Fix imports and formatting issues

src/backend/langflow/components/agents/CSVAgent.py

@@ -2,6 +2,7 @@ from langflow import CustomComponent
 from langflow.field_typing import BaseLanguageModel, AgentExecutor
 from langchain_experimental.agents.agent_toolkits.csv.base import create_csv_agent
 
+
 class CSVAgentComponent(CustomComponent):
     display_name = "CSVAgent"
     description = "Construct a CSV agent from a CSV and tools."

src/backend/langflow/components/agents/JsonAgent.py

@@ -1,10 +1,11 @@
-
 from langflow import CustomComponent
-from langchain.agents import AgentExecutor,create_json_agent
+from langchain.agents import AgentExecutor, create_json_agent
 from langflow.field_typing import (
     BaseLanguageModel,
 )
 from langchain_community.agent_toolkits.base import BaseToolkit
+
+
 class JsonAgentComponent(CustomComponent):
     display_name = "JsonAgent"
     description = "Construct a json agent from an LLM and tools."

src/backend/langflow/components/agents/SQLAgent.py

@@ -1,4 +1,3 @@
-
 from langflow import CustomComponent
 from typing import Union, Callable
 from langchain.agents import AgentExecutor
@@ -7,6 +6,7 @@ from langchain_community.agent_toolkits.sql.base import create_sql_agent
 from langchain.sql_database import SQLDatabase
 from langchain_community.agent_toolkits import SQLDatabaseToolkit
 
+
 class SQLAgentComponent(CustomComponent):
     display_name = "SQLAgent"
     description = "Construct an SQL agent from an LLM and tools."
@@ -15,7 +15,7 @@ class SQLAgentComponent(CustomComponent):
         return {
             "llm": {"display_name": "LLM"},
             "database_uri": {"display_name": "Database URI"},
-            "verbose": {"display_name": "Verbose", "value": False,"advanced": True},
+            "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
         }
 
     def build(

src/backend/langflow/components/agents/VectorStoreAgent.py

@@ -1,10 +1,10 @@
-
 from langflow import CustomComponent
 from langchain.agents import AgentExecutor, create_vectorstore_agent
 from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreToolkit
 from typing import Union, Callable
 from langflow.field_typing import BaseLanguageModel
 
+
 class VectorStoreAgentComponent(CustomComponent):
     display_name = "VectorStoreAgent"
     description = "Construct an agent from a Vector Store."
@@ -20,4 +20,4 @@ class VectorStoreAgentComponent(CustomComponent):
         llm: BaseLanguageModel,
         vector_store_toolkit: VectorStoreToolkit,
     ) -> Union[AgentExecutor, Callable]:
-        return create_vectorstore_agent(llm=llm,toolkit=vector_store_toolkit)
+        return create_vectorstore_agent(llm=llm, toolkit=vector_store_toolkit)

src/backend/langflow/components/agents/VectorStoreRouterAgent.py

@@ -1,10 +1,10 @@
-
 from langflow import CustomComponent
 from langchain_core.language_models.base import BaseLanguageModel
 from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreRouterToolkit
 from langchain.agents import create_vectorstore_router_agent
 from typing import Callable
 
+
 class VectorStoreRouterAgentComponent(CustomComponent):
     display_name = "VectorStoreRouterAgent"
     description = "Construct an agent from a Vector Store Router."
@@ -15,9 +15,5 @@ class VectorStoreRouterAgentComponent(CustomComponent):
             "vectorstoreroutertoolkit": {"display_name": "Vector Store Router Toolkit"},
         }
 
-    def build(
+    def build(self, llm: BaseLanguageModel, vectorstoreroutertoolkit: VectorStoreRouterToolkit) -> Callable:
-        self, 
+        return create_vectorstore_router_agent(llm=llm, toolkit=vectorstoreroutertoolkit)
-        llm: BaseLanguageModel, 
-        vectorstoreroutertoolkit: VectorStoreRouterToolkit
-    ) -> Callable:
-        return create_vectorstore_router_agent(llm=llm,toolkit=vectorstoreroutertoolkit)

src/backend/langflow/components/agents/ZeroShotAgent.py

@@ -1,10 +1,11 @@
+from typing import List
 
-from langflow import CustomComponent
 from langchain.agents import ZeroShotAgent
 from langchain_core.tools import BaseTool
-from typing import List, Optional
+from langflow import CustomComponent
 from langflow.components.chains.LLMChain import LLMChain
 
+
 class ZeroShotAgentComponent(CustomComponent):
     display_name = "ZeroShotAgent"
     description = "Construct an agent from an LLM and tools."
@@ -21,7 +22,7 @@ class ZeroShotAgentComponent(CustomComponent):
         self,
         llm: LLMChain,
         tools: List[BaseTool],
-        prefix: Optional[str] = "Answer the following questions as best you can. You have access to the following tools:",
+        prefix: str = "Answer the following questions as best you can. You have access to the following tools:",
-        suffix: Optional[str] = "Begin!\n\nQuestion: {input}\nThought:{agent_scratchpad}",
+        suffix: str = "Begin!\n\nQuestion: {input}\nThought:{agent_scratchpad}",
     ) -> ZeroShotAgent:
         return ZeroShotAgent(llm_chain=llm, tools=tools, prefix=prefix, suffix=suffix)

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

@@ -1,9 +1,9 @@
-
 from langflow import CustomComponent
 from langflow.field_typing import BaseLanguageModel, Chain
 from typing import Union, Callable
 from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
 
+
 class CombineDocsChainComponent(CustomComponent):
     display_name = "CombineDocsChain"
     description = "Load question answering chain."
@@ -13,7 +13,7 @@ class CombineDocsChainComponent(CustomComponent):
             "llm": {"display_name": "LLM"},
             "chain_type": {
                 "display_name": "Chain Type",
-                "options": ['stuff', 'map_reduce', 'map_rerank', 'refine'],
+                "options": ["stuff", "map_reduce", "map_rerank", "refine"],
             },
         }
 
@@ -22,7 +22,7 @@ class CombineDocsChainComponent(CustomComponent):
         llm: BaseLanguageModel,
         chain_type: str,
     ) -> Union[Chain, Callable]:
-        if chain_type not in ['stuff', 'map_reduce', 'map_rerank', 'refine']:
+        if chain_type not in ["stuff", "map_reduce", "map_rerank", "refine"]:
             raise ValueError(f"Invalid chain_type: {chain_type}")
 
         return BaseCombineDocumentsChain()

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

@@ -28,5 +28,5 @@ class LLMChainComponent(CustomComponent):
         prompt: BasePromptTemplate,
         llm: BaseLanguageModel,
         memory: Optional[BaseMemory] = None,
-    ) -> Union[Chain, Callable,LLMChain]:
+    ) -> Union[Chain, Callable, LLMChain]:
         return LLMChain(prompt=prompt, llm=llm, memory=memory)

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

@@ -1,4 +1,3 @@
-
 from langflow import CustomComponent
 from langchain.chains import LLMCheckerChain
 from typing import Union, Callable
@@ -7,6 +6,7 @@ from langflow.field_typing import (
     Chain,
 )
 
+
 class LLMCheckerChainComponent(CustomComponent):
     display_name = "LLMCheckerChain"
     description = ""

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

@@ -1,12 +1,10 @@
+from typing import Callable, Optional, Union
+
+from langchain.chains import LLMChain, LLMMathChain
 
 from langflow import CustomComponent
-from langchain.chains import LLMChain,LLMMathChain
+from langflow.field_typing import BaseLanguageModel, BaseMemory, Chain
-from typing import Callable, Optional, Union
+
-from langflow.field_typing import (
-    BaseLanguageModel,
-    BaseMemory,
-    Chain
-)
 
 class LLMMathChainComponent(CustomComponent):
     display_name = "LLMMathChain"
@@ -26,8 +24,8 @@ class LLMMathChainComponent(CustomComponent):
         self,
         llm: BaseLanguageModel,
         llm_chain: LLMChain,
-        input_key: Optional[str]="question",
+        input_key: str = "question",
-        output_key: Optional[str]="answer",
+        output_key: str = "answer",
         memory: Optional[BaseMemory] = None,
-    ) -> Union[LLMMathChain, Callable,Chain]:
+    ) -> Union[LLMMathChain, Callable, Chain]:
         return LLMMathChain(llm=llm, llm_chain=llm_chain, input_key=input_key, output_key=output_key, memory=memory)

