Update input_value type to Text

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

@@ -23,7 +23,7 @@ class ConversationChainComponent(CustomComponent):
 
     def build(
         self,
-        input_value: str,
+        input_value: Text,
         llm: BaseLanguageModel,
         memory: Optional[BaseMemory] = None,
     ) -> Text:
@@ -34,7 +34,7 @@ class ConversationChainComponent(CustomComponent):
         result = chain.invoke({chain.input_key: input_value})
         # result is an AIMessage which is a subclass of BaseMessage
         # We need to check if it is a string or a BaseMessage
-        result_str: str = ""
+        result_str: Text = ""
         if hasattr(result, "content") and isinstance(result.content, str):
 
             result_str = result.content
@@ -43,6 +43,6 @@ class ConversationChainComponent(CustomComponent):
             result_str = result
         else:
             # is dict
-            result_str = result.get("response")
+            result_str = Text(result.get("response"))
         self.status = result_str
         return result_str

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

@@ -18,13 +18,13 @@ class LLMCheckerChainComponent(CustomComponent):
 
     def build(
         self,
-        input_value: str,
+        input_value: Text,
         llm: BaseLanguageModel,
     ) -> Text:
 
         chain = LLMCheckerChain.from_llm(llm=llm)
         response = chain.invoke({chain.input_key: input_value})
         result = response.get(chain.output_key, "")
-        result_str = str(result)
+        result_str = Text(result)
         self.status = result_str
         return result_str

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

@@ -40,6 +40,6 @@ class LLMMathChainComponent(CustomComponent):
         )
         response = chain.invoke({input_key: input_value})
         result = response.get(output_key)
-        result_str = str(result)
+        result_str = Text(result)
         self.status = result_str
         return result_str

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

@@ -57,6 +57,6 @@ class RetrievalQAComponent(CustomComponent):
             references_str = self.create_references_from_records(records)
         result_str = result.get("result", "")
 
-        final_result = "\n".join([str(result_str), references_str])
+        final_result = "\n".join([Text(result_str), references_str])
         self.status = final_result
         return final_result  # OK

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

@@ -26,7 +26,7 @@ class RetrievalQAWithSourcesChainComponent(CustomComponent):
 
     def build(
         self,
-        input_value: str,
+        input_value: Text,
         retriever: BaseRetriever,
         llm: BaseLanguageModel,
         chain_type: str,
@@ -52,7 +52,7 @@ class RetrievalQAWithSourcesChainComponent(CustomComponent):
         references_str = ""
         if return_source_documents:
             references_str = self.create_references_from_records(records)
-        result_str = str(result.get("answer", ""))
+        result_str = Text(result.get("answer", ""))
         final_result = "\n".join([result_str, references_str])
         self.status = final_result
         return final_result

src/backend/langflow/components/documentloaders/GatherRecords.py

@@ -1,6 +1,6 @@
 from concurrent import futures
 from pathlib import Path
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Text
 
 from langflow import CustomComponent
 from langflow.schema import Record
@@ -71,7 +71,9 @@ class GatherRecordsComponent(CustomComponent):
         glob = "**/*" if recursive else "*"
         paths = walk_level(path_obj, depth) if depth else path_obj.glob(glob)
         file_paths = [
-            str(p) for p in paths if p.is_file() and match_types(p) and is_not_hidden(p)
+            Text(p)
+            for p in paths
+            if p.is_file() and match_types(p) and is_not_hidden(p)
         ]
 
         return file_paths
@@ -90,7 +92,7 @@ class GatherRecordsComponent(CustomComponent):
             return None
 
