merge dev

src/backend/langflow/alembic/versions/67cc006d50bf_add_profile_image_column.py

@@ -0,0 +1,49 @@
+"""Add profile-image column
+
+Revision ID: 67cc006d50bf
+Revises: 260dbcc8b680
+Create Date: 2023-09-08 07:36:13.387318
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+import sqlmodel
+from sqlalchemy.engine.reflection import Inspector
+
+# revision identifiers, used by Alembic.
+revision: str = "67cc006d50bf"
+down_revision: Union[str, None] = "260dbcc8b680"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    conn = op.get_bind()
+    inspector = Inspector.from_engine(conn)
+    if "user" in inspector.get_table_names() and "profile_image" not in [
+        column["name"] for column in inspector.get_columns("user")
+    ]:
+        with op.batch_alter_table("user", schema=None) as batch_op:
+            batch_op.add_column(
+                sa.Column(
+                    "profile_image", sqlmodel.sql.sqltypes.AutoString(), nullable=True
+                )
+            )
+
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    conn = op.get_bind()
+    inspector = Inspector.from_engine(conn)
+    if "user" in inspector.get_table_names() and "profile_image" in [
+        column["name"] for column in inspector.get_columns("user")
+    ]:
+        with op.batch_alter_table("user", schema=None) as batch_op:
+            batch_op.drop_column("profile_image")
+
+    # ### end Alembic commands ###

src/backend/langflow/api/v1/chat.py

@@ -13,7 +13,6 @@ from langflow.api.v1.schemas import BuildStatus, BuiltResponse, InitResponse, St
 
 from langflow.graph.graph.base import Graph
 from langflow.services.auth.utils import get_current_active_user, get_current_user
-from langflow.services.utils import get_session
 from loguru import logger
 from langflow.services.utils import get_chat_manager, get_session
 from cachetools import LRUCache

src/backend/langflow/api/v1/users.py

@@ -18,15 +18,17 @@ from langflow.services.auth.utils import (
     get_current_active_superuser,
     get_current_active_user,
     get_password_hash,
+    verify_password,
 )
 from langflow.services.database.models.user.crud import (
+    get_user_by_id,
     update_user,
 )
 
-router = APIRouter(tags=["Users"])
+router = APIRouter(tags=["Users"], prefix="/users")
 
 
-@router.post("/user", response_model=UserRead, status_code=201)
+@router.post("/", response_model=UserRead, status_code=201)
 def add_user(
     user: UserCreate,
     session: Session = Depends(get_session),
@@ -50,7 +52,7 @@ def add_user(
     return new_user
 
 
-@router.get("/user", response_model=UserRead)
+@router.get("/whoami", response_model=UserRead)
 def read_current_user(
     current_user: User = Depends(get_current_active_user),
 ) -> User:
@@ -60,7 +62,7 @@ def read_current_user(
     return current_user
 
 
-@router.get("/users", response_model=UsersResponse)
+@router.get("/", response_model=UsersResponse)
 def read_all_users(
     skip: int = 0,
     limit: int = 10,
@@ -82,20 +84,61 @@ def read_all_users(
     )
 
 
-@router.patch("/user/{user_id}", response_model=UserRead)
+@router.patch("/{user_id}", response_model=UserRead)
 def patch_user(
     user_id: UUID,
-    user: UserUpdate,
-    _: Session = Depends(get_current_active_user),
+    user_update: UserUpdate,
+    user: User = Depends(get_current_active_user),
     session: Session = Depends(get_session),
 ) -> User:
     """
     Update an existing user's data.
     """
-    return update_user(user_id, user, session)
+    if not user.is_superuser and user.id != user_id:
+        raise HTTPException(
+            status_code=403, detail="You don't have the permission to update this user"
+        )
+    if user_update.password:
+        raise HTTPException(
+            status_code=400, detail="You can't change your password here"
+        )
+
+    if user_db := get_user_by_id(session, user_id):
+        return update_user(user_db, user_update, session)
+    else:
+        raise HTTPException(status_code=404, detail="User not found")
 
