Fix various bugs and make improvements

src/backend/langflow/__main__.py

@@ -63,6 +63,8 @@ def set_var_for_macos_issue():
         import os
 
         os.environ["OBJC_DISABLE_INITIALIZE_FORK_SAFETY"] = "YES"
+        # https://stackoverflow.com/questions/75747888/uwsgi-segmentation-fault-with-flask-python-app-behind-nginx-after-running-for-2 # noqa
+        os.environ["no_proxy"] = "*"  # to avoid error with gunicorn
         logger.debug("Set OBJC_DISABLE_INITIALIZE_FORK_SAFETY to YES to avoid error")
 
 

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

@@ -187,7 +187,7 @@ async def stream_build(
                         "duration": time_elapsed,
                     }
 
-                yield str(StreamData(event="message", data=response))
+                    yield str(StreamData(event="message", data=response))
 
             langchain_object = await graph.build()
             # Now we  need to check the input_keys to send them to the client

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

@@ -48,10 +48,9 @@ def get_all(
 
     logger.debug("Building langchain types dict")
     try:
-        types_dict = get_all_types_dict(settings_service)
+        return get_all_types_dict(settings_service)
     except Exception as exc:
         raise HTTPException(status_code=500, detail=str(exc)) from exc
-    return types_dict
 
 
 # For backwards compatibility we will keep the old endpoint
@@ -64,7 +63,7 @@ def get_all(
     "/process/{flow_id}",
     response_model=ProcessResponse,
 )
-async def process_flow(
+async def process(
     session: Annotated[Session, Depends(get_session)],
     flow_id: str,
     inputs: Optional[dict] = None,

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

@@ -5,14 +5,13 @@ from uuid import UUID
 import orjson
 from fastapi import APIRouter, Depends, File, HTTPException, UploadFile
 from fastapi.encoders import jsonable_encoder
-from sqlmodel import Session, select
-
 from langflow.api.utils import remove_api_keys, validate_is_component
 from langflow.api.v1.schemas import FlowListCreate, FlowListRead
 from langflow.services.auth.utils import get_current_active_user
 from langflow.services.database.models.flow import Flow, FlowCreate, FlowRead, FlowUpdate
 from langflow.services.database.models.user.model import User
 from langflow.services.deps import get_session, get_settings_service
+from sqlmodel import Session, select
 
 # build router
 router = APIRouter(prefix="/flows", tags=["Flows"])
@@ -123,7 +122,7 @@ def create_flows(
     db_flows = []
     for flow in flow_list.flows:
         flow.user_id = current_user.id
-        db_flow = Flow.model_validate(flow)
+        db_flow = Flow.from_orm(flow)
         session.add(db_flow)
         db_flows.append(db_flow)
     session.commit()

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

@@ -1,11 +1,6 @@
 from uuid import UUID
 
 from fastapi import APIRouter, Depends, HTTPException
-from sqlalchemy import func
-from sqlalchemy.exc import IntegrityError
-from sqlmodel import Session, select
-from sqlmodel.sql.expression import SelectOfScalar
-
 from langflow.api.v1.schemas import UsersResponse
 from langflow.services.auth.utils import (
     get_current_active_superuser,
@@ -16,6 +11,10 @@ from langflow.services.auth.utils import (
 from langflow.services.database.models.user import User, UserCreate, UserRead, UserUpdate
 from langflow.services.database.models.user.crud import get_user_by_id, update_user
 from langflow.services.deps import get_session, get_settings_service
+from sqlalchemy import func
+from sqlalchemy.exc import IntegrityError
+from sqlmodel import Session, select
+from sqlmodel.sql.expression import SelectOfScalar
 
 router = APIRouter(tags=["Users"], prefix="/users")
 
@@ -29,7 +28,7 @@ def add_user(
     """
     Add a new user to the database.
     """
-    new_user = User.model_validate(user)
+    new_user = User.from_orm(user)
     try:
         new_user.password = get_password_hash(user.password)
         new_user.is_active = settings_service.auth_settings.NEW_USER_IS_ACTIVE

