Add io module for easier access to Inputs and Outputs

src/backend/base/langflow/base/vectorstores/model.py

@@ -3,11 +3,10 @@ from typing import List, Union
 from langchain_core.documents import Document
 
 from langflow.custom import Component
-from langflow.field_typing import Text
+from langflow.field_typing import BaseRetriever, Text, VectorStore
 from langflow.helpers.data import docs_to_data
+from langflow.io import Output
 from langflow.schema import Data
-from langflow.template import Output
-from langflow.field_typing import BaseRetriever, VectorStore
 
 
 class LCVectorStoreComponent(Component):

src/backend/base/langflow/components/data/APIRequest.py

@@ -7,10 +7,9 @@ from loguru import logger
 
 from langflow.base.curl.parse import parse_context
 from langflow.custom import Component
-from langflow.inputs import DropdownInput, IntInput, NestedDictInput, TextInput
+from langflow.io import DropdownInput, IntInput, NestedDictInput, Output, TextInput
 from langflow.schema import Data
 from langflow.schema.dotdict import dotdict
-from langflow.template import Output
 
 
 class APIRequestComponent(Component):

src/backend/base/langflow/components/data/Directory.py

@@ -1,10 +1,12 @@
 from typing import List, Optional
 
-from langflow.base.data.utils import parallel_load_data, parse_text_file_to_data, retrieve_file_paths
-from langflow.custom import Component
-from langflow.inputs import TextInput, IntInput, BoolInput
-from langflow.template import Output
+from langflow.base.data.utils import (parallel_load_data,
+                                      parse_text_file_to_data,
+from langflow.io import BoolInput, IntInput, TextInput
 from langflow.schema import Data
+from langflow.io import Output
+from langflow.schema import Data
+from langflow.io import Output
 
 
 class DirectoryComponent(Component):

src/backend/base/langflow/components/data/File.py

@@ -2,9 +2,8 @@ from pathlib import Path
 
 from langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_data
 from langflow.custom import Component
-from langflow.inputs import BoolInput, FileInput
+from langflow.io import BoolInput, FileInput, Output
 from langflow.schema import Data
-from langflow.template import Output
 
 
 class FileComponent(Component):

src/backend/base/langflow/components/data/URL.py

@@ -3,9 +3,8 @@ import re
 from langchain_community.document_loaders.web_base import WebBaseLoader
 
 from langflow.custom import Component
-from langflow.inputs import TextInput
+from langflow.io import Output, TextInput
 from langflow.schema import Data
-from langflow.template import Output
 
 
 class URLComponent(Component):

src/backend/base/langflow/components/data/Webhook.py

@@ -1,9 +1,8 @@
 import json
 
 from langflow.custom import Component
-from langflow.inputs import TextInput
+from langflow.io import Output, TextInput
 from langflow.schema import Data
-from langflow.template import Output
 
 
 class WebhookComponent(Component):

src/backend/base/langflow/components/embeddings/OpenAIEmbeddings.py

@@ -2,8 +2,7 @@ from langchain_openai.embeddings.base import OpenAIEmbeddings
 
 from langflow.base.models.model import LCModelComponent
 from langflow.field_typing import Embeddings
-from langflow.inputs import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, SecretStrInput, TextInput
-from langflow.template import Output
+from langflow.io import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, Output, SecretStrInput, TextInput
 
 
 class OpenAIEmbeddingsComponent(LCModelComponent):

src/backend/base/langflow/components/experimental/ConditionalRouter.py

@@ -1,6 +1,5 @@
 from langflow.custom import Component
-from langflow.inputs import BoolInput, DropdownInput, TextInput, MessageInput
-from langflow.template import Output
+from langflow.io import BoolInput, DropdownInput, MessageInput, Output, TextInput
 from langflow.schema.message import Message
 
 
@@ -26,7 +25,7 @@ class ConditionalRouterComponent(Component):
             options=["equals", "not equals", "contains", "starts with", "ends with"],
             info="The operator to apply for comparing the texts.",
             value="equals",
-            advanced=True
+            advanced=True,
         ),
         BoolInput(
             name="case_sensitive",

src/backend/base/langflow/components/experimental/SelectivePassThrough.py

@@ -1,7 +1,6 @@
 from langflow.custom import Component
 from langflow.field_typing import Text
-from langflow.inputs import BoolInput, DropdownInput, TextInput
-from langflow.template import Output
+from langflow.io import BoolInput, DropdownInput, Output, TextInput
 
 
 class SelectivePassThroughComponent(Component):

src/backend/base/langflow/components/experimental/SplitText.py

@@ -2,11 +2,11 @@ from typing import List
 
 from langchain_text_splitters import CharacterTextSplitter
 from langflow.custom import Component
-from langflow.inputs import IntInput, TextInput, HandleInput
+from langflow.io import HandleInput, IntInput, Output, TextInput
 from langflow.schema import Data
-from langflow.template import Output
 from langflow.utils.util import unescape_string
 
+
 class SplitTextComponent(Component):
     display_name: str = "Split Text"
     description: str = "Split text into chunks based on specified criteria."

src/backend/base/langflow/components/helpers/CombineText.py

@@ -1,6 +1,5 @@
 from langflow.custom import Component
-from langflow.inputs import TextInput
-from langflow.template import Output
+from langflow.io import Output, TextInput
 from langflow.schema.message import Message
 
 

src/backend/base/langflow/components/helpers/CustomComponent.py

@@ -1,8 +1,7 @@
 # from langflow.field_typing import Data
 from langflow.custom import Component
-from langflow.inputs import TextInput
+from langflow.io import Output, TextInput
 from langflow.schema import Data
-from langflow.template import Output
 
 
 class CustomComponent(Component):

src/backend/base/langflow/components/helpers/FilterData.py

@@ -1,9 +1,8 @@
 from typing import List
 
 from langflow.custom import Component
-from langflow.inputs import DataInput, TextInput
+from langflow.io import DataInput, Output, TextInput
 from langflow.schema import Data
-from langflow.template import Output
 
 
 class FilterDataComponent(Component):

src/backend/base/langflow/components/helpers/Memory.py

@@ -1,10 +1,9 @@
 from langflow.custom import Component
-from langflow.inputs import DropdownInput, IntInput, TextInput
+from langflow.helpers.data import data_to_text
+from langflow.io import DropdownInput, IntInput, MultilineInput, Output, TextInput
 from langflow.memory import get_messages
 from langflow.schema import Data
 from langflow.schema.message import Message
-from langflow.template import Output
-from langflow.helpers.data import data_to_text
 
 
 class MemoryComponent(Component):

src/backend/base/langflow/components/helpers/ParseData.py

@@ -1,7 +1,6 @@
 from langflow.custom import Component
 from langflow.helpers.data import data_to_text
-from langflow.inputs import MultilineInput, DataInput, StrInput
-from langflow.template import Output
+from langflow.io import DataInput, MultilineInput, Output, StrInput
 from langflow.schema.message import Message
 
