Fix formatting and icon naming conventions

src/backend/langflow/api/utils.py

@@ -20,9 +20,7 @@ API_WORDS = ["api", "key", "token"]
 
 
 def has_api_terms(word: str):
-    return "api" in word and (
-        "key" in word or ("token" in word and "tokens" not in word)
-    )
+    return "api" in word and ("key" in word or ("token" in word and "tokens" not in word))
 
 
 def remove_api_keys(flow: dict):
@@ -32,11 +30,7 @@ def remove_api_keys(flow: dict):
             node_data = node.get("data").get("node")
             template = node_data.get("template")
             for value in template.values():
-                if (
-                    isinstance(value, dict)
-                    and has_api_terms(value["name"])
-                    and value.get("password")
-                ):
+                if isinstance(value, dict) and has_api_terms(value["name"]) and value.get("password"):
                     value["value"] = None
 
     return flow
@@ -57,9 +51,7 @@ def build_input_keys_response(langchain_object, artifacts):
             input_keys_response["input_keys"][key] = value
     # If the object has memory, that memory will have a memory_variables attribute
     # memory variables should be removed from the input keys
-    if hasattr(langchain_object, "memory") and hasattr(
-        langchain_object.memory, "memory_variables"
-    ):
+    if hasattr(langchain_object, "memory") and hasattr(langchain_object.memory, "memory_variables"):
         # Remove memory variables from input keys
         input_keys_response["input_keys"] = {
             key: value
@@ -69,9 +61,7 @@ def build_input_keys_response(langchain_object, artifacts):
         # Add memory variables to memory_keys
         input_keys_response["memory_keys"] = langchain_object.memory.memory_variables
 
-    if hasattr(langchain_object, "prompt") and hasattr(
-        langchain_object.prompt, "template"
-    ):
+    if hasattr(langchain_object, "prompt") and hasattr(langchain_object.prompt, "template"):
         input_keys_response["template"] = langchain_object.prompt.template
 
     return input_keys_response
@@ -106,11 +96,7 @@ def raw_frontend_data_is_valid(raw_frontend_data):
 def is_valid_data(frontend_node, raw_frontend_data):
     """Check if the data is valid for processing."""
 
-    return (
-        frontend_node
-        and "template" in frontend_node
-        and raw_frontend_data_is_valid(raw_frontend_data)
-    )
+    return frontend_node and "template" in frontend_node and raw_frontend_data_is_valid(raw_frontend_data)
 
 
 def update_template_values(frontend_template, raw_template):
@@ -150,9 +136,7 @@ def get_file_path_value(file_path):
     # If the path is not in the cache dir, return empty string
     # This is to prevent access to files outside the cache dir
     # If the path is not a file, return empty string
-    if not path.exists() or not str(path).startswith(
-        user_cache_dir("langflow", "langflow")
-    ):
+    if not path.exists() or not str(path).startswith(user_cache_dir("langflow", "langflow")):
         return ""
     return file_path
 
@@ -183,9 +167,7 @@ async def check_langflow_version(component: StoreComponentCreate):
 
     langflow_version = get_lf_version_from_pypi()
     if langflow_version is None:
-        raise HTTPException(
-            status_code=500, detail="Unable to verify the latest version of Langflow"
-        )
+        raise HTTPException(status_code=500, detail="Unable to verify the latest version of Langflow")
     elif langflow_version != component.last_tested_version:
         warnings.warn(
             f"Your version of Langflow ({component.last_tested_version}) is outdated. "

src/backend/langflow/api/v1/callback.py

@@ -28,9 +28,7 @@ class AsyncStreamingLLMCallbackHandleSIO(AsyncCallbackHandler):
         resp = ChatResponse(message=token, type="stream", intermediate_steps="")
         await self.socketio_service.emit_token(to=self.sid, data=resp.model_dump())
 
-    async def on_tool_start(
-        self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
-    ) -> Any:
+    async def on_tool_start(self, serialized: Dict[str, Any], input_str: str, **kwargs: Any) -> Any:
         """Run when tool starts running."""
         resp = ChatResponse(
             message="",
@@ -68,9 +66,7 @@ class AsyncStreamingLLMCallbackHandleSIO(AsyncCallbackHandler):
         try:
             # This is to emulate the stream of tokens
             for resp in resps:
-                await self.socketio_service.emit_token(
-                    to=self.sid, data=resp.model_dump()
-                )
+                await self.socketio_service.emit_token(to=self.sid, data=resp.model_dump())
         except Exception as exc:
             logger.error(f"Error sending response: {exc}")
 
@@ -96,9 +92,7 @@ class AsyncStreamingLLMCallbackHandleSIO(AsyncCallbackHandler):
             resp = PromptResponse(
                 prompt=text,
             )
-            await self.socketio_service.emit_message(
-                to=self.sid, data=resp.model_dump()
-            )
+            await self.socketio_service.emit_message(to=self.sid, data=resp.model_dump())
 
     async def on_agent_action(self, action: AgentAction, **kwargs: Any):
         log = f"Thought: {action.log}"
@@ -108,9 +102,7 @@ class AsyncStreamingLLMCallbackHandleSIO(AsyncCallbackHandler):
             logs = log.split("\n")
             for log in logs:
                 resp = ChatResponse(message="", type="stream", intermediate_steps=log)
-                await self.socketio_service.emit_token(
-                    to=self.sid, data=resp.model_dump()
-                )
+                await self.socketio_service.emit_token(to=self.sid, data=resp.model_dump())
         else:
             resp = ChatResponse(message="", type="stream", intermediate_steps=log)
             await self.socketio_service.emit_token(to=self.sid, data=resp.model_dump())

src/backend/langflow/api/v1/chat.py

@@ -98,12 +98,8 @@ async def build_vertex(
         cache = chat_service.get_cache(flow_id)
         if not cache:
             # If there's no cache
-            logger.warning(
-                f"No cache found for {flow_id}. Building graph starting at {vertex_id}"
-            )
-            graph = build_and_cache_graph(
-                flow_id=flow_id, session=next(get_session()), chat_service=chat_service
-            )
+            logger.warning(f"No cache found for {flow_id}. Building graph starting at {vertex_id}")
+            graph = build_and_cache_graph(flow_id=flow_id, session=next(get_session()), chat_service=chat_service)
         else:
             graph = cache.get("result")
         result_data_response = ResultDataResponse(results={})
@@ -195,9 +191,7 @@ async def build_vertex_stream(
                     else:
                         graph = cache.get("result")
                 else:
-                    session_data = await session_service.load_session(
-                        session_id, flow_id=flow_id
-                    )
+                    session_data = await session_service.load_session(session_id, flow_id=flow_id)
                     graph, artifacts = session_data if session_data else (None, None)
                     if not graph:
                         raise ValueError(f"No graph found for {flow_id}.")

src/backend/langflow/api/v1/endpoints.py

@@ -49,9 +49,7 @@ def get_all(
         raise HTTPException(status_code=500, detail=str(exc)) from exc
 
 
-@router.post(
-    "/run/{flow_id}", response_model=RunResponse, response_model_exclude_none=True
-)
+@router.post("/run/{flow_id}", response_model=RunResponse, response_model_exclude_none=True)
 async def run_flow_with_caching(
     session: Annotated[Session, Depends(get_session)],
     flow_id: str,
@@ -69,9 +67,7 @@ async def run_flow_with_caching(
             input_values_dict = {}
 
         if session_id:
-            session_data = await session_service.load_session(
-                session_id, flow_id=flow_id
-            )
+            session_data = await session_service.load_session(session_id, flow_id=flow_id)
             graph, artifacts = session_data if session_data else (None, None)
             task_result: Any = None
             if not graph:
@@ -89,11 +85,7 @@ async def run_flow_with_caching(
         else:
             # Get the flow that matches the flow_id and belongs to the user
             # flow = session.query(Flow).filter(Flow.id == flow_id).filter(Flow.user_id == api_key_user.id).first()
-            flow = session.exec(
-                select(Flow)
-                .where(Flow.id == flow_id)
-                .where(Flow.user_id == api_key_user.id)
-            ).first()
+            flow = session.exec(select(Flow).where(Flow.id == flow_id).where(Flow.user_id == api_key_user.id)).first()
             if flow is None:
                 raise ValueError(f"Flow {flow_id} not found")
 