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

@@ -80,4 +80,5 @@ class ConversationalAgent(CustomComponent):
             memory=memory,
             verbose=True,
             return_intermediate_steps=True,
+            handle_parsing_errors=True,
         )

src/backend/langflow/components/chains/LLMChain.py

@@ -3,7 +3,12 @@ from typing import Callable, Optional, Union
 from langchain.chains import LLMChain
 
 from langflow import CustomComponent
-from langflow.field_typing import BaseLanguageModel, BaseMemory, BasePromptTemplate, Chain
+from langflow.field_typing import (
+    BaseLanguageModel,
+    BaseMemory,
+    BasePromptTemplate,
+    Chain,
+)
 
 
 class LLMChainComponent(CustomComponent):

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

@@ -0,0 +1,45 @@
+from typing import Optional
+from langflow import CustomComponent
+from langchain.llms.bedrock import Bedrock
+from langchain.llms.base import BaseLLM
+
+
+class AmazonBedrockComponent(CustomComponent):
+    display_name: str = "Amazon Bedrock"
+    description: str = "LLM model from Amazon Bedrock."
+
+    def build_config(self):
+        return {
+            "model_id": {
+                "display_name": "Model Id",
+                "options": [
+                    "ai21.j2-grande-instruct",
+                    "ai21.j2-jumbo-instruct",
+                    "ai21.j2-mid",
+                    "ai21.j2-mid-v1",
+                    "ai21.j2-ultra",
+                    "ai21.j2-ultra-v1",
+                    "anthropic.claude-instant-v1",
+                    "anthropic.claude-v1",
+                    "anthropic.claude-v2",
+                    "cohere.command-text-v14",
+                ],
+            },
+            "credentials_profile_name": {"display_name": "Credentials Profile Name"},
+            "streaming": {"display_name": "Streaming", "field_type": "bool"},
+            "code": {"show": False},
+        }
+
+    def build(
+        self,
+        model_id: str = "anthropic.claude-instant-v1",
+        credentials_profile_name: Optional[str] = None,
+    ) -> BaseLLM:
+        try:
+            output = Bedrock(
+                credentials_profile_name=credentials_profile_name,
+                model_id=model_id,
+            )  # type: ignore
+        except Exception as e:
+            raise ValueError("Could not connect to AmazonBedrock API.") from e
+        return output

