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>

src/backend/base/langflow/api/v2/files.py

@@ -23,6 +23,7 @@ router = APIRouter(tags=["Files"], prefix="/files")
 
 # Set the static name of the MCP servers file
 MCP_SERVERS_FILE = "_mcp_servers"
+SAMPLE_DATA_DIR = Path(__file__).parent / "sample_data"
 
 
 async def byte_stream_generator(file_input, chunk_size: int = 8192) -> AsyncGenerator[bytes, None]:
@@ -61,6 +62,21 @@ async def fetch_file_object(file_id: uuid.UUID, current_user: CurrentActiveUser,
     return file
 
 
+async def save_file_routine(file, storage_service, current_user: CurrentActiveUser, file_content=None, file_name=None):
+    """Routine to save the file content to the storage service."""
+    file_id = uuid.uuid4()
+
+    if not file_content:
+        file_content = await file.read()
+    if not file_name:
+        file_name = file.filename
+
+    # Save the file using the storage service.
+    await storage_service.save_file(flow_id=str(current_user.id), file_name=file_name, data=file_content)
+
+    return file_id, file_name
+
+
 @router.post("", status_code=HTTPStatus.CREATED)
 @router.post("/", status_code=HTTPStatus.CREATED)
 async def upload_user_file(
@@ -90,18 +106,7 @@ async def upload_user_file(
 
     # Read file content and create a unique file name
     try:
-        # Create a unique file name
+        file_id, file_name = await save_file_routine(file, storage_service, current_user)
-        file_id = uuid.uuid4()
-        file_content = await file.read()
-
-        # Get file extension of the file
-        file_extension = "." + file.filename.split(".")[-1] if file.filename and "." in file.filename else ""
-        anonymized_file_name = f"{file_id!s}{file_extension}"
-
-        # Here we use the current user's id as the folder name
-        folder = str(current_user.id)
-        # Save the file using the storage service.
-        await storage_service.save_file(flow_id=folder, file_name=anonymized_file_name, data=file_content)
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"Error saving file: {e}") from e
 
@@ -138,17 +143,17 @@ async def upload_user_file(
             root_filename = f"{root_filename} ({count + 1})"
 
         # Compute the file size based on the path
-        file_size = await storage_service.get_file_size(flow_id=folder, file_name=anonymized_file_name)
+        file_size = await storage_service.get_file_size(
-
+            flow_id=str(current_user.id),
-        # Compute the file path
+            file_name=file_name,
-        file_path = f"{folder}/{anonymized_file_name}"
+        )
 
         # Create a new file record
         new_file = UserFile(
             id=file_id,
             user_id=current_user.id,
             name=root_filename,
-            path=file_path,
+            path=f"{current_user.id}/{file_name}",
             size=file_size,
         )
         session.add(new_file)
@@ -178,14 +183,61 @@ async def get_file_by_name(
         raise HTTPException(status_code=500, detail=f"Error fetching file: {e}") from e
 
 
+async def load_sample_files(current_user: CurrentActiveUser, session: DbSession, storage_service: StorageService):
+    # Check if the sample files in the SAMPLE_DATA_DIR exist
+    for sample_file_path in Path(SAMPLE_DATA_DIR).iterdir():
+        sample_file_name = sample_file_path.name
+        root_filename, _ = sample_file_name.rsplit(".", 1)
+
+        # Check if the sample file exists in the storage service
+        existing_sample_file = await get_file_by_name(
+            file_name=root_filename, current_user=current_user, session=session
+        )
+        if existing_sample_file:
+            continue
+
+        # Read the binary data of the sample file
+        binary_data = sample_file_path.read_bytes()
+
+        # Write the sample file content to the storage service
+        file_id, _ = await save_file_routine(
+            sample_file_path,
+            storage_service,
+            current_user,
+            file_content=binary_data,
+            file_name=sample_file_name,
+        )
+        file_size = await storage_service.get_file_size(
+            flow_id=str(current_user.id),
+            file_name=sample_file_name,
+        )
+        # Create a UserFile object for the sample file
+        sample_file = UserFile(
+            id=file_id,
+            user_id=current_user.id,
+            name=root_filename,
+            path=sample_file_name,
+            size=file_size,
+        )
+
+        session.add(sample_file)
+
+        await session.commit()
+        await session.refresh(sample_file)
+
+
 @router.get("")
 @router.get("/", status_code=HTTPStatus.OK)
 async def list_files(
     current_user: CurrentActiveUser,
     session: DbSession,
+    # storage_service: Annotated[StorageService, Depends(get_storage_service)],
 ) -> list[UserFile]:
     """List the files available to the current user."""
     try:
+        # Load sample files if they don't exist
+        # TODO: Pending further testing
+        # await load_sample_files(current_user, session, get_storage_service())
         # Fetch from the UserFile table
         stmt = select(UserFile).where(UserFile.user_id == current_user.id)
         results = await session.exec(stmt)