 
@@ -16,14 +15,9 @@ class ParseDataComponent(Component):
             name="template",
             display_name="Template",
             info="The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.",
-            value="{text}"
+            value="{text}",
         ),
-        StrInput(
-            name="sep",
-            display_name="Separator",
-            advanced=True,
-            value='\n'
-            )
+        StrInput(name="sep", display_name="Separator", advanced=True, value="\n"),
     ]
 
     outputs = [

src/backend/base/langflow/components/inputs/ChatInput.py

@@ -1,8 +1,7 @@
 from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES
 from langflow.base.io.chat import ChatComponent
-from langflow.inputs import DropdownInput, FileInput, TextInput
+from langflow.io import DropdownInput, FileInput, Output, TextInput
 from langflow.schema.message import Message
-from langflow.template import Output
 
 
 class ChatInput(ChatComponent):

src/backend/base/langflow/components/inputs/TextInput.py

@@ -1,6 +1,5 @@
 from langflow.base.io.text import TextComponent
-from langflow.inputs import TextInput
-from langflow.template import Output
+from langflow.io import Output, TextInput
 from langflow.schema.message import Message
 
 

src/backend/base/langflow/components/models/AmazonBedrockModel.py

@@ -3,8 +3,7 @@ from langchain_community.chat_models.bedrock import BedrockChat
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
 from langflow.field_typing import BaseLanguageModel, Text
-from langflow.inputs import BoolInput, DictInput, DropdownInput, MessageInput, StrInput
-from langflow.template import Output
+from langflow.io import BoolInput, DictInput, DropdownInput, MessageInput, Output, StrInput
 
 
 class AmazonBedrockComponent(LCModelComponent):

src/backend/base/langflow/components/models/AnthropicModel.py

@@ -4,8 +4,7 @@ from pydantic.v1 import SecretStr
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
 from langflow.field_typing import BaseLanguageModel, Text
-from langflow.inputs import BoolInput, DropdownInput, FloatInput, IntInput, SecretStrInput, StrInput
-from langflow.template import Output
+from langflow.io import BoolInput, DropdownInput, FloatInput, IntInput, Output, SecretStrInput, StrInput
 
 
 class AnthropicModelComponent(LCModelComponent):

src/backend/base/langflow/components/models/AzureOpenAIModel.py

@@ -4,8 +4,7 @@ from pydantic.v1 import SecretStr
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
 from langflow.field_typing import BaseLanguageModel, Text
-from langflow.inputs import BoolInput, DropdownInput, FloatInput, IntInput, MessageInput, StrInput
-from langflow.template import Output
+from langflow.io import BoolInput, DropdownInput, FloatInput, IntInput, MessageInput, Output, StrInput
 
 
 class AzureChatOpenAIComponent(LCModelComponent):

src/backend/base/langflow/components/models/BaiduQianfanChatModel.py

@@ -1,11 +1,10 @@
-from langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint
+from langchain_community.chat_models.baidu_qianfan_endpoint import \
 from pydantic.v1 import SecretStr
+
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
 from langflow.field_typing import BaseLanguageModel, Text
-from langflow.inputs import BoolInput, FloatInput, SecretStrInput, StrInput
-from langflow.template import Output
-
+from langflow.io import BoolInput, FloatInput, Output, SecretStrInput, StrInput
 
 class QianfanChatEndpointComponent(LCModelComponent):
     display_name: str = "Qianfan"

src/backend/base/langflow/components/models/ChatLiteLLMModel.py

@@ -4,10 +4,9 @@ from langchain_community.chat_models.litellm import ChatLiteLLM, ChatLiteLLMExce
 
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
-from langflow.field_typing import BaseLanguageModel
-from langflow.inputs import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, MessageInput, StrInput
+from langflow.io import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, MessageInput, Output, StrInput
+from langflow.schema.message import Message
 from langflow.schema.message import Message
-from langflow.template import Output
 
 
 class ChatLiteLLMModelComponent(LCModelComponent):

src/backend/base/langflow/components/models/CohereModel.py

@@ -4,8 +4,7 @@ from pydantic.v1 import SecretStr
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
 from langflow.field_typing import BaseLanguageModel, Text
-from langflow.inputs import BoolInput, FloatInput, IntInput, MessageInput, SecretStrInput, StrInput
-from langflow.template import Output
+from langflow.io import BoolInput, FloatInput, IntInput, MessageInput, Output, SecretStrInput, StrInput
 
 
 class CohereComponent(LCModelComponent):

src/backend/base/langflow/components/models/GoogleGenerativeAIModel.py

@@ -4,11 +4,7 @@ from pydantic.v1 import SecretStr
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
 from langflow.field_typing import BaseLanguageModel, Text
-from langflow.inputs import BoolInput, DropdownInput, FloatInput, IntInput, SecretStrInput, StrInput
-from langflow.template import Output
-
-
-from langflow.inputs import MessageInput
+from langflow.io import BoolInput, DropdownInput, FloatInput, IntInput, MessageInput, Output, SecretStrInput, StrInput
 
 
 class GoogleGenerativeAIComponent(LCModelComponent):

src/backend/base/langflow/components/models/GroqModel.py

@@ -1,11 +1,11 @@
 from langchain_groq import ChatGroq
-from langflow.base.models.groq_constants import MODEL_NAMES
 from pydantic.v1 import SecretStr
+
 from langflow.base.constants import STREAM_INFO_TEXT
+from langflow.base.models.groq_constants import MODEL_NAMES
 from langflow.base.models.model import LCModelComponent
 from langflow.field_typing import BaseLanguageModel, Text
-from langflow.inputs import BoolInput, FloatInput, IntInput, SecretStrInput, StrInput, DropdownInput
-from langflow.template import Output
+from langflow.io import BoolInput, DropdownInput, FloatInput, IntInput, Output, SecretStrInput, StrInput
 
 
 class GroqModel(LCModelComponent):

src/backend/base/langflow/components/models/HuggingFaceModel.py

@@ -4,8 +4,7 @@ from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
 from langflow.field_typing import BaseLanguageModel, Text
-from langflow.inputs import BoolInput, DictInput, DropdownInput, MessageInput, SecretStrInput, StrInput
-from langflow.template import Output
+from langflow.io import BoolInput, DictInput, DropdownInput, MessageInput, Output, SecretStrInput, StrInput
 
 
 class HuggingFaceEndpointsComponent(LCModelComponent):

src/backend/base/langflow/components/models/MistralModel.py

@@ -4,8 +4,7 @@ from pydantic.v1 import SecretStr
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
 from langflow.field_typing import BaseLanguageModel, Text
-from langflow.inputs import BoolInput, DropdownInput, FloatInput, IntInput, MessageInput, SecretStrInput, StrInput
-from langflow.template import Output
+from langflow.io import BoolInput, DropdownInput, FloatInput, IntInput, MessageInput, Output, SecretStrInput, StrInput
 