 
-@router.delete("/user/{user_id}")
+@router.patch("/{user_id}/reset-password", response_model=UserRead)
+def reset_password(
+    user_id: UUID,
+    user_update: UserUpdate,
+    user: User = Depends(get_current_active_user),
+    session: Session = Depends(get_session),
+) -> User:
+    """
+    Reset a user's password.
+    """
+    if user_id != user.id:
+        raise HTTPException(
+            status_code=400, detail="You can't change another user's password"
+        )
+
+    if not user:
+        raise HTTPException(status_code=404, detail="User not found")
+    if verify_password(user_update.password, user.password):
+        raise HTTPException(
+            status_code=400, detail="You can't use your current password"
+        )
+    new_password = get_password_hash(user_update.password)
+    user.password = new_password
+    session.commit()
+    session.refresh(user)
+
+    return user
+
+
+@router.delete("/{user_id}", response_model=dict)
 def delete_user(
     user_id: UUID,
     current_user: User = Depends(get_current_active_superuser),

src/backend/langflow/components/agents/OpenAIConversationalAgent.py

@@ -42,8 +42,8 @@ class ConversationalAgent(CustomComponent):
         self,
         model_name: str,
         openai_api_key: str,
-        openai_api_base: str,
         tools: Tool,
+        openai_api_base: Optional[str] = None,
         memory: Optional[BaseMemory] = None,
         system_message: Optional[SystemMessagePromptTemplate] = None,
         max_token_limit: int = 2000,

src/backend/langflow/components/llms/HuggingFaceEndpoints.py

@@ -0,0 +1,42 @@
+from typing import Optional
+from langflow import CustomComponent
+from langchain.llms import HuggingFaceEndpoint
+from langchain.llms.base import BaseLLM
+
+
+class HuggingFaceEndpointsComponent(CustomComponent):
+    display_name: str = "Hugging Face Inference API"
+    description: str = "LLM model from Hugging Face Inference API."
+
+    def build_config(self):
+        return {
+            "endpoint_url": {"display_name": "Endpoint URL", "password": True},
+            "task": {
+                "display_name": "Task",
+                "type": "select",
+                "options": ["text2text-generation", "text-generation", "summarization"],
+            },
+            "huggingfacehub_api_token": {"display_name": "API token", "password": True},
+            "model_kwargs": {
+                "display_name": "Model Keyword Arguments",
+                "field_type": "code",
+            },
+            "code": {"show": False},
+        }
+
+    def build(
+        self,
+        endpoint_url: str,
+        task="text2text-generation",
+        huggingfacehub_api_token: Optional[str] = None,
+        model_kwargs: Optional[dict] = None,
+    ) -> BaseLLM:
+        try:
+            output = HuggingFaceEndpoint(
+                endpoint_url=endpoint_url,
+                task=task,
+                huggingfacehub_api_token=huggingfacehub_api_token,
+            )
+        except Exception as e:
+            raise ValueError("Could not connect to HuggingFace Endpoints API.") from e
+        return output

src/backend/langflow/components/retrievers/MetalRetriever.py

@@ -0,0 +1,28 @@
+from typing import Optional
+from langflow import CustomComponent
+from langchain.retrievers import MetalRetriever
+from langchain.schema import BaseRetriever
+from metal_sdk.metal import Metal  # type: ignore
+
+
+class MetalRetrieverComponent(CustomComponent):
+    display_name: str = "Metal Retriever"
+    description: str = "Retriever that uses the Metal API."
+
+    def build_config(self):
+        return {
+            "api_key": {"display_name": "API Key", "password": True},
+            "client_id": {"display_name": "Client ID", "password": True},
+            "index_id": {"display_name": "Index ID"},
+            "params": {"display_name": "Parameters", "field_type": "code"},
+            "code": {"show": False},
+        }
+
+    def build(
+        self, api_key: str, client_id: str, index_id: str, params: Optional[dict] = None
+    ) -> BaseRetriever:
+        try:
+            metal = Metal(api_key=api_key, client_id=client_id, index_id=index_id)
+        except Exception as e:
+            raise ValueError("Could not connect to Metal API.") from e
+        return MetalRetriever(client=metal, params=params or {})