         # Create a Record
-        text = "\n\n".join([str(el) for el in elements])
+        text = "\n\n".join([Text(el) for el in elements])
         metadata = elements.metadata if hasattr(elements, "metadata") else {}
         metadata["file_path"] = file_path
         record = Record(text=text, data=metadata)
@@ -136,7 +138,7 @@ class GatherRecordsComponent(CustomComponent):
         recursive: bool = True,
         silent_errors: bool = False,
         use_multithreading: bool = True,
-    ) -> List[Record]:
+    ) -> List[Optional[Record]]:
         if types is None:
             types = []
         resolved_path = self.resolve_path(path)

src/backend/langflow/components/io/base/chat.py

@@ -98,7 +98,7 @@ class ChatComponent(CustomComponent):
         if not input_value:
             input_value = ""
         if return_record and input_value_record:
-            result = input_value_record
+            result: Union[Text, Record] = input_value_record
         else:
             result = input_value
         self.status = result

src/backend/langflow/components/model_specs/ChatLiteLLMSpecs.py

@@ -139,7 +139,7 @@ class ChatLiteLLMComponent(CustomComponent):
             "OpenRouter": "openrouter_api_key",
         }
         # Set the API key based on the provider
-        api_keys = {v: None for v in provider_map.values()}
+        api_keys: dict[str, Optional[str]] = {v: None for v in provider_map.values()}
 
         if variable_name := provider_map.get(provider):
             api_keys[variable_name] = api_key

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

@@ -44,7 +44,7 @@ class AmazonBedrockComponent(LCModelComponent):
 
     def build(
         self,
-        input_value: str,
+        input_value: Text,
         model_id: str = "anthropic.claude-instant-v1",
         credentials_profile_name: Optional[str] = None,
         region_name: Optional[str] = None,

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

@@ -60,7 +60,7 @@ class AnthropicLLM(LCModelComponent):
     def build(
         self,
         model: str,
-        input_value: str,
+        input_value: Text,
         anthropic_api_key: Optional[str] = None,
         max_tokens: Optional[int] = None,
         temperature: Optional[float] = None,

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

@@ -2,9 +2,10 @@ from typing import Optional
 
 from langchain.llms.base import BaseLanguageModel
 from langchain_openai import AzureChatOpenAI
+from pydantic.v1 import SecretStr
 
 from langflow.components.models.base.model import LCModelComponent
-from pydantic.v1 import SecretStr
+from langflow.field_typing import Text
 
 
 class AzureChatOpenAIComponent(LCModelComponent):
@@ -86,7 +87,7 @@ class AzureChatOpenAIComponent(LCModelComponent):
         self,
         model: str,
         azure_endpoint: str,
-        input_value: str,
+        input_value: Text,
         azure_deployment: str,
         api_key: str,
         api_version: str,

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

@@ -78,7 +78,7 @@ class QianfanChatEndpointComponent(LCModelComponent):
 
     def build(
         self,
-        input_value: str,
+        input_value: Text,
         model: str = "ERNIE-Bot-turbo",
         qianfan_ak: Optional[str] = None,
         qianfan_sk: Optional[str] = None,

src/backend/langflow/components/models/CTransformersModel.py

@@ -39,7 +39,7 @@ class CTransformersComponent(LCModelComponent):
         self,
         model: str,
         model_file: str,
-        input_value: str,
+        input_value: Text,
         model_type: str,
         stream: bool = False,
         config: Optional[Dict] = None,

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

@@ -40,7 +40,7 @@ class CohereComponent(LCModelComponent):
     def build(
         self,
         cohere_api_key: str,
-        input_value: str,
+        input_value: Text,
         temperature: float = 0.75,
         stream: bool = False,
     ) -> Text:

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

@@ -62,7 +62,7 @@ class GoogleGenerativeAIComponent(LCModelComponent):
         self,
         google_api_key: str,
         model: str,
-        input_value: str,
+        input_value: Text,
         max_output_tokens: Optional[int] = None,
         temperature: float = 0.1,
         top_k: Optional[int] = None,

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

@@ -34,7 +34,7 @@ class HuggingFaceEndpointsComponent(LCModelComponent):
 
     def build(
         self,
-        input_value: str,
+        input_value: Text,
         endpoint_url: str,
         model: Optional[str] = None,
         task: str = "text2text-generation",

src/backend/langflow/components/models/LlamaCppModel.py

@@ -66,7 +66,7 @@ class LlamaCppComponent(LCModelComponent):
     def build(
         self,
         model_path: str,
-        input_value: str,
+        input_value: Text,
         grammar: Optional[str] = None,
         cache: Optional[bool] = None,
         client: Optional[Any] = None,