src/backend/base/langflow/base/constants.py

@@ -43,4 +43,4 @@ FIELD_FORMAT_ATTRIBUTES = [
 ]
 SKIPPED_FIELD_ATTRIBUTES = ["advanced"]
 ORJSON_OPTIONS = orjson.OPT_INDENT_2 | orjson.OPT_SORT_KEYS | orjson.OPT_OMIT_MICROSECONDS
-SKIPPED_COMPONENTS = {"LanguageModelComponent", "Agent"}
+SKIPPED_COMPONENTS = {"LanguageModelComponent", "Agent", "File", "FileComponent"}

src/backend/base/langflow/base/data/base_file.py

@@ -1,8 +1,11 @@
+import ast
+import json
 import shutil
 import tarfile
 from abc import ABC, abstractmethod
 from pathlib import Path
 from tempfile import TemporaryDirectory
+from typing import TYPE_CHECKING
 from zipfile import ZipFile, is_zipfile
 
 import pandas as pd
@@ -13,6 +16,9 @@ from langflow.schema.data import Data
 from langflow.schema.dataframe import DataFrame
 from langflow.schema.message import Message
 
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
 
 class BaseFileComponent(Component, ABC):
     """Base class for handling file processing components.
@@ -174,8 +180,7 @@ class BaseFileComponent(Component, ABC):
     ]
 
     _base_outputs = [
-        Output(display_name="Loaded Files", name="dataframe", method="load_files"),
+        Output(display_name="Files", name="dataframe", method="load_files"),
-        Output(display_name="Raw Content", name="message", method="load_files_message"),
     ]
 
     @abstractmethod
@@ -257,6 +262,104 @@ class BaseFileComponent(Component, ABC):
 
         return Message(text=sep.join(parts))
 
+    def load_files_path(self) -> Message:
+        """Returns a Message containing file paths from loaded files.
+
+        Returns:
+            Message: Message containing file paths
+        """
+        files = self._validate_and_resolve_paths()
+        paths = [file.path.as_posix() for file in files if file.path.exists()]
+
+        return Message(text="\n".join(paths) if paths else "")
+
+    def load_files_structured_helper(self, file_path: str) -> list[dict] | None:
+        if not file_path:
+            return None
+
+        # Map file extensions to pandas read functions with type annotation
+        file_readers: dict[str, Callable[[str], pd.DataFrame]] = {
+            ".csv": pd.read_csv,
+            ".xlsx": pd.read_excel,
+            ".parquet": pd.read_parquet,
+            # TODO: sqlite and json support?
+        }
+
+        # Get file extension in lowercase
+        ext = Path(file_path).suffix.lower()
+
+        # Get the appropriate reader function or None
+        reader = file_readers.get(ext)
+
+        if reader:
+            result = reader(file_path)  # MyPy now knows reader is callable
+            return result.to_dict("records")
+
+        return None
+
+    def load_files_structured(self) -> DataFrame:
+        """Load files and return as DataFrame with structured content.
+
+        Returns:
+            DataFrame: DataFrame containing structured content from all files
+        """
+        data_list = self.load_files_core()
+        if not data_list:
+            return DataFrame()
+
+        # Get the file path from the first Data object
+        file_path = data_list[0].data.get(self.SERVER_FILE_PATH_FIELDNAME, None)
+
+        # If file_path is provided and is a CSV, read it directly
+        if file_path and str(file_path).lower().endswith((".csv", ".xlsx", ".parquet")):
+            rows = self.load_files_structured_helper(file_path)
+        else:
+            # Convert Data objects to a list of dictionaries
+            # TODO: Parse according to docling standards
+            rows = [data_list[0].data]
+
+        self.status = DataFrame(rows)
+
+        return DataFrame(rows)
+
+    def parse_string_to_dict(self, s: str) -> dict:
+        # Try JSON first (handles true/false/null)
+        try:
+            result = json.loads(s)
+            if isinstance(result, dict):
+                return result
+        except json.JSONDecodeError:
+            pass
+
+        # Fall back to Python literal evaluation
+        try:
+            result = ast.literal_eval(s)
+            if isinstance(result, dict):
+                return result
+        except (SyntaxError, ValueError):
+            pass
+
+        # If all parsing fails, return the fallback
+        return {"value": s}
+
+    def load_files_json(self) -> Data:
+        """Load files and return as a single Data object containing JSON content.
+
+        Returns:
+            Data: Data object containing JSON content from all files
+        """
+        data_list = self.load_files_core()
+        if not data_list:
+            return Data()
+
+        # Grab the JSON data
+        json_data = data_list[0].data[data_list[0].text_key]
+        json_data = self.parse_string_to_dict(json_data)
+
+        self.status = Data(data=json_data)
+
+        return Data(data=json_data)
+
     def load_files(self) -> DataFrame:
         """Load files and return as DataFrame.
 
