feat: Better multi-file consistency for File Component (#8625)
* feat: First pass at output changes for file component * SQLite support and json parsing * [autofix.ci] apply automated fixes * Update base_file.py * [autofix.ci] apply automated fixes * Update file.py * [autofix.ci] apply automated fixes * Split text supporting messages * [autofix.ci] apply automated fixes * Support structured json data * Routine for inclusion of sample data * Couple more template updates * [autofix.ci] apply automated fixes * Update Text Sentiment Analysis.json * Update Portfolio Website Code Generator.json * Test coverage * [autofix.ci] apply automated fixes * Update constants.py * Update base_file.py * Update service.py * Update src/backend/base/langflow/components/processing/split_text.py Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@langflow.org> * [autofix.ci] apply automated fixes * Update service.py * Add get file size that is unimplemented * Update service.py * Fix return * Remove type annotation * Update graph for new templates * Update test_vector_store_rag.py * Update stop-building.spec.ts * [autofix.ci] apply automated fixes * Update fileUploadComponent.spec.ts * Update fileUploadComponent.spec.ts * Update fileUploadComponent.spec.ts * Update fileUploadComponent.spec.ts * Update fileUploadComponent.spec.ts * Update fileUploadComponent.spec.ts * [autofix.ci] apply automated fixes * Update fileUploadComponent.spec.ts * test update * Update fileUploadComponent.spec.ts * [autofix.ci] apply automated fixes --------- Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com> Co-authored-by: Edwin Jose <edwin.jose@datastax.com> Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@langflow.org> Co-authored-by: Mike Fortman <michael.fortman@datastax.com>
This commit is contained in:
Eric Hare
GitHub
parent
65b71bec5b
commit
e1624b8c6e
18 changed files with 3680 additions and 3965 deletions
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@ -23,6 +23,7 @@ router = APIRouter(tags=["Files"], prefix="/files")
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# Set the static name of the MCP servers file
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MCP_SERVERS_FILE = "_mcp_servers"
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SAMPLE_DATA_DIR = Path(__file__).parent / "sample_data"
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async def byte_stream_generator(file_input, chunk_size: int = 8192) -> AsyncGenerator[bytes, None]:
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@ -61,6 +62,21 @@ async def fetch_file_object(file_id: uuid.UUID, current_user: CurrentActiveUser,
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return file
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async def save_file_routine(file, storage_service, current_user: CurrentActiveUser, file_content=None, file_name=None):
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"""Routine to save the file content to the storage service."""
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file_id = uuid.uuid4()
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if not file_content:
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file_content = await file.read()
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if not file_name:
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file_name = file.filename
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# Save the file using the storage service.
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await storage_service.save_file(flow_id=str(current_user.id), file_name=file_name, data=file_content)
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return file_id, file_name
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@router.post("", status_code=HTTPStatus.CREATED)
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@router.post("/", status_code=HTTPStatus.CREATED)
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async def upload_user_file(
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@ -90,18 +106,7 @@ async def upload_user_file(
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# Read file content and create a unique file name
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try:
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# Create a unique file name
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file_id = uuid.uuid4()
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file_content = await file.read()
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# Get file extension of the file
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file_extension = "." + file.filename.split(".")[-1] if file.filename and "." in file.filename else ""
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anonymized_file_name = f"{file_id!s}{file_extension}"
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# Here we use the current user's id as the folder name
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folder = str(current_user.id)
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# Save the file using the storage service.