@@ -116,18 +108,12 @@ async def run_flow_with_caching(
         # StatementError('(builtins.ValueError) badly formed hexadecimal UUID string')
         if "badly formed hexadecimal UUID string" in str(exc):
             # This means the Flow ID is not a valid UUID which means it can't find the flow
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND, detail=str(exc)
-            ) from exc
+            raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=str(exc)) from exc
     except ValueError as exc:
         if f"Flow {flow_id} not found" in str(exc):
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND, detail=str(exc)
-            ) from exc
+            raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=str(exc)) from exc
         else:
-            raise HTTPException(
-                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(exc)
-            ) from exc
+            raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(exc)) from exc
 
 
 @router.post(
@@ -156,8 +142,7 @@ async def process(
     """
     # Raise a depreciation warning
     logger.warning(
-        "The /process endpoint is deprecated and will be removed in a future version. "
-        "Please use /run instead."
+        "The /process endpoint is deprecated and will be removed in a future version. " "Please use /run instead."
     )
     raise HTTPException(
         status_code=status.HTTP_400_BAD_REQUEST,
@@ -229,16 +214,12 @@ async def custom_component(
 
     built_frontend_node = build_custom_component_template(component, user_id=user.id)
 
-    built_frontend_node = update_frontend_node_with_template_values(
-        built_frontend_node, raw_code.frontend_node
-    )
+    built_frontend_node = update_frontend_node_with_template_values(built_frontend_node, raw_code.frontend_node)
     return built_frontend_node
 
 
 @router.post("/custom_component/reload", status_code=HTTPStatus.OK)
-async def reload_custom_component(
-    path: str, user: User = Depends(get_current_active_user)
-):
+async def reload_custom_component(path: str, user: User = Depends(get_current_active_user)):
     from langflow.interface.custom.utils import build_custom_component_template
 
     try:
@@ -260,8 +241,6 @@ async def custom_component_update(
 ):
     component = CustomComponent(code=raw_code.code)
 
-    component_node = build_custom_component_template(
-        component, user_id=user.id, update_field=raw_code.field
-    )
+    component_node = build_custom_component_template(component, user_id=user.id, update_field=raw_code.field)
     # Update the field
     return component_node

src/backend/langflow/api/v1/schemas.py

@@ -21,26 +21,6 @@ class BuildStatus(Enum):
     IN_PROGRESS = "in_progress"
 
 
-class GraphData(BaseModel):
-    """Data inside the exported flow."""
-
-    nodes: List[Dict[str, Any]]
-    edges: List[Dict[str, Any]]
-
-
-class ExportedFlow(BaseModel):
-    """Exported flow from Langflow."""
-
-    description: str
-    name: str
-    id: str
-    data: GraphData
-
-
-class InputRequest(BaseModel):
-    input: dict
-
-
 class TweaksRequest(BaseModel):
     tweaks: Optional[Dict[str, Dict[str, str]]] = Field(default_factory=dict)
 
@@ -178,9 +158,7 @@ class StreamData(BaseModel):
     data: dict
 
     def __str__(self) -> str:
-        return (
-            f"event: {self.event}\ndata: {orjson_dumps(self.data, indent_2=False)}\n\n"
-        )
+        return f"event: {self.event}\ndata: {orjson_dumps(self.data, indent_2=False)}\n\n"
 
 
 class CustomComponentCode(BaseModel):

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

@@ -17,7 +17,7 @@ class ConversationalAgent(CustomComponent):
     display_name: str = "OpenAI Conversational Agent"
     description: str = "Conversational Agent that can use OpenAI's function calling API"
     icon = "OpenAI"
-    
+
     def build_config(self):
         openai_function_models = [
             "gpt-4-turbo-preview",

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

@@ -36,10 +36,8 @@ class ConversationChainComponent(CustomComponent):
         # We need to check if it is a string or a BaseMessage
         result_str: Text = ""
         if hasattr(result, "content") and isinstance(result.content, str):
-
             result_str = result.content
         elif isinstance(result, str):
-
             result_str = result
         else:
             # is dict