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

@@ -177,7 +177,7 @@ class ChatOllamaComponent(LCModelComponent):
         self,
         base_url: Optional[str],
         model: str,
-        input_value: str,
+        input_value: Text,
         mirostat: Optional[str],
         mirostat_eta: Optional[float] = None,
         mirostat_tau: Optional[float] = None,

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

@@ -9,8 +9,7 @@ from langflow.field_typing import Text
 class ChatVertexAIComponent(LCModelComponent):
     display_name = "ChatVertexAIModel"
     description = "Generate text using Vertex AI Chat large language models API."
-    icon="VertexAI"
+    icon = "VertexAI"
-
 
     def build_config(self):
         return {
@@ -68,7 +67,7 @@ class ChatVertexAIComponent(LCModelComponent):
 
     def build(
         self,
-        input_value: str,
+        input_value: Text,
         credentials: Optional[str],
         project: str,
         examples: Optional[List[BaseMessage]] = [],

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

@@ -20,7 +20,7 @@ class PromptComponent(CustomComponent):
         template: Prompt,
         **kwargs,
     ) -> Text:
-        prompt_template = PromptTemplate.from_template(str(template))
+        prompt_template = PromptTemplate.from_template(Text(template))
 
         attributes_to_check = ["text", "page_content"]
         for key, value in kwargs.items():

src/backend/langflow/components/utilities/GetRequest.py

@@ -1,7 +1,8 @@
-from typing import Optional
+from typing import Optional, Text
 
 import requests
 from langchain_core.documents import Document
+
 from langflow import CustomComponent
 from langflow.services.database.models.base import orjson_dumps
 
@@ -31,7 +32,9 @@ class GetRequest(CustomComponent):
         },
     }
 
-    def get_document(self, session: requests.Session, url: str, headers: Optional[dict], timeout: int) -> Document:
+    def get_document(
+        self, session: requests.Session, url: str, headers: Optional[dict], timeout: int
+    ) -> Document:
         try:
             response = session.get(url, headers=headers, timeout=int(timeout))
             try:
@@ -55,7 +58,7 @@ class GetRequest(CustomComponent):
             )
         except Exception as exc:
             return Document(
-                page_content=str(exc),
+                page_content=Text(exc),
                 metadata={"source": url, "headers": headers, "status_code": 500},
             )
 

src/backend/langflow/components/utilities/IDGenerator.py

@@ -9,7 +9,7 @@ class UUIDGeneratorComponent(CustomComponent):
     description = "Generates a unique ID."
 
     def generate(self, *args, **kwargs):
-        return str(uuid.uuid4().hex)
+        return Text(uuid.uuid4().hex)
 
     def build_config(self):
         return {"unique_id": {"display_name": "Value", "value": self.generate}}

src/backend/langflow/components/utilities/PostRequest.py

@@ -1,7 +1,8 @@
-from typing import Optional
+from typing import Optional, Text
 
 import requests
 from langchain_core.documents import Document
+
 from langflow import CustomComponent
 from langflow.services.database.models.base import orjson_dumps
 
@@ -47,7 +48,7 @@ class PostRequest(CustomComponent):
             )
         except Exception as exc:
             return Document(
-                page_content=str(exc),
+                page_content=Text(exc),
                 metadata={
                     "source": url,
                     "headers": headers,
@@ -66,12 +67,16 @@ class PostRequest(CustomComponent):
 
         if not isinstance(document, list) and isinstance(document, Document):
             documents: list[Document] = [document]
-        elif isinstance(document, list) and all(isinstance(doc, Document) for doc in document):
+        elif isinstance(document, list) and all(
+            isinstance(doc, Document) for doc in document
+        ):
             documents = document
         else:
             raise ValueError("document must be a Document or a list of Documents")
 