 
 class MistralAIModelComponent(LCModelComponent):

src/backend/base/langflow/components/models/OllamaModel.py

@@ -1,9 +1,9 @@
 from langchain_community.chat_models import ChatOllama
+
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
 from langflow.field_typing import BaseLanguageModel, Text
-from langflow.inputs import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, StrInput
-from langflow.template import Output
+from langflow.io import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, Output, StrInput
 
 
 class ChatOllamaComponent(LCModelComponent):

src/backend/base/langflow/components/models/VertexAiModel.py

@@ -3,8 +3,7 @@ from langchain_google_vertexai import ChatVertexAI
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
 from langflow.field_typing import BaseLanguageModel, Text
-from langflow.inputs import BoolInput, FloatInput, IntInput, MessageInput, StrInput
-from langflow.template import Output
+from langflow.io import BoolInput, FloatInput, IntInput, MessageInput, Output, StrInput
 
 
 class ChatVertexAIComponent(LCModelComponent):

src/backend/base/langflow/components/outputs/ChatOutput.py

@@ -1,7 +1,6 @@
 from langflow.base.io.chat import ChatComponent
-from langflow.inputs import DropdownInput, TextInput
+from langflow.io import DropdownInput, Output, TextInput
 from langflow.schema.message import Message
-from langflow.template import Output
 
 
 class ChatOutput(ChatComponent):
@@ -23,7 +22,9 @@ class ChatOutput(ChatComponent):
             advanced=True,
             info="Type of sender.",
         ),
-        TextInput(name="sender_name", display_name="Sender Name", info="Name of the sender.", value="AI", advanced=True),
+        TextInput(
+            name="sender_name", display_name="Sender Name", info="Name of the sender.", value="AI", advanced=True
+        ),
         TextInput(name="session_id", display_name="Session ID", info="Session ID for the message.", advanced=True),
         TextInput(
             name="data_template",

src/backend/base/langflow/components/outputs/TextOutput.py

@@ -1,6 +1,5 @@
 from langflow.base.io.text import TextComponent
-from langflow.template import Output
-from langflow.inputs import TextInput
+from langflow.io import Output, TextInput
 from langflow.schema.message import Message
 
 

src/backend/base/langflow/components/prompts/Prompt.py

@@ -1,7 +1,6 @@
 from langflow.custom import Component
-from langflow.inputs import PromptInput
+from langflow.io import Output, PromptInput
 from langflow.schema.message import Message
-from langflow.template import Output
 
 
 class PromptComponent(Component):

src/backend/base/langflow/components/vectorstores/AstraDB.py

@@ -1,7 +1,7 @@
 from loguru import logger
 
 from langflow.base.vectorstores.model import LCVectorStoreComponent
-from langflow.inputs import BoolInput, DropdownInput, HandleInput, IntInput, MultilineInput, SecretStrInput, StrInput
+from langflow.io import BoolInput, DropdownInput, HandleInput, IntInput, MultilineInput, SecretStrInput, StrInput
 from langflow.schema import Data
 
 

src/backend/base/langflow/components/vectorstores/Cassandra.py

@@ -1,13 +1,12 @@
 from typing import List
 
 from langchain_community.vectorstores import Cassandra
+from langchain_core.retrievers import BaseRetriever
 
 from langflow.custom import Component
-from langflow.schema import Data
-from langflow.inputs import BoolInput, IntInput, StrInput, HandleInput, DropdownInput
-from langflow.template import Output
 from langflow.helpers.data import docs_to_data
-from langchain_core.retrievers import BaseRetriever
+from langflow.io import BoolInput, DropdownInput, HandleInput, IntInput, Output, StrInput
+from langflow.schema import Data
 
 
 class CassandraVectorStoreComponent(Component):

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

@@ -7,7 +7,7 @@ from loguru import logger
 
 from langflow.base.vectorstores.model import LCVectorStoreComponent
 from langflow.base.vectorstores.utils import chroma_collection_to_data
-from langflow.inputs import BoolInput, DataInput, DropdownInput, HandleInput, IntInput, StrInput, TextInput
+from langflow.io import BoolInput, DataInput, DropdownInput, HandleInput, IntInput, StrInput, TextInput
 from langflow.schema import Data
 
 if TYPE_CHECKING:

src/backend/base/langflow/components/vectorstores/Couchbase.py

@@ -2,13 +2,12 @@ from datetime import timedelta
 from typing import List
 
 from langchain_community.vectorstores import CouchbaseVectorStore
+from langchain_core.retrievers import BaseRetriever
 
 from langflow.custom import Component
-from langflow.schema import Data
-from langflow.inputs import BoolInput, IntInput, StrInput, HandleInput
-from langflow.template import Output
 from langflow.helpers.data import docs_to_data
-from langchain_core.retrievers import BaseRetriever
+from langflow.io import BoolInput, HandleInput, IntInput, Output, StrInput
+from langflow.schema import Data
 
 
 class CouchbaseVectorStoreComponent(Component):

src/backend/base/langflow/components/vectorstores/FAISS.py

@@ -1,15 +1,14 @@
 from typing import List
 
-from langflow.custom import Component
-from langflow.inputs import BoolInput, IntInput, StrInput, HandleInput
-from langflow.schema import Data
-from langflow.template import Output
-from langflow.field_typing import Text
-from langflow.helpers.data import docs_to_data
-
-from loguru import logger
 from langchain_community.vectorstores import FAISS
 from langchain_core.retrievers import BaseRetriever
+from loguru import logger
+
+from langflow.custom import Component
+from langflow.field_typing import Text
+from langflow.helpers.data import docs_to_data
+from langflow.io import BoolInput, HandleInput, IntInput, Output, StrInput
+from langflow.schema import Data
 
 
 class FaissVectorStoreComponent(Component):

src/backend/base/langflow/components/vectorstores/MongoDBAtlasVector.py

@@ -1,13 +1,12 @@
 from typing import List
 
 from langchain_community.vectorstores import MongoDBAtlasVectorSearch
+from langchain_core.retrievers import BaseRetriever
 
 from langflow.custom import Component
-from langflow.schema import Data
-from langflow.inputs import BoolInput, IntInput, StrInput, HandleInput
-from langflow.template import Output
 from langflow.helpers.data import docs_to_data
-from langchain_core.retrievers import BaseRetriever
+from langflow.io import BoolInput, HandleInput, IntInput, Output, StrInput
+from langflow.schema import Data
 
 
 class MongoVectorStoreComponent(Component):

src/backend/base/langflow/components/vectorstores/Pinecone.py

@@ -1,13 +1,13 @@
 from typing import List
 
-from langflow.custom import Component
-from langflow.schema import Data
-from langflow.inputs import BoolInput, IntInput, StrInput, HandleInput, DropdownInput
-from langflow.template import Output
-from langflow.helpers.data import docs_to_data
 from langchain_core.retrievers import BaseRetriever
 from langchain_pinecone import Pinecone
 
