name == \"embedding_service\":\n if field_value == \"Astra Vectorize\":\n for field in [\"embedding\"]:\n if field in build_config:\n del build_config[field]\n\n new_parameter = DropdownInput(\n name=\"provider\",\n display_name=\"Vectorize Provider\",\n options=self.VECTORIZE_PROVIDERS_MAPPING.keys(),\n value=\"\",\n required=True,\n real_time_refresh=True,\n ).to_dict()\n\n self.insert_in_dict(build_config, \"embedding_service\", {\"provider\": new_parameter})\n else:\n for field in [\n \"provider\",\n \"z_00_model_name\",\n \"z_01_model_parameters\",\n \"z_02_api_key_name\",\n \"z_03_provider_api_key\",\n \"z_04_authentication\",\n ]:\n if field in build_config:\n del build_config[field]\n\n new_parameter = HandleInput(\n name=\"embedding\",\n display_name=\"Embedding Model\",\n input_types=[\"Embeddings\"],\n info=\"Allows an embedding model configuration.\",\n ).to_dict()\n\n self.insert_in_dict(build_config, \"embedding_service\", {\"embedding\": new_parameter})\n\n elif field_name == \"provider\":\n for field in [\n \"z_00_model_name\",\n \"z_01_model_parameters\",\n \"z_02_api_key_name\",\n \"z_03_provider_api_key\",\n \"z_04_authentication\",\n ]:\n if field in build_config:\n del build_config[field]\n\n model_options = self.VECTORIZE_PROVIDERS_MAPPING[field_value][1]\n\n new_parameter_0 = DropdownInput(\n name=\"z_00_model_name\",\n display_name=\"Model Name\",\n info=\"The embedding model to use for the selected provider. Each provider has a different set of \"\n \"models available (full list at \"\n \"https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html):\\n\\n\"\n f\"{', '.join(model_options)}\",\n options=model_options,\n required=True,\n ).to_dict()\n\n new_parameter_1 = DictInput(\n name=\"z_01_model_parameters\",\n display_name=\"Model Parameters\",\n is_list=True,\n ).to_dict()\n\n new_parameter_2 = MessageTextInput(\n name=\"z_02_api_key_name\",\n display_name=\"API Key name\",\n info=\"The name of the embeddings provider API key stored on Astra. \"\n \"If set, it will override the 'ProviderKey' in the authentication parameters.\",\n ).to_dict()\n\n new_parameter_3 = SecretStrInput(\n name=\"z_03_provider_api_key\",\n display_name=\"Provider API Key\",\n info=\"An alternative to the Astra Authentication that passes an API key for the provider \"\n \"with each request to Astra DB. \"\n \"This may be used when Vectorize is configured for the collection, \"\n \"but no corresponding provider secret is stored within Astra's key management system.\",\n ).to_dict()\n\n new_parameter_4 = DictInput(\n name=\"z_04_authentication\",\n display_name=\"Authentication parameters\",\n is_list=True,\n ).to_dict()\n\n self.insert_in_dict(\n build_config,\n \"provider\",\n {\n \"z_00_model_name\": new_parameter_0,\n \"z_01_model_parameters\": new_parameter_1,\n \"z_02_api_key_name\": new_parameter_2,\n \"z_03_provider_api_key\": new_parameter_3,\n \"z_04_authentication\": new_parameter_4,\n },\n )\n\n return build_config\n\n def build_vectorize_options(self, **kwargs):\n for attribute in [\n \"provider\",\n \"z_00_model_name\",\n \"z_01_model_parameters\",\n \"z_02_api_key_name\",\n \"z_03_provider_api_key\",\n \"z_04_authentication\",\n ]:\n if not hasattr(self, attribute):\n setattr(self, attribute, None)\n\n # Fetch values from kwargs if any self.