@@ -267,30 +370,21 @@ class BaseFileComponent(Component, ABC):
         if not data_list:
             return DataFrame()
 
-        # First handle CSV files specially
+        # Convert Data objects to a list of dictionaries
-        csv_data = []
+        all_rows = []
-        non_csv_rows = []
-
         for data in data_list:
             file_path = data.data.get(self.SERVER_FILE_PATH_FIELDNAME)
-            if file_path and str(file_path).lower().endswith(".csv"):
+            row = dict(data.data) if data.data else {}
-                try:
+
-                    csv_data.extend(pd.read_csv(file_path).to_dict("records"))
+            # Add text if available, otherwise use the data's text property
-                except Exception as e:
+            if "text" in data.data:
-                    self.log(f"Error processing CSV file {file_path}: {e}")
+                row["text"] = data.data["text"]
-                    if not self.silent_errors:
+            if file_path:
-                        raise
+                row["file_path"] = file_path
-            else:
+            all_rows.append(row)
-                # Handle non-CSV files as before
+
-                row = dict(data.data) if data.data else {}
+        self.status = DataFrame(all_rows)
-                if "text" in data.data:
-                    row["text"] = data.data["text"]
-                if file_path:
-                    row["file_path"] = file_path
-                non_csv_rows.append(row)
 
-        # Combine CSV and non-CSV data
-        all_rows = csv_data + non_csv_rows
         return DataFrame(all_rows)
 
     @property

src/backend/base/langflow/components/data/file.py

@@ -1,6 +1,9 @@
+from copy import deepcopy
+from typing import Any
+
 from langflow.base.data.base_file import BaseFileComponent
 from langflow.base.data.utils import TEXT_FILE_TYPES, parallel_load_data, parse_text_file_to_data
-from langflow.io import BoolInput, IntInput
+from langflow.io import BoolInput, FileInput, IntInput, Output
 from langflow.schema.data import Data
 
 
@@ -18,8 +21,15 @@ class FileComponent(BaseFileComponent):
 
     VALID_EXTENSIONS = TEXT_FILE_TYPES
 
+    _base_inputs = deepcopy(BaseFileComponent._base_inputs)
+
+    for input_item in _base_inputs:
+        if isinstance(input_item, FileInput) and input_item.name == "path":
+            input_item.real_time_refresh = True
+            break
+
     inputs = [
-        *BaseFileComponent._base_inputs,
+        *_base_inputs,
         BoolInput(
             name="use_multithreading",
             display_name="[Deprecated] Use Multithreading",
@@ -37,9 +47,45 @@ class FileComponent(BaseFileComponent):
     ]
 
     outputs = [
-        *BaseFileComponent._base_outputs,
+        Output(display_name="Raw Content", name="message", method="load_files_message"),
     ]
 