+from langflow.custom import Component
+from langflow.helpers.data import docs_to_data
+from langflow.io import BoolInput, DropdownInput, HandleInput, IntInput, Output, StrInput
+from langflow.schema import Data
+
 
 class PineconeVectorStoreComponent(Component):
     display_name = "Pinecone"

src/backend/base/langflow/components/vectorstores/Qdrant.py

@@ -1,13 +1,13 @@
 from typing import List
 
-from langflow.custom import Component
-from langflow.schema import Data
-from langflow.inputs import BoolInput, IntInput, StrInput, HandleInput, DropdownInput
-from langflow.template import Output
-from langflow.helpers.data import docs_to_data
 from langchain_community.vectorstores import Qdrant
 from langchain_core.retrievers import BaseRetriever
 
+from langflow.custom import Component
+from langflow.helpers.data import docs_to_data
+from langflow.io import BoolInput, DropdownInput, HandleInput, IntInput, Output, StrInput
+from langflow.schema import Data
+
 
 class QdrantVectorStoreComponent(Component):
     display_name = "Qdrant"

src/backend/base/langflow/components/vectorstores/SupabaseVectorStore.py

@@ -1,14 +1,13 @@
 from typing import List
 
 from langchain_community.vectorstores import SupabaseVectorStore
+from langchain_core.retrievers import BaseRetriever
 from supabase.client import Client, create_client
 
 from langflow.custom import Component
-from langflow.schema import Data
-from langflow.inputs import IntInput, StrInput, HandleInput
-from langflow.template import Output
 from langflow.helpers.data import docs_to_data
-from langchain_core.retrievers import BaseRetriever
+from langflow.io import HandleInput, IntInput, Output, StrInput
+from langflow.schema import Data
 
 
 class SupabaseVectorStoreComponent(Component):

src/backend/base/langflow/components/vectorstores/Upstash.py

@@ -1,13 +1,12 @@
 from typing import List
 
 from langchain_community.vectorstores import UpstashVectorStore
+from langchain_core.retrievers import BaseRetriever
 
 from langflow.custom import Component
-from langflow.schema import Data
-from langflow.inputs import BoolInput, IntInput, StrInput, HandleInput
-from langflow.template import Output
 from langflow.helpers.data import docs_to_data
-from langchain_core.retrievers import BaseRetriever
+from langflow.io import BoolInput, HandleInput, IntInput, Output, StrInput
+from langflow.schema import Data
 
 
 class UpstashVectorStoreComponent(Component):

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

@@ -1,14 +1,14 @@
 from typing import List
 
-from langflow.custom import Component
-from langflow.schema import Data
-from langflow.inputs import BoolInput, IntInput, StrInput, HandleInput
-from langflow.template import Output
-from langflow.helpers.data import docs_to_data
 from langchain_community.embeddings import FakeEmbeddings
 from langchain_community.vectorstores import Vectara
 from langchain_core.retrievers import BaseRetriever
 
+from langflow.custom import Component
+from langflow.helpers.data import docs_to_data
+from langflow.io import BoolInput, HandleInput, IntInput, Output, StrInput
+from langflow.schema import Data
+
 
 class VectaraVectorStoreComponent(Component):
     display_name = "Vectara"

src/backend/base/langflow/components/vectorstores/Weaviate.py

@@ -1,15 +1,14 @@
 from typing import List
 
 import weaviate
-
-from langflow.custom import Component
-from langflow.schema import Data
-from langflow.inputs import BoolInput, IntInput, StrInput, HandleInput
-from langflow.template import Output
-from langflow.helpers.data import docs_to_data
 from langchain_community.vectorstores import Weaviate
 from langchain_core.retrievers import BaseRetriever
 
+from langflow.custom import Component
+from langflow.helpers.data import docs_to_data
+from langflow.io import BoolInput, HandleInput, IntInput, Output, StrInput
+from langflow.schema import Data
+
 
 class WeaviateVectorStoreComponent(Component):
     display_name = "Weaviate"

src/backend/base/langflow/components/vectorstores/pgvector.py

@@ -1,13 +1,12 @@
 from typing import List
 
-from langchain_community.vectorstores import PGVector
 from langchain.schema import BaseRetriever
+from langchain_community.vectorstores import PGVector
 
 from langflow.custom import Component
-from langflow.schema import Data
-from langflow.inputs import BoolInput, IntInput, StrInput, HandleInput
-from langflow.template import Output
 from langflow.helpers.data import docs_to_data
+from langflow.io import BoolInput, HandleInput, IntInput, Output, StrInput
+from langflow.schema import Data
 
 
 class PGVectorStoreComponent(Component):

src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json

@@ -72,7 +72,8 @@
             "fieldName": "input_value",
             "id": "ChatOutput-xfhJ1",
             "inputTypes": [
-              "Message"
+              "Message",
+              "str"
             ],
             "type": "str"
           }
@@ -81,7 +82,7 @@
         "source": "OpenAIModel-SsPHS",
         "sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-SsPHSœ, œnameœ: œtext_outputœ, œoutput_typesœ: [œMessageœ]}",
         "target": "ChatOutput-xfhJ1",
-        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-xfhJ1œ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-xfhJ1œ, œinputTypesœ: [œMessageœ, œstrœ], œtypeœ: œstrœ}"
       }
     ],
     "nodes": [
@@ -147,7 +148,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._arguments)\n        self.status = prompt.text\n        return prompt\n"
+                "value": "from langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._arguments)\n        self.status = prompt.text\n        return prompt\n"
               },
               "template": {
                 "advanced": false,
@@ -274,7 +275,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import DropdownInput, TextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        TextInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n        TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        TextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        TextInput(\n            name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n        ),\n        TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        TextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
               },
               "input_value": {
                 "advanced": false,
@@ -284,7 +285,8 @@
                 "file_path": "",
                 "info": "Message to be passed as output.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -332,7 +334,8 @@
                 "file_path": "",
                 "info": "Name of the sender.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -354,7 +357,8 @@
                 "file_path": "",
                 "info": "Session ID for the message.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -444,7 +448,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import DropdownInput, FileInput, TextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            multiline=True,\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"User\",\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        TextInput(\n            name=\"sender_name\",\n            type=str,\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=\"User\",\n            advanced=True,\n        ),\n        TextInput(\n            name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, FileInput, Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            multiline=True,\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"User\",\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        TextInput(\n            name=\"sender_name\",\n            type=str,\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=\"User\",\n            advanced=True,\n        ),\n        TextInput(\n            name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
               },
               "input_value": {
                 "advanced": false,
@@ -454,7 +458,8 @@
                 "file_path": "",
                 "info": "Message to be passed as input.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -502,7 +507,8 @@
                 "file_path": "",
                 "info": "Name of the sender.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -524,7 +530,8 @@
                 "file_path": "",
                 "info": "Session ID for the message.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,