src/backend/langflow/components/textsplitters/LanguageRecursiveTextSplitter.py

@@ -0,0 +1,80 @@
+from typing import Optional
+from langflow import CustomComponent
+from langchain.text_splitter import Language
+from langchain.schema import Document
+
+
+class LanguageRecursiveTextSplitterComponent(CustomComponent):
+    display_name: str = "Language Recursive Text Splitter"
+    description: str = "Split text into chunks of a specified length based on language."
+    documentation: str = "https://docs.langflow.org/components/text-splitters#languagerecursivetextsplitter"
+
+    def build_config(self):
+        options = [x.value for x in Language]
+        return {
+            "documents": {
+                "display_name": "Documents",
+                "info": "The documents to split.",
+            },
+            "separator_type": {
+                "display_name": "Separator Type",
+                "info": "The type of separator to use.",
+                "field_type": "str",
+                "options": options,
+                "value": "Python",
+            },
+            "separators": {
+                "display_name": "Separators",
+                "info": "The characters to split on.",
+                "is_list": True,
+            },
+            "chunk_size": {
+                "display_name": "Chunk Size",
+                "info": "The maximum length of each chunk.",
+                "field_type": "int",
+                "value": 1000,
+            },
+            "chunk_overlap": {
+                "display_name": "Chunk Overlap",
+                "info": "The amount of overlap between chunks.",
+                "field_type": "int",
+                "value": 200,
+            },
+            "code": {"show": False},
+        }
+
+    def build(
+        self,
+        documents: list[Document],
+        chunk_size: Optional[int] = 1000,
+        chunk_overlap: Optional[int] = 200,
+        separator_type: Optional[str] = "Python",
+    ) -> list[Document]:
+        """
+        Split text into chunks of a specified length.
+
+        Args:
+            separators (list[str]): The characters to split on.
+            chunk_size (int): The maximum length of each chunk.
+            chunk_overlap (int): The amount of overlap between chunks.
+            length_function (function): The function to use to calculate the length of the text.
+
+        Returns:
+            list[str]: The chunks of text.
+        """
+        from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+        # Make sure chunk_size and chunk_overlap are ints
+        if isinstance(chunk_size, str):
+            chunk_size = int(chunk_size)
+        if isinstance(chunk_overlap, str):
+            chunk_overlap = int(chunk_overlap)
+
+        splitter = RecursiveCharacterTextSplitter.from_language(
+            language=Language(separator_type),
+            chunk_size=chunk_size,
+            chunk_overlap=chunk_overlap,
+        )
+
+        docs = splitter.split_documents(documents)
+        return docs

src/backend/langflow/components/textsplitters/RecursiveCharacterTextSplitter.py

@@ -0,0 +1,79 @@
+from typing import Optional
+from langflow import CustomComponent
+from langchain.schema import Document
+from langflow.utils.util import build_loader_repr_from_documents
+
+
+class RecursiveCharacterTextSplitterComponent(CustomComponent):
+    display_name: str = "Recursive Character Text Splitter"
+    description: str = "Split text into chunks of a specified length."
+    documentation: str = "https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter"
+
+    def build_config(self):
+        return {
+            "documents": {
+                "display_name": "Documents",
+                "info": "The documents to split.",
+            },
+            "separators": {
+                "display_name": "Separators",
+                "info": 'The characters to split on.\nIf left empty defaults to ["\\n\\n", "\\n", " ", ""].',
+                "is_list": True,
+            },
+            "chunk_size": {
+                "display_name": "Chunk Size",
+                "info": "The maximum length of each chunk.",
+                "field_type": "int",
+                "value": 1000,
+            },
+            "chunk_overlap": {
+                "display_name": "Chunk Overlap",
+                "info": "The amount of overlap between chunks.",
+                "field_type": "int",
+                "value": 200,
+            },
+            "code": {"show": False},
+        }
+
+    def build(
+        self,
+        documents: list[Document],
+        separators: Optional[list[str]] = None,
+        chunk_size: Optional[int] = 1000,
+        chunk_overlap: Optional[int] = 200,
+    ) -> list[Document]:
+        """
+        Split text into chunks of a specified length.
+
+        Args:
+            separators (list[str]): The characters to split on.
+            chunk_size (int): The maximum length of each chunk.
+            chunk_overlap (int): The amount of overlap between chunks.
+            length_function (function): The function to use to calculate the length of the text.
+
+        Returns:
+            list[str]: The chunks of text.
+        """
+        from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+        if separators == "":
+            separators = None
+        elif separators:
+            # check if the separators list has escaped characters
+            # if there are escaped characters, unescape them
+            separators = [x.encode().decode("unicode-escape") for x in separators]
+
+        # Make sure chunk_size and chunk_overlap are ints
+        if isinstance(chunk_size, str):
+            chunk_size = int(chunk_size)
+        if isinstance(chunk_overlap, str):
+            chunk_overlap = int(chunk_overlap)
+        splitter = RecursiveCharacterTextSplitter(
+            separators=separators,
+            chunk_size=chunk_size,
+            chunk_overlap=chunk_overlap,
+        )
+
+        docs = splitter.split_documents(documents)
+        self.repr_value = build_loader_repr_from_documents(docs)
+        return docs