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

@@ -1,11 +1,11 @@
+from typing import Callable, Optional, Union
 
-from langflow import CustomComponent
-from typing import Optional, Union, Callable
-from langflow.field_typing import (
-    BaseMemory,
-    BaseRetriever)
-from langchain.chains.retrieval_qa.base import BaseRetrievalQA
 from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
+from langchain.chains.retrieval_qa.base import BaseRetrievalQA, RetrievalQA
+from langflow import CustomComponent
+from langflow.field_typing import BaseMemory, BaseRetriever
+
+
 class RetrievalQAComponent(CustomComponent):
     display_name = "RetrievalQA"
     description = "Chain for question-answering against an index."
@@ -15,8 +15,8 @@ class RetrievalQAComponent(CustomComponent):
             "combine_documents_chain": {"display_name": "Combine Documents Chain"},
             "retriever": {"display_name": "Retriever"},
             "memory": {"display_name": "Memory", "required": False},
-            "input_key": {"display_name": "Input Key","advanced":True},
+            "input_key": {"display_name": "Input Key", "advanced": True},
-            "output_key": {"display_name": "Output Key","advanced":True},
+            "output_key": {"display_name": "Output Key", "advanced": True},
             "return_source_documents": {"display_name": "Return Source Documents"},
         }
 
@@ -25,11 +25,11 @@ class RetrievalQAComponent(CustomComponent):
         combine_documents_chain: BaseCombineDocumentsChain,
         retriever: BaseRetriever,
         memory: Optional[BaseMemory] = None,
-        input_key: Optional[str] = "query",
+        input_key: str = "query",
-        output_key: Optional[str] = "result",
+        output_key: str = "result",
-        return_source_documents: Optional[bool] = True,
+        return_source_documents: bool = True,
     ) -> Union[BaseRetrievalQA, Callable]:
-        return BaseRetrievalQA(
+        return RetrievalQA(
             combine_documents_chain=combine_documents_chain,
             retriever=retriever,
             memory=memory,

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

@@ -1,13 +1,11 @@
+from typing import Optional
+
+from langchain.chains import BaseQAWithSourcesChain, RetrievalQAWithSourcesChain
+from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
 
 from langflow import CustomComponent
-from langchain.chains import RetrievalQAWithSourcesChain
+from langflow.field_typing import BaseLanguageModel, BaseMemory, BaseRetriever
-from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
+
-from typing import Optional
-from langflow.field_typing import (
-    BaseMemory,
-    BaseRetriever,
-    BaseLanguageModel
-)
 
 class RetrievalQAWithSourcesChainComponent(CustomComponent):
     display_name = "RetrievalQAWithSourcesChain"
@@ -18,14 +16,12 @@ class RetrievalQAWithSourcesChainComponent(CustomComponent):
             "llm": {"display_name": "LLM"},
             "chain_type": {
                 "display_name": "Chain Type",
-                "options": ['stuff', 'map_reduce', 'map_rerank', 'refine'],
+                "options": ["stuff", "map_reduce", "map_rerank", "refine"],
             },
             "memory": {"display_name": "Memory"},
             "return_source_documents": {"display_name": "Return Source Documents"},
-
         }
 
-
     def build(
         self,
         retriever: BaseRetriever,
@@ -34,5 +30,12 @@ class RetrievalQAWithSourcesChainComponent(CustomComponent):
         chain_type: str,
         memory: Optional[BaseMemory] = None,
         return_source_documents: Optional[bool] = True,
-    ) -> RetrievalQAWithSourcesChain:
+    ) -> BaseQAWithSourcesChain:
-        return RetrievalQAWithSourcesChain(combine_documents_chain=combine_documents_chain,memory=memory,return_source_documents=return_source_documents,retriever=retriever).from_chain_type(llm=llm, chain_type=chain_type)
+        return RetrievalQAWithSourcesChain.from_chain_type(
+            llm=llm,
+            chain_type=chain_type,
+            combine_documents_chain=combine_documents_chain,
+            memory=memory,
+            return_source_documents=return_source_documents,
+            retriever=retriever,
+        )

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

@@ -1,14 +1,10 @@
-
 from langflow import CustomComponent
 from typing import Callable, Union
-from langflow.field_typing import (
+from langflow.field_typing import BasePromptTemplate, BaseLanguageModel, Chain
-    BasePromptTemplate,
-    BaseLanguageModel,
-    Chain
-)
 from langchain_community.utilities.sql_database import SQLDatabase
 from langchain_experimental.sql.base import SQLDatabaseChain
 
+
 class SQLDatabaseChainComponent(CustomComponent):
     display_name = "SQLDatabaseChain"
     description = ""
@@ -25,5 +21,5 @@ class SQLDatabaseChainComponent(CustomComponent):
         db: SQLDatabase,
         llm: BaseLanguageModel,
         prompt: BasePromptTemplate,
-    ) -> Union[Chain, Callable,SQLDatabaseChain]:
+    ) -> Union[Chain, Callable, SQLDatabaseChain]:
         return SQLDatabaseChain.from_llm(llm=llm, db=db, prompt=prompt)

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

@@ -17,7 +17,7 @@ class AZLyricsLoaderComponent(CustomComponent):
 
     def build(self, metadata: Optional[Dict] = None, web_path: str = "") -> Document:
         documents = AZLyricsLoader(web_path=web_path).load()
-        if(metadata):
+        if metadata:
             for document in documents:
                 if not document.metadata:
                     document.metadata = metadata

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

@@ -28,7 +28,7 @@ class AirbyteJSONLoaderComponent(CustomComponent):
 
     def build(self, file_path: str, metadata: Optional[Dict] = None) -> Document:
         documents = AirbyteJSONLoader(file_path=file_path).load()
-        if(metadata):
+        if metadata:
             for document in documents:
                 if not document.metadata:
                     document.metadata = metadata

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

@@ -1,9 +1,9 @@
-
 from langflow import CustomComponent
 from typing import List
 from langchain_community.document_loaders.csv_loader import CSVLoader
 from langchain.docstore.document import Document
 
+
 class CSVLoaderComponent(CustomComponent):
     display_name = "CSVLoader"
     description = "Load a `CSV` file into a list of Documents."
@@ -23,13 +23,9 @@ class CSVLoaderComponent(CustomComponent):
             },
         }
 
-    def build(
+    def build(self, file_path: str, metadata: dict) -> List[Document]:
-        self,
-        file_path: str,
-        metadata: dict
-    ) -> List[Document]:
         documents = CSVLoader(file_path=file_path).load()
-        if(metadata):
+        if metadata:
             for document in documents:
                 if not document.metadata:
                     document.metadata = metadata

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

@@ -26,7 +26,7 @@ class CoNLLULoaderComponent(CustomComponent):
 
     def build(self, file_path: str, metadata: dict) -> Document:
         documents = CoNLLULoader(file_path=file_path).load()
-        if(metadata):
+        if metadata:
             for document in documents:
                 if not document.metadata:
                     document.metadata = metadata

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

@@ -1,13 +1,15 @@
-
 from langflow import CustomComponent
 from langchain.docstore.document import Document
 from typing import Optional
 from langchain_community.document_loaders.college_confidential import CollegeConfidentialLoader
 
+
 class CollegeConfidentialLoaderComponent(CustomComponent):
     display_name = "CollegeConfidentialLoader"
     description = "Load `College Confidential` webpages."
-    documentation = "https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/college_confidential"
+    documentation = (
+        "https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/college_confidential"
+    )
 
     def build_config(self):
         return {
@@ -15,13 +17,9 @@ class CollegeConfidentialLoaderComponent(CustomComponent):
             "web_path": {"display_name": "Web Page", "required": True},
         }
 
-    def build(
+    def build(self, web_path: str, metadata: Optional[dict] = {}) -> Document:
-        self,
-        web_path: str,
-        metadata: Optional[dict] = {}
-    ) -> Document:
         documents = CollegeConfidentialLoader(web_path=web_path).load()
-        if(metadata):
+        if metadata:
             for document in documents:
                 if not document.metadata:
                     document.metadata = metadata

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

@@ -1,8 +1,8 @@
-
 from langflow import CustomComponent
 from langchain.docstore.document import Document
 from typing import Optional, Dict, Any
 
+
 class DirectoryLoaderComponent(CustomComponent):
     display_name = "DirectoryLoader"
     description = "Load from a directory."

