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metadata
title: SentinelAI RAG Gateway
emoji: ⚡
colorFrom: yellow
colorTo: indigo
sdk: docker
app_port: 7860
pinned: false
short_description: C++17 AVX2 Semantic Cache, Sub-0.5ms Guardrails & Hybrid RAG
SentinelAI-RAG
Enterprise AI Gateway, C++17 AVX2 Semantic Cache Proxy, 4-Pillar Production RAG Pipeline & 3D WebCrypto BYOK Vault.
📖 System Documentation
- System Architecture Specification (HLD + LLD): Exhaustive technical specification detailing the 4-pillar production RAG engine, SIMD AVX2 unrolled loops, RRF $k=60$ mathematics, cross-attention weights, and WebCrypto AES-256-GCM vault.
- Eraser.io Diagram-as-Code Script: Ready-to-paste architecture canvas script for Eraser.io.
- Production Benchmark Report: Empirical 120-query evaluation over
novapay_enterprise_rag_corpus.pdf.
1. System Architecture
┌────────────────────────────────────────┐
│ Client Request (HTTP / SSE / UI) │
│ Interactive 3D Rabbit Three.js Vault │
│ Client-Side WebCrypto AES-256-GCM │
└───────────────────┬────────────────────┘
│
▼
┌────────────────────────────────────────┐
│ Redis Lua Token-Bucket Rate Limiter │
└───────────────────┬────────────────────┘
│
▼
┌────────────────────────────────────────┐
│ Sub-0.5ms PII Redactor (+ Luhn) │
└───────────────────┬────────────────────┘
│
▼
┌────────────────────────────────────────┐
│ Prompt Injection & Jailbreak Guard │
└───────────────────┬────────────────────┘
│
▼
┌────────────────────────────────────────┐
│ ONNX Embedder (all-MiniLM-L6-v2, 384d) │
└───────────────────┬────────────────────┘
│
Single Embedding Vector Reused (vq)
(Zero-Double-Embedding Principle)
/ \
/ \
▼ ▼
┌───────────────────────────────────┐ ┌───────────────────────────────────┐
│ C++17 AVX2 Semantic Cache │ │ PILLAR 1: PRE-RETRIEVAL REWRITER │
│ (Cosine >= 0.86 | 8.4µs p50) │ │ Domain Mapping & Policy Acronyms │
└─────────────────┬─────────────────┘ └─────────────────┬─────────────────┘
│ │
[Cache Hit? (>=0.86)] ▼
/ \ ┌───────────────────────────────────┐
YES / \ NO │ PILLAR 2: HYBRID SEARCH VIA RRF │
▼ └─────►│ Dense: C++17 AVX2 (9.77µs, w=0.6)│
┌────────────────────────┐ │ Sparse: Okapi BM25 (1,467 terms) │
│ Return Response (24ms) │ └─────────────────┬─────────────────┘
│ Zero LLM API Cost │ │
└────────────────────────┘ ▼
┌───────────────────────────────────┐
│ PILLAR 3: CROSS-ATTENTION RERANK │
│ Multi-Factor: Code+Window+Header │
└─────────────────┬─────────────────┘
│
┌─────────────────▼─────────────────┐
│ PILLAR 4: QUALITY GATING & SCORE │
│ Sufficiency >= 0.35 | Grounding │
└─────────────────┬─────────────────┘
│
┌─────────────────▼─────────────────┐
│ Async Dynamic LLM Router │
│ BYOK: OpenAI/Groq/Anthropic/Gemini│
│ (Sliding-Window Circuit Breaker) │
└─────────────────┬─────────────────┘
│
┌─────────────────▼─────────────────┐
│ Output Guard (PII & Refusal Fltr)│
└─────────────────┬─────────────────┘
│
┌─────────────────▼─────────────────┐
│ Store in Cache + Stream to UI │
│ Telemetry Chip: 🎯 Quality: XX% │
└───────────────────────────────────┘
2. Key Technical Innovations
🧠 The 4-Pillar Production RAG Engine
- Pre-Retrieval Query Rewriting & Expansion (
app/rag/query_rewriter.py): Resolves ambiguous queries ("what is policy of company") into exhaustive domain queries covering all 10 NovaPay policies (FIN-101throughSLA-1001) and expands critical payment acronyms (SLA, DLQ, VDMP, RPO, RTO, KYC, KYB, PCI-DSS). - Hybrid Search with Reciprocal Rank Fusion (
app/rag/bm25_retriever.py&retriever.py): Fuses dense vector semantics with an in-memory Okapi BM25 sparse index (1,467 vocabulary terms, $k_1=1.5, b=0.75$, $2.0\times$ title weighting) using Reciprocal Rank Fusion (RRF, $k=60$): $$\text{RRF_Score}(d) = 0.60 \cdot \frac{1}{60 + \text{rank}{\text{dense}}(d)} + 0.40 \cdot \frac{1}{60 + \text{rank}{\text{bm25}}(d)}$$ - Two-Stage Cross-Attention Neural Reranking (
app/rag/reranker.py): Evaluates top candidate chunks against policy reference codes ($35%$), token window proximity density ($35%$), section title match ($20%$), and numerical SLA anchors ($10%$). - Pre-Generation Retrieval Quality Gating (
app/rag/quality_evaluator.py): Computes context grounding sufficiency ($Q \ge 0.35$) before invoking the upstream model. Emits real-time quality confidence in the UI (🎯 Quality: 78%).
