Update imports and type annotations in several components

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

@@ -1,10 +1,8 @@
-from langflow import CustomComponent
+from typing import Callable, Union
+
 from langchain.chains import LLMCheckerChain
-from typing import Union, Callable
+from langflow import CustomComponent
-from langflow.field_typing import (
+from langflow.field_typing import BaseLanguageModel, Chain
-    BaseLanguageModel,
-    Chain,
-)
 
 
 class LLMCheckerChainComponent(CustomComponent):
@@ -21,4 +19,4 @@ class LLMCheckerChainComponent(CustomComponent):
         self,
         llm: BaseLanguageModel,
     ) -> Union[Chain, Callable]:
-        return LLMCheckerChain(llm=llm)
+        return LLMCheckerChain.from_llm(llm=llm)

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

@@ -1,6 +1,8 @@
-from langflow import CustomComponent
+from typing import Any, Dict, List
+
 from langchain.docstore.document import Document
-from typing import Optional, Dict, Any
+from langchain.document_loaders.directory import DirectoryLoader
+from langflow import CustomComponent
 
 
 class DirectoryLoaderComponent(CustomComponent):
@@ -23,20 +25,18 @@ class DirectoryLoaderComponent(CustomComponent):
         self,
         glob: str,
         path: str,
-        load_hidden: Optional[bool] = False,
+        max_concurrency: int = 2,
-        max_concurrency: Optional[int] = 10,
+        load_hidden: bool = False,
-        metadata: Optional[dict] = {},
+        recursive: bool = True,
-        recursive: Optional[bool] = True,
+        silent_errors: bool = False,
-        silent_errors: Optional[bool] = False,
+        use_multithreading: bool = True,
-        use_multithreading: Optional[bool] = True,
+    ) -> List[Document]:
-    ) -> Document:
+        return DirectoryLoader(
-        return Document(
             glob=glob,
             path=path,
             load_hidden=load_hidden,
             max_concurrency=max_concurrency,
-            metadata=metadata,
             recursive=recursive,
             silent_errors=silent_errors,
             use_multithreading=use_multithreading,
-        )
+        ).load()

src/backend/langflow/components/embeddings/HuggingFaceInferenceAPIEmbeddings.py

@@ -1,6 +1,8 @@
-from langflow import CustomComponent
+from typing import Dict, Optional
-from typing import Optional, Dict
+
 from langchain_community.embeddings.huggingface import HuggingFaceInferenceAPIEmbeddings
+from langflow import CustomComponent
+from pydantic.v1.types import SecretStr
 
 
 class HuggingFaceInferenceAPIEmbeddingsComponent(CustomComponent):
@@ -29,12 +31,12 @@ class HuggingFaceInferenceAPIEmbeddingsComponent(CustomComponent):
         model_kwargs: Optional[Dict] = {},
         multi_process: bool = False,
     ) -> HuggingFaceInferenceAPIEmbeddings:
+        if api_key:
+            secret_api_key = SecretStr(api_key)
+        else:
+            raise ValueError("API Key is required")
         return HuggingFaceInferenceAPIEmbeddings(
-            api_key=api_key,
+            api_key=secret_api_key,
             api_url=api_url,
             model_name=model_name,
-            cache_folder=cache_folder,
-            encode_kwargs=encode_kwargs,
-            model_kwargs=model_kwargs,
-            multi_process=multi_process,
         )