         with requests.Session() as session:
-            documents = [self.post_document(session, doc, url, headers) for doc in documents]
+            documents = [
+                self.post_document(session, doc, url, headers) for doc in documents
+            ]
             self.repr_value = documents
         return documents

src/backend/langflow/components/utilities/RunnableExecutor.py

@@ -32,7 +32,7 @@ class RunnableExecComponent(CustomComponent):
     def build(
         self,
         input_key: str,
-        input_value: str,
+        input_value: Text,
         runnable: Runnable,
         output_key: str = "output",
     ) -> Text:

src/backend/langflow/components/utilities/SQLExecutor.py

@@ -40,7 +40,7 @@ class SQLExecutorComponent(CustomComponent):
             result = tool.run(query, include_columns=include_columns)
             self.status = result
         except Exception as e:
-            result = str(e)
+            result = Text(e)
             self.status = result
             if not passthrough:
                 raise e

src/backend/langflow/components/utilities/ShouldRunNext.py

@@ -1,4 +1,5 @@
 # Implement ShouldRunNext component
+from typing import Text
 from langchain_core.prompts import PromptTemplate
 
 from langflow import CustomComponent
@@ -23,7 +24,7 @@ class ShouldRunNext(CustomComponent):
 
     def build(self, template: Prompt, llm: BaseLanguageModel, **kwargs) -> dict:
         # This is a simple component that always returns True
-        prompt_template = PromptTemplate.from_template(str(template))
+        prompt_template = PromptTemplate.from_template(Text(template))
 
         attributes_to_check = ["text", "page_content"]
         for key, value in kwargs.items():

src/backend/langflow/components/utilities/UpdateRequest.py

@@ -1,7 +1,8 @@
-from typing import List, Optional
+from typing import List, Optional, Text
 
 import requests
 from langchain_core.documents import Document
+
 from langflow import CustomComponent
 from langflow.services.database.models.base import orjson_dumps
 
@@ -40,7 +41,9 @@ class UpdateRequest(CustomComponent):
     ) -> Document:
         try:
             if method == "PATCH":
-                response = session.patch(url, headers=headers, data=document.page_content)
+                response = session.patch(
+                    url, headers=headers, data=document.page_content
+                )
             elif method == "PUT":
                 response = session.put(url, headers=headers, data=document.page_content)
             else:
@@ -61,7 +64,7 @@ class UpdateRequest(CustomComponent):
             )
         except Exception as exc:
             return Document(
-                page_content=str(exc),
+                page_content=Text(exc),
                 metadata={"source": url, "headers": headers, "status_code": 500},
             )
 
@@ -77,12 +80,17 @@ class UpdateRequest(CustomComponent):
 
         if not isinstance(document, list) and isinstance(document, Document):
             documents: list[Document] = [document]
-        elif isinstance(document, list) and all(isinstance(doc, Document) for doc in document):
+        elif isinstance(document, list) and all(
+            isinstance(doc, Document) for doc in document
+        ):
             documents = document
         else:
             raise ValueError("document must be a Document or a list of Documents")
 
         with requests.Session() as session:
-            documents = [self.update_document(session, doc, url, headers, method) for doc in documents]
+            documents = [
+                self.update_document(session, doc, url, headers, method)
+                for doc in documents
+            ]
             self.repr_value = documents
         return documents

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

@@ -1,4 +1,4 @@
-from typing import List, Union
+from typing import List, Text, Union
 
 from langchain.schema import BaseRetriever
 from langchain_community.vectorstores import VectorStore
@@ -35,5 +35,5 @@ class FAISSComponent(CustomComponent):
         if not folder_path:
             raise ValueError("Folder path is required to save the FAISS index.")
         path = self.resolve_path(folder_path)
-        vector_store.save_local(str(path), index_name)
+        vector_store.save_local(Text(path), index_name)
         return vector_store

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

@@ -3,7 +3,7 @@ from typing import List
 from langchain_community.vectorstores.faiss import FAISS
 
 from langflow.components.vectorstores.base.model import LCVectorStoreComponent
-from langflow.field_typing import Embeddings
+from langflow.field_typing import Embeddings, Text
 from langflow.schema import Record
 