* attributes are None\n provider_value = self.VECTORIZE_PROVIDERS_MAPPING.get(self.provider, [None])[0] or kwargs.get(\"provider\")\n authentication = {**(self.z_04_authentication or kwargs.get(\"z_04_authentication\", {}))}\n\n api_key_name = self.z_02_api_key_name or kwargs.get(\"z_02_api_key_name\")\n provider_key = self.z_03_provider_api_key or kwargs.get(\"z_03_provider_api_key\")\n if api_key_name:\n authentication[\"providerKey\"] = api_key_name\n\n return {\n # must match astrapy.info.CollectionVectorServiceOptions\n \"collection_vector_service_options\": {\n \"provider\": provider_value,\n \"modelName\": self.z_00_model_name or kwargs.get(\"z_00_model_name\"),\n \"authentication\": authentication,\n \"parameters\": self.z_01_model_parameters or kwargs.get(\"z_01_model_parameters\", {}),\n },\n \"collection_embedding_api_key\": provider_key,\n }\n\n @check_cached_vector_store\n def build_vector_store(self, vectorize_options=None):\n try:\n from langchain_astradb import AstraDBVectorStore\n from langchain_astradb.utils.astradb import SetupMode\n except ImportError as e:\n msg = (\n \"Could not import langchain Astra DB integration package. \"\n \"Please install it with `pip install langchain-astradb`.\"\n )\n raise ImportError(msg) from e\n\n try:\n if not self.setup_mode:\n self.setup_mode = self._inputs[\"setup_mode\"].options[0]\n\n setup_mode_value = SetupMode[self.setup_mode.upper()]\n except KeyError as e:\n msg = f\"Invalid setup mode: {self.setup_mode}\"\n raise ValueError(msg) from e\n\n if self.embedding:\n embedding_dict = {\"embedding\": self.embedding}\n else:\n from astrapy.info import CollectionVectorServiceOptions\n\n dict_options = vectorize_options or self.build_vectorize_options()\n dict_options[\"authentication\"] = {\n k: v for k, v in dict_options.get(\"authentication\", {}).items() if k and v\n }\n dict_options[\"parameters\"] = {k: v for k, v in dict_options.get(\"parameters\", {}).items() if k and v}\n\n embedding_dict = {\n \"collection_vector_service_options\": CollectionVectorServiceOptions.from_dict(\n dict_options.get(\"collection_vector_service_options\", {})\n ),\n }\n\n vector_store_kwargs = {\n **embedding_dict,\n \"collection_name\": self.collection_name,\n \"token\": self.token,\n \"api_endpoint\": self.api_endpoint,\n \"namespace\": self.namespace or None,\n \"environment\": parse_api_endpoint(self.api_endpoint).environment,\n \"metric\": self.metric or None,\n \"batch_size\": self.batch_size or None,\n \"bulk_insert_batch_concurrency\": self.bulk_insert_batch_concurrency or None,\n \"bulk_insert_overwrite_concurrency\": self.bulk_insert_overwrite_concurrency or None,\n \"bulk_delete_concurrency\": self.bulk_delete_concurrency or None,\n \"setup_mode\": setup_mode_value,\n \"pre_delete_collection\": self.pre_delete_collection or False,\n }\n\n if self.metadata_indexing_include:\n vector_store_kwargs[\"metadata_indexing_include\"] = self.metadata_indexing_include\n elif self.metadata_indexing_exclude:\n vector_store_kwargs[\"metadata_indexing_exclude\"] = self.metadata_indexing_exclude\n elif self.collection_indexing_policy:\n vector_store_kwargs[\"collection_indexing_policy\"] = self.collection_indexing_policy\n\n try:\n vector_store = AstraDBVectorStore(**vector_store_kwargs)\n except Exception as e:\n msg = f\"Error initializing AstraDBVectorStore: {e}\"\n raise ValueError(msg) from e\n\n self._add_documents_to_vector_store(vector_store)\n\n return vector_store\n\n def _add_documents_to_vector_store(self, vector_store):\n documents = []\n for _input in self.ingest_data or []:\n if isinstance(_input, Data):\n documents.append(_input.to_lc_document())\n else:\n msg = \"Vector Store Inputs must be Data objects.\"\n raise ValueError(msg)\n\n if documents:\n logger.debug(f\"Adding {len(documents)} documents to the Vector Store.\")\n try:\n vector_store.add_documents(documents)\n except Exception as e:\n msg = f\"Error adding documents to AstraDBVectorStore: {e}\"\n raise ValueError(msg) from e\n else:\n logger.debug(\"No documents to add to the Vector Store.