🚀 feat(utils.py): add support for configurable LLM caching

This commit adds support for configurable LLM caching. The `setup_llm_caching` function now imports the cache class from the `langchain.cache` module based on the `settings.cache` value. If the import is successful, the `langchain.llm_cache` is set to an instance of the cache class. If the import fails, a warning is logged. If an exception is raised during the setup, a warning is logged with the error message.

src/backend/langflow/interface/utils.py

@@ -64,7 +64,16 @@ def extract_input_variables_from_prompt(prompt: str) -> list[str]:
 def setup_llm_caching():
  """Setup LLM caching."""
  import langchain
- from langchain.cache import SQLiteCache
-
- logger.debug("Setting up LLM caching")
- langchain.llm_cache = SQLiteCache()
+ from langflow.settings import settings
+ from langflow.interface.importing.utils import import_class
+
+ try:
+ cache_class = import_class(f"langchain.cache.{settings.cache}")
+
+ logger.debug(f"Setting up LLM caching with {cache_class.__name__}")
+ langchain.llm_cache = cache_class()
+ logger.info(f"LLM caching setup with {cache_class.__name__}")
+ except ImportError:
+ logger.warning(f"Could not import {settings.cache}. ")
+ except Exception as exc:
+ logger.warning(f"Could not setup LLM caching. Error: {exc}")