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

@@ -7,9 +7,7 @@ from langflow.field_typing import BaseLanguageModel, Text
 class LLMCheckerChainComponent(CustomComponent):
     display_name = "LLMCheckerChain"
     description = ""
-    documentation = (
-        "https://python.langchain.com/docs/modules/chains/additional/llm_checker"
-    )
+    documentation = "https://python.langchain.com/docs/modules/chains/additional/llm_checker"
 
     def build_config(self):
         return {
@@ -21,7 +19,6 @@ class LLMCheckerChainComponent(CustomComponent):
         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, "")

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

@@ -9,9 +9,7 @@ from langflow.field_typing import BaseLanguageModel, BaseMemory, Text
 class LLMMathChainComponent(CustomComponent):
     display_name = "LLMMathChain"
     description = "Chain that interprets a prompt and executes python code to do math."
-    documentation = (
-        "https://python.langchain.com/docs/modules/chains/additional/llm_math"
-    )
+    documentation = "https://python.langchain.com/docs/modules/chains/additional/llm_math"
 
     def build_config(self):
         return {

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

@@ -47,19 +47,10 @@ class SQLGeneratorComponent(CustomComponent):
             sql_query_chain = create_sql_query_chain(llm=llm, db=db, k=top_k)
         else:
             # Check if {question} is in the prompt
-            if (
-                "{question}" not in prompt_template.template
-                or "question" not in prompt_template.input_variables
-            ):
-                raise ValueError(
-                    "Prompt must contain `{question}` to be used with Natural Language to SQL."
-                )
-            sql_query_chain = create_sql_query_chain(
-                llm=llm, db=db, prompt=prompt_template, k=top_k
-            )
-        query_writer: Runnable = sql_query_chain | {
-            "query": lambda x: x.replace("SQLQuery:", "").strip()
-        }
+            if "{question}" not in prompt_template.template or "question" not in prompt_template.input_variables:
+                raise ValueError("Prompt must contain `{question}` to be used with Natural Language to SQL.")
+            sql_query_chain = create_sql_query_chain(llm=llm, db=db, prompt=prompt_template, k=top_k)
+        query_writer: Runnable = sql_query_chain | {"query": lambda x: x.replace("SQLQuery:", "").strip()}
         response = query_writer.invoke({"question": input_value})
         query = response.get("query")
         self.status = query

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

@@ -70,17 +70,11 @@ class GatherRecordsComponent(CustomComponent):
 
         glob = "**/*" if recursive else "*"
         paths = walk_level(path_obj, depth) if depth else path_obj.glob(glob)
-        file_paths = [
-            Text(p)
-            for p in paths
-            if p.is_file() and match_types(p) and is_not_hidden(p)
-        ]
+        file_paths = [Text(p) for p in paths if p.is_file() and match_types(p) and is_not_hidden(p)]
 
         return file_paths
 
-    def parse_file_to_record(
-        self, file_path: str, silent_errors: bool
-    ) -> Optional[Record]:
+    def parse_file_to_record(self, file_path: str, silent_errors: bool) -> Optional[Record]:
         # Use the partition function to load the file
         from unstructured.partition.auto import partition  # type: ignore
 
@@ -106,14 +100,9 @@ class GatherRecordsComponent(CustomComponent):
         use_multithreading: bool,
     ) -> List[Optional[Record]]:
         if use_multithreading:
-            records = self.parallel_load_records(
-                file_paths, silent_errors, max_concurrency
-            )
+            records = self.parallel_load_records(file_paths, silent_errors, max_concurrency)
         else:
-            records = [
-                self.parse_file_to_record(file_path, silent_errors)
-                for file_path in file_paths
-            ]
+            records = [self.parse_file_to_record(file_path, silent_errors) for file_path in file_paths]
         records = list(filter(None, records))
         return records
 
