Improve chatbot memory using Google Cloud

When scaling conversational agents, the data layer design often determines success or failure. To support millions of users, agents need conversational continuity — the ability to maintain responsive chats while preserving the context backend models need.

This article covers how to use Google Cloud solutions to solve two data challenges in AI: fast context updates for real-time chat, and efficient retrieval for long-term history. We’ll share a polyglot approach using Redis, Bigtable, and BigQuery that ensures your agent retains detail and continuity, from recent interactions to months-old archives.

Polyglot storage approach for short, mid, and long-term history

What is a polyglot approach?

A polyglot approach uses a multi-tiered storage strategy that leverages several specialized data services rather than a single database to manage different data lifecycles. This allows an application to use the specific strengths of various tools—such as in-memory caches for speed, NoSQL databases for scale, blob storage ...