src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writer.json

@@ -144,7 +144,8 @@
             "fieldName": "input_value",
             "id": "ChatOutput-n5T1m",
             "inputTypes": [
-              "Message"
+              "Message",
+              "str"
             ],
             "type": "str"
           }
@@ -153,7 +154,7 @@
         "source": "OpenAIModel-NXVBv",
         "sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-NXVBvœ, œnameœ: œtext_outputœ, œoutput_typesœ: [œMessageœ]}",
         "target": "ChatOutput-n5T1m",
-        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-n5T1mœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-n5T1mœ, œinputTypesœ: [œMessageœ, œstrœ], œtypeœ: œstrœ}"
       },
       {
         "data": {
@@ -247,7 +248,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._arguments)\n        self.status = prompt.text\n        return prompt\n"
+                "value": "from langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._arguments)\n        self.status = prompt.text\n        return prompt\n"
               },
               "instructions": {
                 "advanced": false,
@@ -412,7 +413,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import re\n\nfrom langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.custom import Component\nfrom langflow.inputs import TextInput\nfrom langflow.schema import Data\nfrom langflow.template import Output\n\n\nclass URLComponent(Component):\n    display_name = \"URL\"\n    description = \"Fetch content from one or more URLs.\"\n    icon = \"layout-template\"\n\n    inputs = [\n        TextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs, separated by commas.\",\n            value=\"\",\n            is_list=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"fetch_content\"),\n    ]\n\n    def ensure_url(self, string: str) -> str:\n        \"\"\"\n        Ensures the given string is a URL by adding 'http://' if it doesn't start with 'http://' or 'https://'.\n        Raises an error if the string is not a valid URL.\n\n        Parameters:\n            string (str): The string to be checked and possibly modified.\n\n        Returns:\n            str: The modified string that is ensured to be a URL.\n\n        Raises:\n            ValueError: If the string is not a valid URL.\n        \"\"\"\n        if not string.startswith((\"http://\", \"https://\")):\n            string = \"http://\" + string\n\n        # Basic URL validation regex\n        url_regex = re.compile(\n            r\"^(http://|https://)?\"  # http:// or https://\n            r\"(([a-zA-Z0-9\\.-]+)\"  # domain\n            r\"(\\.[a-zA-Z]{2,}))\"  # top-level domain\n            r\"(:[0-9]{1,5})?\"  # optional port\n            r\"(\\/.*)?$\"  # optional path\n        )\n\n        if not re.match(url_regex, string):\n            raise ValueError(f\"Invalid URL: {string}\")\n\n        return string\n\n    def fetch_content(self) -> Data:\n        urls = [self.ensure_url(url.strip()) for url in self.urls if url.strip()]\n        loader = WebBaseLoader(web_paths=urls, encoding=\"utf-8\")\n        docs = loader.load()\n        data = [Data(text=doc.page_content, **doc.metadata) for doc in docs]\n        self.status = data\n        return data\n"
+                "value": "import re\n\nfrom langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.custom import Component\nfrom langflow.io import Output, TextInput\nfrom langflow.schema import Data\n\n\nclass URLComponent(Component):\n    display_name = \"URL\"\n    description = \"Fetch content from one or more URLs.\"\n    icon = \"layout-template\"\n\n    inputs = [\n        TextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs, separated by commas.\",\n            is_list=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"fetch_content\"),\n    ]\n\n    def ensure_url(self, string: str) -> str:\n        \"\"\"\n        Ensures the given string is a URL by adding 'http://' if it doesn't start with 'http://' or 'https://'.\n        Raises an error if the string is not a valid URL.\n\n        Parameters:\n            string (str): The string to be checked and possibly modified.\n\n        Returns:\n            str: The modified string that is ensured to be a URL.\n\n        Raises:\n            ValueError: If the string is not a valid URL.\n        \"\"\"\n        if not string.startswith((\"http://\", \"https://\")):\n            string = \"http://\" + string\n\n        # Basic URL validation regex\n        url_regex = re.compile(\n            r\"^(http://|https://)?\"  # http:// or https://\n            r\"(([a-zA-Z0-9\\.-]+)\"  # domain\n            r\"(\\.[a-zA-Z]{2,}))\"  # top-level domain\n            r\"(:[0-9]{1,5})?\"  # optional port\n            r\"(\\/.*)?$\"  # optional path\n        )\n\n        if not re.match(url_regex, string):\n            raise ValueError(f\"Invalid URL: {string}\")\n\n        return string\n\n    def fetch_content(self) -> Data:\n        urls = [self.ensure_url(url.strip()) for url in self.urls if url.strip()]\n        loader = WebBaseLoader(web_paths=urls, encoding=\"utf-8\")\n        docs = loader.load()\n        data = [Data(text=doc.page_content, **doc.metadata) for doc in docs]\n        self.status = data\n        return data\n"
               },
               "urls": {
                 "advanced": false,
@@ -500,7 +501,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import re\n\nfrom langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.custom import Component\nfrom langflow.inputs import TextInput\nfrom langflow.schema import Data\nfrom langflow.template import Output\n\n\nclass URLComponent(Component):\n    display_name = \"URL\"\n    description = \"Fetch content from one or more URLs.\"\n    icon = \"layout-template\"\n\n    inputs = [\n        TextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs, separated by commas.\",\n            value=\"\",\n            is_list=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"fetch_content\"),\n    ]\n\n    def ensure_url(self, string: str) -> str:\n        \"\"\"\n        Ensures the given string is a URL by adding 'http://' if it doesn't start with 'http://' or 'https://'.\n        Raises an error if the string is not a valid URL.\n\n        Parameters:\n            string (str): The string to be checked and possibly modified.\n\n        Returns:\n            str: The modified string that is ensured to be a URL.\n\n        Raises:\n            ValueError: If the string is not a valid URL.\n        \"\"\"\n        if not string.startswith((\"http://\", \"https://\")):\n            string = \"http://\" + string\n\n        # Basic URL validation regex\n        url_regex = re.compile(\n            r\"^(http://|https://)?\"  # http:// or https://\n            r\"(([a-zA-Z0-9\\.-]+)\"  # domain\n            r\"(\\.[a-zA-Z]{2,}))\"  # top-level domain\n            r\"(:[0-9]{1,5})?\"  # optional port\n            r\"(\\/.*)?$\"  # optional path\n        )\n\n        if not re.match(url_regex, string):\n            raise ValueError(f\"Invalid URL: {string}\")\n\n        return string\n\n    def fetch_content(self) -> Data:\n        urls = [self.ensure_url(url.strip()) for url in self.urls if url.strip()]\n        loader = WebBaseLoader(web_paths=urls, encoding=\"utf-8\")\n        docs = loader.load()\n        data = [Data(text=doc.page_content, **doc.metadata) for doc in docs]\n        self.status = data\n        return data\n"
