James Murray has designed the Atlas Vector RAG API, a high-throughput retrieval-augmented generation microservice. This API integrates a queue of embeddings for enhanced performance in real-time data retrieval and AI reasoning.

The service handles 10K+ QPS with sub-50ms p95 latency using async embedding queues, vector database sharding, and GPU-accelerated inference. It supports hybrid search (keyword + semantic) and reranking with cross-encoders for 23% precision boost.

Designed for enterprise chatbots, internal knowledge bases, and real-time decision systems with strict SLAs.

Key Features

• High Throughput: 10K+ QPS with horizontal pod autoscaling.
• Embeddings Queue: Decouples embedding generation from query serving via Redis Streams.
• Microservice Architecture: Stateless, containerized, Kubernetes-native.
• Hybrid Search: BM25 + dense vector with alpha blending.
• Reranking Layer: Cross-encoder (deberta-v3) on top-20 results.
• Rate Limiting & Auth: JWT + per-namespace, per-IP quotas.

System Design & Architecture

Atlas Vector RAG uses state-of-the-art vector search technologies to provide rapid, reliable results. The system is deployed in containerized environments with Helm charts for scalability and zero-downtime updates.

Technical Stack

• API: FastAPI + Uvicorn
• Vector DB: Qdrant with HNSW + product quantization
• Queue: Redis Streams + RQ workers
• Orchestration: Kubernetes + ArgoCD

Related Projects

Explore other related projects:

• RAG Pipelines
• Semantic SEO
• Render.com RAG Service