02. Caching (Cache)
Caching
LangChain offers a selective caching layer above LLM.
This is useful for two reasons.
For LLM providers if multiple requests for the same completion Reduce the number of API calls to reduce costs You can.
For LLM providers Reduce the number of API calls to speed up your application There is.
Copy
# API KEY a configuration file for managing environment variables
from dotenv import load_dotenv
# API KEY Load information
load_dotenv()Copy
trueCopy
# LangSmith Set up tracking. https://smith.langchain.com
# !pip install langchain-teddynote
from langchain_teddynote import logging
# Enter a project name.
logging.langsmith("CH04-Models")Copy
Generates models and prompts
Copy
Copy
Copy
InMemoryCache
Save the answer to the same question using the inmemory cache, and return the answer stored in the cache.
Copy
Copy
Copy
Copy
SQLite Cache
Copy
Copy
Copy
Last updated