+    def update_outputs(self, frontend_node: dict, field_name: str, field_value: Any) -> dict:
+        """Dynamically show only the relevant output based on the number of files processed."""
+        if field_name == "path":
+            # Add outputs based on the number of files in the path
+            if len(field_value) == 0:
+                return frontend_node
+
+            frontend_node["outputs"] = []
+
+            if len(field_value) == 1:
+                # We need to check if the file is structured content
+                file_path = frontend_node["template"]["path"]["file_path"][0]
+                if file_path.endswith((".csv", ".xlsx", ".parquet")):
+                    frontend_node["outputs"].append(
+                        Output(display_name="Structured Content", name="dataframe", method="load_files_structured"),
+                    )
+                elif file_path.endswith(".json"):
+                    frontend_node["outputs"].append(
+                        Output(display_name="Structured Content", name="json", method="load_files_json"),
+                    )
+
+                # All files get the raw content and path outputs
+                frontend_node["outputs"].append(
+                    Output(display_name="Raw Content", name="message", method="load_files_message"),
+                )
+                frontend_node["outputs"].append(
+                    Output(display_name="File Path", name="path", method="load_files_path"),
+                )
+            else:
+                # For multiple files, we only show the files output
+                frontend_node["outputs"].append(
+                    Output(display_name="Files", name="dataframe", method="load_files"),
+                )
+
+        return frontend_node
+
     def process_files(self, file_list: list[BaseFileComponent.BaseFile]) -> list[BaseFileComponent.BaseFile]:
         """Processes files either sequentially or in parallel, depending on concurrency settings.
 

src/backend/base/langflow/components/processing/split_text.py

@@ -4,6 +4,7 @@ from langflow.custom.custom_component.component import Component
 from langflow.io import DropdownInput, HandleInput, IntInput, MessageTextInput, Output
 from langflow.schema.data import Data
 from langflow.schema.dataframe import DataFrame
+from langflow.schema.message import Message
 from langflow.utils.util import unescape_string
 
 
@@ -16,9 +17,9 @@ class SplitTextComponent(Component):
     inputs = [
         HandleInput(
             name="data_inputs",
-            display_name="Data or DataFrame",
+            display_name="Input",
             info="The data with texts to split in chunks.",
-            input_types=["Data", "DataFrame"],
+            input_types=["Data", "DataFrame", "Message"],
             required=True,
         ),
         IntInput(
@@ -93,6 +94,9 @@ class SplitTextComponent(Component):
             except Exception as e:
                 msg = f"Error converting DataFrame to documents: {e}"
                 raise TypeError(msg) from e
+        elif isinstance(self.data_inputs, Message):
+            self.data_inputs = [self.data_inputs.to_data()]
+            return self.split_text_base()
         else:
             if not self.data_inputs:
                 msg = "No data inputs provided"

src/backend/base/langflow/initial_setup/starter_projects/document_qa.py

@@ -1,7 +1,6 @@
 from langflow.components.data import FileComponent
 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.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 = LanguageModelComponent()
-    openai_component.set(input_value=prompt_component.build_prompt)
+    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)

src/backend/base/langflow/initial_setup/starter_projects/vector_store_rag.py

@@ -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()

src/backend/base/langflow/services/storage/local.py

@@ -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

src/backend/base/langflow/services/storage/s3.py

@@ -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

src/backend/base/langflow/services/storage/service.py

@@ -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

src/backend/tests/unit/components/data/test_file_component.py

@@ -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

src/backend/tests/unit/initial_setup/starter_projects/test_vector_store_rag.py

@@ -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"

src/frontend/tests/core/features/stop-building.spec.ts

@@ -82,9 +82,7 @@ test(
     await page
       .getByTestId("handle-urlcomponent-shownode-extracted pages-right")
       .click();
-    await page
+    await page.getByTestId("handle-splittext-shownode-input-left").click();
-      .getByTestId("handle-splittext-shownode-data or dataframe-left")
-      .click();
 
     //connection 2
     await page

src/frontend/tests/core/unit/fileUploadComponent.spec.ts

@@ -250,33 +250,8 @@ test(
 
     await adjustScreenView(page);
 
-    await page
+    await page.getByTestId("handle-file-shownode-files-right").first().click();
-      .getByTestId("handle-file-shownode-loaded 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();