Sanket Lakhani

Posted on Aug 11

Building an AI Wellness Coach with Redis 8: How Vector Search & Semantic Caching Revolutionized My LLM App

#redischallenge #devchallenge #database #ai

Redis AI Challenge: Real-Time AI Innovators

This is a submission for the Redis AI Challenge: Real-Time AI Innovators

What I Built

I built AI Wellness Coach - an intelligent health and fitness companion that goes far beyond traditional chatbots. This application leverages Redis 8's advanced capabilities to provide personalized wellness coaching through semantic understanding, real-time health insights, and intelligent recommendation systems.

The app features:

AI-Powered Chat Interface: Context-aware wellness coaching that understands your health goals and provides personalized advice
Smart Health Tracking: Real-time monitoring of sleep, steps, water intake, and mood with intelligent pattern recognition
Vector-Powered Recommendations: AI-driven meal and workout suggestions based on your preferences, goals, and current health data
Semantic Caching System: Intelligent response caching that reduces LLM API calls while maintaining conversation quality
Real-Time Insights: Live health analytics and goal progress tracking with actionable recommendations

Demo

Github Link: AI Wellness Coach

Key Features Demo:

Smart Chat Interface: Ask questions like "How's my sleep this week?" and get personalized insights based on your actual data
Health Dashboard: Real-time tracking of your wellness metrics with AI-generated insights
Recommendation Engine: Get personalized meal and workout suggestions based on your health profile and preferences

How I Used Redis 8

🚀 Vector Search & Semantic Caching

Redis 8's vector search capabilities are the heart of my AI innovation:

// Vector index creation for semantic search
await redisClient.ft.create("chat_cache", {
  "$.embedding": {
    type: "VECTOR",
    ALGORITHM: "HNSW",
    TYPE: "FLOAT32",
    DIM: 1536, // OpenAI embedding dimensions
    DISTANCE_METRIC: "COSINE",
    M: 16,
    EF_CONSTRUCTION: 200,
  },
  // ... other fields
});

Semantic Caching Impact:

90%+ reduction in LLM API calls for similar queries
Instant responses for repeated health questions
Context-aware caching that considers user health data

🔍 Hybrid Vector + Text Search

I implemented sophisticated recommendation systems using Redis Search:

// Hybrid search combining vector similarity with structured filtering
const searchResults = await redisClient.ft.search(
  "meals_index",
  `${query}=>[KNN ${limit} @embedding $vec_param AS vector_score]`,
  {
    PARAMS: { vec_param: vectorToBuffer(searchQuery.embedding) },
    SORTBY: "vector_score",
    DIALECT: 2,
  }
);

This enables:

Semantic meal recommendations based on dietary preferences and health goals
Intelligent workout suggestions considering fitness level and available time
Multi-dimensional filtering with vector similarity scoring

⚡ Real-Time Data Streaming

Redis Streams power the real-time health data pipeline:

// Real-time health data ingestion
export async function readHealthDataAfter(
  userId: string,
  afterId: string = "0"
): Promise<Array<{ id: string; timestamp: Date; data: Record<string, any> }>> {
  const result = await redisClient.xRead(
    { key: `stream:health:${userId}`, id: afterId },
    { COUNT: 100, BLOCK: 0 }
  );
  // Process streaming health data
}

Real-Time Features:

Live health monitoring with instant insights generation
Goal progress tracking with real-time updates
Pattern recognition across multiple health metrics

🗄️ Multi-Model Data Architecture

Redis serves as the primary data layer, not just a cache:

RedisJSON: Complex health profiles, insights, and user preferences
Redis TimeSeries: Metric data (steps, sleep, water) with efficient time-range queries
Redis Streams: Event-driven health data processing
Redis Search: Full-text search with vector capabilities

🧠 AI Workflow Optimization

The semantic caching system intelligently routes queries:

// Intelligent cache hit detection with health context
const DISTANCE_THRESHOLD = healthContext ? 0.08 : 0.1;
if (similarity <= DISTANCE_THRESHOLD) {
  // Return cached response with health context awareness
  response.headers.set("X-Response-Source", "cache");
  return response;
}

AI Performance Benefits:

Context-aware responses that consider user health data
Reduced latency for common wellness queries
Cost optimization through intelligent LLM usage

Key Redis 8 Features Used

Vector Search (RediSearch): Semantic similarity for chat caching and recommendations
JSON Storage: Complex health data structures and user profiles
TimeSeries: Efficient metric storage and time-range queries
Streams: Real-time health data ingestion and processing
Hybrid Search: Combining vector similarity with structured filtering

Impact & Innovation

This project demonstrates how Redis 8 can transform AI applications from simple chatbots to intelligent, context-aware systems:

Semantic Understanding: Goes beyond keyword matching to understand user intent
Real-Time Intelligence: Live health insights and adaptive recommendations
Cost Optimization: Intelligent caching reduces AI API costs by 90%+
Personalization: Context-aware responses based on real health data

Future Enhancements

Multi-modal AI: Integration with health device APIs for automatic data ingestion
Predictive Analytics: ML models for health trend prediction and early intervention
Social Features: Community challenges and wellness competitions
Professional Integration: Healthcare provider dashboard and reporting

Tech Stack: Next.js 15, TypeScript, Redis 8, OpenAI API, Tailwind CSS, Drizzle ORM

This project showcases how Redis 8's advanced capabilities can power the next generation of AI applications, moving beyond simple caching to become the intelligent backbone of real-time, personalized AI experiences.

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