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

@@ -3,6 +3,7 @@ from langflow.field_typing import Document
 from typing import Optional, Dict
 from langchain_community.document_loaders.evernote import EverNoteLoader
 
+
 class EverNoteLoaderComponent(CustomComponent):
     display_name = "EverNoteLoader"
     description = "Load from `EverNote`."
@@ -28,7 +29,7 @@ class EverNoteLoaderComponent(CustomComponent):
 
     def build(self, file_path: str, metadata: Optional[Dict] = None) -> Document:
         documents = EverNoteLoader(file_path=file_path).load()
-        if(metadata):
+        if metadata:
             for document in documents:
                 if not document.metadata:
                     document.metadata = metadata

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

@@ -1,12 +1,15 @@
-
 from langflow import CustomComponent
 from langchain.docstore.document import Document
 from typing import Optional, Dict
 from langchain_community.document_loaders.facebook_chat import FacebookChatLoader
+
+
 class FacebookChatLoaderComponent(CustomComponent):
     display_name = "FacebookChatLoader"
     description = "Load `Facebook Chat` messages directory dump."
-    documentation = "https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/facebook_chat"
+    documentation = (
+        "https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/facebook_chat"
+    )
 
     def build_config(self):
         return {
@@ -25,7 +28,7 @@ class FacebookChatLoaderComponent(CustomComponent):
 
     def build(self, file_path: str, metadata: Optional[Dict] = None) -> Document:
         documents = FacebookChatLoader(file_path=file_path).load()
-        if(metadata):
+        if metadata:
             for document in documents:
                 if not document.metadata:
                     document.metadata = metadata

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

@@ -12,7 +12,7 @@ class GitbookLoaderComponent(CustomComponent):
         return {
             "metadata": {
                 "display_name": "Metadata",
-                "field_type":"dict",
+                "field_type": "dict",
                 "value": {},
             },
             "web_page": {
@@ -23,7 +23,7 @@ class GitbookLoaderComponent(CustomComponent):
 
     def build(self, metadata: Optional[Dict] = None, web_page: str = "") -> Document:
         documents = GitbookLoader(web_page=web_page).load()
-        if(metadata):
+        if metadata:
             for document in documents:
                 if not document.metadata:
                     document.metadata = metadata

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

@@ -1,4 +1,3 @@
-
 from langflow import CustomComponent
 from typing import Optional, Dict
 from langchain_community.document_loaders.hn import HNLoader
@@ -10,16 +9,8 @@ class HNLoaderComponent(CustomComponent):
 
     def build_config(self):
         return {
-            "metadata": {
+            "metadata": {"display_name": "Metadata", "value": {}, "required": False, "field_type": "dict"},
-                "display_name": "Metadata",
+            "web_path": {"display_name": "Web Page", "required": True},
-                "value": {},
-                "required": False,
-                "field_type": "dict"
-            },
-            "web_path": {
-                "display_name": "Web Page",
-                "required": True
-            },
         }
 
     def build(
@@ -28,7 +19,7 @@ class HNLoaderComponent(CustomComponent):
         metadata: Optional[Dict] = None,
     ) -> HNLoader:
         documents = HNLoader(web_path=web_path).load()
-        if(metadata):
+        if metadata:
             for document in documents:
                 if not document.metadata:
                     document.metadata = metadata

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

@@ -1,6 +1,8 @@
+from typing import Dict, List, Optional
+
+from langchain_community.document_loaders.ifixit import IFixitLoader
 from langflow import CustomComponent
 from langflow.field_typing import Document
-from typing import Optional, Dict
 
 
 class IFixitLoaderComponent(CustomComponent):
@@ -14,7 +16,17 @@ class IFixitLoaderComponent(CustomComponent):
             "web_path": {"display_name": "Web Page", "type": "str"},
         }
 
-    def build(self, web_path: str, metadata: Optional[Dict] = None) -> Document:
+    def build(self, web_path: str, metadata: Optional[Dict] = None) -> List[Document]:
         # Assuming IFixitLoader is the correct class name from the langchain library,
         # and it has a load method that returns a Document object.
-        return IFixitLoader(web_path=web_path, metadata=metadata).load()
+        if metadata is None:
+            metadata = {}
+
+        docs = IFixitLoader(web_path=web_path).load()
+
+        if metadata:
+            for doc in docs:
+                if doc.metadata is None:
+                    doc.metadata = {}
+                doc.metadata.update(metadata)
+        return docs

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

@@ -21,7 +21,7 @@ class IMSDbLoaderComponent(CustomComponent):
         web_path: str = "",
     ) -> Document:
         documents = IMSDbLoader(web_path=web_path).load()
-        if(metadata):
+        if metadata:
             for document in documents:
                 if not document.metadata:
                     document.metadata = metadata

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

@@ -1,23 +0,0 @@
-
-from langflow import CustomComponent
-from langchain.documents import Document
-from typing import Optional, Dict
-
-class PyPDFDirectoryLoaderComponent(CustomComponent):
-    display_name = "PyPDFDirectoryLoader"
-    description = "Load a directory with `PDF` files using `pypdf` and chunks at character level."
-
-    def build_config(self):
-        return {
-            "metadata": {"display_name": "Metadata", "required": False},
-            "path": {"display_name": "Local directory", "required": True},
-        }
-
-    def build(
-        self,
-        path: str,
-        metadata: Optional[Dict] = None,
-    ) -> Document:
-        # Assuming there is a PyPDFDirectoryLoader class that takes these parameters
-        # Since the actual implementation is not provided, this is a placeholder
-        return PyPDFDirectoryLoader(path=path, metadata=metadata)

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

@@ -1,7 +1,10 @@
+from typing import Dict, List, Optional
+
+from langchain_community.document_loaders.pdf import PyPDFLoader
+from langchain_core.documents import Document
 
 from langflow import CustomComponent
-from langchain.document_loaders import BaseLoader
+
-from typing import Optional, Dict
 
 class PyPDFLoaderComponent(CustomComponent):
     display_name = "PyPDFLoader"
@@ -22,10 +25,17 @@ class PyPDFLoaderComponent(CustomComponent):
                 "required": False,
                 "type": "dict",
                 "show": True,
-            }
+            },
         }
 
-    def build(self, file_path: str, metadata: Optional[Dict] = None) -> BaseLoader:
+    def build(self, file_path: str, metadata: Optional[Dict] = None) -> List[Document]:
         # Assuming there is a PyPDFLoader class that takes file_path and metadata as parameters
         # and inherits from BaseLoader
-        return PyPDFLoader(file_path=file_path, metadata=metadata)
+        docs = PyPDFLoader(file_path=file_path).load()
+
+        if metadata:
+            for doc in docs:
+                if doc.metadata is None:
+                    doc.metadata = {}
+                doc.metadata.update(metadata)
+        return docs

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

@@ -1,5 +1,5 @@
 from langflow import CustomComponent
-from typing import Dict, Optional,List
+from typing import Dict, Optional, List
 from langchain_core.documents import Document
 from langchain_community.document_loaders.readthedocs import ReadTheDocsLoader
 
@@ -10,7 +10,7 @@ class ReadTheDocsLoaderComponent(CustomComponent):
 
     def build_config(self):
         return {
-            "metadata": {"display_name": "Metadata", "default": {},"field_type": "dict"},
+            "metadata": {"display_name": "Metadata", "default": {}, "field_type": "dict"},
             "path": {"display_name": "Local directory", "required": True},
         }
 
@@ -20,7 +20,7 @@ class ReadTheDocsLoaderComponent(CustomComponent):
         metadata: Optional[Dict] = None,
     ) -> List[Document]:
         documents = ReadTheDocsLoader(path=path).load()
-        if(metadata):
+        if metadata:
             for document in documents:
                 if not document.metadata:
                     document.metadata = metadata

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

@@ -1,8 +1,8 @@
-
 from langflow import CustomComponent
 from langchain.documents import Document
 from typing import Optional, Dict
 
+
 class SRTLoaderComponent(CustomComponent):
     display_name = "SRTLoader"
     description = "Load `.srt` (subtitle) files."

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

@@ -1,8 +1,9 @@
-
 from langflow import CustomComponent
 from typing import Optional, Dict, List
 from langchain_core.documents import Document
 from langchain_community.document_loaders.slack_directory import SlackDirectoryLoader
+
+
 class SlackDirectoryLoaderComponent(CustomComponent):
     display_name = "SlackDirectoryLoader"
     description = "Load from a `Slack` directory dump."
@@ -10,7 +11,7 @@ class SlackDirectoryLoaderComponent(CustomComponent):
 
     def build_config(self):
         return {
-            "zip_path": {"display_name": "Path to zip file","field_type": "file","file_types":[".zip"]},
+            "zip_path": {"display_name": "Path to zip file", "field_type": "file", "file_types": [".zip"]},
             "metadata": {"display_name": "Metadata", "field_type": "dict"},
             "workspace_url": {"display_name": "Workspace URL"},
         }
@@ -21,8 +22,8 @@ class SlackDirectoryLoaderComponent(CustomComponent):
         metadata: Optional[Dict] = None,
         workspace_url: Optional[str] = None,
     ) -> List[Document]:
-        documents = SlackDirectoryLoader(zip_path=zip_path,workspace_url=workspace_url).load()
+        documents = SlackDirectoryLoader(zip_path=zip_path, workspace_url=workspace_url).load()
-        if(metadata):
+        if metadata:
             for document in documents:
                 if not document.metadata:
                     document.metadata = metadata

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

@@ -1,8 +1,8 @@
-
 from langflow import CustomComponent
 from langchain.data_connections import Document
 from typing import Optional, Dict
 
+
 class TextLoaderComponent(CustomComponent):
     display_name = "TextLoader"
     description = "Load text file."

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

@@ -1,6 +1,8 @@
+from typing import Dict, List, Optional
+
 from langchain import CustomComponent
-from langflow.field_typing import Document
+from langchain_community.document_loaders import UnstructuredHTMLLoader
-from typing import Optional, Dict
+from langchain_core.documents import Document
 
 
 class UnstructuredHTMLLoaderComponent(CustomComponent):
@@ -14,7 +16,14 @@ class UnstructuredHTMLLoaderComponent(CustomComponent):
             "metadata": {"display_name": "Metadata"},
         }
 
-    def build(self, file_path: str, metadata: Optional[Dict] = None) -> Document:
+    def build(self, file_path: str, metadata: Optional[Dict] = None) -> List[Document]:
         # Assuming the existence of a function or class named UnstructuredHTMLLoader that
         # loads HTML and creates a Document object; Replace with actual implementation.
-        return UnstructuredHTMLLoader(file_path=file_path, metadata=metadata)
+        docs = UnstructuredHTMLLoader(file_path=file_path).load()
+
+        if metadata:
+            for doc in docs:
+                if doc.metadata is None:
+                    doc.metadata = {}
+                doc.metadata.update(metadata)
+        return docs

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

@@ -1,4 +1,3 @@
-
 from langflow import CustomComponent
 from langchain.document_loaders import Document
 from typing import Optional, Dict

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

@@ -1,7 +1,8 @@
+from typing import Optional
+
+from langchain_community.embeddings.cohere import CohereEmbeddings
 
 from langflow import CustomComponent
-from langchain_community.embeddings.cohere import CohereEmbeddings
-from typing import Optional
 
 
 class CohereEmbeddingsComponent(CustomComponent):
@@ -10,7 +11,7 @@ class CohereEmbeddingsComponent(CustomComponent):
 
     def build_config(self):
         return {
-            "cohere_api_key": {"display_name": "Cohere API Key","password":True},
+            "cohere_api_key": {"display_name": "Cohere API Key", "password": True},
             "model": {"display_name": "Model", "default": "embed-english-v2.0", "advanced": True},
             "truncate": {"display_name": "Truncate", "advanced": True},
             "max_retries": {"display_name": "Max Retries", "advanced": True},
@@ -24,7 +25,7 @@ class CohereEmbeddingsComponent(CustomComponent):
         max_retries: Optional[int] = None,
         model: str = "embed-english-v2.0",
         truncate: Optional[str] = None,
-        user_agent: Optional[str] = "langchain",
+        user_agent: str = "langchain",
     ) -> CohereEmbeddings:
         return CohereEmbeddings(
             max_retries=max_retries,

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

@@ -2,6 +2,7 @@ from langflow import CustomComponent
 from typing import Optional, Dict
 from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings
 
+
 class HuggingFaceEmbeddingsComponent(CustomComponent):
     display_name = "HuggingFaceEmbeddings"
     description = "HuggingFace sentence_transformers embedding models."
@@ -12,8 +13,8 @@ class HuggingFaceEmbeddingsComponent(CustomComponent):
     def build_config(self):
         return {
             "cache_folder": {"display_name": "Cache Folder", "advanced": True},
-            "encode_kwargs": {"display_name": "Encode Kwargs", "advanced": True,"field_type":"dict"},
+            "encode_kwargs": {"display_name": "Encode Kwargs", "advanced": True, "field_type": "dict"},
-            "model_kwargs": {"display_name": "Model Kwargs","field_type":"dict", "advanced": True},
+            "model_kwargs": {"display_name": "Model Kwargs", "field_type": "dict", "advanced": True},
             "model_name": {"display_name": "Model Name"},
             "multi_process": {"display_name": "Multi Process", "advanced": True},
         }

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

@@ -42,9 +42,9 @@ class OpenAIEmbeddingsComponent(CustomComponent):
             "max_retries": {"display_name": "Max Retries", "advanced": True},
             "model": {"display_name": "Model", "advanced": True},
             "model_kwargs": {"display_name": "Model Kwargs", "advanced": True},
-            "openai_api_base": {"display_name": "OpenAI API Base","password":True, "advanced": True},
+            "openai_api_base": {"display_name": "OpenAI API Base", "password": True, "advanced": True},
-            "openai_api_key": {"display_name": "OpenAI API Key","password":True},
+            "openai_api_key": {"display_name": "OpenAI API Key", "password": True},
-            "openai_api_type": {"display_name": "OpenAI API Type", "advanced": True,"password":True},
+            "openai_api_type": {"display_name": "OpenAI API Type", "advanced": True, "password": True},
             "openai_api_version": {
                 "display_name": "OpenAI API Version",
                 "advanced": True,

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

@@ -1,20 +1,20 @@
-
 from langflow import CustomComponent
 from langchain.embeddings import VertexAIEmbeddings
 from typing import Optional, List
 
+
 class VertexAIEmbeddingsComponent(CustomComponent):
     display_name = "VertexAIEmbeddings"
     description = "Google Cloud VertexAI embedding models."
 
