diff --git a/.env.example b/.env.example
index b3e592bdf..ccab7c28e 100644
--- a/.env.example
+++ b/.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=
diff --git a/pyproject.toml b/pyproject.toml
index 70c291bdc..e968f1e79 100644
--- a/pyproject.toml
+++ b/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]
diff --git a/src/backend/langflow/components/llms/HuggingFaceEndpoints.py b/src/backend/langflow/components/llms/HuggingFaceEndpoints.py
index ea2b4f20b..0d28d5b9b 100644
--- a/src/backend/langflow/components/llms/HuggingFaceEndpoints.py
+++ b/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
diff --git a/src/backend/langflow/field_typing/__init__.py b/src/backend/langflow/field_typing/__init__.py
index 927716b11..9a2161d3f 100644
--- a/src/backend/langflow/field_typing/__init__.py
+++ b/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",
+]
diff --git a/src/backend/langflow/field_typing/base.py b/src/backend/langflow/field_typing/base.py
deleted file mode 100644
index ed3219888..000000000
--- a/src/backend/langflow/field_typing/base.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from typing import Union, Dict
-
-# Type alias for more complex dicts
-NestedDict = Dict[str, Union[str, Dict]]
diff --git a/src/backend/langflow/field_typing/constants.py b/src/backend/langflow/field_typing/constants.py
new file mode 100644
index 000000000..3ce429548
--- /dev/null
+++ b/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,
+}
diff --git a/src/backend/langflow/graph/vertex/base.py b/src/backend/langflow/graph/vertex/base.py
index 8ae0bb5f8..de64b3598 100644
--- a/src/backend/langflow/graph/vertex/base.py
+++ b/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")
diff --git a/src/backend/langflow/interface/custom/constants.py b/src/backend/langflow/interface/custom/constants.py
index 58ecef637..6df72500c 100644
--- a/src/backend/langflow/interface/custom/constants.py
+++ b/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 langchain.chains import LLMChain
-from langchain.prompts import PromptTemplate
-from langchain.schema import Document
+from langflow.field_typing import (
+ Tool,
+ PromptTemplate,
+ 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))
"""
diff --git a/src/backend/langflow/interface/custom/custom_component.py b/src/backend/langflow/interface/custom/custom_component.py
index ccc0b08b2..4b889e77b 100644
--- a/src/backend/langflow/interface/custom/custom_component.py
+++ b/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
diff --git a/src/backend/langflow/interface/types.py b/src/backend/langflow/interface/types.py
index 6708c11c9..0046c84fb 100644
--- a/src/backend/langflow/interface/types.py
+++ b/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:
diff --git a/src/backend/langflow/processing/base.py b/src/backend/langflow/processing/base.py
index e5816306c..6c56a59dd 100644
--- a/src/backend/langflow/processing/base.py
+++ b/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}")
diff --git a/src/backend/langflow/template/frontend_node/custom_components.py b/src/backend/langflow/template/frontend_node/custom_components.py
index 4f36a1c9f..e239775bc 100644
--- a/src/backend/langflow/template/frontend_node/custom_components.py
+++ b/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):
diff --git a/src/backend/langflow/utils/util.py b/src/backend/langflow/utils/util.py
index b563c5973..c23c559e4 100644
--- a/src/backend/langflow/utils/util.py
+++ b/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"]):
diff --git a/src/frontend/src/contexts/tabsContext.tsx b/src/frontend/src/contexts/tabsContext.tsx
index 620894594..cba2376b5 100644
--- a/src/frontend/src/contexts/tabsContext.tsx
+++ b/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() {
diff --git a/src/frontend/src/modals/exportModal/index.tsx b/src/frontend/src/modals/exportModal/index.tsx
index 01b655b39..89bc292c9 100644
--- a/src/frontend/src/modals/exportModal/index.tsx
+++ b/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(
{
setChecked(event);
}}
@@ -52,18 +55,26 @@ const ExportModal = forwardRef(
Save with my API keys
+
+ Caution: Uncheck this box only removes API keys from fields
+ specifically designated for API keys.
+