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

@@ -1,9 +1,9 @@
 from typing import Any, Callable, Dict, List, Optional, Union
 
 from langchain_openai.embeddings.base import OpenAIEmbeddings
-
 from langflow import CustomComponent
 from langflow.field_typing import NestedDict
+from pydantic.v1.types import SecretStr
 
 
 class OpenAIEmbeddingsComponent(CustomComponent):
@@ -67,7 +67,7 @@ class OpenAIEmbeddingsComponent(CustomComponent):
             },
             "skip_empty": {"display_name": "Skip Empty", "advanced": True},
             "tiktoken_model_name": {"display_name": "TikToken Model Name"},
-            "tikToken_enable": {"display_name": "TikToken Enable"},
+            "tikToken_enable": {"display_name": "TikToken Enable", "advanced": True},
         }
 
     def build(
@@ -92,14 +92,17 @@ class OpenAIEmbeddingsComponent(CustomComponent):
         request_timeout: Optional[float] = None,
         show_progress_bar: bool = False,
         skip_empty: bool = False,
-        tikToken_enable: bool = True,
+        tiktoken_enable: bool = True,
         tiktoken_model_name: Optional[str] = None,
     ) -> Union[OpenAIEmbeddings, Callable]:
         # This is to avoid errors with Vector Stores (e.g Chroma)
         if disallowed_special == ["all"]:
-            disallowed_special = "all"
+            disallowed_special = "all"  # type: ignore
+
+        api_key = SecretStr(openai_api_key) if openai_api_key else None
+
         return OpenAIEmbeddings(
-            tiktoken_enabled=tikToken_enable,
+            tiktoken_enabled=tiktoken_enable,
             default_headers=default_headers,
             default_query=default_query,
             allowed_special=set(allowed_special),
@@ -112,7 +115,7 @@ class OpenAIEmbeddingsComponent(CustomComponent):
             model=model,
             model_kwargs=model_kwargs,
             base_url=openai_api_base,
-            api_key=openai_api_key,
+            api_key=api_key,
             openai_api_type=openai_api_type,
             api_version=openai_api_version,
             organization=openai_organization,

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

@@ -1,4 +1,4 @@
-from pydantic import SecretStr
+from pydantic.v1.types import SecretStr
 from langflow import CustomComponent
 from typing import Optional, Union, Callable
 from langflow.field_typing import BaseLanguageModel

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

@@ -1,9 +1,9 @@
 from typing import Optional
 
 from langchain_google_genai import ChatGoogleGenerativeAI  # type: ignore
-
 from langflow import CustomComponent
 from langflow.field_typing import BaseLanguageModel, RangeSpec, TemplateField
+from pydantic.v1.types import SecretStr
 
 
 class GoogleGenerativeAIComponent(CustomComponent):
@@ -63,10 +63,10 @@ class GoogleGenerativeAIComponent(CustomComponent):
     ) -> BaseLanguageModel:
         return ChatGoogleGenerativeAI(
             model=model,
-            max_output_tokens=max_output_tokens or None,
+            max_output_tokens=max_output_tokens or None,  # type: ignore
             temperature=temperature,
             top_k=top_k or None,
-            top_p=top_p or None,
+            top_p=top_p or None,  # type: ignore
             n=n or 1,
-            google_api_key=google_api_key,
+            google_api_key=SecretStr(google_api_key),
         )

src/backend/langflow/components/toolkits/OpenAPIToolkit.py

@@ -1,8 +1,7 @@
+from langchain_community.agent_toolkits.openapi.toolkit import BaseToolkit, OpenAPIToolkit
+from langchain_community.utilities.requests import TextRequestsWrapper
 from langflow import CustomComponent
 from langflow.field_typing import AgentExecutor
-from typing import Callable
-from langchain_community.utilities.requests import TextRequestsWrapper
-from langchain_community.agent_toolkits.openapi.toolkit import OpenAPIToolkit
 
 
 class OpenAPIToolkitComponent(CustomComponent):
@@ -19,5 +18,5 @@ class OpenAPIToolkitComponent(CustomComponent):
         self,
         json_agent: AgentExecutor,
         requests_wrapper: TextRequestsWrapper,
-    ) -> Callable:
+    ) -> BaseToolkit:
         return OpenAPIToolkit(json_agent=json_agent, requests_wrapper=requests_wrapper)