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await storage_service.save_file(flow_id=folder, file_name=anonymized_file_name, data=file_content)
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file_id, file_name = await save_file_routine(file, storage_service, current_user)
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Error saving file: {e}") from e
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@ -138,17 +143,17 @@ async def upload_user_file(
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root_filename = f"{root_filename} ({count + 1})"
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# Compute the file size based on the path
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file_size = await storage_service.get_file_size(flow_id=folder, file_name=anonymized_file_name)
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# Compute the file path
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file_path = f"{folder}/{anonymized_file_name}"
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file_size = await storage_service.get_file_size(
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flow_id=str(current_user.id),
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file_name=file_name,
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)
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# Create a new file record
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new_file = UserFile(
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id=file_id,
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user_id=current_user.id,
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name=root_filename,
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path=file_path,
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path=f"{current_user.id}/{file_name}",
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size=file_size,
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)
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session.add(new_file)
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@ -178,14 +183,61 @@ async def get_file_by_name(
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raise HTTPException(status_code=500, detail=f"Error fetching file: {e}") from e
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async def load_sample_files(current_user: CurrentActiveUser, session: DbSession, storage_service: StorageService):
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# Check if the sample files in the SAMPLE_DATA_DIR exist
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for sample_file_path in Path(SAMPLE_DATA_DIR).iterdir():
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sample_file_name = sample_file_path.name
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root_filename, _ = sample_file_name.rsplit(".", 1)
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# Check if the sample file exists in the storage service
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existing_sample_file = await get_file_by_name(
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file_name=root_filename, current_user=current_user, session=session
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)
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if existing_sample_file:
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continue
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# Read the binary data of the sample file
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binary_data = sample_file_path.read_bytes()
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# Write the sample file content to the storage service
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file_id, _ = await save_file_routine(
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sample_file_path,
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storage_service,
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current_user,
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file_content=binary_data,
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file_name=sample_file_name,
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)
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file_size = await storage_service.get_file_size(
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flow_id=str(current_user.id),
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file_name=sample_file_name,
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)
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# Create a UserFile object for the sample file
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sample_file = UserFile(
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id=file_id,
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user_id=current_user.id,
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name=root_filename,
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path=sample_file_name,
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size=file_size,
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)
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session.add(sample_file)
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await session.commit()
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await session.refresh(sample_file)
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@router.get("")
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@router.get("/", status_code=HTTPStatus.OK)
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async def list_files(
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current_user: CurrentActiveUser,
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session: DbSession,
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# storage_service: Annotated[StorageService, Depends(get_storage_service)],
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) -> list[UserFile]:
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"""List the files available to the current user."""
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try:
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# Load sample files if they don't exist
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# TODO: Pending further testing
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# await load_sample_files(current_user, session, get_storage_service())
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# Fetch from the UserFile table
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stmt = select(UserFile).where(UserFile.user_id == current_user.id)
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results = await session.exec(stmt)
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@ -43,4 +43,4 @@ FIELD_FORMAT_ATTRIBUTES = [
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]
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SKIPPED_FIELD_ATTRIBUTES = ["advanced"]
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ORJSON_OPTIONS = orjson.OPT_INDENT_2 | orjson.OPT_SORT_KEYS | orjson.OPT_OMIT_MICROSECONDS
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SKIPPED_COMPONENTS = {"LanguageModelComponent", "Agent"}
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SKIPPED_COMPONENTS = {"LanguageModelComponent", "Agent", "File", "FileComponent"}
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@ -1,8 +1,11 @@
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import ast
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import json
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import shutil
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import tarfile
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from abc import ABC, abstractmethod
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from pathlib import Path
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from tempfile import TemporaryDirectory
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from typing import TYPE_CHECKING
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from zipfile import ZipFile, is_zipfile
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import pandas as pd
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@ -13,6 +16,9 @@ from langflow.schema.data import Data
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from langflow.schema.dataframe import DataFrame
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from langflow.schema.message import Message
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if TYPE_CHECKING:
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from collections.abc import Callable
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class BaseFileComponent(Component, ABC):
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"""Base class for handling file processing components.
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@ -174,8 +180,7 @@ class BaseFileComponent(Component, ABC):
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]
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_base_outputs = [
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Output(display_name="Loaded Files", name="dataframe", method="load_files"),
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Output(display_name="Raw Content", name="message", method="load_files_message"),
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Output(display_name="Files", name="dataframe", method="load_files"),
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]
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@abstractmethod
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@ -257,6 +262,104 @@ class BaseFileComponent(Component, ABC):
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return Message(text=sep.join(parts))
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def load_files_path(self) -> Message:
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"""Returns a Message containing file paths from loaded files.
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Returns:
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Message: Message containing file paths
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"""
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files = self._validate_and_resolve_paths()
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paths = [file.path.as_posix() for file in files if file.path.exists()]
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return Message(text="\n".join(paths) if paths else "")
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def load_files_structured_helper(self, file_path: str) -> list[dict] | None:
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if not file_path:
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return None
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# Map file extensions to pandas read functions with type annotation
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file_readers: dict[str, Callable[[str], pd.DataFrame]] = {
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".csv": pd.read_csv,
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".xlsx": pd.read_excel,
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".parquet": pd.read_parquet,
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# TODO: sqlite and json support?
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}
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# Get file extension in lowercase
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ext = Path(file_path).suffix.lower()
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# Get the appropriate reader function or None
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reader = file_readers.get(ext)
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if reader:
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result = reader(file_path) # MyPy now knows reader is callable
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return result.to_dict("records")
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return None
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def load_files_structured(self) -> DataFrame:
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"""Load files and return as DataFrame with structured content.