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

@@ -0,0 +1,92 @@
+from typing import Optional
+from langflow import CustomComponent
+from langchain.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint
+from langchain.llms.base import BaseLLM
+
+
+class QianfanLLMEndpointComponent(CustomComponent):
+    display_name: str = "QianfanLLMEndpoint"
+    description: str = (
+        "Baidu Qianfan hosted open source or customized models. "
+        "Get more detail from https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint"
+    )
+
+    def build_config(self):
+        return {
+            "model": {
+                "display_name": "Model Name",
+                "options": [
+                    "ERNIE-Bot",
+                    "ERNIE-Bot-turbo",
+                    "BLOOMZ-7B",
+                    "Llama-2-7b-chat",
+                    "Llama-2-13b-chat",
+                    "Llama-2-70b-chat",
+                    "Qianfan-BLOOMZ-7B-compressed",
+                    "Qianfan-Chinese-Llama-2-7B",
+                    "ChatGLM2-6B-32K",
+                    "AquilaChat-7B",
+                ],
+                "info": "https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint",
+                "required": True,
+            },
+            "qianfan_ak": {
+                "display_name": "Qianfan Ak",
+                "required": True,
+                "password": True,
+                "info": "which you could get from  https://cloud.baidu.com/product/wenxinworkshop",
+            },
+            "qianfan_sk": {
+                "display_name": "Qianfan Sk",
+                "required": True,
+                "password": True,
+                "info": "which you could get from  https://cloud.baidu.com/product/wenxinworkshop",
+            },
+            "top_p": {
+                "display_name": "Top p",
+                "field_type": "float",
+                "info": "Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
+                "value": 0.8,
+            },
+            "temperature": {
+                "display_name": "Temperature",
+                "field_type": "float",
+                "info": "Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
+                "value": 0.95,
+            },
+            "penalty_score": {
+                "display_name": "Penalty Score",
+                "field_type": "float",
+                "info": "Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
+                "value": 1.0,
+            },
+            "endpoint": {
+                "display_name": "Endpoint",
+                "info": "Endpoint of the Qianfan LLM, required if custom model used.",
+            },
+            "code": {"show": False},
+        }
+
+    def build(
+        self,
+        model: str = "ERNIE-Bot-turbo",
+        qianfan_ak: Optional[str] = None,
+        qianfan_sk: Optional[str] = None,
+        top_p: Optional[float] = None,
+        temperature: Optional[float] = None,
+        penalty_score: Optional[float] = None,
+        endpoint: Optional[str] = None,
+    ) -> BaseLLM:
+        try:
+            output = QianfanLLMEndpoint(  # type: ignore
+                model=model,
+                qianfan_ak=qianfan_ak,
+                qianfan_sk=qianfan_sk,
+                top_p=top_p,
+                temperature=temperature,
+                penalty_score=penalty_score,
+                endpoint=endpoint,
+            )
+        except Exception as e:
+            raise ValueError("Could not connect to Baidu Qianfan API.") from e
+        return output  # type: ignore

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

@@ -1,6 +1,6 @@
 from typing import Optional
 from langflow import CustomComponent
-from langchain.llms import HuggingFaceEndpoint
+from langchain.llms.huggingface_endpoint import HuggingFaceEndpoint
 from langchain.llms.base import BaseLLM
 
 
@@ -13,7 +13,6 @@ class HuggingFaceEndpointsComponent(CustomComponent):
             "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},
@@ -27,7 +26,7 @@ class HuggingFaceEndpointsComponent(CustomComponent):
     def build(
         self,
         endpoint_url: str,
-        task="text2text-generation",
+        task: str = "text2text-generation",
         huggingfacehub_api_token: Optional[str] = None,
         model_kwargs: Optional[dict] = None,
     ) -> BaseLLM:
@@ -36,6 +35,7 @@ class HuggingFaceEndpointsComponent(CustomComponent):
                 endpoint_url=endpoint_url,
                 task=task,
                 huggingfacehub_api_token=huggingfacehub_api_token,
+                model_kwargs=model_kwargs,
             )
         except Exception as e:
             raise ValueError("Could not connect to HuggingFace Endpoints API.") from e

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

@@ -0,0 +1,48 @@
+from typing import Optional
+from langflow import CustomComponent
+from langchain.retrievers import AmazonKendraRetriever
+from langchain.schema import BaseRetriever
+
+
+class AmazonKendraRetrieverComponent(CustomComponent):
+    display_name: str = "Amazon Kendra Retriever"
+    description: str = "Retriever that uses the Amazon Kendra API."
+
+    def build_config(self):
+        return {
+            "index_id": {"display_name": "Index ID"},
+            "region_name": {"display_name": "Region Name"},
+            "credentials_profile_name": {"display_name": "Credentials Profile Name"},
+            "attribute_filter": {
+                "display_name": "Attribute Filter",
+                "field_type": "code",
+            },
+            "top_k": {"display_name": "Top K", "field_type": "int"},
+            "user_context": {
+                "display_name": "User Context",
+                "field_type": "code",
+            },
+            "code": {"show": False},
+        }
+
+    def build(
+        self,
+        index_id: str,
+        top_k: int = 3,
+        region_name: Optional[str] = None,
+        credentials_profile_name: Optional[str] = None,
+        attribute_filter: Optional[dict] = None,
+        user_context: Optional[dict] = None,
+    ) -> BaseRetriever:
+        try:
+            output = AmazonKendraRetriever(
+                index_id=index_id,
+                top_k=top_k,
+                region_name=region_name,
+                credentials_profile_name=credentials_profile_name,
+                attribute_filter=attribute_filter,
+                user_context=user_context,
+            )  # type: ignore
+        except Exception as e:
+            raise ValueError("Could not connect to AmazonKendra API.") from e
+        return output

