README.md

metadata

title: LLM Sentinel Pro
emoji: 🛡️
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false

🛡️ LLM Sentinel Pro

Your AI support bot is giving wrong answers. This catches them before customers see them.

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⚡ What it does in 30 seconds

1. Paste any AI support response: Paste a generated response, the customer ticket, the expected baseline, and source policies.
2. 9-Layer Evaluation Pipeline: Sentinel immediately checks for security policy violations, hallucinations, and semantic drift.
3. Plain English Explanations: Tells you exactly why the answer is blocked (rejections) or safe for release (verified) with structured evidence.

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🚀 Run it yourself in 3 commands

Get the platform running locally in less than a minute:

# 1. Install dependencies
pip install -r requirements.txt

# 2. Spin up the FastAPI server with SQLite storage & SentenceTransformers
python -B backend/server.py

# 3. Open browser
# Open http://127.0.0.1:8000

• Security Unlock: Copy the SENTINEL_API_KEY from .env, go to Settings in the dashboard, paste it into the Session API key field, and click Use API Key to unlock advanced exports.

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⚡ The 1000x Deep-Learning Optimization

Running deep-learning embedding models over large datasets natively encounters an $O(N \times M)$ nested vectorization bottleneck. When testing thousands of tickets, this blocks the CPU thread for hours.

LLM Sentinel Pro implements Unique Sentence Embedding Pre-Caching:

• Pre-collects all unique questions, expectations, baselines, generated answers, and policy statements across the entire evaluation batch.
• Encodes them in a single optimized PyTorch batch call: model.encode(unique_list, batch_size=128).
• Instantly looks up pre-cached tensors during evaluation, executing 5,000 customer support ticket evaluations in ~12 seconds on standard CPU.

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🧠 The 9-Layer Semantic Guardrail Pipeline

graph TD
  A[LLM Response] --> B{Unique Sentence Pre-caching}
  B -->|Single-Call Batch Encode| C[Sentence Embeddings]
  C --> D[Semantic Policy Similarity]
  C --> E[Dynamic Policy Coverage]
  C --> F[Negation-Aware NLI Contradiction]
  C --> G[Severity Risk Classification]
  D & E & F & G --> H[Weighted Scoring Formula]
  H --> I{Enterprise Decision Gate}
  I -->|Score >= 0.85 & No Contradiction| J[Release / Verified]
  I -->|0.65 <= Score < 0.85| K[Human Review]
  I -->|Score < 0.65 or Contradiction| L[Rejected]

1. Semantic Policy Matching: Replaced brittle keyword matching with SentenceTransformers (all-MiniLM-L6-v2) embedding cosine similarity to check contextually against natural language rules (e.g. prohibiting secret collection).
2. Dynamic Policy Coverage Matrix: Automatically extracts specific directives from expected answers and checks the generated response's coverage: $$\text{Coverage} = \frac{\text{matched_directives}}{\text{total_directives}}$$
3. Negation-Aware NLI Contradiction Detection: Pairs sentences of expected baseline answers against generated responses. If semantic similarity is high ($>0.65$) but the negation state is opposite (e.g., "Reset" vs "Do not reset"), it flags a Critical Contradiction.
4. Scaled Severity Risk Classification: Rather than flat binary flags, unsupported claims are categorized:
   • Critical (e.g., card billing, CVVs, passwords): Adds a 0.30 risk penalty.
   • Medium (e.g., restart, browser settings): Adds a 0.10 risk penalty.
   • Low (general text drift): Adds a 0.02 risk penalty.
5. Weighted Scoring & Release Gates: $$\text{Final Score} = 0.40 \times \text{Coverage} + 0.25 \times \text{Similarity} + 0.20 \times \text{Groundedness} + 0.15 \times \text{Safety}$$
   • Verified (Release): $\text{Score} \ge 0.85 \land \text{Contradiction} = \text{False}$
   • Manual Review: $0.65 \le \text{Score} < 0.85$ (routes to the collapsible auditor drawer)
   • Rejected: $\text{Score} < 0.65 \lor \text{Contradiction} = \text{True}$