⚡ C++17 AVX2 + FMA SIMD Engine (8.40 µs p50)
- Hand-tuned AVX2 vector intrinsics unrolled 4-wide (
4 x 8 = 32floats per cycle). - Computes 384-dimensional dot products in sub-microsecond latencies (
8.40 µsp50,11.40 µsp95). - Fused Multiply-Add (
_mm256_fmadd_ps) eliminates roundoff error and doubles floating point throughput. - Zero-copy Python integration via
pybind11buffer protocol.
🛡️ Pre-Execution Sub-Millisecond Guardrails (< 0.5ms)
- PII Redaction: Pre-compiled regex patterns coupled with strict ISO/IEC 7812 Luhn checksum card validation. Redacts SSNs, credit cards, phones, emails, IPv4s, passports, and IBANs in
0.076 ms(p95). - Prompt Injection Defense: Multi-tier heuristic scoring detecting DAN roleplay jailbreaks, system-prompt extraction attempts, delimiter injection (
<system>,[INST]), and encoded payload attacks (100%block rate,0%false positives). - Output Guard: Post-generation scanner verifying responses for leaked PII, repetition loops, and disclaimers while strictly preserving newlines
\nfor Markdown tables.
🐰 Interactive 3D Rabbit BYOK Vault & WebCrypto AES-256-GCM
- Interactive Three.js WebGL Mascot: Floating 3D rabbit with procedural breathing and mouse-follow ear physics.
- Client-Side Key Encryption: Encrypts API keys (OpenAI, Groq, Anthropic, Gemini) with AES-256-GCM derived via PBKDF2 (100,000 iterations). Plaintext keys never touch the server disk or environment variables.
- Dynamic Model Selection: Switch seamlessly between
GPT-4o,Llama 3.3 70B,Claude 3.5 Sonnet, andGemini 1.5 Proon the fly.
🍏 Apple visionOS Frosted-Glass Web UI
- Served directly at
GET http://localhost:8000/. - Soft champagne sunlit-yellow canvas (
#FDFBF2), optical glass physics, real-time typing indicators, and telemetry pill badges (Pipeline latency, AVX2 search microseconds, cost saved, and🎯 Quality: XX%grounding score). - Full Markdown table parsing, unescaped
<br>support, and frosted-glass horizontal rules (<hr class="ios-hr">).
3. Benchmark Headline KPIs
From the empirical 120-query enterprise workload (BENCHMARK_REPORT.md):
| Metric | Baseline (Zero Cache) | Warm Cache (Steady State) | Improvement |
|---|---|---|---|
| LLM API Cost (120 Queries) | $0.0139 |
$0.0032 |
76.61% Cost Decrease |
| Pipeline Latency (p50) | 111.21 ms |
25.98 ms |
76.64% Latency Reduction |
| Direct Cache Hit Latency (p50) | N/A |
24.24 ms |
Sub-25ms Execution |
| Semantic Cache Hit Ratio | 0.0% |
65.00% |
65.00% Traffic Offloaded |
| C++17 AVX2 SIMD Search (p50) | 29.40 µs (NumPy) |
8.40 µs (AVX2) |
3.50x SIMD Hardware Speedup |
| Sparse Index Coverage | — | 1,467 terms | Zero-Dependency Okapi BM25 |
| Retrieval Quality Gating | Unmeasured | 78% - 94% | Grounded Sufficiency Verified |
| Prompt Injection Defense | — | 100.0% (20/20) |
0% False Positives (0/100) |
| Automated Test Suite | — | 31 / 31 Passing | 100% Pass Rate |
4. Directory Structure
sentinelai-rag/
├── ARCHITECTURE.md # Production systems architecture (HLD + LLD)
├── BENCHMARK_REPORT.md # Empirical 120-query benchmark report
├── README.md # Project overview and quickstart
├── render.yaml # Zero-secret Render cloud deployment config
├── data/
│ └── novapay_enterprise_rag_corpus.pdf # 10-page enterprise manual (80 chunks)
├── docs/
│ └── eraser_hld.txt # Ready-to-paste Eraser.io Diagram-as-Code
├── cpp_engine/
│ ├── simd_engine.cpp # C++17 AVX2 + FMA vector search engine
│ └── setup.py # pybind11 compilation flags (-O3 -mavx2 -mfma)
├── app/
│ ├── main.py # FastAPI production application & routes
│ ├── config.py # Central configuration & pricing matrix
│ ├── static/
│ │ └── index.html # Apple visionOS glass UI + 3D Three.js Rabbit
│ ├── guardrails/
│ │ ├── pii_redactor.py # <0.5ms Regex + Luhn PII filter
│ │ ├── prompt_injection.py # Jailbreak & injection blocker
│ │ ├── output_guard.py # Newline-preserving response sanitizer
│ │ └── rate_limiter.py # Redis Lua token-bucket limiter
│ ├── embeddings/
│ │ └── onnx_embedder.py # 384-dim L2 unit vector embedder
│ ├── rag/
│ │ ├── __init__.py # RAG module exports
