This is a submission for the Redis AI Challenge: Real-Time AI Innovators.
What I Built
LeaseGuard analyzes residential lease agreements in real time to flag risky or unlawful clauses. It isn’t a chatbot wrapper; it’s an AI application with Redis as the real-time data layer:
- Vectorized clauses stored in RedisJSON, indexed with RediSearch for KNN and hybrid retrieval
- Two-tier semantic caching to cut LLM calls and latency
- Event-driven pipeline and collaboration powered by Redis Streams and Pub/Sub
- Conversation/session state fully in Redis structures (Lists/Sets/JSON), with optional TimeSeries
Outcome: sub-second retrieval, grounded answers, and live telemetry/alerts.
Demo
How I Used Redis 8
Vector index (RediSearch over RedisJSON)
Storing clauses in RedisJSON with embeddings
Violation detection via vector similarity (KNN)
Hybrid search (text + vector + filters) with RediSearch
Semantic caching (L1 memory + L2 RedisJSON + similarity fallback)
Streams (event sourcing + pipeline telemetry)
Pub/Sub (violation alerts and collaboration)
Conversation state in Redis (Lists + TTL)
Optional TimeSeries (performance/analytics where supported)
Real-Time AI Innovators: I went beyond a chatbot by combining vector search, semantic caching, streams, and pub/sub to accelerate AI.
Beyond the Cache: I use Redis as a multi‑model platform: JSON as primary store, RediSearch for hybrid vector, TimeSeries for analytics, Streams for event sourcing, Lists/Sets for session and UX, Pub/Sub for real-time collaboration.
Thanks for Reading!
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