\")\n\n def _map_search_type(self):\n if self.search_type == \"Similarity with score threshold\":\n return \"similarity_score_threshold\"\n if self.search_type == \"MMR (Max Marginal Relevance)\":\n return \"mmr\"\n return \"similarity\"\n\n def _build_search_args(self):\n args = {\n \"k\": self.number_of_results,\n \"score_threshold\": self.search_score_threshold,\n }\n\n if self.search_filter:\n clean_filter = {k: v for k, v in self.search_filter.items() if k and v}\n if len(clean_filter) > 0:\n args[\"filter\"] = clean_filter\n return args\n\n def search_documents(self, vector_store=None) -> list[Data]:\n if not vector_store:\n vector_store = self.build_vector_store()\n\n logger.debug(f\"Search input: {self.search_input}\")\n logger.debug(f\"Search type: {self.search_type}\")\n logger.debug(f\"Number of results: {self.number_of_results}\")\n\n if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():\n try:\n search_type = self._map_search_type()\n search_args = self._build_search_args()\n\n docs = vector_store.search(query=self.search_input, search_type=search_type, **search_args)\n except Exception as e:\n msg = f\"Error performing search in AstraDBVectorStore: {e}\"\n raise ValueError(msg) from e\n\n logger.debug(f\"Retrieved documents: {len(docs)}\")\n\n data = docs_to_data(docs)\n logger.debug(f\"Converted documents to data: {len(data)}\")\n self.status = data\n return data\n logger.debug(\"No search input provided. Skipping search.\")\n return []\n\n def get_retriever_kwargs(self):\n search_args = self._build_search_args()\n return {\n \"search_type\": self._map_search_type(),\n \"search_kwargs\": search_args,\n }\n" }, "collection_indexing_policy": { "advanced": true, @@ -3327,7 +3327,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = LCModelComponent._base_inputs + [\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" + "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = [\n *LCModelComponent._base_inputs,\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. \"\n \"You must pass the word JSON in the prompt. \"\n \"If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. \"\n \"Defaults to https://api.openai.com/v1. \"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n HandleInput(\n name=\"output_parser\",\n display_name=\"Output Parser\",\n info=\"The parser to use to parse the output of the model\",\n advanced=True,\n input_types=[\"OutputParser\"],\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n api_key = SecretStr(openai_api_key) if openai_api_key else None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return None\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return None\n" }, "input_value": { "advanced": false, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/__init__.py b/src/backend/base/langflow/initial_setup/starter_projects/__init__.py index d5b19c6e3..c9bdb7d42 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/__init__.py +++ b/src/backend/base/langflow/initial_setup/starter_projects/__init__.py @@ -8,12 +8,12 @@ from .sequential_tasks_agent import sequential_tasks_agent_graph from .vector_store_rag import vector_store_rag_graph __all__ = [ - "blog_writer_graph", - "document_qa_graph", - "memory_chatbot_graph", - "vector_store_rag_graph", "basic_prompting_graph", - "sequential_tasks_agent_graph", - "hierarchical_tasks_agent_graph", + "blog_writer_graph", "complex_agent_graph", + "document_qa_graph", + "hierarchical_tasks_agent_graph", + "memory_chatbot_graph", + "sequential_tasks_agent_graph", + "vector_store_rag_graph", ] diff --git a/src/backend/base/langflow/inputs/__init__.py b/src/backend/base/langflow/inputs/__init__.py index d41f7d728..043f539be 100644 --- a/src/backend/base/langflow/inputs/__init__.py +++ b/src/backend/base/langflow/inputs/__init__.py @@ -25,25 +25,25 @@ from .inputs import ( __all__ = [ "BoolInput", + "CodeInput", "DataInput", + "DefaultPromptField", "DictInput", "DropdownInput", - "MultiselectInput", "FileInput", "FloatInput", "HandleInput", + "Input", "IntInput", + "LinkInput", "MessageInput", + "MessageTextInput", "MultilineInput", "MultilineSecretInput", + "MultiselectInput", "NestedDictInput", "PromptInput", - "CodeInput", "SecretStrInput", "StrInput", - "MessageTextInput", "TableInput", - "Input", - "DefaultPromptField", - "LinkInput", ] diff --git a/src/backend/base/langflow/io/__init__.py b/src/backend/base/langflow/io/__init__.py index 9d9e39d09..fb4a6a43f 100644 --- a/src/backend/base/langflow/io/__init__.py +++ b/src/backend/base/langflow/io/__init__.py @@ -25,25 +25,25 @@ from langflow.template import Output __all__ = [ "BoolInput", + "CodeInput", "DataInput", + "DefaultPromptField", "DictInput", "DropdownInput", - "MultiselectInput", "FileInput", "FloatInput", "HandleInput", "IntInput", + "LinkInput", "MessageInput", + "MessageTextInput", "MultilineInput", "MultilineSecretInput", + "MultiselectInput", "NestedDictInput", + "Output", "PromptInput", - "CodeInput", "SecretStrInput", "StrInput", - "MessageTextInput", - "Output", "TableInput", - "DefaultPromptField", - "LinkInput", ] diff --git a/src/backend/base/langflow/load/__init__.py b/src/backend/base/langflow/load/__init__.py index ca8afbe9e..7c3369761 100644 --- a/src/backend/base/langflow/load/__init__.py +++ b/src/backend/base/langflow/load/__init__.py @@ -1,4 +1,4 @@ from .load import load_flow_from_json, run_flow_from_json from .utils import get_flow, upload_file -__all__ = ["load_flow_from_json", "run_flow_from_json", "upload_file", "get_flow"] +__all__ = ["get_flow", "load_flow_from_json", "run_flow_from_json", "upload_file"] diff --git a/src/backend/base/langflow/logging/__init__.py b/src/backend/base/langflow/logging/__init__.py index b7d7bb5a1..cc1b11cb1 100644 --- a/src/backend/base/langflow/logging/__init__.py +++ b/src/backend/base/langflow/logging/__init__.py @@ -1,4 +1,4 @@ from .logger import configure, logger from .setup import disable_logging, enable_logging -__all__ = ["configure", "logger", "disable_logging", "enable_logging"] +__all__ = ["configure", "disable_logging", "enable_logging", "logger"] diff --git a/src/backend/base/langflow/schema/artifact.py b/src/backend/base/langflow/schema/artifact.py index 78e35b1a4..026bc47ef 100644 --- a/src/backend/base/langflow/schema/artifact.py +++ b/src/backend/base/langflow/schema/artifact.py @@ -42,10 +42,8 @@ def get_artifact_type(value, build_result=None) -> str: result = ArtifactType.ARRAY if result == ArtifactType.UNKNOWN and ( - build_result - and isinstance(build_result, Generator) - or isinstance(value, Message) - and isinstance(value.text, Generator) + (build_result and isinstance(build_result, Generator)) + or (isinstance(value, Message) and isinstance(value.text, Generator)) ): result = ArtifactType.STREAM diff --git a/src/backend/base/langflow/schema/schema.py b/src/backend/base/langflow/schema/schema.py index c83fb7d64..685b05c5e 100644 --- a/src/backend/base/langflow/schema/schema.py +++ b/src/backend/base/langflow/schema/schema.py @@ -57,10 +57,8 @@ def get_type(payload): result = LogType.TEXT if result == LogType.UNKNOWN and ( - payload - and isinstance(payload, Generator) - or isinstance(payload, Message) - and isinstance(payload.text, Generator) + (payload and isinstance(payload, Generator)) + or (isinstance(payload, Message) and isinstance(payload.text, Generator)) ): result = LogType.STREAM diff --git a/src/backend/base/langflow/services/__init__.py b/src/backend/base/langflow/services/__init__.py index 8ac74b5b9..a4c3bc2be 100644 --- a/src/backend/base/langflow/services/__init__.py +++ b/src/backend/base/langflow/services/__init__.py @@ -1,4 +1,4 @@ from .manager import service_manager from .schema import ServiceType -__all__ = ["service_manager", "ServiceType"] +__all__ = ["ServiceType", "service_manager"] diff --git a/src/backend/base/langflow/services/cache/__init__.py