src/backend/langflow/config.yaml

@@ -171,8 +171,6 @@ prompts:
 textsplitters:
   CharacterTextSplitter:
     documentation: "https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter"
-  RecursiveCharacterTextSplitter:
-    documentation: "https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/recursive_text_splitter"
 toolkits:
   OpenAPIToolkit:
     documentation: ""

src/backend/langflow/interface/types.py

@@ -5,6 +5,7 @@ from langflow.api.utils import merge_nested_dicts_with_renaming
 from langflow.interface.agents.base import agent_creator
 from langflow.interface.chains.base import chain_creator
 from langflow.interface.custom.constants import CUSTOM_COMPONENT_SUPPORTED_TYPES
+from langflow.interface.custom.utils import extract_inner_type
 from langflow.interface.document_loaders.base import documentloader_creator
 from langflow.interface.embeddings.base import embedding_creator
 from langflow.interface.importing.utils import get_function_custom
@@ -84,6 +85,8 @@ def build_langchain_types_dict():  # sourcery skip: dict-assign-update-to-union
 
 
 def process_type(field_type: str):
+    if field_type.startswith("list") or field_type.startswith("List"):
+        return extract_inner_type(field_type)
     return "prompt" if field_type == "Prompt" else field_type
 
 
@@ -100,6 +103,7 @@ def add_new_custom_field(
     # if it is, update the value
     display_name = field_config.pop("display_name", field_name)
     field_type = field_config.pop("field_type", field_type)
+    field_contains_list = "list" in field_type.lower()
     field_type = process_type(field_type)
     field_value = field_config.pop("value", field_value)
     field_advanced = field_config.pop("advanced", False)
@@ -110,7 +114,9 @@ def add_new_custom_field(
     # If options is a list, then it's a dropdown
     # If options is None, then it's a list of strings
     is_list = isinstance(field_config.get("options"), list)
-    field_config["is_list"] = is_list or field_config.get("is_list", False)
+    field_config["is_list"] = (
+        is_list or field_config.get("is_list", False) or field_contains_list
+    )
 
     if "name" in field_config:
         warnings.warn(
@@ -172,7 +178,7 @@ def extract_type_from_optional(field_type):
     Returns:
     str: The extracted type, or an empty string if no type was found.
     """
-    match = re.search(r"\[(.*?)\]", field_type)
+    match = re.search(r"\[(.*?)\]$", field_type)
     return match[1] if match else None
 
 

src/backend/langflow/main.py

@@ -11,6 +11,7 @@ from langflow.api import router
 from langflow.interface.utils import setup_llm_caching
 from langflow.services.database.utils import initialize_database
 from langflow.services.manager import initialize_services, teardown_services
+from langflow.services.plugins.langfuse import LangfuseInstance
 from langflow.utils.logger import configure
 
 
@@ -41,6 +42,8 @@ def create_app():
     app.on_event("startup")(initialize_database)
     app.on_event("startup")(setup_llm_caching)
     app.on_event("shutdown")(teardown_services)
+    app.on_event("startup")(LangfuseInstance.update)
+    app.on_event("shutdown")(LangfuseInstance.teardown)
     return app
 