@@ -142,20 +131,13 @@ class GatherRecordsComponent(CustomComponent):
         if types is None:
             types = []
         resolved_path = self.resolve_path(path)
-        file_paths = self.retrieve_file_paths(
-            resolved_path, types, load_hidden, recursive, depth
-        )
+        file_paths = self.retrieve_file_paths(resolved_path, types, load_hidden, recursive, depth)
         loaded_records = []
 
         if use_multithreading:
-            loaded_records = self.parallel_load_records(
-                file_paths, silent_errors, max_concurrency
-            )
+            loaded_records = self.parallel_load_records(file_paths, silent_errors, max_concurrency)
         else:
-            loaded_records = [
-                self.parse_file_to_record(file_path, silent_errors)
-                for file_path in file_paths
-            ]
+            loaded_records = [self.parse_file_to_record(file_path, silent_errors) for file_path in file_paths]
         loaded_records = list(filter(None, loaded_records))
         self.status = loaded_records
         return loaded_records

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

@@ -9,7 +9,7 @@ class HuggingFaceEmbeddingsComponent(CustomComponent):
     documentation = (
         "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/sentence_transformers"
     )
-    icon="HuggingFace"
+    icon = "HuggingFace"
 
     def build_config(self):
         return {

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

@@ -9,8 +9,7 @@ class HuggingFaceInferenceAPIEmbeddingsComponent(CustomComponent):
     display_name = "HuggingFaceInferenceAPIEmbeddings"
     description = "HuggingFace sentence_transformers embedding models, API version."
     documentation = "https://github.com/huggingface/text-embeddings-inference"
-    icon="HuggingFace"
-
+    icon = "HuggingFace"
 
     def build_config(self):
         return {

src/backend/langflow/components/io/StoreMessages.py

@@ -54,9 +54,7 @@ class StoreMessages(CustomComponent):
         if not records:
             records = []
             if not session_id or not sender or not sender_name:
-                raise ValueError(
-                    "If passing texts, session_id, sender, and sender_name must be provided."
-                )
+                raise ValueError("If passing texts, session_id, sender, and sender_name must be provided.")
             for text in texts:
                 record = Record(
                     text=text,

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

@@ -45,9 +45,7 @@ class ChatComponent(CustomComponent):
             return []
 
         if not session_id or not sender or not sender_name:
-            raise ValueError(
-                "All of session_id, sender, and sender_name must be provided."
-            )
+            raise ValueError("All of session_id, sender, and sender_name must be provided.")
         if isinstance(message, Record):
             record = message
             record.data.update(

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

@@ -12,7 +12,6 @@ class AmazonBedrockComponent(CustomComponent):
     description: str = "LLM model from Amazon Bedrock."
     icon = "Amazon"
 
-
     def build_config(self):
         return {
             "model_id": {

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

@@ -10,7 +10,7 @@ from langflow import CustomComponent
 class AnthropicLLM(CustomComponent):
     display_name: str = "AnthropicLLM"
     description: str = "Anthropic Chat&Completion large language models."
-    icon ="Anthropic"
+    icon = "Anthropic"
 
     def build_config(self):
         return {

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

@@ -10,8 +10,7 @@ from langflow.field_typing import BaseLanguageModel, NestedDict
 class AnthropicComponent(CustomComponent):
     display_name = "Anthropic"
     description = "Anthropic large language models."
-    icon ="Anthropic"
-
+    icon = "Anthropic"
 
     def build_config(self):
         return {

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

@@ -9,7 +9,7 @@ class ChatAnthropicComponent(CustomComponent):
     display_name = "ChatAnthropic"
     description = "`Anthropic` chat large language models."
     documentation = "https://python.langchain.com/docs/modules/model_io/models/chat/integrations/anthropic"
-    icon ="Anthropic"
+    icon = "Anthropic"
 
     def build_config(self):
         return {

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

@@ -127,8 +127,7 @@ class ChatLiteLLMComponent(CustomComponent):
             litellm.set_verbose = verbose
         except ImportError:
             raise ChatLiteLLMException(
-                "Could not import litellm python package. "
-                "Please install it with `pip install litellm`"
+                "Could not import litellm python package. " "Please install it with `pip install litellm`"
             )
         provider_map = {
             "OpenAI": "openai_api_key",