+                "value": "import re\n\nfrom langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.custom import Component\nfrom langflow.io import Output, TextInput\nfrom langflow.schema import Data\n\n\nclass URLComponent(Component):\n    display_name = \"URL\"\n    description = \"Fetch content from one or more URLs.\"\n    icon = \"layout-template\"\n\n    inputs = [\n        TextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs, separated by commas.\",\n            is_list=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"fetch_content\"),\n    ]\n\n    def ensure_url(self, string: str) -> str:\n        \"\"\"\n        Ensures the given string is a URL by adding 'http://' if it doesn't start with 'http://' or 'https://'.\n        Raises an error if the string is not a valid URL.\n\n        Parameters:\n            string (str): The string to be checked and possibly modified.\n\n        Returns:\n            str: The modified string that is ensured to be a URL.\n\n        Raises:\n            ValueError: If the string is not a valid URL.\n        \"\"\"\n        if not string.startswith((\"http://\", \"https://\")):\n            string = \"http://\" + string\n\n        # Basic URL validation regex\n        url_regex = re.compile(\n            r\"^(http://|https://)?\"  # http:// or https://\n            r\"(([a-zA-Z0-9\\.-]+)\"  # domain\n            r\"(\\.[a-zA-Z]{2,}))\"  # top-level domain\n            r\"(:[0-9]{1,5})?\"  # optional port\n            r\"(\\/.*)?$\"  # optional path\n        )\n\n        if not re.match(url_regex, string):\n            raise ValueError(f\"Invalid URL: {string}\")\n\n        return string\n\n    def fetch_content(self) -> Data:\n        urls = [self.ensure_url(url.strip()) for url in self.urls if url.strip()]\n        loader = WebBaseLoader(web_paths=urls, encoding=\"utf-8\")\n        docs = loader.load()\n        data = [Data(text=doc.page_content, **doc.metadata) for doc in docs]\n        self.status = data\n        return data\n"
               },
               "urls": {
                 "advanced": false,
@@ -594,7 +595,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.inputs import TextInput\nfrom langflow.template import Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n        )\n        return message\n"
+                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import TextInput\nfrom langflow.io import Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n        )\n        return message\n"
               },
               "input_value": {
                 "advanced": false,
@@ -687,7 +688,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs import MultilineInput, DataInput, StrInput\nfrom langflow.template import Output\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\"\n        ),\n        StrInput(\n            name=\"sep\",\n            display_name=\"Separator\",\n            advanced=True,\n            value='---'\n            )\n    ]\n\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n    ]\n\n    def parse_data(self) -> Message:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n\n        result_string = data_to_text(template, data, sep=self.sep)\n        self.status = result_string\n        return Message(text=result_string)\n"
+                "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\",\n        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n    ]\n\n    def parse_data(self) -> Message:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n\n        result_string = data_to_text(template, data, sep=self.sep)\n        self.status = result_string\n        return Message(text=result_string)\n"
               },
               "data": {
                 "advanced": false,
@@ -808,7 +809,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs import MultilineInput, DataInput, StrInput\nfrom langflow.template import Output\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\"\n        ),\n        StrInput(\n            name=\"sep\",\n            display_name=\"Separator\",\n            advanced=True,\n            value='---'\n            )\n    ]\n\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n    ]\n\n    def parse_data(self) -> Message:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n\n        result_string = data_to_text(template, data, sep=self.sep)\n        self.status = result_string\n        return Message(text=result_string)\n"
+                "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\",\n        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n    ]\n\n    def parse_data(self) -> Message:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n\n        result_string = data_to_text(template, data, sep=self.sep)\n        self.status = result_string\n        return Message(text=result_string)\n"
               },
               "data": {
                 "advanced": false,
@@ -1196,7 +1197,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import DropdownInput, TextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        TextInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n        TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        TextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        TextInput(\n            name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n        ),\n        TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        TextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
               },
               "data_template": {
                 "advanced": true,
@@ -1204,7 +1205,8 @@
                 "dynamic": false,
                 "info": "Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -1223,7 +1225,8 @@
                 "dynamic": false,
                 "info": "Message to be passed as output.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -1259,7 +1262,8 @@
                 "dynamic": false,
                 "info": "Name of the sender.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -1278,7 +1282,8 @@
                 "dynamic": false,
                 "info": "Session ID for the message.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,