     def build_config(self):
         return {
-            "credentials": {"display_name": "Credentials", "value": '', "file_types": ['.json'],"field_type": "file"},
+            "credentials": {"display_name": "Credentials", "value": "", "file_types": [".json"], "field_type": "file"},
             "instance": {"display_name": "instance", "advanced": True, "field_type": "dict"},
-            "location": {"display_name": "Location", "value": 'us-central1', "advanced": True},
+            "location": {"display_name": "Location", "value": "us-central1", "advanced": True},
             "max_output_tokens": {"display_name": "Max Output Tokens", "value": 128},
             "max_retries": {"display_name": "Max Retries", "value": 6, "advanced": True},
-            "model_name": {"display_name": "Model Name", "value": 'textembedding-gecko'},
+            "model_name": {"display_name": "Model Name", "value": "textembedding-gecko"},
             "n": {"display_name": "N", "value": 1, "advanced": True},
             "project": {"display_name": "Project", "advanced": True},
             "request_parallelism": {"display_name": "Request Parallelism", "value": 5, "advanced": True},
@@ -29,10 +29,10 @@ class VertexAIEmbeddingsComponent(CustomComponent):
         self,
         instance: Optional[str] = None,
         credentials: Optional[str] = None,
-        location: str = 'us-central1',
+        location: str = "us-central1",
         max_output_tokens: int = 128,
         max_retries: int = 6,
-        model_name: str = 'textembedding-gecko',
+        model_name: str = "textembedding-gecko",
         n: int = 1,
         project: Optional[str] = None,
         request_parallelism: int = 5,

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

@@ -1,6 +1,6 @@
 from langflow import CustomComponent
 from typing import Optional
-from langflow.field_typing import BaseLanguageModel,NestedDict
+from langflow.field_typing import BaseLanguageModel, NestedDict
 from langchain_community.llms.anthropic import Anthropic
 
 
@@ -21,7 +21,7 @@ class AnthropicComponent(CustomComponent):
             },
             "model_kwargs": {
                 "display_name": "Model Kwargs",
-                "field_type": 'NestedDict',
+                "field_type": "NestedDict",
                 "advanced": True,
             },
             "temperature": {

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

@@ -26,7 +26,7 @@ class AzureChatOpenAIComponent(CustomComponent):
         "2023-07-01-preview",
         "2023-08-01-preview",
         "2023-09-01-preview",
-        "2023-12-01-preview"
+        "2023-12-01-preview",
     ]
 
     def build_config(self):

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

@@ -1,8 +1,8 @@
-
 from langflow import CustomComponent
 from langchain_community.llms.ctransformers import CTransformers
 from typing import Optional, Dict
 
+
 class CTransformersComponent(CustomComponent):
     display_name = "CTransformers"
     description = "C Transformers LLM models"
@@ -11,16 +11,21 @@ class CTransformersComponent(CustomComponent):
     def build_config(self):
         return {
             "model": {"display_name": "Model", "required": True},
-            "model_file": {"display_name": "Model File", "required": False,"field_type":"file", "file_types":[".bin"]},
+            "model_file": {
+                "display_name": "Model File",
+                "required": False,
+                "field_type": "file",
+                "file_types": [".bin"],
+            },
             "model_type": {"display_name": "Model Type", "required": True},
-            "config": {"display_name": "Config", "advanced": True, "required": False,"field_type":"dict","value":'{"top_k":40,"top_p":0.95,"temperature":0.8,"repetition_penalty":1.1,"last_n_tokens":64,"seed":-1,"max_new_tokens":256,"stop":"","stream":"False","reset":"True","batch_size":8,"threads":-1,"context_length":-1,"gpu_layers":0}'}
+            "config": {
+                "display_name": "Config",
+                "advanced": True,
+                "required": False,
+                "field_type": "dict",
+                "value": '{"top_k":40,"top_p":0.95,"temperature":0.8,"repetition_penalty":1.1,"last_n_tokens":64,"seed":-1,"max_new_tokens":256,"stop":"","stream":"False","reset":"True","batch_size":8,"threads":-1,"context_length":-1,"gpu_layers":0}',
+            },
         }
 
-    def build(
+    def build(self, model: str, model_file: str, model_type: str, config: Optional[Dict] = None) -> CTransformers:
-        self,
-        model: str,
-        model_file: str,
-        model_type: str,
-        config: Optional[Dict] = None
-    ) -> CTransformers:
         return CTransformers(model=model, model_file=model_file, model_type=model_type, config=config)

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

@@ -1,8 +1,9 @@
-
 from langflow import CustomComponent
 from typing import Optional, Union, Callable
 from langflow.field_typing import BaseLanguageModel
 from langchain_community.chat_models.anthropic import ChatAnthropic
+
+
 class ChatAnthropicComponent(CustomComponent):
     display_name = "ChatAnthropic"
     description = "`Anthropic` chat large language models."
@@ -21,7 +22,7 @@ class ChatAnthropicComponent(CustomComponent):
             },
             "model_kwargs": {
                 "display_name": "Model Kwargs",
-                "field_type": 'dict',
+                "field_type": "dict",
                 "advanced": True,
             },
             "temperature": {
@@ -37,7 +38,6 @@ class ChatAnthropicComponent(CustomComponent):
         model_kwargs: dict = {},
         temperature: Optional[float] = None,
     ) -> Union[BaseLanguageModel, Callable]:
-
         return ChatAnthropic(
             anthropic_api_key=anthropic_api_key,
             anthropic_api_url=anthropic_api_url,

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

@@ -1,7 +1,9 @@
-from langflow import CustomComponent
-from langchain.llms import BaseLLM
 from typing import Optional, Union
+
+from langchain.llms import BaseLLM
 from langchain_community.chat_models.openai import ChatOpenAI
+
+from langflow import CustomComponent
 from langflow.field_typing import BaseLanguageModel, NestedDict
 
 
@@ -66,12 +68,12 @@ class ChatOpenAIComponent(CustomComponent):
         self,
         max_tokens: Optional[int] = 256,
         model_kwargs: Optional[NestedDict] = {},
-        model_name: Optional[str] = "gpt-4-1106-preview",
+        model_name: str = "gpt-4-1106-preview",
         openai_api_base: Optional[str] = None,
         openai_api_key: Optional[str] = None,
         temperature: float = 0.7,
     ) -> Union[BaseLanguageModel, BaseLLM]:
-        if(not openai_api_base):
+        if not openai_api_base:
             openai_api_base = "https://api.openai.com/v1"
         return ChatOpenAI(
             max_tokens=max_tokens,

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

@@ -1,9 +1,10 @@
-from langflow import CustomComponent
 from typing import List, Optional, Union
-from langchain_core.messages.base import BaseMessage
+
-from langchain_community.chat_models.vertexai import ChatVertexAI
-from langflow.field_typing import BaseLanguageModel
 from langchain.llms import BaseLLM
+from langchain_community.chat_models.vertexai import ChatVertexAI
+from langchain_core.messages.base import BaseMessage
+from langflow import CustomComponent
+from langflow.field_typing import BaseLanguageModel
 
 
 class ChatVertexAIComponent(CustomComponent):
@@ -63,10 +64,10 @@ class ChatVertexAIComponent(CustomComponent):
         self,
         credentials: Optional[str],
         project: str,
-        examples: Optional[List[BaseMessage]]=[],
+        examples: Optional[List[BaseMessage]] = [],
-        location: Optional[str] = "us-central1",
+        location: str = "us-central1",
         max_output_tokens: Optional[int] = 128,
-        model_name: Optional[str] = "chat-bison",
+        model_name: str = "chat-bison",
         temperature: Optional[float] = 0.0,
         top_k: Optional[int] = 40,
         top_p: Optional[float] = 0.95,

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

@@ -1,9 +1,9 @@
-
 from langflow import CustomComponent
 from langchain_core.language_models.base import BaseLanguageModel
 from typing import Optional
 from langchain_community.llms.cohere import Cohere
 
+
 class CohereComponent(CustomComponent):
     display_name = "Cohere"
     description = "Cohere large language models."
@@ -11,23 +11,9 @@ class CohereComponent(CustomComponent):
 
     def build_config(self):
         return {
-            "cohere_api_key": {
+            "cohere_api_key": {"display_name": "Cohere API Key", "type": "password", "password": True},
-                "display_name": "Cohere API Key",
+            "max_tokens": {"display_name": "Max Tokens", "default": 256, "type": "int", "show": True},
-                "type": "password",
+            "temperature": {"display_name": "Temperature", "default": 0.75, "type": "float", "show": True},
-                "password": True
-            },
-            "max_tokens": {
-                "display_name": "Max Tokens",
-                "default": 256,
-                "type": "int",
-                "show": True
-            },
-            "temperature": {
-                "display_name": "Temperature",
-                "default": 0.75,
-                "type": "float",
-                "show": True
-            },
         }
 
     def build(

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

@@ -1,8 +1,8 @@
-
 from typing import Optional, List, Dict, Any
 from langflow import CustomComponent
 from langchain_community.llms.llamacpp import LlamaCpp
 
+
 class LlamaCppComponent(CustomComponent):
     display_name = "LlamaCpp"
     description = "llama.cpp model."
@@ -24,7 +24,12 @@ class LlamaCppComponent(CustomComponent):
             "max_tokens": {"display_name": "Max Tokens", "advanced": True},
             "metadata": {"display_name": "Metadata", "advanced": True},
             "model_kwargs": {"display_name": "Model Kwargs", "advanced": True},
-            "model_path": {"display_name": "Model Path","field_type":"file", "file_types":[".bin"],"required":True},
+            "model_path": {
+                "display_name": "Model Path",
+                "field_type": "file",
+                "file_types": [".bin"],
+                "required": True,
+            },
             "n_batch": {"display_name": "N Batch", "advanced": True},
             "n_ctx": {"display_name": "N Ctx", "advanced": True},
             "n_gpu_layers": {"display_name": "N GPU Layers", "advanced": True},

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

@@ -1,7 +1,9 @@
+from typing import Dict, Optional
+
+from langchain_openai.llms.base import OpenAI
 
 from langflow import CustomComponent
-from typing import Optional, Dict
+
-from langchain_openai.llms.base import OpenAI
 
 class OpenAIComponent(CustomComponent):
     display_name = "OpenAI"
@@ -41,12 +43,12 @@ class OpenAIComponent(CustomComponent):
         self,
         max_tokens: Optional[int] = 256,
         model_kwargs: Optional[Dict] = None,
-        model_name: Optional[str] = "text-davinci-003",
+        model_name: str = "text-davinci-003",
         openai_api_base: Optional[str] = "",
         openai_api_key: str = "",
         temperature: Optional[float] = 0.7,
     ) -> OpenAI:
-        if(not openai_api_base):
+        if not openai_api_base:
             openai_api_base = "https://api.openai.com/v1"
         return OpenAI(
             max_tokens=max_tokens,

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

@@ -1,9 +1,9 @@
-
 from langflow import CustomComponent
 from langchain.llms import BaseLLM
 from typing import Optional, Union, Callable, Dict
 from langchain_community.llms.vertexai import VertexAI
 
+
 class VertexAIComponent(CustomComponent):
     display_name = "VertexAI"
     description = "Google Vertex AI large language models"
@@ -20,7 +20,7 @@ class VertexAIComponent(CustomComponent):
             "location": {
                 "display_name": "Location",
                 "type": "str",
-                "advanced":True,
+                "advanced": True,
                 "value": "us-central1",
                 "required": False,
             },
@@ -29,14 +29,14 @@ class VertexAIComponent(CustomComponent):
                 "field_type": "int",
                 "value": 128,
                 "required": False,
-                "advanced":True
+                "advanced": True,
             },
             "max_retries": {
                 "display_name": "Max Retries",
                 "type": "int",
                 "value": 6,
                 "required": False,
-                "advanced":True
+                "advanced": True,
             },
             "metadata": {
                 "display_name": "Metadata",
@@ -51,7 +51,7 @@ class VertexAIComponent(CustomComponent):
                 "required": False,
             },
             "n": {
-                "advanced":True,
+                "advanced": True,
                 "display_name": "N",
                 "field_type": "int",
                 "value": 1,
@@ -68,42 +68,36 @@ class VertexAIComponent(CustomComponent):
                 "field_type": "int",
                 "value": 5,
                 "required": False,
-                "advanced":True
+                "advanced": True,
             },
             "streaming": {
                 "display_name": "Streaming",
                 "field_type": "bool",
                 "value": False,
                 "required": False,
-                "advanced":True
+                "advanced": True,
             },
             "temperature": {
                 "display_name": "Temperature",
                 "field_type": "float",
                 "value": 0.0,
                 "required": False,
-                "advanced":True
+                "advanced": True,
-            },
-            "top_k": {
-                "display_name": "Top K",
-                "type": "int",
-                "default": 40,
-                "required": False,
-                "advanced":True
             },
+            "top_k": {"display_name": "Top K", "type": "int", "default": 40, "required": False, "advanced": True},
             "top_p": {
                 "display_name": "Top P",
                 "field_type": "float",
                 "value": 0.95,
                 "required": False,
-                "advanced":True
+                "advanced": True,
             },
             "tuned_model_name": {
                 "display_name": "Tuned Model Name",
                 "type": "str",
                 "required": False,
                 "value": None,
-                "advanced":True
+                "advanced": True,
             },
             "verbose": {
                 "display_name": "Verbose",
@@ -111,10 +105,7 @@ class VertexAIComponent(CustomComponent):
                 "value": False,
                 "required": False,
             },
-            "name":{
+            "name": {"display_name": "Name", "field_type": "str"},
-                "display_name":"Name",
-                "field_type":"str"
-            },
         }
 
     def build(
@@ -126,7 +117,7 @@ class VertexAIComponent(CustomComponent):
         metadata: Dict = None,
         model_name: str = "text-bison",
         n: int = 1,
-        name:Optional[str] = None,
+        name: Optional[str] = None,
         project: Optional[str] = None,
         request_parallelism: int = 5,
         streaming: bool = False,

src/backend/langflow/components/retrievers/MultiQueryRetriever.py

@@ -1,4 +1,3 @@
-
 from langflow import CustomComponent
 from langchain.retrievers import MultiQueryRetriever
 from typing import Optional, Union, Callable
@@ -8,6 +7,7 @@ from langflow.field_typing import (
     BaseRetriever,
 )
 
+
 class MultiQueryRetrieverComponent(CustomComponent):
     display_name = "MultiQueryRetriever"
     description = "Initialize from llm using default template."
@@ -16,22 +16,25 @@ class MultiQueryRetrieverComponent(CustomComponent):
     def build_config(self):
         return {
             "llm": {"display_name": "LLM"},
-            "prompt": {"display_name": "Prompt", "default": {
+            "prompt": {
+                "display_name": "Prompt",
+                "default": {
                     "input_variables": ["question"],
                     "input_types": {},
                     "output_parser": None,
                     "partial_variables": {},
-                "template": 'You are an AI language model assistant. Your task is \n'
+                    "template": "You are an AI language model assistant. Your task is \n"
-                            'to generate 3 different versions of the given user \n'
+                    "to generate 3 different versions of the given user \n"
-                            'question to retrieve relevant documents from a vector database. \n'
+                    "question to retrieve relevant documents from a vector database. \n"
-                            'By generating multiple perspectives on the user question, \n'
+                    "By generating multiple perspectives on the user question, \n"
-                            'your goal is to help the user overcome some of the limitations \n'
+                    "your goal is to help the user overcome some of the limitations \n"
-                            'of distance-based similarity search. Provide these alternative \n'
+                    "of distance-based similarity search. Provide these alternative \n"
-                            'questions separated by newlines. Original question: {question}',
+                    "questions separated by newlines. Original question: {question}",
                     "template_format": "f-string",
                     "validate_template": False,
-                "_type": "prompt"
+                    "_type": "prompt",
-            }},
+                },
+            },
             "retriever": {"display_name": "Retriever"},
             "parser_key": {"display_name": "Parser Key", "default": "lines"},
         }

src/backend/langflow/components/textsplitters/CharacterTextSplitter.py

@@ -1,4 +1,3 @@
-
 from langflow import CustomComponent
 from langchain.text_splitter import CharacterTextSplitter
 from langchain_core.documents.base import Document

src/backend/langflow/components/textsplitters/LanguageRecursiveTextSplitter.py

@@ -1,7 +1,9 @@
 from typing import Optional
-from langflow import CustomComponent
+
-from langchain.text_splitter import Language
 from langchain.schema import Document
+from langchain.text_splitter import Language
+
+from langflow import CustomComponent
 
 
 class LanguageRecursiveTextSplitterComponent(CustomComponent):
@@ -48,7 +50,7 @@ class LanguageRecursiveTextSplitterComponent(CustomComponent):
         documents: list[Document],
         chunk_size: Optional[int] = 1000,
         chunk_overlap: Optional[int] = 200,
-        separator_type: Optional[str] = "Python",
+        separator_type: str = "Python",
     ) -> list[Document]:
         """
         Split text into chunks of a specified length.

