Handle customer support queries with cache-first RAG using Redis, LangCache and OpenAI

About This Workflow

What This Workflow Does

This workflow is designed to handle customer support queries using a Retrieval-Augmented Generation (RAG) approach, combining a cache-first strategy with Redis vector storage and OpenAI. The workflow aims to provide efficient and effective customer support by leveraging AI-powered generation capabilities. It uses LangCache to cache frequently used information and Redis for fast vector storage.

Who Should Use This

This workflow is suitable for developers, businesses, and organizations looking to create a comprehensive customer support system that utilizes AI-powered generation capabilities to provide fast and effective responses to customer queries.

Key Features

• Cache-first strategy: The workflow uses LangCache to quickly retrieve and cache frequently used information, reducing the load on external APIs and improving response times.
• Redis vector storage: The workflow utilizes Redis for fast vector storage, enabling efficient storage and retrieval of large amounts of data.
• OpenAI integration: The workflow integrates with OpenAI to generate high-quality responses to customer queries, leveraging the power of AI-powered generation capabilities.
• RAG approach: The workflow employs a Retrieval-Augmented Generation approach, combining the strengths of knowledge retrieval and AI-powered generation to provide accurate and effective responses to customer queries.

How to Get Started

To use this workflow, import it into your n8n instance and configure the workflow's settings to connect to your Redis and OpenAI accounts. Customize the workflow to suit your organization's specific needs, ensuring that the cache-first strategy and Redis vector storage are properly configured to optimize performance and efficiency.