We present an intelligent semantic search system with an AI-powered cache layer and vector similarity search, designed to speed up queries, reduce model usage costs, and improve the user experience.
Key features
AI for semantic cache Reduces model API calls with a cache that stores responses and vector representations
Intelligent retry logic Robust error handling with exponential backoff for resilience against temporary failures
Multiple model support Integration with OpenAI GPT models and SentenceTransformers for generation and embeddings
Performance analytics Real-time tracking of cache hits and misses and acceleration metrics
What the project demonstrates
Performance acceleration Repeated queries show 100x to 300x improvements in response time
Semantic similarity Similar questions retrieve cached responses thanks to vector search
Cost optimization Reduction of model API consumption by around 80-90 percent by reusing responses
Demo and access
The repository with the code and instructions is available on GitHub in the author's redis-semantic-cache-demo folder, and there is a temporary demo where you can observe the system's behavior and the effect of the cache on latency. During execution, the first generation may take around 9 seconds because it comes from the LLM, and subsequent retrievals achieve accelerations on the order of 150x or higher.
How we use Redis 8
The project leverages several advanced Redis capabilities to build the high-performance semantic layer
RedisVL Vector Library Storage and search of 768-dimensional embeddings for semantic similarity and vector cache
RedisJSON Storage of documents and responses with flexible schemas and easy access
RediSearch Full-text indexes and ultra-fast searches for hybrid text-vector queries
Benefits for businesses
The solution is ideal for applications where latency and cost are critical, such as virtual assistants, corporate search engines, and automated customer service systems. The combination of semantic embeddings with persistent cache allows for consistent and fast responses and significantly reduces spending on language APIs.
About Q2BSTUDIO
Q2BSTUDIO is a software development company specialized in custom applications and custom software, with experience in artificial intelligence and cybersecurity. We offer AWS and Azure cloud services, business intelligence services, AI implementation for companies, AI agent design, and Power BI visualization solutions. Our team combines expertise in software engineering and AI models to create secure, scalable, and business-optimized solutions.
How Q2BSTUDIO can help
We can adapt this semantic cache layer to your needs, integrate it with your existing systems, optimize model consumption costs, and deploy it on AWS or Azure cloud infrastructures with security and monitoring policies. We offer consulting to define artificial intelligence use cases, implement AI agents, and deploy data pipelines for business intelligence and Power BI.
Keywords for positioning
custom applications custom software artificial intelligence cybersecurity AWS and Azure cloud services business intelligence services AI for companies AI agents Power BI
If you want a personalized demo or a technical audit to evaluate cost savings and latency improvement, Q2BSTUDIO can help design and implement a Semantic Caching Layer for High-Throughput solution adapted to your organization.