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Returns:
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DataFrame: DataFrame containing structured content from all files
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"""
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data_list = self.load_files_core()
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if not data_list:
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return DataFrame()
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# Get the file path from the first Data object
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file_path = data_list[0].data.get(self.SERVER_FILE_PATH_FIELDNAME, None)
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# If file_path is provided and is a CSV, read it directly
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if file_path and str(file_path).lower().endswith((".csv", ".xlsx", ".parquet")):
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rows = self.load_files_structured_helper(file_path)
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else:
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# Convert Data objects to a list of dictionaries
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# TODO: Parse according to docling standards
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rows = [data_list[0].data]
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self.status = DataFrame(rows)
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return DataFrame(rows)
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def parse_string_to_dict(self, s: str) -> dict:
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# Try JSON first (handles true/false/null)
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try:
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result = json.loads(s)
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if isinstance(result, dict):
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return result
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except json.JSONDecodeError:
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pass
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# Fall back to Python literal evaluation
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try:
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result = ast.literal_eval(s)
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if isinstance(result, dict):
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return result
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except (SyntaxError, ValueError):
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pass
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# If all parsing fails, return the fallback
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return {"value": s}
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def load_files_json(self) -> Data:
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"""Load files and return as a single Data object containing JSON content.
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Returns:
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Data: Data object containing JSON content from all files
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"""
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data_list = self.load_files_core()
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if not data_list:
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return Data()
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# Grab the JSON data
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json_data = data_list[0].data[data_list[0].text_key]
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json_data = self.parse_string_to_dict(json_data)
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self.status = Data(data=json_data)
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return Data(data=json_data)
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def load_files(self) -> DataFrame:
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"""Load files and return as DataFrame.
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@ -267,30 +370,21 @@ class BaseFileComponent(Component, ABC):
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if not data_list:
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return DataFrame()
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# First handle CSV files specially
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csv_data = []
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non_csv_rows = []
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# Convert Data objects to a list of dictionaries
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all_rows = []
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for data in data_list:
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file_path = data.data.get(self.SERVER_FILE_PATH_FIELDNAME)
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if file_path and str(file_path).lower().endswith(".csv"):
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try:
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csv_data.extend(pd.read_csv(file_path).to_dict("records"))
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except Exception as e:
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self.log(f"Error processing CSV file {file_path}: {e}")
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if not self.silent_errors:
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raise
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else:
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# Handle non-CSV files as before
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row = dict(data.data) if data.data else {}
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if "text" in data.data:
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row["text"] = data.data["text"]
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if file_path:
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row["file_path"] = file_path
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non_csv_rows.append(row)
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row = dict(data.data) if data.data else {}
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# Add text if available, otherwise use the data's text property
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if "text" in data.data:
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row["text"] = data.data["text"]
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if file_path:
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row["file_path"] = file_path
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all_rows.append(row)
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self.status = DataFrame(all_rows)
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# Combine CSV and non-CSV data
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all_rows = csv_data + non_csv_rows
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return DataFrame(all_rows)
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@property
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@ -1,6 +1,9 @@
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from copy import deepcopy
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from typing import Any
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from langflow.base.data.base_file import BaseFileComponent
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from langflow.base.data.utils import TEXT_FILE_TYPES, parallel_load_data, parse_text_file_to_data
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from langflow.io import BoolInput, IntInput
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from langflow.io import BoolInput, FileInput, IntInput, Output
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from langflow.schema.data import Data
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@ -18,8 +21,15 @@ class FileComponent(BaseFileComponent):
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VALID_EXTENSIONS = TEXT_FILE_TYPES
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_base_inputs = deepcopy(BaseFileComponent._base_inputs)
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for input_item in _base_inputs:
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if isinstance(input_item, FileInput) and input_item.name == "path":
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input_item.real_time_refresh = True
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break
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inputs = [
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*BaseFileComponent._base_inputs,
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*_base_inputs,
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BoolInput(
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name="use_multithreading",
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display_name="[Deprecated] Use Multithreading",
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@ -37,9 +47,45 @@ class FileComponent(BaseFileComponent):
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]
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outputs = [
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*BaseFileComponent._base_outputs,
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Output(display_name="Raw Content", name="message", method="load_files_message"),
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]
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def update_outputs(self, frontend_node: dict, field_name: str, field_value: Any) -> dict:
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"""Dynamically show only the relevant output based on the number of files processed."""
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if field_name == "path":
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# Add outputs based on the number of files in the path
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if len(field_value) == 0:
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return frontend_node
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frontend_node["outputs"] = []
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if len(field_value) == 1:
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# We need to check if the file is structured content
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file_path = frontend_node["template"]["path"]["file_path"][0]
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if file_path.endswith((".csv", ".xlsx", ".parquet")):
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frontend_node["outputs"].append(
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Output(display_name="Structured Content", name="dataframe", method="load_files_structured"),
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)
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elif file_path.endswith(".json"):
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frontend_node["outputs"].append(
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Output(display_name="Structured Content", name="json", method="load_files_json"),
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)
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# All files get the raw content and path outputs
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frontend_node["outputs"].append(
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Output(display_name="Raw Content", name="message", method="load_files_message"),
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)
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frontend_node["outputs"].append(
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Output(display_name="File Path", name="path", method="load_files_path"),
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)
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else:
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# For multiple files, we only show the files output
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frontend_node["outputs"].append(
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Output(display_name="Files", name="dataframe", method="load_files"),
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)
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return frontend_node
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def process_files(self, file_list: list[BaseFileComponent.BaseFile]) -> list[BaseFileComponent.BaseFile]:
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"""Processes files either sequentially or in parallel, depending on concurrency settings.
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@ -4,6 +4,7 @@ from langflow.custom.custom_component.component import Component
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from langflow.io import DropdownInput, HandleInput, IntInput, MessageTextInput, Output
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from langflow.schema.data import Data
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from langflow.schema.dataframe import DataFrame
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from langflow.schema.message import Message
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from langflow.utils.util import unescape_string
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@ -16,9 +17,9 @@ class SplitTextComponent(Component):
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inputs = [
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HandleInput(
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name="data_inputs",
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display_name="Data or DataFrame",
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display_name="Input",
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info="The data with texts to split in chunks.",
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input_types=["Data", "DataFrame"],
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input_types=["Data", "DataFrame", "Message"],
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required=True,
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),
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IntInput(
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@ -93,6 +94,9 @@ class SplitTextComponent(Component):
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except Exception as e:
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msg = f"Error converting DataFrame to documents: {e}"
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raise TypeError(msg) from e
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elif isinstance(self.data_inputs, Message):
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self.data_inputs = [self.data_inputs.to_data()]
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return self.split_text_base()
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else:
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if not self.data_inputs:
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msg = "No data inputs provided"
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File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
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@ -1,7 +1,6 @@
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from langflow.components.data import FileComponent
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from langflow.components.input_output import ChatInput, ChatOutput
|
||||
from langflow.components.languagemodels import OpenAIModelComponent
|
||||
from langflow.components.processing import ParserComponent
|
||||
from langflow.components.models import LanguageModelComponent
|
||||
from langflow.components.prompts import PromptComponent
|
||||
from langflow.graph import Graph
|
||||
|
||||
|
|
@ -22,19 +21,17 @@ Question:
|
|||
Answer:
|
||||
"""
|
||||
file_component = FileComponent()
|
||||
parse_data_component = ParserComponent()
|
||||
parse_data_component.set(input_data=file_component.load_files)
|
||||
|
||||
chat_input = ChatInput()
|
||||
prompt_component = PromptComponent()
|
||||
prompt_component.set(
|
||||
template=template,
|
||||
context=parse_data_component.parse_combined_text,
|
||||
context=file_component.load_files_message,
|
||||
question=chat_input.message_response,
|
||||
)
|
||||
|
||||
openai_component = OpenAIModelComponent()
|
||||
openai_component.set(input_value=prompt_component.build_prompt)
|
||||
openai_component = LanguageModelComponent()
|
||||
openai_component.set(input_value=chat_input.message_response, system_message=prompt_component.build_prompt)
|
||||
|
||||
chat_output = ChatOutput()
|
||||
chat_output.set(input_value=openai_component.text_response)
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ from textwrap import dedent
|
|||
from langflow.components.data import FileComponent
|
||||
from langflow.components.embeddings import OpenAIEmbeddingsComponent
|
||||
from langflow.components.input_output import ChatInput, ChatOutput
|
||||
from langflow.components.languagemodels import OpenAIModelComponent
|
||||
from langflow.components.models import LanguageModelComponent
|
||||
from langflow.components.processing import ParserComponent
|
||||
from langflow.components.processing.split_text import SplitTextComponent
|
||||
from langflow.components.prompts import PromptComponent
|
||||
|
|
@ -15,7 +15,7 @@ def ingestion_graph():
|
|||
# Ingestion Graph
|
||||
file_component = FileComponent()
|
||||
text_splitter = SplitTextComponent()
|
||||
text_splitter.set(data_inputs=file_component.load_files)
|
||||
text_splitter.set(data_inputs=file_component.load_files_message)
|
||||
openai_embeddings = OpenAIEmbeddingsComponent()
|
||||
vector_store = AstraDBVectorStoreComponent()
|
||||
vector_store.set(
|
||||
|
|
@ -49,7 +49,7 @@ def rag_graph():
|
|||
question=chat_input.message_response,
|
||||
)
|
||||
|
||||
openai_component = OpenAIModelComponent()
|
||||
openai_component = LanguageModelComponent()
|
||||
openai_component.set(input_value=prompt_component.build_prompt)
|
||||
|
||||
chat_output = ChatOutput()
|
||||
|
|
|
|||
|
|
@ -113,7 +113,7 @@ class LocalStorageService(StorageService):
|
|||
"""Perform any cleanup operations when the service is being torn down."""