 
@@ -26,7 +26,7 @@ class FAISSSearchComponent(LCVectorStoreComponent):
 
     def build(
         self,
-        input_value: str,
+        input_value: Text,
         embedding: Embeddings,
         folder_path: str,
         index_name: str = "langflow_index",
@@ -35,7 +35,7 @@ class FAISSSearchComponent(LCVectorStoreComponent):
             raise ValueError("Folder path is required to save the FAISS index.")
         path = self.resolve_path(folder_path)
         vector_store = FAISS.load_local(
-            folder_path=str(path), embeddings=embedding, index_name=index_name
+            folder_path=Text(path), embeddings=embedding, index_name=index_name
         )
         if not vector_store:
             raise ValueError("Failed to load the FAISS index.")

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

@@ -2,7 +2,7 @@ from typing import List, Optional
 
 from langflow.components.vectorstores.base.model import LCVectorStoreComponent
 from langflow.components.vectorstores.MongoDBAtlasVector import MongoDBAtlasComponent
-from langflow.field_typing import Embeddings, NestedDict
+from langflow.field_typing import Embeddings, NestedDict, Text
 from langflow.schema import Record
 
 
@@ -27,7 +27,7 @@ class MongoDBAtlasSearchComponent(MongoDBAtlasComponent, LCVectorStoreComponent)
 
     def build(  # type: ignore[override]
         self,
-        input_value: str,
+        input_value: Text,
         search_type: str,
         embedding: Embeddings,
         collection_name: str = "",

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

@@ -2,7 +2,7 @@ from typing import List, Optional
 
 from langflow.components.vectorstores.base.model import LCVectorStoreComponent
 from langflow.components.vectorstores.Pinecone import PineconeComponent
-from langflow.field_typing import Embeddings
+from langflow.field_typing import Embeddings, Text
 from langflow.schema import Record
 
 
@@ -42,7 +42,7 @@ class PineconeSearchComponent(PineconeComponent, LCVectorStoreComponent):
 
     def build(  # type: ignore[override]
         self,
-        input_value: str,
+        input_value: Text,
         embedding: Embeddings,
         pinecone_env: str,
         text_key: str = "text",

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

@@ -2,15 +2,14 @@ from typing import List, Optional
 
 from langflow.components.vectorstores.base.model import LCVectorStoreComponent
 from langflow.components.vectorstores.Qdrant import QdrantComponent
-from langflow.field_typing import Embeddings, NestedDict
+from langflow.field_typing import Embeddings, NestedDict, Text
 from langflow.schema import Record
 
 
 class QdrantSearchComponent(QdrantComponent, LCVectorStoreComponent):
     display_name = "Qdrant Search"
     description = "Construct Qdrant wrapper from a list of texts."
-    icon="Qdrant"
+    icon = "Qdrant"
-
 
     def build_config(self):
         return {
@@ -46,7 +45,7 @@ class QdrantSearchComponent(QdrantComponent, LCVectorStoreComponent):
 
     def build(  # type: ignore[override]
         self,
-        input_value: str,
+        input_value: Text,
         embedding: Embeddings,
         collection_name: str,
         search_type: str = "similarity",

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

@@ -4,6 +4,7 @@ from langchain.embeddings.base import Embeddings
 
 from langflow.components.vectorstores.base.model import LCVectorStoreComponent
 from langflow.components.vectorstores.Redis import RedisComponent
+from langflow.field_typing import Text
 from langflow.schema import Record
 
 
@@ -44,7 +45,7 @@ class RedisSearchComponent(RedisComponent, LCVectorStoreComponent):
 
     def build(  # type: ignore[override]
         self,
-        input_value: str,
+        input_value: Text,
         search_type: str,
         embedding: Embeddings,
         redis_server_url: str,