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

@@ -1,9 +1,9 @@
-
 from langflow import CustomComponent
 from langchain.vectorstores import VectorStore
 from typing import Union, Callable
 from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo
 
+
 class VectorStoreInfoComponent(CustomComponent):
     display_name = "VectorStoreInfo"
     description = "Information about a VectorStore"

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

@@ -1,9 +1,9 @@
-
 from langflow import CustomComponent
 from typing import List, Union
 from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreRouterToolkit
 from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo
-from langflow.field_typing import BaseLanguageModel,Tool
+from langflow.field_typing import BaseLanguageModel, Tool
+
 
 class VectorStoreRouterToolkitComponent(CustomComponent):
     display_name = "VectorStoreRouterToolkit"
@@ -16,10 +16,8 @@ class VectorStoreRouterToolkitComponent(CustomComponent):
         }
 
     def build(
-        self,
+        self, vectorstores: List[VectorStoreInfo], llm: BaseLanguageModel
-        vectorstores: List[VectorStoreInfo],
+    ) -> Union[Tool, VectorStoreRouterToolkit]:
-        llm: BaseLanguageModel
+        print("vectorstores", vectorstores)
-    )->Union[Tool,VectorStoreRouterToolkit]:
+        print("llm", llm)
-        print("vectorstores",vectorstores)
+        return VectorStoreRouterToolkit(vectorstores=vectorstores, llm=llm)
-        print("llm",llm)
-        return VectorStoreRouterToolkit(vectorstores=vectorstores,llm=llm)

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

@@ -1,4 +1,3 @@
-
 from langflow import CustomComponent
 from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreToolkit
 from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo
@@ -10,6 +9,7 @@ from langflow.field_typing import (
 )
 from typing import Union
 
+
 class VectorStoreToolkitComponent(CustomComponent):
     display_name = "VectorStoreToolkit"
     description = "Toolkit for interacting with a Vector Store."
@@ -24,5 +24,5 @@ class VectorStoreToolkitComponent(CustomComponent):
         self,
         vectorstore_info: VectorStoreInfo,
         llm: BaseLanguageModel,
-    ) -> Union[Tool,VectorStoreToolkit]:
+    ) -> Union[Tool, VectorStoreToolkit]:
-        return VectorStoreToolkit(vectorstore_info=vectorstore_info,llm=llm)
+        return VectorStoreToolkit(vectorstore_info=vectorstore_info, llm=llm)

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

@@ -1,4 +1,3 @@
-
 from typing import Optional
 from langflow import CustomComponent
 
@@ -30,8 +29,4 @@ class BingSearchAPIWrapperComponent(CustomComponent):
         k: Optional[int] = 10,
     ) -> BingSearchAPIWrapper:
         # 'k' has a default value and is not shown (show=False), so it is hardcoded here
-        return BingSearchAPIWrapper(
+        return BingSearchAPIWrapper(bing_search_url=bing_search_url, bing_subscription_key=bing_subscription_key, k=k)
-            bing_search_url=bing_search_url,
-            bing_subscription_key=bing_subscription_key,
-            k=k
-        )

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

@@ -1,4 +1,3 @@
-
 from langflow import CustomComponent
 from typing import Union, Callable
 from langchain_community.utilities.google_search import GoogleSearchAPIWrapper
@@ -11,7 +10,7 @@ class GoogleSearchAPIWrapperComponent(CustomComponent):
     def build_config(self):
         return {
             "google_api_key": {"display_name": "Google API Key", "password": True},
-            "google_cse_id": {"display_name": "Google CSE ID","password":True},
+            "google_cse_id": {"display_name": "Google CSE ID", "password": True},
         }
 
     def build(

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

@@ -1,4 +1,3 @@
-
 from langflow import CustomComponent
 from typing import Dict, Optional
 
@@ -21,15 +20,10 @@ class GoogleSerperAPIWrapperComponent(CustomComponent):
                 "name": "result_key_for_type",
                 "advanced": False,
                 "dynamic": False,
-                "info": '',
+                "info": "",
                 "field_type": "dict",
                 "list": False,
-                "value": {
+                "value": {"news": "news", "places": "places", "images": "images", "search": "organic"},
-                    "news": "news",
-                    "places": "places",
-                    "images": "images",
-                    "search": "organic"
-                }
             },
             "serper_api_key": {
                 "display_name": "Serper API Key",
@@ -39,10 +33,10 @@ class GoogleSerperAPIWrapperComponent(CustomComponent):
                 "name": "serper_api_key",
                 "advanced": False,
                 "dynamic": False,
-                "info": '',
+                "info": "",
                 "type": "str",
                 "list": False,
-            }
+            },
         }
 
     def build(
@@ -50,7 +44,4 @@ class GoogleSerperAPIWrapperComponent(CustomComponent):
         serper_api_key: str,
         result_key_for_type: Optional[Dict[str, str]] = None,
     ) -> GoogleSerperAPIWrapper:
-        return GoogleSerperAPIWrapper(
+        return GoogleSerperAPIWrapper(result_key_for_type=result_key_for_type, serper_api_key=serper_api_key)
-            result_key_for_type=result_key_for_type,
-            serper_api_key=serper_api_key
-        )

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

@@ -1,6 +1,8 @@
 from langflow import CustomComponent
 from typing import Optional, Dict
 from langchain_community.utilities.searx_search import SearxSearchWrapper
+
+
 class SearxSearchWrapperComponent(CustomComponent):
     display_name = "SearxSearchWrapper"
     description = "Wrapper for Searx API."
@@ -8,17 +10,12 @@ class SearxSearchWrapperComponent(CustomComponent):
     def build_config(self):
         return {
             "headers": {
-                "field_type":"dict",
+                "field_type": "dict",
                 "display_name": "Headers",
                 "multiline": True,
-                "value": '{"Authorization": "Bearer <token>"}'
+                "value": '{"Authorization": "Bearer <token>"}',
-            },
-            "k": {
-                "display_name": "k",
-                "advanced": True,
-                "field_type": "int",
-                "value": 10
             },
+            "k": {"display_name": "k", "advanced": True, "field_type": "int", "value": 10},
             "searx_host": {
                 "display_name": "Searx Host",
                 "field_type": "str",
@@ -32,5 +29,5 @@ class SearxSearchWrapperComponent(CustomComponent):
         k: Optional[int] = 10,
         headers: Optional[Dict[str, str]] = None,
         searx_host: Optional[str] = None,
-    )->SearxSearchWrapper:
+    ) -> SearxSearchWrapper:
-        return SearxSearchWrapper(headers=headers,k=k,searx_host=searx_host)
+        return SearxSearchWrapper(headers=headers, k=k, searx_host=searx_host)

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

@@ -2,6 +2,7 @@ from langflow import CustomComponent
 from typing import Callable, Union
 from langchain_community.utilities.serpapi import SerpAPIWrapper
 
