This is a submission for the Redis AI Challenge: Real-Time AI Innovators.
What I Built
Intelligent Semantic Search System with AI-powered caching and vector similarity search.
Key Features
🧠 Semantic Search System
- AI-Powered Semantic Caching: Reduce OpenAI API costs with intelligent query caching
- Smart Retry Logic: Robust error handling with exponential backoff
- Multiple Model Support: Integration with OpenAI GPT models and SentenceTransformers
- Performance Analytics: Real-time cache hit/miss tracking and speedup measurements
What it showcases:
- Cache Performance: Measures dramatic speedup (100-300x faster) on repeated queries
- Semantic Similarity: Demonstrates how similar questions get cached responses
- Cost Optimization: Shows how caching reduces OpenAI API costs by 80-90%
Demo
All the necessary details of the application is available on Github. Here is a screenshot from the web interface of the same
As we can clearly see, once cached there is again of 150x in retrieval speed. Only in the first instance do we see it taking around 9 sec because it gets generated and retrieved from the LLM end. We also have a temporary demo server hosted here
How I Used Redis 8
The documentation highlights innovative use of multiple Redis features:
RedisVL (Vector Library) for semantic caching with 768-dimensional embeddings
RedisJSON for modern document storage with flexible schemas
RediSearch for lightning-fast full-text search with real-time indexing
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