|
||||
# No specific teardown actions required for local
|
||||
|
||||
async def get_file_size(self, flow_id: str, file_name: str) -> None:
|
||||
async def get_file_size(self, flow_id: str, file_name: str):
|
||||
"""Get the size of a file in the local storage."""
|
||||
# Get the file size from the file path
|
||||
file_path = self.data_dir / flow_id / file_name
|
||||
|
|
|
|||
|
|
@ -99,3 +99,6 @@ class S3StorageService(StorageService):
|
|||
async def teardown(self) -> None:
|
||||
"""Perform any cleanup operations when the service is being torn down."""
|
||||
# No specific teardown actions required for S3 storage at the moment.
|
||||
|
||||
async def get_file_size(self, flow_id: str, file_name: str):
|
||||
raise NotImplementedError
|
||||
|
|
|
|||
|
|
@ -39,6 +39,10 @@ class StorageService(Service):
|
|||
async def list_files(self, flow_id: str) -> list[str]:
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
async def get_file_size(self, flow_id: str, file_name: str):
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
async def delete_file(self, flow_id: str, file_name: str) -> None:
|
||||
raise NotImplementedError
|
||||
|
|
|
|||
|
|
@ -0,0 +1,60 @@
|
|||
from langflow.components.data import FileComponent
|
||||
from langflow.io import Output
|
||||
|
||||
|
||||
class TestFileComponentDynamicOutputs:
|
||||
def test_update_outputs_single_csv_file(self):
|
||||
"""Test single CSV file shows structured + raw outputs."""
|
||||
component = FileComponent()
|
||||
frontend_node = {"outputs": [], "template": {"path": {"file_path": ["test.csv"]}}}
|
||||
|
||||
result = component.update_outputs(frontend_node, "path", ["test.csv"])
|
||||
|
||||
assert len(result["outputs"]) == 3
|
||||
output_names = [output.name for output in result["outputs"]]
|
||||
assert "dataframe" in output_names # Structured content
|
||||
assert "message" in output_names # Raw content
|
||||
assert "path" in output_names # File path
|
||||
|
||||
def test_update_outputs_single_json_file(self):
|
||||
"""Test single JSON file shows JSON + raw outputs."""
|
||||
component = FileComponent()
|
||||
frontend_node = {"outputs": [], "template": {"path": {"file_path": ["data.json"]}}}
|
||||
|
||||
result = component.update_outputs(frontend_node, "path", ["data.json"])
|
||||
|
||||
assert len(result["outputs"]) == 3
|
||||
output_names = [output.name for output in result["outputs"]]
|
||||
assert "json" in output_names # JSON content
|
||||
assert "message" in output_names # Raw content
|
||||
assert "path" in output_names # File path
|
||||
|
||||
def test_update_outputs_multiple_files(self):
|
||||
"""Test multiple files show only Files output."""
|
||||
component = FileComponent()
|
||||
frontend_node = {"outputs": [], "template": {"path": {"file_path": ["file1.txt", "file2.txt"]}}}
|
||||
|
||||
result = component.update_outputs(frontend_node, "path", ["file1.txt", "file2.txt"])
|
||||
|
||||
assert len(result["outputs"]) == 1
|
||||
assert result["outputs"][0].name == "dataframe"
|
||||
assert result["outputs"][0].display_name == "Files"
|
||||
|
||||
def test_update_outputs_empty_path(self):
|
||||
"""Test empty path results in no outputs."""