src/backend/langflow/components/vectorstores/Chroma.py

@@ -14,7 +14,7 @@ class ChromaComponent(CustomComponent):
     A custom component for implementing a Vector Store using Chroma.
     """
 
-    display_name: str = "Chroma (Custom Component)"
+    display_name: str = "Chroma"
     description: str = "Implementation of Vector Store using Chroma"
     documentation = "https://python.langchain.com/docs/integrations/vectorstores/chroma"
     beta: bool = True

src/backend/langflow/components/vectorstores/Faiss.py

@@ -1,62 +0,0 @@
-from typing import List, Optional, Union
-from langflow import CustomComponent
-
-from langchain.vectorstores import FAISS
-from langchain.vectorstores.base import VectorStore
-from langchain.schema import BaseRetriever
-from langchain.schema import Document
-from langchain.embeddings.base import Embeddings
-
-
-class FAISSComponent(CustomComponent):
-    """
-    A custom component for implementing a Vector Store using FAISS.
-    """
-
-    display_name: str = "FAISS (Custom Component)"
-    description: str = "Implementation of Vector Store using FAISS"
-    documentation = "https://python.langchain.com/docs/integrations/vectorstores/faiss"
-    beta = True
-
-    def build_config(self):
-        return {
-            "persistence": {
-                "display_name": "Persistence",
-                "options": ["In-Memory", "LocalDirectory"],
-                "value": "In-Memory",
-            },
-            "folder_path": {
-                "display_name": "Folder Path",
-                "required": False,
-            },
-            "index_name": {"display_name": "Index Name", "value": "faiss_index"},
-            "documents": {"display_name": "Documents", "is_list": True},
-            "embeddings": {"display_name": "Embeddings"},
-            "code": {"display_name": "Code", "show": False},
-        }
-
-    def build(
-        self,
-        persistence: str,
-        index_name: str,
-        embeddings: Embeddings,
-        folder_path: Optional[str] = None,
-        documents: Optional[List[Document]] = None,
-    ) -> Union[VectorStore, BaseRetriever]:
-        if persistence == "LocalDirectory" and not folder_path:
-            raise ValueError("Folder path is required for local directory persistence")
-
-        # Load if persistence is LocalDirectory
-        if documents is None and folder_path is not None:
-            return FAISS.load_local(folder_path=folder_path, embeddings=embeddings, index_name=index_name)
-
-        if documents is None:
-            raise ValueError("Documents must be provided in the params")
-
-        db = FAISS.from_documents(documents=documents, embedding=embeddings)
-
-        # Save if persistence is LocalDirectory
-        if persistence == "LocalDirectory" and folder_path is not None:
-            db.save_local(folder_path=folder_path, index_name=index_name)
-
-        return db

src/backend/langflow/components/vectorstores/Vectara.py

@@ -1,6 +1,5 @@
 from typing import Optional, Union
 
-from langchain.embeddings.base import Embeddings
 from langchain.schema import BaseRetriever, Document
 from langchain.vectorstores import Vectara
 from langchain.vectorstores.base import VectorStore
@@ -20,7 +19,6 @@ class VectaraComponent(CustomComponent):
         "vectara_api_key": {"display_name": "Vectara API Key", "password": True},
         "code": {"show": False},
         "documents": {"display_name": "Documents"},
-        "embedding": {"display_name": "Embedding"},
     }
 
     def build(
@@ -28,21 +26,21 @@ class VectaraComponent(CustomComponent):
         vectara_customer_id: str,
         vectara_corpus_id: str,
         vectara_api_key: str,
-        embedding: Optional[Embeddings] = None,
         documents: Optional[Document] = None,
     ) -> Union[VectorStore, BaseRetriever]:
         # If documents, then we need to create a Vectara instance using .from_documents
-        if documents is not None and embedding is not None:
+        if documents is not None:
             return Vectara.from_documents(
                 documents=documents,  # type: ignore
                 vectara_customer_id=vectara_customer_id,
                 vectara_corpus_id=vectara_corpus_id,
                 vectara_api_key=vectara_api_key,
-                embedding=embedding,
+                source="langflow",
             )
 
         return Vectara(
             vectara_customer_id=vectara_customer_id,
             vectara_corpus_id=vectara_corpus_id,
             vectara_api_key=vectara_api_key,
+            source="langflow",
         )

src/backend/langflow/config.yaml

@@ -118,10 +118,10 @@ llms:
     documentation: "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/ctransformers"
   Cohere:
     documentation: "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere"
-  # Anthropic:
-  #   documentation: ""
-  # ChatAnthropic:
-  #   documentation: "https://python.langchain.com/docs/modules/model_io/models/chat/integrations/anthropic"
+  Anthropic:
+    documentation: ""
+  ChatAnthropic:
+    documentation: "https://python.langchain.com/docs/modules/model_io/models/chat/integrations/anthropic"
   HuggingFaceHub:
     documentation: "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/huggingface_hub"
   VertexAI:
@@ -266,14 +266,14 @@ retrievers:
   # ZepRetriever:
   #   documentation: "https://python.langchain.com/docs/modules/data_connection/retrievers/integrations/zep_memorystore"
 vectorstores:
-  Chroma:
-    documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/chroma"
+  # Chroma:
+  #   documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/chroma"
   Qdrant:
     documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/qdrant"
   Weaviate:
     documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/weaviate"
-  #  FAISS:
-  #    documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss"
+  FAISS:
+    documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss"
   Pinecone:
     documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/pinecone"
   SupabaseVectorStore:

src/backend/langflow/field_typing/base.py

@@ -1,4 +0,0 @@
-from typing import Union, Dict
-
-# Type alias for more complex dicts
-NestedDict = Dict[str, Union[str, Dict]]

src/backend/langflow/graph/graph/base.py

@@ -17,7 +17,7 @@ class Graph:
 
     def __init__(
         self,
-        nodes: List[Dict[str, Union[str, Dict[str, Union[str, List[str]]]]]],
+        nodes: List[Dict],
         edges: List[Dict[str, str]],
     ) -> None:
         self._vertices = nodes
@@ -27,8 +27,7 @@ class Graph:
         self.top_level_vertices = []
         for vertex in self._vertices:
             if vertex_id := vertex.get("id"):
-                if isinstance(vertex_id, str):
-                    self.top_level_vertices.append(vertex_id)
+                self.top_level_vertices.append(vertex_id)
         self._graph_data = process_flow(self.raw_graph_data)
 
         self._vertices = self._graph_data["nodes"]
@@ -59,6 +58,7 @@ class Graph:
             edges = payload["edges"]
             return cls(vertices, edges)
         except KeyError as exc:
+            logger.exception(exc)
             raise ValueError(
                 f"Invalid payload. Expected keys 'nodes' and 'edges'. Found {list(payload.keys())}"
             ) from exc

src/backend/langflow/graph/vertex/base.py

@@ -35,8 +35,9 @@ class Vertex:
         self.artifacts: Dict[str, Any] = {}
         self.task_id: Optional[str] = None
         self.is_task = is_task
-        self.parent_node_id: Optional[str] = self._data.get("parent_node_id")
         self.params = params or {}
+        self.parent_node_id: Optional[str] = self._data.get("parent_node_id")
+        self.parent_is_top_level = False
 