│ │ ├── query_rewriter.py # Pillar 1: Pre-retrieval query rewriter
│ │ ├── bm25_retriever.py # Pillar 2a: Okapi BM25 sparse retriever
│ │ ├── retriever.py # Pillar 2b: C++17 AVX2 hybrid retriever
│ │ ├── reranker.py # Pillar 3: Cross-attention neural reranker
│ │ ├── quality_evaluator.py # Pillar 4: Pre-generation quality gate
│ │ └── pdf_chunker.py # Section-aware 10-page PDF chunker
│ └── gateway/
│ ├── semantic_cache.py # AVX2 similarity cache + response store
│ ├── circuit_breaker.py # Sliding-window circuit breaker state machine
│ └── llm_router.py # Dynamic BYOK multi-model failover router
├── benchmarks/
│ ├── workload_120.py # 120 queries across all 10 PDF pages
│ └── run_benchmark.py # Evaluator & BENCHMARK_REPORT.md generator
├── tests/
│ └── test_production_suite.py # Complete pytest automated suite (31/31 passing)
├── Dockerfile # Multi-stage production container
├── docker-compose.yml # Full stack deployment with Redis
└── requirements.txt
5. Quickstart Guide
Prerequisites
- Python 3.10+
- C++17 compiler (GCC 9+, Clang 10+, or MSVC 2019+) with AVX2 instruction support.
1. Build C++17 AVX2 SIMD Extension
cd cpp_engine
python setup.py build_ext --inplace
cd ..
2. Install Python Dependencies
pip install -r requirements.txt
3. Run Automated Tests (31/31 Passing)
pytest tests/test_production_suite.py -v
4. Execute Benchmark Suite
python benchmarks/run_benchmark.py
This executes the 120-query enterprise workload across all 10 pages and outputs BENCHMARK_REPORT.md.
5. Launch FastAPI Gateway & Web Chat UI
uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
Open http://localhost:8000/ in your browser to experience the Apple visionOS Frosted-Glass Chat Interface with the interactive 3D Rabbit BYOK vault.
6. API Reference
POST /v1/rag/query
Execute full protected hybrid RAG query.
Request:
{
"query": "How fast can I get a refund under FIN-101?",
"client_id": "merchant_tier_1",
"top_k": 5,
"bypass_cache": false
}
Response:
{
"query": "How fast can I get a refund under FIN-101?",
"sanitized_query": "How fast can I get a refund under FIN-101?",
"rewritten_query": "How fast can I get a refund under FIN-101? FIN-101 Payment Processing & Refunds: standard refunds 5-7 business days...",
"response": "Under NovaPay Policy FIN-101 (DOC-OPS-001), standard credit card refunds process in 5 to 7 business days, instant UPI refunds complete in 15 minutes, and automated network reversals resolve within 120 seconds.",
"cache_hit": false,
"cache_similarity": 0.0,
"retrieval_quality_score": 0.78,
"retrieved_contexts": [
{
"doc_id": "DOC-OPS-001",
"page_number": 1,
"section_title": "3 - 5 Business Days",
"similarity_score": 0.8223,
"text": "Standard refund timelines: Credit card 5-7 business days..."
}
],
"guardrails": {
"pii_detected_count": 0,
"pii_findings": [],
"prompt_injection_blocked": false,
"injection_risk_score": 0.0,
"output_guard_passed": true,
"output_issues": []
},
"timing": {
"guardrail_latency_ms": 0.18,
"embedding_latency_ms": 4.12,
"cache_lookup_latency_ms": 0.009,
"retrieval_latency_ms": 0.011,
"llm_latency_ms": 320.4,
"output_guard_latency_ms": 0.12,
"total_pipeline_latency_ms": 324.8
},
"estimated_cost_usd": 0.00012,
"cost_saved_usd": 0.0,
"model_used": "openai:gpt-4o-mini"
}
POST /v1/rag/stream
Server-Sent Events (SSE) streaming endpoint with guardrail interception, hybrid retrieval, quality telemetry, and live token generation.
POST /v1/cache/clear
Purges all entries from the C++17 AVX2 semantic cache.
GET /v1/health
System liveness, readiness, and SIMD hardware status.
GET /v1/metrics
Prometheus/JSON latency percentiles (p50, p95, p99), cost reduction percentages, and cache stats.
7. Cloud Deployment (Render Zero-Secret Setup)
SentinelAI-RAG is configured for zero-secret deployment using render.yaml.
- Push your repository to GitHub.
- In the Render Dashboard, click New Blueprint Instance.
- Select your
SentinelAIrepository. - Render will automatically build the C++17 SIMD extension and start Uvicorn.
- Users connect their own encrypted keys in the browser via the 3D Rabbit Vault!