Caching LLM Output On-Demand with LangChain, Redis and QStash

The process outlined in the text describes how to optimize applications that utilize LangChain for tasks like processing and querying data by employing caching techniques. To avoid delays caused by sending prompts to an API and waiting for responses, a microservice is created using Hono.js, which is hosted on Cloudflare Workers. This microservice sends prompts to LangChain and caches the responses using Upstash Redis. It is equipped with middleware for logging and verifying request signatures and employs Upstash's Rate Limiting SDK to prevent exceeding API call limits. QStash is utilized for flexible on-demand calling of the microservice and provides a dashboard for monitoring usage, with automatic retries for failed HTTP requests. The system integrates with OpenAI's API, ensuring efficient handling of repeated queries by caching results, thus improving user experience by providing instantaneous responses from the cache.