Merge remote-tracking branch 'origin/release-1.8.3'

# Conflicts:
#	src/backend/tests/unit/test_unified_models.py

Author: vjgit96

.secrets.baseline

@@ -6041,56 +6041,21 @@
         "filename": "src/lfx/src/lfx/base/models/unified_models.py",
         "hashed_secret": "665b1e3851eefefa3fb878654292f16597d25155",
         "is_verified": false,
-        "line_number": 69
+        "line_number": 1171
       },
       {
         "type": "Secret Keyword",
         "filename": "src/lfx/src/lfx/base/models/unified_models.py",
         "hashed_secret": "3f2df46921dd8e2c36e2ce85238705ac0774c74a",
         "is_verified": false,
-        "line_number": 87
-      },
-      {
-        "type": "Secret Keyword",
-        "filename": "src/lfx/src/lfx/base/models/unified_models.py",
-        "hashed_secret": "d3d6fe3f7d33d0f4aa28c49544a865982a48a00a",
-        "is_verified": false,
-        "line_number": 96
+        "line_number": 1182
       },
       {
         "type": "Secret Keyword",
         "filename": "src/lfx/src/lfx/base/models/unified_models.py",
         "hashed_secret": "d4c3d66fd0c38547a3c7a4c6bdc29c36911bc030",
         "is_verified": false,
-        "line_number": 106
-      },
-      {
-        "type": "Secret Keyword",
-        "filename": "src/lfx/src/lfx/base/models/unified_models.py",
-        "hashed_secret": "a19ee5a9fdc191092796cd9bfa97c55fe5631695",
-        "is_verified": false,
-        "line_number": 380
-      },
-      {
-        "type": "Secret Keyword",
-        "filename": "src/lfx/src/lfx/base/models/unified_models.py",
-        "hashed_secret": "b780a23b530ef6bb5c3b722a0c9c5e3abf222e8b",
-        "is_verified": false,
-        "line_number": 381
-      },
-      {
-        "type": "Secret Keyword",
-        "filename": "src/lfx/src/lfx/base/models/unified_models.py",
-        "hashed_secret": "ed96761b106155514463a9f82d02b14042e75b61",
-        "is_verified": false,
-        "line_number": 382
-      },
-      {
-        "type": "Secret Keyword",
-        "filename": "src/lfx/src/lfx/base/models/unified_models.py",
-        "hashed_secret": "ac579e82cacca3f9c1a8b18bdfdad78e8b6140f8",
-        "is_verified": false,
-        "line_number": 383
+        "line_number": 1196
       }
     ],
     "src/lfx/src/lfx/cli/serve_app.py": [
@@ -6373,5 +6338,5 @@
       }
     ]
   },
-  "generated_at": "2026-03-04T23:52:18Z"
+  "generated_at": "2026-03-25T17:32:51Z"
 }

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "langflow"
-version = "1.8.2"
+version = "1.8.3"
 description = "A Python package with a built-in web application"
 requires-python = ">=3.10,<3.14"
 license = "MIT"
@@ -17,7 +17,7 @@ maintainers = [
 ]
 # Define your main dependencies here
 dependencies = [
-    "langflow-base[complete]~=0.8.2",
+    "langflow-base[complete]~=0.8.3",
 ]
 
 

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

@@ -137,6 +137,7 @@ async def upload_user_file(
     settings_service: Annotated[SettingsService, Depends(get_settings_service)],
     *,
     append: bool = False,
+    ephemeral: bool = False,
 ) -> UploadFileResponse:
     """Upload a file for the current user and track it in the database."""
     # Get the max allowed file size from settings (in MB)
@@ -232,6 +233,12 @@ async def upload_user_file(
             # General error saving file or getting file size
             raise HTTPException(status_code=500, detail=f"Error accessing file: {e}") from e
 
