Reduce LLM Costs with Semantic Caching using Redis Vector Store and HuggingFace

About This Workflow

What This Workflow Does

This workflow reduces the costs associated with Large Language Models (LLMs) by utilizing semantic caching with Redis Vector Store and HuggingFace. It automates the process of storing and retrieving pre-computed answers to frequently asked questions, preventing redundant computations and saving resources. By leveraging Redis Vector Store, this workflow ensures efficient and scalable caching for improved performance.

Who Should Use This

Developers and engineers working on AI-powered chatbots or applications that rely heavily on LLMs can benefit from this workflow. By implementing semantic caching, they can optimize their models' performance and reduce the financial burden of redundant computations.

Key Features

• Semantic Caching: The workflow stores pre-computed answers in Redis Vector Store, allowing for efficient retrieval and re-use of cached results.
• HuggingFace Integration: The workflow utilizes HuggingFace's vector store capabilities to integrate with popular LLMs, ensuring seamless compatibility.
• Redis Vector Store: This workflow leverages Redis's vector store capabilities to provide a high-performance caching solution for LLM answers.
• Efficient Resource Utilization: By caching and re-using pre-computed answers, the workflow helps reduce the computational resources required by LLMs, leading to cost savings.

How to Get Started

To import and customize this workflow in n8n, follow these steps: Import the workflow into your n8n instance, then configure the settings to match your specific use case. You may need to adjust the workflow's parameters and integrations to suit your AI chatbot or application's requirements.