b/src/backend/base/langflow/services/cache/__init__.py index 48fb9837a..72f74d7da 100644 --- a/src/backend/base/langflow/services/cache/__init__.py +++ b/src/backend/base/langflow/services/cache/__init__.py @@ -3,10 +3,10 @@ from langflow.services.cache.service import AsyncInMemoryCache, CacheService, Re from . import factory, service __all__ = [ - "factory", - "service", - "ThreadingInMemoryCache", "AsyncInMemoryCache", "CacheService", "RedisCache", + "ThreadingInMemoryCache", + "factory", + "service", ] diff --git a/src/backend/base/langflow/services/database/models/__init__.py b/src/backend/base/langflow/services/database/models/__init__.py index 5e4efda4e..4419e7f11 100644 --- a/src/backend/base/langflow/services/database/models/__init__.py +++ b/src/backend/base/langflow/services/database/models/__init__.py @@ -6,4 +6,4 @@ from .transactions import TransactionTable from .user import User from .variable import Variable -__all__ = ["Flow", "User", "ApiKey", "Variable", "Folder", "MessageTable", "TransactionTable"] +__all__ = ["ApiKey", "Flow", "Folder", "MessageTable", "TransactionTable", "User", "Variable"] diff --git a/src/backend/base/langflow/services/database/models/api_key/__init__.py b/src/backend/base/langflow/services/database/models/api_key/__init__.py index 84f734e09..4dbd35026 100644 --- a/src/backend/base/langflow/services/database/models/api_key/__init__.py +++ b/src/backend/base/langflow/services/database/models/api_key/__init__.py @@ -1,3 +1,3 @@ from .model import ApiKey, ApiKeyCreate, ApiKeyRead, UnmaskedApiKeyRead -__all__ = ["ApiKey", "ApiKeyCreate", "UnmaskedApiKeyRead", "ApiKeyRead"] +__all__ = ["ApiKey", "ApiKeyCreate", "ApiKeyRead", "UnmaskedApiKeyRead"] diff --git a/src/backend/base/langflow/services/database/models/message/__init__.py b/src/backend/base/langflow/services/database/models/message/__init__.py index 05005cc62..90d0a37c8 100644 --- a/src/backend/base/langflow/services/database/models/message/__init__.py +++ b/src/backend/base/langflow/services/database/models/message/__init__.py @@ -1,3 +1,3 @@ from .model import MessageCreate, MessageRead, MessageTable, MessageUpdate -__all__ = ["MessageTable", "MessageCreate", "MessageRead", "MessageUpdate"] +__all__ = ["MessageCreate", "MessageRead", "MessageTable", "MessageUpdate"] diff --git a/src/backend/base/langflow/services/deps.py b/src/backend/base/langflow/services/deps.py index 7d5d5ef3b..fdd52d07c 100644 --- a/src/backend/base/langflow/services/deps.py +++ b/src/backend/base/langflow/services/deps.py @@ -42,8 +42,8 @@ def get_service(service_type: ServiceType, default=None): from langflow.services.manager import service_manager if not service_manager.factories: - #! This is a workaround to ensure that the service manager is initialized - #! Not optimal, but it works for now + # ! This is a workaround to ensure that the service manager is initialized + # ! Not optimal, but it works for now service_manager.register_factories() return service_manager.get(service_type, default) # type: ignore diff --git a/src/backend/base/langflow/services/settings/base.py b/src/backend/base/langflow/services/settings/base.py index b778ee7d7..a3b62c4ab 100644 --- a/src/backend/base/langflow/services/settings/base.py +++ b/src/backend/base/langflow/services/settings/base.py @@ -272,14 +272,14 @@ class Settings(BaseSettings): elif Path(new_path).exists(): logger.debug(f"Database already exists at {new_path}, using it") final_path = new_path - elif Path("./{db_file_name}").exists(): + elif Path(f"./{db_file_name}").exists(): try: logger.debug("Copying existing database to new location") - copy2("./{db_file_name}", new_path) + copy2(f"./{db_file_name}", new_path) logger.debug(f"Copied existing database to {new_path}") except Exception: logger.exception("Failed to copy