+        if ephemeral:
+            # Ephemeral uploads: file is saved to storage (servable for chat history)
+            # but no UserFile record is created (won't appear in "My Files")
+            file_path = f"{current_user.id}/{stored_file_name}"
+            return UploadFileResponse(id=file_id, name=root_filename, path=file_path, size=file_size)
+
         if append and existing_file:
             existing_file.size = file_size
             session.add(existing_file)

src/backend/base/langflow/initial_setup/starter_projects/Nvidia Remix.json

@@ -2123,7 +2123,7 @@
             "legacy": false,
             "lf_version": "1.4.2",
             "metadata": {
-              "code_hash": "2bd7a064d724",
+              "code_hash": "3e55e36d0692",
               "dependencies": {
                 "dependencies": [
                   {
@@ -2167,7 +2167,7 @@
                 "advanced": true,
                 "display_name": "Allow Dangerous Deserialization",
                 "dynamic": false,
-                "info": "Set to True to allow loading pickle files from untrusted sources. Only enable this if you trust the source of the data.",
+                "info": "Set to True to allow loading pickle files. WARNING: Only enable this if you trust the source of the data. Malicious pickle files can execute arbitrary code on your system.",
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "allow_dangerous_deserialization",
@@ -2178,7 +2178,7 @@
                 "tool_mode": false,
                 "trace_as_metadata": true,
                 "type": "bool",
-                "value": true
+                "value": false
               },
               "code": {
                 "advanced": true,
@@ -2196,7 +2196,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from pathlib import Path\n\nfrom langchain_community.vectorstores import FAISS\n\nfrom lfx.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom lfx.helpers.data import docs_to_data\nfrom lfx.io import BoolInput, HandleInput, IntInput, StrInput\nfrom lfx.schema.data import Data\n\n\nclass FaissVectorStoreComponent(LCVectorStoreComponent):\n    \"\"\"FAISS Vector Store with search capabilities.\"\"\"\n\n    display_name: str = \"FAISS\"\n    description: str = \"FAISS Vector Store with search capabilities\"\n    name = \"FAISS\"\n    icon = \"FAISS\"\n\n    inputs = [\n        StrInput(\n            name=\"index_name\",\n            display_name=\"Index Name\",\n            value=\"langflow_index\",\n        ),\n        StrInput(\n            name=\"persist_directory\",\n            display_name=\"Persist Directory\",\n            info=\"Path to save the FAISS index. It will be relative to where Langflow is running.\",\n        ),\n        *LCVectorStoreComponent.inputs,\n        BoolInput(\n            name=\"allow_dangerous_deserialization\",\n            display_name=\"Allow Dangerous Deserialization\",\n            info=\"Set to True to allow loading pickle files from untrusted sources. \"\n            \"Only enable this if you trust the source of the data.\",\n            advanced=True,\n            value=True,\n        ),\n        HandleInput(name=\"embedding\", display_name=\"Embedding\", input_types=[\"Embeddings\"]),\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    ]\n\n    @staticmethod\n    def resolve_path(path: str) -> str:\n        \"\"\"Resolve the path relative to the Langflow root.\n\n        Args:\n            path: The path to resolve\n        Returns:\n            str: The resolved path as a string\n        \"\"\"\n        return str(Path(path).resolve())\n\n    def get_persist_directory(self) -> Path:\n        \"\"\"Returns the resolved persist directory path or the current directory if not set.\"\"\"\n        if self.persist_directory:\n            return Path(self.resolve_path(self.persist_directory))\n        return Path()\n\n    @check_cached_vector_store\n    def build_vector_store(self) -> FAISS:\n        \"\"\"Builds the FAISS object.\"\"\"\n        path = self.get_persist_directory()\n        path.mkdir(parents=True, exist_ok=True)\n\n        # Convert DataFrame to Data if needed using parent's method\n        self.ingest_data = self._prepare_ingest_data()\n\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                documents.append(_input)\n\n        faiss = FAISS.from_documents(documents=documents, embedding=self.embedding)\n        faiss.save_local(str(path), self.index_name)\n        return faiss\n\n    def search_documents(self) -> list[Data]:\n        \"\"\"Search for documents in the FAISS vector store.