Nexus RAG & Multi-Agent Engine
Autonomous multi-agent research and reasoning engine powered by hybrid vector search.
System Overview & Motivation
Nexus is an enterprise-grade Retrieval-Augmented Generation (RAG) and autonomous multi-agent reasoning engine built to overcome hallucination and context-window degradation in massive knowledge bases. It features a dual-phase retrieval pipeline combining dense vector embeddings with sparse BM25 keyword matching, followed by cross-encoder re-ranking to yield highly relevant context for LLM agents.
Key Architectural Highlights
- Hybrid dense-sparse retrieval combining Qdrant cosine similarity with BM25 keyword search
- Cross-encoder reranking step cutting context noise and eliminating token window waste
- LangGraph autonomous multi-agent cyclic execution with tool-calling sandbox
- Streaming response protocol via FastAPI WebSockets for instantaneous token delivery
- Sub-120ms p95 retrieval latency across 500k ingested technical documentation chunks
Technical Architecture & Data Pipeline
The ingest service consumes raw PDF/Markdown documents, recursively splits tokens with semantic overlap, computes embeddings using Hugging Face sentence transformers, and indexes vectors into Qdrant. When queries arrive, an orchestrator agent decomposes user intent, performs parallel hybrid searches, reranks candidates, and delegates tasks to specialized sub-agents via LangGraph before synthesizing citations.
Production Challenges & Edge Cases Solved
Balancing high retrieval precision against inference latency. Solved by decoupling embedding generation into an asynchronous queue and caching vector lookups in Redis for high-frequency queries.