Building an AI-Powered FAQ Assistant with Redis 8 and OpenAI

#redischallenge #devchallenge #database #ai

Redis AI Challenge: Real-Time AI Innovators

What I Built

I built an AI-powered FAQ assistant that delivers instant, context-aware answers to user questions using Redis 8 and OpenAI. It supports semantic search with Redis Vector similarity and intelligently caches responses using RedisJSON to speed up repeated queries. Users can interact with a friendly chat interface that retrieves and returns answers in real time.

The goal of this project is to demonstrate how Redis can be used as a real-time AI data layer to power conversational interfaces, specifically for use cases like FAQs, support bots, and knowledge assistants.

Note that the cache duration is set to 15 mins due to the limited storage

Demo

Live App: https://master.d15ripqxac0b7u.amplifyapp.com

Initial page load:

Response from app:

With cached data:

From Redis Insight

Github: https://github.com/dilumdarshana/ai-faq-memory-assistant

How I Used Redis 8

Redis 8 was the core engine that powered both performance and intelligence in this project. Here’s how I used its features:

Redis Stack (RedisJSON + RediSearch + Vector Similarity Search):
I stored FAQ documents in Redis using the JSON data type. Each FAQ includes fields like question, answer, source, and a createdAt timestamp. The documents are indexed for full-text and semantic search using FT.CREATE.

Vector Search for Semantic Understanding:
Each question was embedded into a 1536-dimensional vector using OpenAI's embedding model. These vectors were stored in Redis and indexed using the HNSW algorithm. When a user asks a question, the assistant searches Redis using vector similarity to retrieve the most relevant context.

Intelligent Caching with RedisJSON:
Before sending a user query to the LLM, I check if a cached answer already exists for a similar query using Redis. This reduces token usage and cost while improving response time.

FT.SEARCH Sorting by Date:
For the "Recently Asked Questions" section, I used FT.SEARCH with sorting on the createdAt field to show the latest activity. (15 mins cache duration)

Redis not only made the solution real-time and scalable, but it also gave me a flexible and powerful way to combine structured, vector, and text search in one place.

Design:

Final Thoughts

This project shows how Redis 8 can be a game-changer for real-time AI applications. Whether you're building FAQ bots, search interfaces, or RAG-based assistants, Redis offers a powerful and developer-friendly solution.

Tech Stack: