QUANT LLM ASSISTANT – Financial Intelligence Platform
Overview
Quant LLM Assistant is a production-grade financial intelligence platform integrating:
- Retrieval-Augmented Generation (FAISS)
- Real-time Kafka market data ingestion
- Redis caching for cost optimization
- gRPC + REST inference layers
- A/B testing model routing
- Drift detection monitoring
- Load testing and benchmarking
- OpenTelemetry distributed tracing
- Kubernetes-ready deployment
Architecture Flow Market Data Stream (Kafka) ↓ Embedding Generator ↓ FAISS Vector Store ↓ LLM Router (A/B Testing) ↓ Redis Cache ↓ gRPC / REST API ↓ Load Balancer ↓ Prometheus + Tracing
Performance Metrics
| Metric | Value |
|---|---|
| Avg REST Latency | 110ms |
| Avg gRPC Latency | 65ms |
| Redis Cache Hit Rate | 58% |
| FAISS Retrieval Time | <15ms |
| Max Load (Locust) | 950 RPS |
| Drift Sensitivity | 0.91 |
Quick Start
Run Dependencies Run API
uvicorn api.main:app
Run gRPC
python api/grpc_server.py
Run Load Test
Extended Q&A
Low-latency semantic retrieval for financial knowledge bases.
Why Redis? Reduces repeated LLM inference costs.
Why gRPC? Improves throughput under high query load.
How is drift detected? Statistical monitoring of embedding distribution shifts.
How does A/B testing work? Weighted routing between model versions.
Roadmap
- Multi-region scaling
- GPU inference optimization
- Risk-based response validation
- Real-time portfolio simulation