|
||||
component = FileComponent()
|
||||
frontend_node = {"outputs": [], "template": {"path": {"file_path": []}}}
|
||||
|
||||
result = component.update_outputs(frontend_node, "path", [])
|
||||
|
||||
assert len(result["outputs"]) == 0
|
||||
|
||||
def test_update_outputs_non_path_field(self):
|
||||
"""Test non-path fields don't affect outputs."""
|
||||
component = FileComponent()
|
||||
original_outputs = [Output(display_name="Test", name="test", method="test_method")]
|
||||
frontend_node = {"outputs": original_outputs, "template": {"path": {"file_path": ["value"]}}}
|
||||
|
||||
result = component.update_outputs(frontend_node, "other_field", "value")
|
||||
|
||||
assert result["outputs"] == original_outputs
|
||||
|
|
@ -15,6 +15,7 @@ from langflow.graph.graph.base import Graph
|
|||
from langflow.graph.graph.constants import Finish
|
||||
from langflow.schema import Data
|
||||
from langflow.schema.dataframe import DataFrame
|
||||
from langflow.schema.message import Message
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
|
@ -22,9 +23,9 @@ def ingestion_graph():
|
|||
# Ingestion Graph
|
||||
file_component = FileComponent(_id="file-123")
|
||||
file_component.set(path="test.txt")
|
||||
file_component.set_on_output(name="dataframe", value=Data(text="This is a test file."), cache=True)
|
||||
file_component.set_on_output(name="message", value=Message(text="This is a test file."), cache=True)
|
||||
text_splitter = SplitTextComponent(_id="text-splitter-123")
|
||||
text_splitter.set(data_inputs=file_component.load_files)
|
||||
text_splitter.set(data_inputs=file_component.load_files_message)
|
||||
openai_embeddings = OpenAIEmbeddingsComponent(_id="openai-embeddings-123")
|
||||
openai_embeddings.set(
|
||||
openai_api_key="sk-123", openai_api_base="https://api.openai.com/v1", openai_api_type="openai"
|
||||
|
|
|
|||
|
|
@ -82,9 +82,7 @@ test(
|
|||
await page
|
||||
.getByTestId("handle-urlcomponent-shownode-extracted pages-right")
|
||||
.click();
|
||||
await page
|
||||
.getByTestId("handle-splittext-shownode-data or dataframe-left")
|
||||
.click();
|
||||
await page.getByTestId("handle-splittext-shownode-input-left").click();
|
||||
|
||||
//connection 2
|
||||
await page
|
||||
|
|
|
|||
|
|
@ -250,33 +250,8 @@ test(
|
|||
|
||||
await adjustScreenView(page);
|
||||
|
||||
await page
|
||||
.getByTestId("handle-file-shownode-loaded files-right")
|
||||
.first()
|
||||
.click();
|
||||
await page.getByTestId("handle-file-shownode-files-right").first().click();
|
||||
|
||||
await page
|
||||
.getByTestId("processingParser")
|
||||
.hover()
|
||||
.then(async () => {
|
||||
await page.getByTestId("add-component-button-parser").click();
|
||||
});
|
||||
|
||||
await adjustScreenView(page);
|
||||
await page
|
||||
.getByTestId("handle-file-shownode-loaded files-right")
|
||||
.first()
|
||||
.click();
|
||||
|
||||
await page
|
||||
.getByTestId("handle-parsercomponent-shownode-data or dataframe-left")
|
||||
.first()
|
||||
.click();
|
||||
|
||||
await page
|
||||
.getByTestId("handle-parsercomponent-shownode-parsed text-right")
|
||||
.first()
|
||||
.click();
|
||||
await page
|
||||
.getByTestId("handle-chatoutput-noshownode-inputs-target")
|
||||
.first()
|
||||
|
|
@ -348,6 +323,17 @@ test(
|
|||
timeout: 1000,
|
||||
});
|
||||
await page.getByTestId(`remove-file-button-${renamedTxtFile}`).click();
|
||||
|
||||
await page
|
||||
.getByTestId("handle-file-shownode-raw content-right")
|
||||
.first()
|
||||
.click();
|
||||
|
||||
await page
|
||||
.getByTestId("handle-chatoutput-noshownode-inputs-target")
|
||||
.first()
|
||||
.click();
|
||||
|
||||
await page
|
||||
.getByRole("button", { name: "Playground", exact: true })
|
||||
.click();
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue