Merge remote-tracking branch 'origin/dev' into saveComponent

.env.example

@@ -56,6 +56,14 @@ LANGFLOW_REMOVE_API_KEYS=
 # LANGFLOW_REDIS_CACHE_EXPIRE (default: 3600)
 LANGFLOW_CACHE_TYPE=
 
+# Auto login
+# If set to true then a superuser will be logged in automatically
+# and the login page will be skipped, keeping the
+# default experience of Langflow
+# Values: true, false
+# Example: LANGFLOW_AUTO_LOGIN=true
+LANGFLOW_AUTO_LOGIN=
+
 # Superuser username
 # Example: LANGFLOW_SUPERUSER=admin
 LANGFLOW_SUPERUSER=

pyproject.toml

@@ -90,6 +90,7 @@ langfuse = "^1.0.13"
 pillow = "^10.0.0"
 metal-sdk = "^2.2.0"
 markupsafe = "^2.1.3"
+numexpr = "^2.8.6"
 
 
 [tool.poetry.group.dev.dependencies]

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/field_typing/__init__.py

@@ -1,3 +1,53 @@
-from .base import NestedDict
+# LANGCHAIN_BASE_TYPES = {
+#     "Chain": Chain,
+#     "AgentExecutor": AgentExecutor,
+#     "Tool": Tool,
+#     "BaseLLM": BaseLLM,
+#     "PromptTemplate": PromptTemplate,
+#     "BaseLoader": BaseLoader,
+#     "Document": Document,
+#     "TextSplitter": TextSplitter,
+#     "VectorStore": VectorStore,
+#     "Embeddings": Embeddings,
+#     "BaseRetriever": BaseRetriever,
+#     "BaseOutputParser": BaseOutputParser,
+#     "BaseMemory": BaseMemory,
+#     "BaseChatMemory": BaseChatMemory,
+# }
+from .constants import (
+    Tool,
+    PromptTemplate,
+    Chain,
+    BaseChatMemory,
+    BaseLLM,
+    BaseLoader,
+    BaseMemory,
+    BaseOutputParser,
+    BaseRetriever,
+    VectorStore,
+    Embeddings,
+    TextSplitter,
+    Document,
+    AgentExecutor,
+    NestedDict,
+    Data,
+)
 
-__all__ = ["NestedDict"]
+__all__ = [
+    "NestedDict",
+    "Data",
+    "Tool",
+    "PromptTemplate",
+    "Chain",
+    "BaseChatMemory",
+    "BaseLLM",
+    "BaseLoader",
+    "BaseMemory",
+    "BaseOutputParser",
+    "BaseRetriever",
+    "VectorStore",
+    "Embeddings",
+    "TextSplitter",
+    "Document",
+    "AgentExecutor",
+]

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/field_typing/constants.py

@@ -0,0 +1,50 @@
+from langchain.agents.agent import AgentExecutor
+from langchain.chains.base import Chain
+from langchain.document_loaders.base import BaseLoader
+from langchain.llms.base import BaseLLM
+from langchain.memory.chat_memory import BaseChatMemory
+from langchain.prompts import PromptTemplate
+from langchain.schema import BaseOutputParser, BaseRetriever, Document
+from langchain.schema.embeddings import Embeddings
+from langchain.schema.memory import BaseMemory
+from langchain.text_splitter import TextSplitter
+from langchain.tools import Tool
+from langchain.vectorstores.base import VectorStore
+from typing import Union, Dict
+
+# Type alias for more complex dicts
+NestedDict = Dict[str, Union[str, Dict]]
+
+
+class Data:
+    pass
+
+
+LANGCHAIN_BASE_TYPES = {
+    "Chain": Chain,
+    "AgentExecutor": AgentExecutor,
+    "Tool": Tool,
+    "BaseLLM": BaseLLM,
+    "PromptTemplate": PromptTemplate,
+    "BaseLoader": BaseLoader,
+    "Document": Document,
+    "TextSplitter": TextSplitter,
+    "VectorStore": VectorStore,
+    "Embeddings": Embeddings,
+    "BaseRetriever": BaseRetriever,
+    "BaseOutputParser": BaseOutputParser,
+    "BaseMemory": BaseMemory,
+    "BaseChatMemory": BaseChatMemory,
+}
+# Langchain base types plus Python base types
+CUSTOM_COMPONENT_SUPPORTED_TYPES = {
+    **LANGCHAIN_BASE_TYPES,
+    "str": str,
+    "int": int,
+    "float": float,
+    "bool": bool,
+    "list": list,
+    "dict": dict,
+    "NestedDict": NestedDict,
+    "Data": Data,
+}

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

@@ -216,6 +216,16 @@ class Vertex:
                         }
                     elif isinstance(_value, dict):
                         params[key] = _value
+                elif value.get("type") == "int" and value.get("value") is not None:
+                    try:
+                        params[key] = int(value.get("value"))
+                    except ValueError:
+                        params[key] = value.get("value")
+                elif value.get("type") == "float" and value.get("value") is not None:
+                    try:
+                        params[key] = float(value.get("value"))
+                    except ValueError:
+                        params[key] = value.get("value")
                 else:
                     params[key] = value.get("value")
 

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

@@ -1,65 +1,33 @@
-from langchain.prompts import PromptTemplate
-from langchain.chains.base import Chain
-from langchain.document_loaders.base import BaseLoader
-from langchain.schema.embeddings import Embeddings
-from langchain.llms.base import BaseLLM
-from langchain.schema import BaseRetriever, Document
-from langchain.text_splitter import TextSplitter
-from langchain.tools import Tool
-from langchain.vectorstores.base import VectorStore
-from langchain.schema import BaseOutputParser
-from langchain.schema.memory import BaseMemory
-from langchain.memory.chat_memory import BaseChatMemory
-from langchain.agents.agent import AgentExecutor
-
-LANGCHAIN_BASE_TYPES = {
-    "Chain": Chain,
-    "AgentExecutor": AgentExecutor,
-    "Tool": Tool,
-    "BaseLLM": BaseLLM,
-    "PromptTemplate": PromptTemplate,
-    "BaseLoader": BaseLoader,
-    "Document": Document,
-    "TextSplitter": TextSplitter,
-    "VectorStore": VectorStore,
-    "Embeddings": Embeddings,
-    "BaseRetriever": BaseRetriever,
-    "BaseOutputParser": BaseOutputParser,
-    "BaseMemory": BaseMemory,
-    "BaseChatMemory": BaseChatMemory,
-}
-
-# Langchain base types plus Python base types
-CUSTOM_COMPONENT_SUPPORTED_TYPES = {
-    **LANGCHAIN_BASE_TYPES,
-    "str": str,
-    "int": int,
-    "float": float,
-    "bool": bool,
-    "list": list,
-    "dict": dict,
-}
-
-
 DEFAULT_CUSTOM_COMPONENT_CODE = """from langflow import CustomComponent
 
-from langchain.llms.base import BaseLLM
+from langflow.field_typing import (
-from langchain.chains import LLMChain
+    Tool,
-from langchain.prompts import PromptTemplate
+    PromptTemplate,
-from langchain.schema import Document
+    Chain,
+    BaseChatMemory,
+    BaseLLM,
+    BaseLoader,
+    BaseMemory,
+    BaseOutputParser,
+    BaseRetriever,
+    VectorStore,
+    Embeddings,
+    TextSplitter,
+    Document,
+    AgentExecutor,
+    NestedDict,
+    Data,
+)
 
-import requests
 
-class YourComponent(CustomComponent):
+class Component(CustomComponent):
     display_name: str = "Custom Component"
     description: str = "Create any custom component you want!"
 
     def build_config(self):
-        return { "url": { "multiline": True, "required": True } }
+        return {"param": {"display_name": "Parameter"}}
+
+    def build(self, param: Data) -> Data:
+        return param
 
-    def build(self, url: str, llm: BaseLLM, prompt: PromptTemplate) -> Document:
-        response = requests.get(url)
-        chain = LLMChain(llm=llm, prompt=prompt)
-        result = chain.run(response.text[:300])
-        return Document(page_content=str(result))
 """

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

@@ -1,7 +1,7 @@
 from typing import Any, Callable, List, Optional, Union
 from uuid import UUID
 from fastapi import HTTPException
-from langflow.interface.custom.constants import CUSTOM_COMPONENT_SUPPORTED_TYPES
+from langflow.field_typing.constants import CUSTOM_COMPONENT_SUPPORTED_TYPES
 from langflow.interface.custom.component import Component
 from langflow.interface.custom.directory_reader import DirectoryReader
 from langflow.services.getters import get_db_service
@@ -108,6 +108,9 @@ class CustomComponent(Component, extra=Extra.allow):
                         ),
                     },
                 )
+            elif not arg.get("type"):
+                # Set the type to Data
+                arg["type"] = "Data"
         return args
 
     @property

src/backend/langflow/interface/types.py

@@ -4,7 +4,7 @@ from typing import Any, List
 from langflow.api.utils import get_new_key
 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.field_typing.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
@@ -288,6 +288,24 @@ def add_base_classes(frontend_node, return_types: List[str]):
                 frontend_node.get("base_classes").append(base_class)
 
 
+def add_output_types(frontend_node, return_types: List[str]):
+    """Add output types to the frontend node"""
+    for return_type in return_types:
+        if return_type not in CUSTOM_COMPONENT_SUPPORTED_TYPES or return_type is None:
+            raise HTTPException(
+                status_code=400,
+                detail={
+                    "error": (
+                        "Invalid return type should be one of: "
+                        f"{list(CUSTOM_COMPONENT_SUPPORTED_TYPES.keys())}"
+                    ),
+                    "traceback": traceback.format_exc(),
+                },
+            )
+
+        frontend_node.get("output_types").append(return_type)
+
+
 def build_langchain_template_custom_component(custom_component: CustomComponent):
     """Build a custom component template for the langchain"""
     try:
@@ -314,6 +332,9 @@ def build_langchain_template_custom_component(custom_component: CustomComponent)
         add_base_classes(
             frontend_node, custom_component.get_function_entrypoint_return_type
         )
+        add_output_types(
+            frontend_node, custom_component.get_function_entrypoint_return_type
+        )
         logger.debug("Added base classes")
         return frontend_node
     except Exception as exc:

src/backend/langflow/processing/base.py

@@ -34,7 +34,9 @@ def get_langfuse_callback(trace_id):
     if langfuse := LangfuseInstance.get():
         logger.debug("Langfuse credentials found")
         try:
-            trace = langfuse.trace(CreateTrace(id=trace_id))
+            trace = langfuse.trace(
+                CreateTrace(name="langflow-" + trace_id, id=trace_id)
+            )
             return trace.getNewHandler()
         except Exception as exc:
             logger.error(f"Error initializing langfuse callback: {exc}")

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

@@ -2,6 +2,7 @@ from langflow.template.field.base import TemplateField
 from langflow.template.frontend_node.base import FrontendNode
 from langflow.template.template.base import Template
 from langflow.interface.custom.constants import DEFAULT_CUSTOM_COMPONENT_CODE
+from typing import Optional
 
 
 class CustomComponentFrontendNode(FrontendNode):
@@ -24,7 +25,7 @@ class CustomComponentFrontendNode(FrontendNode):
             )
         ],
     )
-    description: str = "Create any custom component you want!"
+    description: Optional[str] = None
     base_classes: list[str] = []
 
     def to_dict(self):

src/backend/langflow/utils/util.py

@@ -191,7 +191,9 @@ def get_base_classes(cls):
     """Get the base classes of a class.
     These are used to determine the output of the nodes.
     """
-    if bases := cls.__bases__:
+
+    if hasattr(cls, "__bases__") and cls.__bases__:
+        bases = cls.__bases__
         result = []
         for base in bases:
             if any(type in base.__module__ for type in ["pydantic", "abc"]):

src/frontend/src/contexts/tabsContext.tsx

@@ -260,9 +260,6 @@ export function TabsProvider({ children }: { children: ReactNode }) {
 
     // simulate a click on the link element to trigger the download
     link.click();
-    setNoticeData({
-      title: "Warning: Critical data, JSON file may include API keys.",
-    });
   }
 
   function downloadFlows() {

src/frontend/src/modals/exportModal/index.tsx

@@ -4,6 +4,7 @@ import IconComponent from "../../components/genericIconComponent";
 import { Button } from "../../components/ui/button";
 import { Checkbox } from "../../components/ui/checkbox";
 import { EXPORT_DIALOG_SUBTITLE } from "../../constants/constants";
+import { alertContext } from "../../contexts/alertContext";
 import { TabsContext } from "../../contexts/tabsContext";
 import { removeApiKeys } from "../../utils/reactflowUtils";
 import BaseModal from "../baseModal";
@@ -11,7 +12,8 @@ import BaseModal from "../baseModal";
 const ExportModal = forwardRef(
   (props: { children: ReactNode }, ref): JSX.Element => {
     const { flows, tabId, downloadFlow } = useContext(TabsContext);
-    const [checked, setChecked] = useState(false);
+    const { setNoticeData } = useContext(alertContext);
+    const [checked, setChecked] = useState(true);
     const flow = flows.find((f) => f.id === tabId);
     useEffect(() => {
       setName(flow!.name);
@@ -44,6 +46,7 @@ const ExportModal = forwardRef(
           <div className="mt-3 flex items-center space-x-2">
             <Checkbox
               id="terms"
+              checked={checked}
               onCheckedChange={(event: boolean) => {
                 setChecked(event);
               }}
@@ -52,18 +55,26 @@ const ExportModal = forwardRef(
               Save with my API keys
             </label>
           </div>
+          <span className="text-xs text-destructive">
+            Caution: Uncheck this box only removes API keys from fields
+            specifically designated for API keys.
+          </span>
         </BaseModal.Content>
 
         <BaseModal.Footer>
           <Button
             onClick={() => {
-              if (checked)
+              if (checked) {
                 downloadFlow(
                   flows.find((flow) => flow.id === tabId)!,
                   name!,
                   description
                 );
-              else
+                setNoticeData({
+                  title:
+                    "Warning: Critical data, JSON file may include API keys.",
+                });
+              } else
                 downloadFlow(
                   removeApiKeys(flows.find((flow) => flow.id === tabId)!),
                   name!,