 

src/backend/langflow/processing/base.py

@@ -1,4 +1,4 @@
-from typing import Union
+from typing import List, Union, TYPE_CHECKING
 from langflow.api.v1.callback import (
     AsyncStreamingLLMCallbackHandler,
     StreamingLLMCallbackHandler,
@@ -6,6 +6,52 @@ from langflow.api.v1.callback import (
 from langflow.processing.process import fix_memory_inputs, format_actions
 from loguru import logger
 from langchain.agents.agent import AgentExecutor
+from langchain.callbacks.base import BaseCallbackHandler
+
+if TYPE_CHECKING:
+    from langfuse.callback import CallbackHandler  # type: ignore
+
+
+def setup_callbacks(sync, trace_id, **kwargs):
+    """Setup callbacks for langchain object"""
+    callbacks = []
+    if sync:
+        callbacks.append(StreamingLLMCallbackHandler(**kwargs))
+    else:
+        callbacks.append(AsyncStreamingLLMCallbackHandler(**kwargs))
+
+    if langfuse_callback := get_langfuse_callback(trace_id=trace_id):
+        logger.debug("Langfuse callback loaded")
+        callbacks.append(langfuse_callback)
+    return callbacks
+
+
+def get_langfuse_callback(trace_id):
+    from langflow.services.plugins.langfuse import LangfuseInstance
+    from langfuse.callback import CreateTrace
+
+    logger.debug("Initializing langfuse callback")
+    if langfuse := LangfuseInstance.get():
+        logger.debug("Langfuse credentials found")
+        try:
+            trace = langfuse.trace(CreateTrace(id=trace_id))
+            return trace.getNewHandler()
+        except Exception as exc:
+            logger.error(f"Error initializing langfuse callback: {exc}")
+
+    return None
+
+
+def flush_langfuse_callback_if_present(
+    callbacks: List[Union[BaseCallbackHandler, "CallbackHandler"]]
+):
+    """
+    If langfuse callback is present, run callback.langfuse.flush()
+    """
+    for callback in callbacks:
+        if hasattr(callback, "langfuse"):
+            callback.langfuse.flush()
+            break
 
 
 async def get_result_and_steps(langchain_object, inputs: Union[dict, str], **kwargs):
@@ -27,13 +73,18 @@ async def get_result_and_steps(langchain_object, inputs: Union[dict, str], **kwa
             logger.error(f"Error fixing memory inputs: {exc}")
 
         try:
-            async_callbacks = [AsyncStreamingLLMCallbackHandler(**kwargs)]
-            output = await langchain_object.acall(inputs, callbacks=async_callbacks)
+            trace_id = kwargs.pop("session_id", None)
+            callbacks = setup_callbacks(sync=False, trace_id=trace_id, **kwargs)
+            output = await langchain_object.acall(inputs, callbacks=callbacks)
         except Exception as exc:
             # make the error message more informative
             logger.debug(f"Error: {str(exc)}")
-            sync_callbacks = [StreamingLLMCallbackHandler(**kwargs)]
-            output = langchain_object(inputs, callbacks=sync_callbacks)
+            trace_id = kwargs.pop("session_id", None)
+            callbacks = setup_callbacks(sync=True, trace_id=trace_id, **kwargs)
+            output = langchain_object(inputs, callbacks=callbacks)
+
+        # if langfuse callback is present, run callback.langfuse.flush()
+        flush_langfuse_callback_if_present(callbacks)
 
         intermediate_steps = (
             output.get("intermediate_steps", []) if isinstance(output, dict) else []

src/backend/langflow/processing/process.py

@@ -11,6 +11,7 @@ from langflow.graph import Graph
 from langchain.chains.base import Chain
 from langchain.vectorstores.base import VectorStore
 from typing import Any, Dict, List, Optional, Tuple, Union
+from langchain.schema import Document
 
 
 def fix_memory_inputs(langchain_object):
@@ -142,6 +143,8 @@ def generate_result(langchain_object: Union[Chain, VectorStore], inputs: dict):
         logger.debug("Generated result and thought")
     elif isinstance(langchain_object, VectorStore):
         result = langchain_object.search(**inputs)
+    elif isinstance(langchain_object, Document):
+        result = langchain_object.dict()
     else:
         raise ValueError(
             f"Unknown langchain_object type: {type(langchain_object).__name__}"

src/backend/langflow/services/chat/manager.py

@@ -1,4 +1,5 @@
 from collections import defaultdict
+import uuid
 from fastapi import WebSocket, status
 from langflow.api.v1.schemas import ChatMessage, ChatResponse, FileResponse
 from langflow.services.base import Service
@@ -49,6 +50,7 @@ class ChatManager(Service):
 