\"\"\"\n        path = self.get_persist_directory()\n        index_path = path / f\"{self.index_name}.faiss\"\n\n        if not index_path.exists():\n            vector_store = self.build_vector_store()\n        else:\n            vector_store = FAISS.load_local(\n                folder_path=str(path),\n                embeddings=self.embedding,\n                index_name=self.index_name,\n                allow_dangerous_deserialization=self.allow_dangerous_deserialization,\n            )\n\n        if not vector_store:\n            msg = \"Failed to load the FAISS index.\"\n            raise ValueError(msg)\n\n        if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():\n            docs = vector_store.similarity_search(\n                query=self.search_query,\n                k=self.number_of_results,\n            )\n            return docs_to_data(docs)\n        return []\n"
+                "value": "from pathlib import Path\n\nfrom langchain_community.vectorstores import FAISS\n\nfrom lfx.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom lfx.helpers.data import docs_to_data\nfrom lfx.io import BoolInput, HandleInput, IntInput, StrInput\nfrom lfx.schema.data import Data\n\n\nclass FaissVectorStoreComponent(LCVectorStoreComponent):\n    \"\"\"FAISS Vector Store with search capabilities.\"\"\"\n\n    display_name: str = \"FAISS\"\n    description: str = \"FAISS Vector Store with search capabilities\"\n    name = \"FAISS\"\n    icon = \"FAISS\"\n\n    inputs = [\n        StrInput(\n            name=\"index_name\",\n            display_name=\"Index Name\",\n            value=\"langflow_index\",\n        ),\n        StrInput(\n            name=\"persist_directory\",\n            display_name=\"Persist Directory\",\n            info=\"Path to save the FAISS index. It will be relative to where Langflow is running.\",\n        ),\n        *LCVectorStoreComponent.inputs,\n        BoolInput(\n            name=\"allow_dangerous_deserialization\",\n            display_name=\"Allow Dangerous Deserialization\",\n            info=\"Set to True to allow loading pickle files. WARNING: Only enable this if you trust the source \"\n            \"of the data. Malicious pickle files can execute arbitrary code on your system.\",\n            advanced=True,\n            value=False,\n        ),\n        HandleInput(name=\"embedding\", display_name=\"Embedding\", input_types=[\"Embeddings\"]),\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    ]\n\n    @staticmethod\n    def resolve_path(path: str) -> str:\n        \"\"\"Resolve the path relative to the Langflow root.\n\n        Args:\n            path: The path to resolve\n        Returns:\n            str: The resolved path as a string\n        \"\"\"\n        return str(Path(path).resolve())\n\n    def get_persist_directory(self) -> Path:\n        \"\"\"Returns the resolved persist directory path or the current directory if not set.\"\"\"\n        if self.persist_directory:\n            return Path(self.resolve_path(self.persist_directory))\n        return Path()\n\n    @check_cached_vector_store\n    def build_vector_store(self) -> FAISS:\n        \"\"\"Builds the FAISS object.\"\"\"\n        path = self.get_persist_directory()\n        path.mkdir(parents=True, exist_ok=True)\n\n        # Convert DataFrame to Data if needed using parent's method\n        self.ingest_data = self._prepare_ingest_data()\n\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                documents.append(_input)\n\n        faiss = FAISS.from_documents(documents=documents, embedding=self.embedding)\n        faiss.save_local(str(path), self.index_name)\n        return faiss\n\n    def search_documents(self) -> list[Data]:\n        \"\"\"Search for documents in the FAISS vector store.\"\"\"\n        path = self.get_persist_directory()\n        index_path = path / f\"{self.index_name}.faiss\"\n\n        if not index_path.exists():\n            vector_store = self.build_vector_store()\n        else:\n            vector_store = FAISS.load_local(\n                folder_path=str(path),\n                embeddings=self.embedding,\n                index_name=self.index_name,\n                allow_dangerous_deserialization=self.allow_dangerous_deserialization,\n            )\n\n        if not vector_store:\n            msg = \"Failed to load the FAISS index.\"\n            raise ValueError(msg)\n\n        if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():\n            docs = vector_store.similarity_search(\n                query=self.search_query,\n                k=self.number_of_results,\n            )\n            return docs_to_data(docs)\n        return []\n"
               },
               "embedding": {
                 "_input_type": "HandleInput",