src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json

@@ -96,7 +96,8 @@
             "fieldName": "input_value",
             "id": "ChatOutput-y4SCS",
             "inputTypes": [
-              "Message"
+              "Message",
+              "str"
             ],
             "type": "str"
           }
@@ -105,7 +106,7 @@
         "source": "OpenAIModel-8b6nG",
         "sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-8b6nGœ, œnameœ: œtext_outputœ, œoutput_typesœ: [œMessageœ]}",
         "target": "ChatOutput-y4SCS",
-        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-y4SCSœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-y4SCSœ, œinputTypesœ: [œMessageœ, œstrœ], œtypeœ: œstrœ}"
       }
     ],
     "nodes": [
@@ -225,7 +226,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._arguments)\n        self.status = prompt.text\n        return prompt\n"
+                "value": "from langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._arguments)\n        self.status = prompt.text\n        return prompt\n"
               },
               "template": {
                 "advanced": false,
@@ -325,7 +326,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import DropdownInput, FileInput, TextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            multiline=True,\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"User\",\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        TextInput(\n            name=\"sender_name\",\n            type=str,\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=\"User\",\n            advanced=True,\n        ),\n        TextInput(\n            name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, FileInput, Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            multiline=True,\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"User\",\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        TextInput(\n            name=\"sender_name\",\n            type=str,\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=\"User\",\n            advanced=True,\n        ),\n        TextInput(\n            name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
               },
               "input_value": {
                 "advanced": false,
@@ -335,7 +336,8 @@
                 "file_path": "",
                 "info": "Message to be passed as input.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -383,7 +385,8 @@
                 "file_path": "",
                 "info": "Name of the sender.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -405,7 +408,8 @@
                 "file_path": "",
                 "info": "Session ID for the message.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -495,7 +499,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import DropdownInput, TextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        TextInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n        TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        TextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        TextInput(\n            name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n        ),\n        TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        TextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
               },
               "input_value": {
                 "advanced": false,
@@ -505,7 +509,8 @@
                 "file_path": "",
                 "info": "Message to be passed as output.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -553,7 +558,8 @@
                 "file_path": "",
                 "info": "Name of the sender.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -575,7 +581,8 @@
                 "file_path": "",
                 "info": "Session ID for the message.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -663,7 +670,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from pathlib import Path\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_data\nfrom langflow.custom import Component\nfrom langflow.inputs import BoolInput, FileInput\nfrom langflow.schema import Data\nfrom langflow.template import Output\n\n\nclass FileComponent(Component):\n    display_name = \"File\"\n    description = \"A generic file loader.\"\n    icon = \"file-text\"\n\n    inputs = [\n        FileInput(\n            name=\"path\",\n            display_name=\"Path\",\n            file_types=TEXT_FILE_TYPES,\n            info=f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n        ),\n        BoolInput(\n            name=\"silent_errors\",\n            display_name=\"Silent Errors\",\n            advanced=True,\n            info=\"If true, errors will not raise an exception.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"load_file\"),\n    ]\n\n    def load_file(self) -> Data:\n        if not self.path:\n            raise ValueError(\"Please, upload a file to use this component.\")\n        resolved_path = self.resolve_path(self.path)\n        silent_errors = self.silent_errors\n\n        extension = Path(resolved_path).suffix[1:].lower()\n\n        if extension == \"doc\":\n            raise ValueError(\"doc files are not supported. Please save as .docx\")\n        if extension not in TEXT_FILE_TYPES:\n            raise ValueError(f\"Unsupported file type: {extension}\")\n\n        data = parse_text_file_to_data(resolved_path, silent_errors)\n        self.status = data if data else \"No data\"\n        return data or Data()\n"
+                "value": "from pathlib import Path\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_data\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, FileInput, Output\nfrom langflow.schema import Data\n\n\nclass FileComponent(Component):\n    display_name = \"File\"\n    description = \"A generic file loader.\"\n    icon = \"file-text\"\n\n    inputs = [\n        FileInput(\n            name=\"path\",\n            display_name=\"Path\",\n            file_types=TEXT_FILE_TYPES,\n            info=f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n        ),\n        BoolInput(\n            name=\"silent_errors\",\n            display_name=\"Silent Errors\",\n            advanced=True,\n            info=\"If true, errors will not raise an exception.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"load_file\"),\n    ]\n\n    def load_file(self) -> Data:\n        if not self.path:\n            raise ValueError(\"Please, upload a file to use this component.\")\n        resolved_path = self.resolve_path(self.path)\n        silent_errors = self.silent_errors\n\n        extension = Path(resolved_path).suffix[1:].lower()\n\n        if extension == \"doc\":\n            raise ValueError(\"doc files are not supported. Please save as .docx\")\n        if extension not in TEXT_FILE_TYPES:\n            raise ValueError(f\"Unsupported file type: {extension}\")\n\n        data = parse_text_file_to_data(resolved_path, silent_errors)\n        self.status = data if data else \"No data\"\n        return data or Data()\n"
               },
               "path": {
                 "advanced": false,

src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json

@@ -100,7 +100,8 @@
             "fieldName": "input_value",
             "id": "ChatOutput-0wtla",
             "inputTypes": [
-              "Message"
+              "Message",
+              "str"
             ],
             "type": "str"
           }
@@ -109,7 +110,7 @@
         "source": "OpenAIModel-nNBA0",
         "sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-nNBA0œ, œnameœ: œtext_outputœ, œoutput_typesœ: [œMessageœ]}",
         "target": "ChatOutput-0wtla",
-        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-0wtlaœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-0wtlaœ, œinputTypesœ: [œMessageœ, œstrœ], œtypeœ: œstrœ}"
       }
     ],
     "nodes": [
@@ -176,7 +177,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._arguments)\n        self.status = prompt.text\n        return prompt\n"
+                "value": "from langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._arguments)\n        self.status = prompt.text\n        return prompt\n"
               },
               "context": {
                 "advanced": false,
@@ -338,7 +339,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import Component\nfrom langflow.inputs import DropdownInput, IntInput, TextInput\nfrom langflow.memory import get_messages\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass MemoryComponent(Component):\n    display_name = \"Memory\"\n    description = \"Retrieves stored chat messages.\"\n    icon = \"history\"\n\n    inputs = [\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\", \"Machine and User\"],\n            value=\"Machine and User\",\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        TextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Messages\",\n            value=100,\n            info=\"Number of messages to retrieve.\",\n            advanced=True,\n        ),\n        TextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"Session ID of the chat history.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"order\",\n            display_name=\"Order\",\n            options=[\"Ascending\", \"Descending\"],\n            value=\"Ascending\",\n            info=\"Order of the messages.\",\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Message Data\", name=\"messages\", method=\"retrieve_messages\"),\n        Output(display_name=\"Parsed\", name=\"messages_text\", method=\"retrieve_messages_as_text\"),\n    ]\n\n    def retrieve_messages(self) -> Data:\n        sender = self.sender\n        sender_name = self.sender_name\n        session_id = self.session_id\n        n_messages = self.n_messages\n        order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n        if sender == \"Machine and User\":\n            sender = None\n\n        messages = get_messages(\n            sender=sender,\n            sender_name=sender_name,\n            session_id=session_id,\n            limit=n_messages,\n            order=order,\n        )\n        self.status = messages\n        return messages\n\n    def retrieve_messages_as_text(self) -> Message:\n        messages = self.retrieve_messages()\n        messages_text = \"\\n\".join([\"{sender_name}: {text}\".format(**message.data) for message in messages])\n        self.status = messages_text\n        return Message(text=messages_text)\n"
+                "value": "from langflow.custom import Component\nfrom langflow.io import DropdownInput, IntInput, Output, TextInput\nfrom langflow.memory import get_messages\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\n\n\nclass MemoryComponent(Component):\n    display_name = \"Memory\"\n    description = \"Retrieves stored chat messages.\"\n    icon = \"history\"\n\n    inputs = [\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\", \"Machine and User\"],\n            value=\"Machine and User\",\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        TextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Messages\",\n            value=100,\n            info=\"Number of messages to retrieve.\",\n            advanced=True,\n        ),\n        TextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"Session ID of the chat history.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"order\",\n            display_name=\"Order\",\n            options=[\"Ascending\", \"Descending\"],\n            value=\"Ascending\",\n            info=\"Order of the messages.\",\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Message Data\", name=\"messages\", method=\"retrieve_messages\"),\n        Output(display_name=\"Parsed\", name=\"messages_text\", method=\"retrieve_messages_as_text\"),\n    ]\n\n    def retrieve_messages(self) -> Data:\n        sender = self.sender\n        sender_name = self.sender_name\n        session_id = self.session_id\n        n_messages = self.n_messages\n        order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n        if sender == \"Machine and User\":\n            sender = None\n\n        messages = get_messages(\n            sender=sender,\n            sender_name=sender_name,\n            session_id=session_id,\n            limit=n_messages,\n            order=order,\n        )\n        self.status = messages\n        return messages\n\n    def retrieve_messages_as_text(self) -> Message:\n        messages = self.retrieve_messages()\n        messages_text = \"\\n\".join([\"{sender_name}: {text}\".format(**message.data) for message in messages])\n        self.status = messages_text\n        return Message(text=messages_text)\n"
               },
               "n_messages": {
                 "advanced": true,
@@ -501,7 +502,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import DropdownInput, FileInput, TextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            multiline=True,\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"User\",\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        TextInput(\n            name=\"sender_name\",\n            type=str,\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=\"User\",\n            advanced=True,\n        ),\n        TextInput(\n            name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, FileInput, Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            multiline=True,\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"User\",\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        TextInput(\n            name=\"sender_name\",\n            type=str,\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=\"User\",\n            advanced=True,\n        ),\n        TextInput(\n            name=\"session_id\", type=str, display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
               },
               "files": {
                 "advanced": true,
@@ -549,7 +550,8 @@
                 "dynamic": false,
                 "info": "Message to be passed as input.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -585,7 +587,8 @@
                 "dynamic": false,
                 "info": "Name of the sender.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -604,7 +607,8 @@
                 "dynamic": false,
                 "info": "Session ID for the message.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -956,7 +960,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import DropdownInput, TextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        TextInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n        TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        TextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        TextInput(\n            name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n        ),\n        TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        TextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
               },
               "data_template": {
                 "advanced": true,
@@ -964,7 +968,8 @@
                 "dynamic": false,
                 "info": "Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -983,7 +988,8 @@
                 "dynamic": false,
                 "info": "Message to be passed as output.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -1019,7 +1025,8 @@
                 "dynamic": false,
                 "info": "Name of the sender.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -1038,7 +1045,8 @@
                 "dynamic": false,
                 "info": "Session ID for the message.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,

