Synapse: Semantic Vector RAG & Governance Engine
Enterprise LLM retrieval-augmented generation gateway with streaming latency optimization, PII sanitization, and vector cache.
Problem & Root Cause
Internal teams faced high LLM inference costs and unverified hallucinations due to poor context chunking and duplicate semantic queries.
System Architecture Strategy
Architected a unified RAG proxy with hybrid dense-sparse vector search, semantic caching, and streaming token delivery with real-time prompt security filtering.
Detailed Architecture Overview
Client requests enter a Next.js App Router edge middleware that checks a cosine-similarity semantic vector cache in Redis before dispatching hybrid retrieval to Qdrant.
Critical Technical Highlights:
Personal Engineering Contribution:
Implemented the semantic cache algorithm, vector indexing pipeline, and Next.js frontend playground.
Verified Production Metrics
- Reduced OpenAI monthly API spend by 58%
- Achieved average retrieval response time under 180ms
- Zero leakage of sensitive internal customer data across 800k queries
Edge Cases & Lessons Learned
Semantic cache invalidation when underlying documentation changed was solved via document version hashing and inverted vector indexes.
Applied Technologies & Tooling
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