     def __init__(self):
         self.active_connections: Dict[str, WebSocket] = {}
+        self.connection_ids: Dict[str, str] = {}
         self.chat_history = ChatHistory()
         self.cache_manager = service_manager.get(ServiceType.CACHE_MANAGER)
         self.cache_manager.attach(self.update)
@@ -93,9 +95,13 @@ class ChatManager(Service):
 
     async def connect(self, client_id: str, websocket: WebSocket):
         self.active_connections[client_id] = websocket
+        # This is to avoid having multiple clients with the same id
+        #! Temporary solution
+        self.connection_ids[client_id] = f"{client_id}-{uuid.uuid4()}"
 
     def disconnect(self, client_id: str):
         self.active_connections.pop(client_id, None)
+        self.connection_ids.pop(client_id, None)
 
     async def send_message(self, client_id: str, message: str):
         websocket = self.active_connections[client_id]
@@ -137,6 +143,7 @@ class ChatManager(Service):
                 langchain_object=langchain_object,
                 chat_inputs=chat_inputs,
                 websocket=self.active_connections[client_id],
+                session_id=self.connection_ids[client_id],
             )
         except Exception as e:
             # Log stack trace

src/backend/langflow/services/chat/utils.py

@@ -9,6 +9,7 @@ async def process_graph(
     langchain_object,
     chat_inputs: ChatMessage,
     websocket: WebSocket,
+    session_id: str,
 ):
     langchain_object = try_setting_streaming_options(langchain_object, websocket)
     logger.debug("Loaded langchain object")
@@ -27,7 +28,10 @@ async def process_graph(
 
         logger.debug("Generating result and thought")
         result, intermediate_steps = await get_result_and_steps(
-            langchain_object, chat_inputs.message, websocket=websocket
+            langchain_object,
+            chat_inputs.message,
+            websocket=websocket,
+            session_id=session_id,
         )
         logger.debug("Generated result and intermediate_steps")
         return result, intermediate_steps

src/backend/langflow/services/database/models/user/crud.py

@@ -1,12 +1,12 @@
 from datetime import datetime, timezone
 from typing import Union
 from uuid import UUID
-from fastapi import Depends, HTTPException
+from fastapi import Depends, HTTPException, status
 from langflow.services.database.models.user.user import User, UserUpdate
 from langflow.services.utils import get_session
 from sqlalchemy.exc import IntegrityError
 from sqlmodel import Session
-
+from typing import Optional
 
 from sqlalchemy.orm.attributes import flag_modified
 
@@ -20,20 +20,26 @@ def get_user_by_id(db: Session, id: UUID) -> Union[User, None]:
 
 
 def update_user(
-    user_id: UUID, user: UserUpdate, db: Session = Depends(get_session)
+    user_db: Optional[User], user: UserUpdate, db: Session = Depends(get_session)
 ) -> User:
-    user_db = get_user_by_id(db, user_id)
     if not user_db:
         raise HTTPException(status_code=404, detail="User not found")
 
-    user_db_by_username = get_user_by_username(db, user.username)  # type: ignore
-    if user_db_by_username and user_db_by_username.id != user_id:
-        raise HTTPException(status_code=409, detail="Username already exists")
+    # user_db_by_username = get_user_by_username(db, user.username)  # type: ignore
+    # if user_db_by_username and user_db_by_username.id != user_id:
+    #     raise HTTPException(status_code=409, detail="Username already exists")
 
     user_data = user.dict(exclude_unset=True)
+    changed = False
     for attr, value in user_data.items():
         if hasattr(user_db, attr) and value is not None:
             setattr(user_db, attr, value)
+            changed = True
+
+    if not changed:
+        raise HTTPException(
+            status_code=status.HTTP_304_NOT_MODIFIED, detail="Nothing to update"
+        )
 
     user_db.updated_at = datetime.now(timezone.utc)
     flag_modified(user_db, "updated_at")
@@ -49,5 +55,5 @@ def update_user(
 
 def update_user_last_login_at(user_id: UUID, db: Session = Depends(get_session)):
     user_data = UserUpdate(last_login_at=datetime.now(timezone.utc))  # type: ignore
-
-    return update_user(user_id, user_data, db)
+    user = get_user_by_id(db, user_id)
+    return update_user(user, user_data, db)

