Design Vector Database for LLM & RAG Retrieval (Variant #77 - AI Infrastructure & RAG Systems)
Complete FAANG-level system design blueprint for Vector Database for LLM & RAG Retrieval. Covers capacity estimation, high-level architecture, deep-dive components, database schemas, and distributed failure modes.
Functional Requirements
- •Core functional capability: Store high-dimensional dense vector embeddings and perform nearest neighbor search
- •Provide real-time telemetry, monitoring, and audit logging
- •Ensure idempotent operations with zero duplicate executions
Non-Functional Requirements
- •Strict non-functional SLA: Sub-10ms ANN vector search, horizontal scalability
- •High availability (99.999% uptime with zero single points of failure)
- •Horizontally scalable architecture with auto-scaling compute pools
Capacity & Scale Estimation
Core Architectural Components
1Client Layer & API Gateway
Handles TLS termination, JWT authentication, rate limiting, and reverse proxy routing to internal microservices.
2Primary Ingestion & Business Service
Executes core business logic for store high-dimensional dense vector embeddings and perform nearest neighbor search with strict validation bounds.
3Distributed Caching & In-Memory State
Multi-tier Redis cluster caching hot keys to achieve sub-millisecond p99 response times.
4Asynchronous Message Queue & Stream Buffer
Kafka cluster decoupling heavy write loads, facilitating event-driven processing and retry dead-letter queues.
5Persistent Storage & Data Tier
Partitioned SQL / NoSQL database with read replicas, sharded by primary entity ID for horizontal scaling.
Architectural FAQs & Interview Deep Dives
How does this Vector Database for LLM & RAG Retrieval architecture handle sudden traffic spikes?
Traffic spikes are buffered using distributed Kafka message queues and elastic auto-scaling worker groups, while read requests are absorbed by multi-tier Redis caches.
How do you prevent data inconsistencies during network partition failures?
We enforce the CAP theorem trade-offs using Quorum-based Raft consensus for strong consistency or Eventual Consistency with vector clocks for high availability.
What is the single most common failure mode in Vector Database for LLM & RAG Retrieval?
Cascading failures caused by unhandled downstream timeouts. We mitigate this using Circuit Breakers with exponential backoff and jittered retries.