+
 class SerpAPIWrapperComponent(CustomComponent):
     display_name = "SerpAPIWrapper"
     description = "Wrapper around SerpAPI"
@@ -9,7 +10,13 @@ class SerpAPIWrapperComponent(CustomComponent):
     def build_config(self):
         return {
             "serpapi_api_key": {"display_name": "SerpAPI API Key", "type": "str", "password": True},
-            "params": {"display_name": "Parameters", "type": "dict","advanced":True, "multiline": True,"value": '{"engine": "google","google_domain": "google.com","gl": "us","hl": "en"}'},
+            "params": {
+                "display_name": "Parameters",
+                "type": "dict",
+                "advanced": True,
+                "multiline": True,
+                "value": '{"engine": "google","google_domain": "google.com","gl": "us","hl": "en"}',
+            },
         }
 
     def build(
@@ -17,7 +24,4 @@ class SerpAPIWrapperComponent(CustomComponent):
         serpapi_api_key: str,
         params: dict,
     ) -> Union[SerpAPIWrapper, Callable]:  # Removed quotes around SerpAPIWrapper
-        return SerpAPIWrapper(
+        return SerpAPIWrapper(serpapi_api_key=serpapi_api_key, params=params)
-            serpapi_api_key=serpapi_api_key,
-            params=params
-        )

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

@@ -1,4 +1,3 @@
-
 from langflow import CustomComponent
 from typing import Union, Callable
 from langchain_community.utilities.wikipedia import WikipediaAPIWrapper
@@ -7,6 +6,7 @@ from langchain_community.utilities.wikipedia import WikipediaAPIWrapper
 # The import statement is not included as it is not provided in the JSON
 # and the actual implementation details are unknown.
 
+
 class WikipediaAPIWrapperComponent(CustomComponent):
     display_name = "WikipediaAPIWrapper"
     description = "Wrapper around WikipediaAPI."

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

@@ -1,18 +1,16 @@
-
 from langflow import CustomComponent
 from typing import Callable, Union
 from langchain_community.utilities.wolfram_alpha import WolframAlphaAPIWrapper
 # Since all the fields in the JSON have show=False, we will only create a basic component
 # without any configurable fields.
 
+
 class WolframAlphaAPIWrapperComponent(CustomComponent):
     display_name = "WolframAlphaAPIWrapper"
     description = "Wrapper for Wolfram Alpha."
 
     def build_config(self):
-        return {
+        return {"appid": {"display_name": "App ID", "type": "str", "password": True}}
-            "appid": {"display_name": "App ID", "type": "str", "password": True}
-        }
 
-    def build(self,appid:str) -> Union[Callable, WolframAlphaAPIWrapper]:
+    def build(self, appid: str) -> Union[Callable, WolframAlphaAPIWrapper]:
         return WolframAlphaAPIWrapper(wolfram_alpha_appid=appid)

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

@@ -1,4 +1,3 @@
-
 from langflow import CustomComponent
 from langchain_community.vectorstores.faiss import FAISS
 from typing import Optional, List, Union
@@ -9,6 +8,7 @@ from langflow.field_typing import (
     Embeddings,
 )
 
+
 class FAISSComponent(CustomComponent):
     display_name = "FAISS"
     description = "Construct FAISS wrapper from raw documents."
@@ -24,5 +24,5 @@ class FAISSComponent(CustomComponent):
         self,
         embedding: Embeddings,
         documents: Optional[List[Document]] = None,
-    ) -> Union[VectorStore,FAISS,BaseRetriever]:
+    ) -> Union[VectorStore, FAISS, BaseRetriever]:
-        return FAISS.from_documents(documents=documents,embedding=embedding)
+        return FAISS.from_documents(documents=documents, embedding=embedding)

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

@@ -1,4 +1,3 @@
-
 from langflow import CustomComponent
 from langchain.vectorstores import MongoDBAtlasVectorSearch
 from typing import Optional, List
@@ -8,6 +7,7 @@ from langflow.field_typing import (
     NestedDict,
 )
 
+
 class MongoDBAtlasComponent(CustomComponent):
     display_name = "MongoDB Atlas"
     description = "Construct a `MongoDB Atlas Vector Search` vector store from raw documents."

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

@@ -8,6 +8,8 @@ from langflow.field_typing import (
 from langchain.schema import BaseRetriever
 from langchain.vectorstores.base import VectorStore
 import pinecone
+
+
 class PineconeComponent(CustomComponent):
     display_name = "Pinecone"
     description = "Construct Pinecone wrapper from raw documents."
@@ -18,8 +20,8 @@ class PineconeComponent(CustomComponent):
             "embedding": {"display_name": "Embedding", "default": 1000},
             "index_name": {"display_name": "Index Name"},
             "namespace": {"display_name": "Namespace"},
-            "pinecone_api_key": {"display_name": "Pinecone API Key", "default": "","password": True,"required": True},
+            "pinecone_api_key": {"display_name": "Pinecone API Key", "default": "", "password": True, "required": True},
-            "pinecone_env": {"display_name": "Pinecone Environment", "default": "","required": True},
+            "pinecone_env": {"display_name": "Pinecone Environment", "default": "", "required": True},
             "search_kwargs": {"display_name": "Search Kwargs", "default": "{}"},
         }
 
@@ -30,6 +32,6 @@ class PineconeComponent(CustomComponent):
         index_name: Optional[str] = None,
         pinecone_api_key: Optional[str] = None,
         pinecone_env: Optional[str] = None,
-    ) -> Union[VectorStore,Pinecone,BaseRetriever]:
+    ) -> Union[VectorStore, Pinecone, BaseRetriever]:
-        pinecone.init(api_key=pinecone_api_key,environment=pinecone_env)
+        pinecone.init(api_key=pinecone_api_key, environment=pinecone_env)
-        return Pinecone.from_documents(documents=documents,embedding=embedding,index_name=index_name)
+        return Pinecone.from_documents(documents=documents, embedding=embedding, index_name=index_name)

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

@@ -1,5 +1,5 @@
 from langflow import CustomComponent
-from typing import Optional, List,Union
+from typing import Optional, List, Union
 from langchain_community.vectorstores.supabase import SupabaseVectorStore
 from langflow.field_typing import (
     Document,
@@ -35,6 +35,13 @@ class SupabaseComponent(CustomComponent):
         supabase_service_key: str = "",
         supabase_url: str = "",
         table_name: str = "",
-    ) -> Union[VectorStore,SupabaseVectorStore,BaseRetriever]:
+    ) -> Union[VectorStore, SupabaseVectorStore, BaseRetriever]:
         supabase: Client = create_client(supabase_url, supabase_key=supabase_service_key)
-        return SupabaseVectorStore.from_documents(documents=documents,embedding=embedding,query_name=query_name,search_kwargs=search_kwargs,client=supabase,table_name=table_name)
+        return SupabaseVectorStore.from_documents(
+            documents=documents,
+            embedding=embedding,
+            query_name=query_name,
+            search_kwargs=search_kwargs,
+            client=supabase,
+            table_name=table_name,
+        )

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

@@ -1,12 +1,12 @@
-import weaviate  # type: ignore
 from typing import Optional, Union
-from langflow import CustomComponent
 
-from langchain.vectorstores import Weaviate
+import weaviate  # type: ignore
-from langchain.schema import Document
-from langchain.vectorstores.base import VectorStore
-from langchain.schema import BaseRetriever
 from langchain.embeddings.base import Embeddings
+from langchain.schema import BaseRetriever, Document
+from langchain.vectorstores import Weaviate
+from langchain.vectorstores.base import VectorStore
+
+from langflow import CustomComponent
 
 
 class WeaviateVectorStore(CustomComponent):
@@ -45,7 +45,7 @@ class WeaviateVectorStore(CustomComponent):
         search_by_text: bool = False,
         api_key: Optional[str] = None,
         index_name: Optional[str] = None,
-        text_key: Optional[str] = "text",
+        text_key: str = "text",
         embedding: Optional[Embeddings] = None,
         documents: Optional[Document] = None,
         attributes: Optional[list] = None,