src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json

@@ -137,7 +137,8 @@
             "fieldName": "input_value",
             "id": "ChatOutput-EJkG3",
             "inputTypes": [
-              "Message"
+              "Message",
+              "str"
             ],
             "type": "str"
           }
@@ -149,7 +150,7 @@
           "stroke": "#555"
         },
         "target": "ChatOutput-EJkG3",
-        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-EJkG3œ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-EJkG3œ, œinputTypesœ: [œMessageœ, œstrœ], œtypeœ: œstrœ}"
       },
       {
         "className": "stroke-gray-900 stroke-connection",
@@ -223,7 +224,8 @@
             "fieldName": "input_value",
             "id": "ChatOutput-DNmvg",
             "inputTypes": [
-              "Message"
+              "Message",
+              "str"
             ],
             "type": "str"
           }
@@ -235,7 +237,7 @@
           "stroke": "#555"
         },
         "target": "ChatOutput-DNmvg",
-        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-DNmvgœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-DNmvgœ, œinputTypesœ: [œMessageœ, œstrœ], œtypeœ: œstrœ}"
       }
     ],
     "nodes": [
@@ -301,7 +303,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._arguments)\n        self.status = prompt.text\n        return prompt\n"
+                "value": "from langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._arguments)\n        self.status = prompt.text\n        return prompt\n"
               },
               "document": {
                 "advanced": false,
@@ -432,7 +434,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import Component\nfrom langflow.inputs import PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._arguments)\n        self.status = prompt.text\n        return prompt\n"
+                "value": "from langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._arguments)\n        self.status = prompt.text\n        return prompt\n"
               },
               "summary": {
                 "advanced": false,
@@ -555,7 +557,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import DropdownInput, TextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        TextInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n        TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        TextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        TextInput(\n            name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n        ),\n        TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        TextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
               },
               "input_value": {
                 "advanced": false,
@@ -565,7 +567,8 @@
                 "file_path": "",
                 "info": "Message to be passed as output.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -613,7 +616,8 @@
                 "file_path": "",
                 "info": "Name of the sender.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -635,7 +639,8 @@
                 "file_path": "",
                 "info": "Session ID for the message.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -722,7 +727,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import DropdownInput, TextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        TextInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True),\n        TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        TextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        TextInput(\n            name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n        ),\n        TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        TextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
               },
               "input_value": {
                 "advanced": false,
@@ -732,7 +737,8 @@
                 "file_path": "",
                 "info": "Message to be passed as output.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -780,7 +786,8 @@
                 "file_path": "",
                 "info": "Name of the sender.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -802,7 +809,8 @@
                 "file_path": "",
                 "info": "Session ID for the message.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -883,7 +891,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.inputs import TextInput\nfrom langflow.template import Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n        )\n        return message\n"
+                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n        )\n        return message\n"
               },
               "input_value": {
                 "advanced": false,
@@ -893,7 +901,8 @@
                 "file_path": "",
                 "info": "Text to be passed as input.",
                 "input_types": [
-                  "Message"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,

src/backend/base/langflow/inputs/input_mixin.py

@@ -5,7 +5,6 @@ from pydantic import BaseModel, ConfigDict, Field, PlainSerializer, field_valida
 
 from langflow.field_typing.range_spec import RangeSpec
 from langflow.inputs.validators import CoalesceBool
-from langflow.inputs.validators import CoalesceBool
 
 
 class FieldTypes(str, Enum):

src/backend/base/langflow/io/__init__.py

@@ -0,0 +1,37 @@
+from langflow.inputs import (
+    BoolInput,
+    DataInput,
+    DictInput,
+    DropdownInput,
+    FileInput,
+    FloatInput,
+    HandleInput,
+    IntInput,
+    MessageInput,
+    MultilineInput,
+    NestedDictInput,
+    PromptInput,
+    SecretStrInput,
+    StrInput,
+    TextInput,
+)
+from langflow.template import Output
+
+__all__ = [
+    "BoolInput",
+    "DataInput",
+    "DictInput",
+    "DropdownInput",
+    "FileInput",
+    "FloatInput",
+    "HandleInput",
+    "IntInput",
+    "MessageInput",
+    "MultilineInput",
+    "NestedDictInput",
+    "PromptInput",
+    "SecretStrInput",
+    "StrInput",
+    "TextInput",
+    "Output",
+]

src/frontend/tests/end-to-end/dropdownComponent.spec.ts

@@ -190,9 +190,9 @@ test("dropDownComponent", async ({ page }) => {
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
 from langflow.field_typing import BaseLanguageModel, Text
-from langflow.inputs import BoolInput, DictInput, DropdownInput, StrInput
-from langflow.inputs import MessageInput
-from langflow.template import Output
+from langflow.io import BoolInput, DictInput, DropdownInput, StrInput
+from langflow.io import MessageInput
+from langflow.io import Output
 
 
 class AmazonBedrockComponent(LCModelComponent):