database, using default path") - new_path = "./{db_file_name}" + new_path = f"./{db_file_name}" else: final_path = new_path diff --git a/src/backend/base/langflow/services/settings/utils.py b/src/backend/base/langflow/services/settings/utils.py index 80f7b3cc1..3a380a3b9 100644 --- a/src/backend/base/langflow/services/settings/utils.py +++ b/src/backend/base/langflow/services/settings/utils.py @@ -12,7 +12,7 @@ def set_secure_permissions(file_path: Path): import win32con import win32security - user, domain, _ = win32security.LookupAccountName("", win32api.GetUserName()) + user, _, _ = win32security.LookupAccountName("", win32api.GetUserName()) sd = win32security.GetFileSecurity(str(file_path), win32security.DACL_SECURITY_INFORMATION) dacl = win32security.ACL() diff --git a/src/backend/base/langflow/services/store/exceptions.py b/src/backend/base/langflow/services/store/exceptions.py index df86d59bc..5cd36de39 100644 --- a/src/backend/base/langflow/services/store/exceptions.py +++ b/src/backend/base/langflow/services/store/exceptions.py @@ -17,7 +17,7 @@ class ForbiddenError(CustomException): class APIKeyError(CustomException): def __init__(self, detail="API key error"): - super().__init__(detail, 400) #! Should be 401 + super().__init__(detail, 400) # ! Should be 401 class FilterError(CustomException): diff --git a/src/backend/base/langflow/services/tracing/langfuse.py b/src/backend/base/langflow/services/tracing/langfuse.py index 479b52494..f0f51afd6 100644 --- a/src/backend/base/langflow/services/tracing/langfuse.py +++ b/src/backend/base/langflow/services/tracing/langfuse.py @@ -78,7 +78,7 @@ class LangFuseTracer(BaseTracer): _metadata: dict = {} _metadata |= {"trace_type": trace_type} if trace_type else {} - _metadata |= metadata if metadata else {} + _metadata |= metadata or {} _name = trace_name.removesuffix(f" ({trace_id})") content_span = { @@ -108,7 +108,7 @@ class LangFuseTracer(BaseTracer): span = self.spans.get(trace_id, None) if span: _output: dict = {} - _output |= outputs if outputs else {} + _output |= outputs or {} _output |= {"error": str(error)} if error else {} _output |= {"logs": list(logs)} if logs else {} content = {"output": _output, "end_time": end_time} diff --git a/src/backend/base/langflow/template/__init__.py b/src/backend/base/langflow/template/__init__.py index b6afea9ef..f53fb279f 100644 --- a/src/backend/base/langflow/template/__init__.py +++ b/src/backend/base/langflow/template/__init__.py @@ -3,8 +3,8 @@ from langflow.template.frontend_node.base import FrontendNode from langflow.template.template.base import Template __all__ = [ + "FrontendNode", "Input", "Output", - "FrontendNode", "Template", ] diff --git a/src/backend/base/langflow/utils/util.py b/src/backend/base/langflow/utils/util.py index ff845114c..95dec590f 100644 --- a/src/backend/base/langflow/utils/util.py +++ b/src/backend/base/langflow/utils/util.py @@ -42,7 +42,7 @@ def build_template_from_function(name: str, type_to_loader_dict: dict, add_funct variables = {"_type": _type} for class_field_items, value in _class.model_fields.items(): - if class_field_items in ["callback_manager"]: + if class_field_items == "callback_manager": continue variables[class_field_items] = {} for name_, value_ in value.__repr_args__(): @@ -53,7 +53,7 @@ def build_template_from_function(name: str, type_to_loader_dict: dict, add_funct ) except Exception: variables[class_field_items]["default"] = None - elif name_ not in ["name"]: + elif name_ != "name": variables[class_field_items][name_] = value_ variables[class_field_items]["placeholder"] = docs.params.get(class_field_items, "") @@ -209,7 +209,7 @@ def format_dict(dictionary: dict[str, Any], class_name: str | None = None) -> di """ for key, value in dictionary.items(): - 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