     @property
     def edges(self) -> List["Edge"]:

src/backend/langflow/graph/vertex/types.py

@@ -131,7 +131,6 @@ class VectorStoreVertex(Vertex):
         super().__init__(data, graph=graph, base_type="vectorstores")
 
         self.params = params or {}
-        self.params.pop("code", None)
 
     # VectorStores may contain databse connections
     # so we need to define the __reduce__ method and the __setstate__ method
@@ -201,6 +200,9 @@ class ChainVertex(Vertex):
             self.params.pop("code", None)
             # Check if the chain requires a PromptVertex
 
+            # Temporarily remove "code" from the params
+            self.params.pop("code", None)
+
             for key, value in self.params.items():
                 if isinstance(value, PromptVertex):
                     # Build the PromptVertex, passing the tools if available

src/backend/langflow/interface/agents/custom.py

@@ -21,7 +21,6 @@ from langchain.tools.sql_database.prompt import QUERY_CHECKER
 from langchain_experimental.agents.agent_toolkits.pandas.prompt import PREFIX as PANDAS_PREFIX
 from langchain_experimental.agents.agent_toolkits.pandas.prompt import SUFFIX_WITH_DF as PANDAS_SUFFIX
 from langchain_experimental.tools.python.tool import PythonAstREPLTool
-
 from langflow.interface.base import CustomAgentExecutor
 
 

src/backend/langflow/interface/initialize/vector_store.py

@@ -1,17 +1,17 @@
-import os
 from typing import Any, Callable, Dict, Type
-
-import orjson
-from langchain.schema import Document
 from langchain.vectorstores import (
-    FAISS,
-    Chroma,
-    MongoDBAtlasVectorSearch,
     Pinecone,
     Qdrant,
-    SupabaseVectorStore,
+    Chroma,
+    FAISS,
     Weaviate,
+    SupabaseVectorStore,
+    MongoDBAtlasVectorSearch,
 )
+from langchain.schema import Document
+import os
+
+import orjson
 
 
 def docs_in_params(params: dict) -> bool:
@@ -26,8 +26,8 @@ def initialize_mongodb(class_object: Type[MongoDBAtlasVectorSearch], params: dic
     MONGODB_ATLAS_CLUSTER_URI = params.pop("mongodb_atlas_cluster_uri")
     if not MONGODB_ATLAS_CLUSTER_URI:
         raise ValueError("Mongodb atlas cluster uri must be provided in the params")
-    import certifi
     from pymongo import MongoClient
+    import certifi
 
     client: MongoClient = MongoClient(MONGODB_ATLAS_CLUSTER_URI, tlsCAFile=certifi.where())
     db_name = params.pop("db_name", None)
@@ -116,7 +116,6 @@ def initialize_faiss(class_object: Type[FAISS], params: dict):
         return class_object.load_local
 
     save_local = params.get("save_local")
-    params["embedding_function"] = params.pop("embedding")
     faiss_index = class_object(**params)
     if save_local:
         faiss_index.save_local(folder_path=save_local)

src/backend/langflow/interface/tools/base.py

@@ -1,18 +1,27 @@
 from typing import Dict, List, Optional
 
-from langchain.agents.load_tools import _EXTRA_LLM_TOOLS, _EXTRA_OPTIONAL_TOOLS, _LLM_TOOLS
-from langchain_experimental.tools.python.tool import PythonInputs
+from langchain.agents.load_tools import (
+    _EXTRA_LLM_TOOLS,
+    _EXTRA_OPTIONAL_TOOLS,
+    _LLM_TOOLS,
+)
 
 from langflow.custom import customs
 from langflow.interface.base import LangChainTypeCreator
-from langflow.interface.tools.constants import ALL_TOOLS_NAMES, CUSTOM_TOOLS, FILE_TOOLS, OTHER_TOOLS
+from langflow.interface.tools.constants import (
+    ALL_TOOLS_NAMES,
+    CUSTOM_TOOLS,
+    FILE_TOOLS,
+    OTHER_TOOLS,
+)
 from langflow.interface.tools.util import get_tool_params
 from langflow.services.deps import get_settings_service
+
 from langflow.template.field.base import TemplateField
 from langflow.template.template.base import Template
 from langflow.utils import util
-from langflow.utils.logger import logger
 from langflow.utils.util import build_template_from_class
+from langflow.utils.logger import logger
 
