Release 0.4.18 (#925)

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langflow"
-version = "0.4.18"
+version = "0.4.19"
 description = "A Python package with a built-in web application"
 authors = ["Logspace <contact@logspace.ai>"]
 maintainers = [

src/backend/langflow/__main__.py

@@ -22,7 +22,7 @@ app = typer.Typer()
 
 def update_settings(
     config: str,
-    cache: str,
+    cache: Optional[str] = None,
     dev: bool = False,
     remove_api_keys: bool = False,
     components_path: Optional[Path] = None,
@@ -117,10 +117,10 @@ def serve(
     log_file: Path = typer.Option(
         "logs/langflow.log", help="Path to the log file.", envvar="LANGFLOW_LOG_FILE"
     ),
-    cache: str = typer.Option(
+    cache: Optional[str] = typer.Option(
         envvar="LANGFLOW_LANGCHAIN_CACHE",
         help="Type of cache to use. (InMemoryCache, SQLiteCache)",
-        default="SQLiteCache",
+        default=None,
     ),
     jcloud: bool = typer.Option(False, help="Deploy on Jina AI Cloud"),
     dev: bool = typer.Option(False, help="Run in development mode (may contain bugs)"),

src/backend/langflow/interface/utils.py

@@ -78,9 +78,16 @@ def set_langchain_cache(settings):
     import langchain
     from langflow.interface.importing.utils import import_class
 
-    cache_type = os.getenv("LANGFLOW_LANGCHAIN_CACHE")
+    if cache_type := os.getenv("LANGFLOW_LANGCHAIN_CACHE"):
-    cache_class = import_class(f"langchain.cache.{cache_type or settings.CACHE}")
+        try:
+            cache_class = import_class(
+                f"langchain.cache.{cache_type or settings.CACHE}"
+            )
 
-    logger.debug(f"Setting up LLM caching with {cache_class.__name__}")
+            logger.debug(f"Setting up LLM caching with {cache_class.__name__}")
-    langchain.llm_cache = cache_class()
+            langchain.llm_cache = cache_class()
-    logger.info(f"LLM caching setup with {cache_class.__name__}")
+            logger.info(f"LLM caching setup with {cache_class.__name__}")
+        except ImportError:
+            logger.warning(f"Could not import {cache_type}. ")
+    else:
+        logger.info("No LLM cache set.")

src/backend/langflow/processing/process.py

@@ -130,9 +130,8 @@ def process_graph_cached(
     elif isinstance(langchain_object, Document):
         result = langchain_object.dict()
     else:
-        raise ValueError(
+        logger.warning(f"Unknown langchain_object type: {type(langchain_object)}")
-            f"Unknown langchain_object type: {type(langchain_object).__name__}"
+
-        )
     return result
 
 

src/backend/langflow/services/plugins/langfuse.py

@@ -16,7 +16,7 @@ class LangfuseInstance:
 
     @classmethod
     def create(cls):
-        logger.debug("Creating Langfuse instance")
+        logger.debug("Checking Langfuse credentials")
         from langflow.settings import settings
         from langfuse import Langfuse  # type: ignore
 

src/backend/langflow/settings.py

@@ -32,7 +32,7 @@ class Settings(BaseSettings):
 
     DEV: bool = False
     DATABASE_URL: Optional[str] = None
-    CACHE: str = "InMemoryCache"
+    CACHE: Optional[str] = None
     REMOVE_API_KEYS: bool = False
     COMPONENTS_PATH: List[str] = []
 

src/frontend/src/components/inputListComponent/index.tsx

@@ -19,7 +19,9 @@ export default function InputListComponent({
   }, [disabled]);
 
   // @TODO Recursive Character Text Splitter - the value might be in string format, whereas the InputListComponent specifically requires an array format. To ensure smooth operation and prevent potential errors, it's crucial that we handle the conversion from a string to an array with the string as its element.
-  typeof value === 'string' ? value = [value] : value = value;
+  if (typeof value === "string") {
+    value = [value];
+  }
 
   return (
     <div