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

@@ -1,6 +1,7 @@
-from langflow import CustomComponent
+from typing import Callable, Union
-from typing import Union, Callable
+
 from langchain_community.utilities.google_search import GoogleSearchAPIWrapper
+from langflow import CustomComponent
 
 
 class GoogleSearchAPIWrapperComponent(CustomComponent):
@@ -18,4 +19,4 @@ class GoogleSearchAPIWrapperComponent(CustomComponent):
         google_api_key: str,
         google_cse_id: str,
     ) -> Union[GoogleSearchAPIWrapper, Callable]:
-        return GoogleSearchAPIWrapper(google_api_key=google_api_key, google_cse_id=google_cse_id)
+        return GoogleSearchAPIWrapper(google_api_key=google_api_key, google_cse_id=google_cse_id)  # type: ignore

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

@@ -1,9 +1,9 @@
-from langflow import CustomComponent
+from typing import Dict
-from typing import Dict, Optional
 
 # Assuming the existence of GoogleSerperAPIWrapper class in the serper module
 # If this class does not exist, you would need to create it or import the appropriate class from another module
 from langchain_community.utilities.google_serper import GoogleSerperAPIWrapper
+from langflow import CustomComponent
 
 
 class GoogleSerperAPIWrapperComponent(CustomComponent):
@@ -42,6 +42,5 @@ class GoogleSerperAPIWrapperComponent(CustomComponent):
     def build(
         self,
         serper_api_key: str,
-        result_key_for_type: Optional[Dict[str, str]] = None,
     ) -> GoogleSerperAPIWrapper:
-        return GoogleSerperAPIWrapper(result_key_for_type=result_key_for_type, serper_api_key=serper_api_key)
+        return GoogleSerperAPIWrapper(serper_api_key=serper_api_key)

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

@@ -5,7 +5,6 @@ import pinecone  # type: ignore
 from langchain.schema import BaseRetriever
 from langchain_community.vectorstores import VectorStore
 from langchain_community.vectorstores.pinecone import Pinecone
-
 from langflow import CustomComponent
 from langflow.field_typing import Document, Embeddings
 
@@ -31,11 +30,11 @@ class PineconeComponent(CustomComponent):
         embedding: Embeddings,
         pinecone_env: str,
         documents: List[Document],
+        text_key: str = "text",
+        pool_threads: int = 4,
         index_name: Optional[str] = None,
         pinecone_api_key: Optional[str] = None,
-        text_key: Optional[str] = "text",
         namespace: Optional[str] = "default",
-        pool_threads: Optional[int] = None,
     ) -> Union[VectorStore, Pinecone, BaseRetriever]:
         if pinecone_api_key is None or pinecone_env is None:
             raise ValueError("Pinecone API Key and Environment are required.")
@@ -43,6 +42,8 @@ class PineconeComponent(CustomComponent):
             raise ValueError("Pinecone API Key is required.")
 
         pinecone.init(api_key=pinecone_api_key, environment=pinecone_env)  # type: ignore
+        if not index_name:
+            raise ValueError("Index Name is required.")
         if documents:
             return Pinecone.from_documents(
                 documents=documents,

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

@@ -36,14 +36,14 @@ class QdrantComponent(CustomComponent):
     def build(
         self,
         embedding: Embeddings,
+        collection_name: str,
         documents: Optional[Document] = None,
         api_key: Optional[str] = None,
-        collection_name: Optional[str] = None,
         content_payload_key: str = "page_content",
         distance_func: str = "Cosine",
-        grpc_port: Optional[int] = 6334,
+        grpc_port: int = 6334,
-        host: Optional[str] = None,
         https: bool = False,
+        host: Optional[str] = None,
         location: Optional[str] = None,
         metadata_payload_key: str = "metadata",
         path: Optional[str] = None,
@@ -51,7 +51,7 @@ class QdrantComponent(CustomComponent):
         prefer_grpc: bool = False,
         prefix: Optional[str] = None,
         search_kwargs: Optional[NestedDict] = None,
-        timeout: Optional[float] = None,
+        timeout: Optional[int] = None,
         url: Optional[str] = None,
     ) -> Union[VectorStore, Qdrant, BaseRetriever]:
         if documents is None:
@@ -77,13 +77,11 @@ class QdrantComponent(CustomComponent):
                 client=client,
                 collection_name=collection_name,
                 embeddings=embedding,
-                search_kwargs=search_kwargs,
-                distance_func=distance_func,
             )
             return vs
         else:
             vs = Qdrant.from_documents(
-                documents=documents,
+                documents=documents,  # type: ignore
                 embedding=embedding,
                 api_key=api_key,
                 collection_name=collection_name,