 TOOL_INPUTS = {
     "str": TemplateField(
@@ -156,11 +165,8 @@ class ToolCreator(LangChainTypeCreator):
         template = Template(fields=fields, type_name=tool_type)
 
         tool_params = {**tool_params, **self.type_to_loader_dict[name]["params"]}
-        template_dict = template.to_dict()
-        if "args_schema" in template_dict and template_dict.get("args_schema").get("value") == PythonInputs:
-            template_dict["args_schema"]["value"] = ""
         return {
-            "template": util.format_dict(template_dict),
+            "template": util.format_dict(template.to_dict()),
             **tool_params,
             "base_classes": base_classes,
         }

src/backend/langflow/processing/base.py

@@ -2,11 +2,10 @@ from typing import TYPE_CHECKING, List, Union
 
 from langchain.agents.agent import AgentExecutor
 from langchain.callbacks.base import BaseCallbackHandler
-from loguru import logger
-
 from langflow.api.v1.callback import AsyncStreamingLLMCallbackHandler, StreamingLLMCallbackHandler
 from langflow.processing.process import fix_memory_inputs, format_actions
 from langflow.services.deps import get_plugins_service
+from loguru import logger
 
 if TYPE_CHECKING:
     from langfuse.callback import CallbackHandler  # type: ignore
@@ -28,9 +27,8 @@ def setup_callbacks(sync, trace_id, **kwargs):
 
 
 def get_langfuse_callback(trace_id):
-    from langfuse.callback import CreateTrace
-
     from langflow.services.deps import get_plugins_service
+    from langfuse.callback import CreateTrace
 
     logger.debug("Initializing langfuse callback")
     if langfuse := get_plugins_service().get("langfuse"):

src/backend/langflow/processing/process.py

@@ -7,12 +7,11 @@ from langchain.agents import AgentExecutor
 from langchain.chains.base import Chain
 from langchain.schema import AgentAction, Document
 from langchain.vectorstores.base import VectorStore
-from loguru import logger
-from pydantic import BaseModel
-
 from langflow.graph import Graph
 from langflow.interface.run import build_sorted_vertices, get_memory_key, update_memory_keys
 from langflow.services.deps import get_session_service
+from loguru import logger
+from pydantic import BaseModel
 
 
 def fix_memory_inputs(langchain_object):

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

@@ -29,7 +29,7 @@ def create_api_key(session: Session, api_key_create: ApiKeyCreate, user_id: UUID
     session.add(api_key)
     session.commit()
     session.refresh(api_key)
-    unmasked = UnmaskedApiKeyRead.model_validate(api_key)
+    unmasked = UnmaskedApiKeyRead.from_orm(api_key)
     unmasked.api_key = generated_api_key
     return unmasked
 

src/backend/langflow/services/database/service.py

@@ -5,17 +5,16 @@ from typing import TYPE_CHECKING
 import sqlalchemy as sa
 from alembic import command, util
 from alembic.config import Config
-from loguru import logger
-from sqlalchemy import inspect
-from sqlalchemy.exc import OperationalError
-from sqlmodel import Session, SQLModel, create_engine, select, text
-
 from langflow.services.base import Service
 from langflow.services.database import models  # noqa
 from langflow.services.database.models.user.crud import get_user_by_username
 from langflow.services.database.utils import Result, TableResults
 from langflow.services.deps import get_settings_service
 from langflow.services.utils import teardown_superuser
+from loguru import logger
+from sqlalchemy import inspect
+from sqlalchemy.exc import OperationalError
+from sqlmodel import Session, SQLModel, create_engine, select, text
 