src/backend/langflow/services/database/models/user/user.py

@@ -15,6 +15,7 @@ class User(SQLModelSerializable, table=True):
     id: UUID = Field(default_factory=uuid4, primary_key=True, unique=True)
     username: str = Field(index=True, unique=True)
     password: str = Field()
+    profile_image: Optional[str] = Field(default=None)
     is_active: bool = Field(default=False)
     is_superuser: bool = Field(default=False)
     create_at: datetime = Field(default_factory=datetime.utcnow)
@@ -32,6 +33,7 @@ class UserCreate(SQLModel):
 class UserRead(SQLModel):
     id: UUID = Field(default_factory=uuid4)
     username: str = Field()
+    profile_image: Optional[str] = Field()
     is_active: bool = Field()
     is_superuser: bool = Field()
     create_at: datetime = Field()
@@ -40,7 +42,8 @@ class UserRead(SQLModel):
 
 
 class UserUpdate(SQLModel):
-    username: Optional[str] = Field()
+    profile_image: Optional[str] = Field()
+    password: Optional[str] = Field()
     is_active: Optional[bool] = Field()
     is_superuser: Optional[bool] = Field()
     last_login_at: Optional[datetime] = Field()

src/backend/langflow/services/plugins/langfuse.py

@@ -0,0 +1,49 @@
+from langflow.services.utils import get_settings_manager
+from langflow.utils.logger import logger
+
+### Temporary implementation
+# This will be replaced by a plugin system once merged into 0.5.0
+
+
+class LangfuseInstance:
+    _instance = None
+
+    @classmethod
+    def get(cls):
+        logger.debug("Getting Langfuse instance")
+        if cls._instance is None:
+            cls.create()
+        return cls._instance
+
+    @classmethod
+    def create(cls):
+        logger.debug("Creating Langfuse instance")
+        from langfuse import Langfuse  # type: ignore
+
+        settings_manager = get_settings_manager()
+
+        if (
+            settings_manager.settings.LANGFUSE_PUBLIC_KEY
+            and settings_manager.settings.LANGFUSE_SECRET_KEY
+        ):
+            logger.debug("Langfuse credentials found")
+            cls._instance = Langfuse(
+                public_key=settings_manager.settings.LANGFUSE_PUBLIC_KEY,
+                secret_key=settings_manager.settings.LANGFUSE_SECRET_KEY,
+            )
+        else:
+            logger.debug("No Langfuse credentials found")
+            cls._instance = None
+
+    @classmethod
+    def update(cls):
+        logger.debug("Updating Langfuse instance")
+        cls._instance = None
+        cls.create()
+
+    @classmethod
+    def teardown(cls):
+        logger.debug("Tearing down Langfuse instance")
+        if cls._instance is not None:
+            cls._instance.flush()
+        cls._instance = None

src/backend/langflow/services/settings/base.py

@@ -41,6 +41,10 @@ class Settings(BaseSettings):
     REMOVE_API_KEYS: bool = False
     COMPONENTS_PATH: List[str] = []
 
+    LANGFUSE_SECRET_KEY: Optional[str] = None
+    LANGFUSE_PUBLIC_KEY: Optional[str] = None
+    LANGFUSE_HOST: Optional[str] = None
+
     @validator("CONFIG_DIR", pre=True, allow_reuse=True)
     def set_langflow_dir(cls, value):
         if not value:

src/backend/langflow/utils/util.py

@@ -2,12 +2,13 @@ import re
 import inspect
 import importlib
 from functools import wraps
-from typing import Optional, Dict, Any, Union
+from typing import List, Optional, Dict, Any, Union
 
 from docstring_parser import parse
 
 from langflow.template.frontend_node.constants import FORCE_SHOW_FIELDS
 from langflow.utils import constants
+from langchain.schema import Document
 
 
 def build_template_from_function(
@@ -456,3 +457,12 @@ def add_options_to_field(
         value["options"] = options_map[class_name]
         value["list"] = True
         value["value"] = options_map[class_name][0]
+
+
+def build_loader_repr_from_documents(documents: List[Document]) -> str:
+    if documents:
+        avg_length = sum(len(doc.page_content) for doc in documents) / len(documents)
+        return f"""{len(documents)} documents
+        \nAvg. Document Length (characters): {int(avg_length)}
+        Documents: {documents[:3]}..."""
+    return "0 documents"