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

@@ -4,14 +4,14 @@ from langchain_community.vectorstores.supabase import SupabaseVectorStore
 from supabase.client import Client, create_client
 
 from langflow.components.vectorstores.base.model import LCVectorStoreComponent
-from langflow.field_typing import Embeddings
+from langflow.field_typing import Embeddings, Text
 from langflow.schema import Record
 
 
 class SupabaseSearchComponent(LCVectorStoreComponent):
     display_name = "Supabase Search"
     description = "Search a Supabase Vector Store for similar documents."
-    icon="Supabase"
+    icon = "Supabase"
 
     def build_config(self):
         return {
@@ -30,7 +30,7 @@ class SupabaseSearchComponent(LCVectorStoreComponent):
 
     def build(
         self,
-        input_value: str,
+        input_value: Text,
         search_type: str,
         embedding: Embeddings,
         query_name: str = "",

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

@@ -4,6 +4,7 @@ from langchain_community.vectorstores.vectara import Vectara
 
 from langflow.components.vectorstores.base.model import LCVectorStoreComponent
 from langflow.components.vectorstores.Vectara import VectaraComponent
+from langflow.field_typing import Text
 from langflow.schema import Record
 
 
@@ -14,7 +15,7 @@ class VectaraSearchComponent(VectaraComponent, LCVectorStoreComponent):
         "https://python.langchain.com/docs/integrations/vectorstores/vectara"
     )
     beta = True
-    icon="Vectara"
+    icon = "Vectara"
 
     field_config = {
         "search_type": {
@@ -44,7 +45,7 @@ class VectaraSearchComponent(VectaraComponent, LCVectorStoreComponent):
 
     def build(  # type: ignore[override]
         self,
-        input_value: str,
+        input_value: Text,
         search_type: str,
         vectara_customer_id: str,
         vectara_corpus_id: str,

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

@@ -4,6 +4,7 @@ from langchain.embeddings.base import Embeddings
 
 from langflow.components.vectorstores.base.model import LCVectorStoreComponent
 from langflow.components.vectorstores.Weaviate import WeaviateVectorStoreComponent
+from langflow.field_typing import Text
 from langflow.schema import Record
 
 
@@ -14,7 +15,7 @@ class WeaviateSearchVectorStore(WeaviateVectorStoreComponent, LCVectorStoreCompo
         "https://python.langchain.com/docs/integrations/vectorstores/weaviate"
     )
     beta = True
-    icon="Weaviate"
+    icon = "Weaviate"
 
     field_config = {
         "search_type": {
@@ -57,7 +58,7 @@ class WeaviateSearchVectorStore(WeaviateVectorStoreComponent, LCVectorStoreCompo
 
     def build(  # type: ignore[override]
         self,
-        input_value: str,
+        input_value: Text,
         search_type: str,
         url: str,
         search_by_text: bool = False,

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

@@ -5,7 +5,8 @@ from langchain_core.retrievers import BaseRetriever
 from langchain_core.vectorstores import VectorStore
 
 from langflow import CustomComponent
-from langflow.schema import Record, docs_to_records
+from langflow.field_typing import Text, docs_to_records
+from langflow.schema import Record
 
 
 class LCVectorStoreComponent(CustomComponent):
@@ -16,7 +17,7 @@ class LCVectorStoreComponent(CustomComponent):
 
     def search_with_vector_store(
         self,
-        input_value: str,
+        input_value: Text,
         search_type: str,
         vector_store: Union[VectorStore, BaseRetriever],
     ) -> List[Record]:

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

@@ -4,6 +4,7 @@ from langchain.embeddings.base import Embeddings
 
 from langflow.components.vectorstores.base.model import LCVectorStoreComponent
 from langflow.components.vectorstores.pgvector import PGVectorComponent
+from langflow.field_typing import Text
 from langflow.schema import Record
 
 
@@ -42,7 +43,7 @@ class PGVectorSearchComponent(PGVectorComponent, LCVectorStoreComponent):
 
     def build(  # type: ignore[override]
         self,
-        input_value: str,
+        input_value: Text,
         embedding: Embeddings,
         search_type: str,
         pg_server_url: str,