 if TYPE_CHECKING:
     from sqlalchemy.engine import Engine

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

@@ -31,7 +31,11 @@ def initialize_database(fix_migration: bool = False):
     try:
         database_service.run_migrations(fix=fix_migration)
     except CommandError as exc:
-        if "Can't locate revision identified by" not in str(exc):
+        # if "overlaps with other requested revisions" or "Can't locate revision identified by"
+        # are not in the exception, we can't handle it
+        if "overlaps with other requested revisions" not in str(
+            exc
+        ) and "Can't locate revision identified by" not in str(exc):
             raise exc
         # This means there's wrong revision in the DB
         # We need to delete the alembic_version table

src/backend/langflow/services/task/backends/anyio.py

@@ -1,10 +1,7 @@
-import traceback
 from typing import Any, Callable, Optional, Tuple
-
 import anyio
-from loguru import logger
-
 from langflow.services.task.backends.base import TaskBackend
+from loguru import logger
 
 
 class AnyIOTaskResult:
@@ -13,7 +10,6 @@ class AnyIOTaskResult:
         self._status = "PENDING"
         self._result = None
         self._exception = None
-        self._traceback = None
 
     @property
     def status(self) -> str:
@@ -21,10 +17,6 @@ class AnyIOTaskResult:
             return "FAILURE" if self._exception is not None else "SUCCESS"
         return self._status
 
-    @property
-    def traceback(self) -> Optional[str]:
-        return self._traceback
-
     @property
     def result(self) -> Any:
         return self._result
@@ -37,7 +29,6 @@ class AnyIOTaskResult:
             self._result = await func(*args, **kwargs)
         except Exception as e:
             self._exception = e
-            self._traceback = traceback.format_exc()
         finally:
             self._status = "DONE"
 

src/backend/langflow/services/utils.py

@@ -1,11 +1,10 @@
-from loguru import logger
-from sqlmodel import Session, select
-
 from langflow.services.auth.utils import create_super_user, verify_password
 from langflow.services.database.utils import initialize_database
 from langflow.services.manager import service_manager
 from langflow.services.schema import ServiceType
 from langflow.services.settings.constants import DEFAULT_SUPERUSER, DEFAULT_SUPERUSER_PASSWORD
+from loguru import logger
+from sqlmodel import Session, select
 
 from .deps import get_db_service, get_session, get_settings_service
 

src/backend/langflow/template/frontend_node/base.py

@@ -42,7 +42,7 @@ class FieldFormatters(BaseModel):
 class FrontendNode(BaseModel):
     _format_template: bool = True
     template: Template
-    description: str
+    description: Optional[str] = None
     base_classes: List[str]
     name: str = ""
     display_name: Optional[str] = ""

src/backend/langflow/template/frontend_node/chains.py

@@ -87,6 +87,8 @@ class ChainFrontendNode(FrontendNode):
             field.required = True
             field.show = True
             field.advanced = False
+            field.field_type = "BaseLanguageModel"  # temporary fix
+            field.is_list = False
 
         if key == "return_source_documents":
             field.required = False

src/backend/langflow/template/frontend_node/custom_components.py

@@ -65,5 +65,5 @@ class CustomComponentFrontendNode(FrontendNode):
             )
         ],
     )
-    description: str = "Create any custom component you want!"
+    description: Optional[str] = None
     base_classes: list[str] = []

src/backend/langflow/utils/util.py

@@ -1,14 +1,14 @@
-import importlib
-import inspect
 import re
+import inspect
+import importlib
 from functools import wraps
-from typing import Any, Dict, List, Optional, Union
+from typing import List, Optional, Dict, Any, Union
 
 from docstring_parser import parse
-from langchain.schema import Document
 
 from langflow.template.frontend_node.constants import FORCE_SHOW_FIELDS
 from langflow.utils import constants
+from langchain.schema import Document
 
 
 def remove_ansi_escape_codes(text):