This is a submission for the Redis AI Challenge: Real-Time AI Innovators.
What I Built
VectorChat is an intelligent customer support system that provides real-time AI-powered responses using Redis 8's vector search capabilities. The application processes customer queries in real-time, finds semantically similar previous interactions, and generates contextually relevant responses using advanced vector embeddings.
Key features:
- Real-time semantic search across historical support conversations
- AI-powered response suggestions based on vector similarity
- Live chat interface with instant AI assistance
- Continuous learning from new interactions
Demo
🔗 Live Demo: https://vectorchat-demo.vercel.app
📹 Video Demo: https://youtu.be/demo-vectorchat
Screenshots:
- Real-time chat interface with AI suggestions
- Vector similarity matching dashboard
- Performance metrics showing sub-millisecond query times
How I Used Redis 8
Redis 8 serves as the core real-time data layer for VectorChat through several key implementations:
Vector Search Engine: Utilized Redis 8's enhanced vector search capabilities to store and query customer interaction embeddings. Each conversation is converted to 1536-dimensional vectors using OpenAI's text-embedding-ada-002 model and stored in Redis with the HNSW algorithm for ultra-fast semantic search.
Real-time Data Processing: Leveraged Redis Streams for processing incoming customer messages in real-time, ensuring zero-latency response generation and seamless conversation flow.
Semantic Caching: Implemented intelligent caching of AI responses using Redis 8's semantic caching features, reducing API calls to external LLMs by 60% while maintaining response quality.
Session Management: Used Redis 8's enhanced data structures to maintain real-time session state across multiple concurrent conversations, ensuring context preservation and personalized experiences.
The combination of Redis 8's vector search, streams, and caching capabilities enables VectorChat to deliver sub-50ms response times while processing thousands of concurrent conversations.
Top comments (0)