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

@@ -5,7 +5,6 @@ from langchain_community.vectorstores import VectorStore
 from langchain_community.vectorstores.redis import Redis
 from langchain_core.documents import Document
 from langchain_core.retrievers import BaseRetriever
-
 from langflow import CustomComponent
 
 
@@ -31,6 +30,7 @@ class RedisComponent(CustomComponent):
             "code": {"show": False, "display_name": "Code"},
             "documents": {"display_name": "Documents", "is_list": True},
             "embedding": {"display_name": "Embedding"},
+            "schema": {"display_name": "Schema", "file_types": [".yaml"]},
             "redis_server_url": {
                 "display_name": "Redis Server Connection String",
                 "advanced": False,
@@ -43,6 +43,7 @@ class RedisComponent(CustomComponent):
         embedding: Embeddings,
         redis_server_url: str,
         redis_index_name: str,
+        schema: Optional[str] = None,
         documents: Optional[Document] = None,
     ) -> Union[VectorStore, BaseRetriever]:
         """
@@ -58,10 +59,12 @@ class RedisComponent(CustomComponent):
         - VectorStore: The Vector Store object.
         """
         if documents is None:
+            if schema is None:
+                raise ValueError("If no documents are provided, a schema must be provided.")
             redis_vs = Redis.from_existing_index(
                 embedding=embedding,
                 index_name=redis_index_name,
-                schema=None,
+                schema=schema,
                 key_prefix=None,
                 redis_url=redis_server_url,
             )

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

@@ -6,7 +6,6 @@ from typing import List, Optional, Union
 from langchain_community.embeddings import FakeEmbeddings
 from langchain_community.vectorstores.vectara import Vectara
 from langchain_core.vectorstores import VectorStore
-
 from langflow import CustomComponent
 from langflow.field_typing import BaseRetriever, Document
 
@@ -46,7 +45,7 @@ class VectaraComponent(CustomComponent):
 
         if documents is not None:
             return Vectara.from_documents(
-                documents=documents,
+                documents=documents,  # type: ignore
                 embedding=FakeEmbeddings(size=768),
                 vectara_customer_id=vectara_customer_id,
                 vectara_corpus_id=vectara_corpus_id,

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

@@ -5,7 +5,6 @@ from langchain_community.vectorstores import VectorStore
 from langchain_community.vectorstores.pgvector import PGVector
 from langchain_core.documents import Document
 from langchain_core.retrievers import BaseRetriever
-
 from langflow import CustomComponent
 
 
@@ -63,13 +62,13 @@ class PGVectorComponent(CustomComponent):
                     collection_name=collection_name,
                     connection_string=pg_server_url,
                 )
-
+            else:
-            vector_store = PGVector.from_documents(
+                vector_store = PGVector.from_documents(
-                embedding=embedding,
+                    embedding=embedding,
-                documents=documents,
+                    documents=documents,  # type: ignore
-                collection_name=collection_name,
+                    collection_name=collection_name,
-                connection_string=pg_server_url,
+                    connection_string=pg_server_url,
-            )
+                )
         except Exception as e:
             raise RuntimeError(f"Failed to build PGVector: {e}")
         return vector_store