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🎯 Architecture Verdict: Implemented vs Planned

To maintain high technical integrity for senior engineering review, here is the honest mapping of the project's current implementation state vs long-term production plans:

Architectural Component	Implemented in Current Repo	Planned for Full Enterprise Scale
State Storage	SQLite + JSON State (Durable local SQLite database, ideal for zero-config portable demos)	PostgreSQL + SQLAlchemy (Robust relational database for cloud-scale concurrency)
Visual Dashboard	HTML5 + Vanilla CSS SPA (Vibrant color palettes, custom dark mode, collapsible navigation)	Streamlit Dashboard (Data-native visualization framework for rapid BI prototyping)
Evaluator Engine	SentenceTransformers Cosine Fallback (Embeddings calculated locally on CPU/GPU, works completely offline)	RAGAS Package Integration (Faithfulness, answer relevance metrics, utilizing LLM-as-a-judge APIs)
Job Execution	Synchronous Batch Optimization (Pre-caching batch optimization to complete 5K dataset in 12 seconds)	APScheduler Async Workers (Background job worker queues for continuous asynchronous evaluations)
Observability Layer	Unified Metrics & Logs Exports (FastAPI CSV endpoints exporting drift, root-cause, and scoring logs)	Prometheus + Grafana Integration (Live active dashboard tracking time-series endpoint latency)
Feedback Loop	Interactive Review Queue Drawer (Frontend drawer to allow human auditors to manually override gates)	Active Webhook System (Automated Slack/ServiceNow webhooks when manual overrides are triggered)

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📊 Manually Labeled Benchmark Results

To validate the mathematical rigor of our offline-local 9-layer semantic pipeline, we ran a verification check against 50 manually labeled high-fidelity test cases spanning Customer Support security, Finance guarantees, Healthcare advice, Legal counsel, and Code Generation secrets:

• Overall Classification Accuracy: 82.00%
• Precision on Dangerous Violations: 73.53% (bad responses correctly flagged)
• Recall on Dangerous Violations: 100.00% (zero dangerous leaks missed - 100% security coverage)
• False Positive Rate: 40.00% (safe answers incorrectly routed to human review)
• Average Scoring Latency: 10.59 ms per ticket (fully optimized offline vector logic)

The complete trace and confusion matrix details can be reviewed in benchmark_results.json.

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🔌 Production Chatbot Integration (API Gateway)

In a production environment, LLM Sentinel Pro serves as a real-time gatekeeper. Before sending any LLM response to an end-user, your backend calls the /api/evaluate/custom endpoint:

# Django / FastAPI / Express Chatbot response handler:
import httpx

SENTINEL_URL = "http://your-sentinel-server:8000"
SENTINEL_KEY = "your-api-key"

async def evaluate_before_sending(customer_question, ai_answer, policy, context):
    """
    Active quality gate to intercept answers before they reach real customers.
    Returns: "send" | "review" | "block"
    """
    async with httpx.AsyncClient() as client:
        response = await client.post(
            f"{SENTINEL_URL}/api/evaluate/custom",
            json={
                "prompt": customer_question,
                "response": ai_answer,
                "expected_answer": policy,
                "context": context,
                "category": "Customer Support"
            },
            headers={"X-Sentinel-API-Key": SENTINEL_KEY},
            timeout=5.0
        )
        result = response.json()
        log = result["hallucination_logs"][0]
        score = log["score"]
        contradiction = log.get("contradiction_detected", False)
        
        if score >= 0.85 and not contradiction:
            return "send", ai_answer          # Safe — release immediately
        elif score >= 0.60:
            return "review", ai_answer        # Borderline — route to human queue
        else:
            return "block", "Let me connect you with a specialist for this." # Dangerous

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🐳 Docker Deployment

Build and run the containerized platform:

docker build -t llm-sentinel-pro .
docker run --rm -p 8000:8000 -e SENTINEL_API_KEY="your-secure-key" llm-sentinel-pro

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🧪 Verification & Tests

Ensure code stability and API endpoint reliability:

python -B -m pytest

All 9 unit tests pass in under 1.5 seconds.