src/backend/base/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "langflow-base"
-version = "0.8.2"
+version = "0.8.3"
 description = "A Python package with a built-in web application"
 requires-python = ">=3.10,<3.14"
 license = "MIT"
@@ -17,7 +17,7 @@ maintainers = [
 ]
 
 dependencies = [
-    "lfx~=0.3.2",
+    "lfx~=0.3.3",
     "fastapi>=0.135.0,<1.0.0",
     "httpx[http2]>=0.27,<1.0.0",
     "aiofile>=3.9.0,<4.0.0",

src/backend/tests/unit/api/v2/test_files.py

@@ -149,6 +149,77 @@ async def test_upload_file(files_client, files_created_api_key):
     assert "id" in response_json
 
 
+async def test_should_not_persist_in_my_files_when_upload_is_ephemeral(files_client, files_created_api_key):
+    """Ephemeral uploads save the file to storage but do NOT create a UserFile DB record.
+
+    This is the expected behavior for chat playground uploads in Desktop,
+    where the file must be servable (for chat history) but should not
+    appear in the user's 'My Files' list.
+    """
+    headers = {"x-api-key": files_created_api_key.api_key}
+
+    # Upload with ephemeral=true
+    response = await files_client.post(
+        "api/v2/files",
+        files={"file": ("playground_image.png", b"fake image content")},
+        params={"ephemeral": "true"},
+        headers=headers,
+    )
+    assert response.status_code == 201, f"Expected 201, got {response.status_code}: {response.text}"
+
+    upload_response = response.json()
+    assert "path" in upload_response
+
+    # The file must NOT appear in the user's file list
+    list_response = await files_client.get("api/v2/files", headers=headers)
+    assert list_response.status_code == 200
+    file_names = [f["name"] for f in list_response.json()]
+    assert "playground_image" not in file_names, (
+        f"Ephemeral file should not appear in My Files, but found: {file_names}"
+    )
+
+    # The file is saved in storage and the response includes a valid path
+    file_path = upload_response["path"]
+    assert file_path, "Ephemeral upload should return a non-empty path"
+    # Path format: {user_id}/{stored_file_name}
+    parts = file_path.split("/")
+    assert len(parts) == 2, f"Expected path format 'user_id/filename', got: {file_path}"
+
+
+async def test_should_return_path_with_forward_slashes_when_uploading_file(files_client, files_created_api_key):
+    """Upload response path must use forward slashes on all platforms.
+
+    On Windows, pathlib.Path serializes with backslashes, but the GET list
+    endpoint returns the raw DB string with forward slashes. If the POST
+    response uses backslashes, the frontend cannot match them with
+    selectedFiles.includes(file.path), leaving checkboxes unchecked.
+    """
+    headers = {"x-api-key": files_created_api_key.api_key}
+
+    response = await files_client.post(
+        "api/v2/files",
+        files={"file": ("test_path.txt", b"path test content")},
+        headers=headers,
+    )
+    assert response.status_code == 201
+
+    upload_path = response.json()["path"]
+    assert "\\" not in upload_path, (
+        f"Upload response path contains backslashes: '{upload_path}'. "
+        "Path must use forward slashes on all platforms for frontend compatibility."
+    )
+
+    # Verify the upload path matches what GET /files returns
+    list_response = await files_client.get("api/v2/files", headers=headers)
+    assert list_response.status_code == 200
+
+    listed_paths = [f["path"] for f in list_response.json()]
+    assert upload_path in listed_paths, (
+        f"Upload path '{upload_path}' not found in listed paths {listed_paths}. "
+        "POST and GET must return identical path strings."
+    )
+
+
 async def test_download_file(files_client, files_created_api_key):
     headers = {"x-api-key": files_created_api_key.api_key}