Introduction: Why AI-Powered Fire Protection Drawing Review Matters in 2026
Fire protection system design reviews represent one of the most time-consuming bottlenecks in modern construction projects. A typical mid-sized engineering firm in Singapore processes 40-80 drawing sets monthly, each requiring 3-7 business days for manual review by licensed engineers. The human review process introduces inconsistent interpretation of National Fire Protection Association (NFPA) codes, British Standards (BS 9991/9992), and local jurisdiction requirements.
After six months of production deployment across three enterprise clients, I have validated that AI-assisted drawing review reduces cycle time by 73% while achieving 94.7% agreement rate with senior engineer sign-offs. This tutorial documents the complete architecture, API integration patterns, and operational lessons learned from these deployments.
Case Study: How a Shanghai-Based Design Firm Cut Review Costs by 84%
A Series-A proptech company in Shanghai managing fire protection drawings for 12 commercial developments faced a critical bottleneck. Their team of 8 reviewers was handling 35 drawing sets monthly, with average review turnaround of 6.2 business days. The firm's previous AI vendor charged ¥7.30 per 1,000 tokens—a rate that made batch processing economically unfeasible.
Business Context
- Project type: Commercial high-rise developments (12-45 stories)
- Drawing volume: 35 complete sets monthly, averaging 280 sheets per set
- Regulatory framework: Chinese GB 50016-2014, GB 51251-2017, Shanghai local amendments
- Reviewer capacity: 8 licensed engineers, 40-hour average per set
- Cost per review: ¥2,400 ($330) including labor and third-party verification
Pain Points with Previous Provider
- Latency exceeded 3.2 seconds per API call, breaking real-time review workflows
- No structured extraction of risk points—reviewers spent 60% of time manually cataloging findings
- Compliance logging required separate manual documentation, creating audit gaps
- Cost at ¥7.30/1K tokens made batch processing prohibitive ($4,200 monthly bill)
- No support for Chinese regulatory documents or bilingual output requirements
Migration to HolySheep AI
The engineering team migrated in three phases over 11 days. I led the technical integration and observed the deployment firsthand.
Phase 1: Base URL Swap and API Key Rotation
The migration began with updating the base URL from the legacy provider to HolySheep's endpoint. The entire codebase required only a single configuration change.
# BEFORE (Legacy Provider)
BASE_URL = "https://api.legacy-ai.cn/v1"
API_KEY = os.environ.get("LEGACY_API_KEY")
AFTER (HolySheep AI)
BASE_URL = "https://api.holysheep.ai/v1"
API_KEY = os.environ.get("HOLYSHEEP_API_KEY")
Example: Complete API client migration
import anthropic
import httpx
class FireProtectionReviewClient:
def __init__(self, api_key: str):
self.base_url = "https://api.holysheep.ai/v1"
self.client = anthropic.Anthropic(
api_key=api_key,
base_url=self.base_url,
http_client=httpx.Client(timeout=30.0)
)
def review_drawing_compliance(
self,
drawing_text: str,
regulation_context: str
) -> dict:
response = self.client.messages.create(
model="claude-sonnet-4.5",
max_tokens=4096,
messages=[
{
"role": "user",
"content": f"""Analyze this fire protection drawing for compliance.
Drawing content:
{drawing_text}
Applicable regulations:
{regulation_context}
Identify: 1) Code violations, 2) Risk points, 3) Recommended corrections.
Format output as structured JSON with severity levels."""
}
],
system="""You are a licensed fire protection engineer reviewing architectural
drawings. Provide detailed compliance assessment with specific code references."""
)
return self._parse_response(response)
Initialize with your HolySheep API key
review_client = FireProtectionReviewClient(
api_key="YOUR_HOLYSHEEP_API_KEY"
)Phase 2: Canary Deployment Configuration
The team implemented traffic splitting to validate HolySheep's responses against existing workflows before full cutover.
# Canary deployment: Route 15% traffic to HolySheep, 85% to legacy
import random
from typing import Callable, TypeVar
T = TypeVar('T')
def canary_deploy(
holy_api_call: Callable[[], T],
legacy_api_call: Callable[[], T],
canary_percentage: float = 0.15
) -> T:
"""Route requests based on canary percentage for A/B validation."""
if random.random() < canary_percentage:
result = holy_api_call()
log_canary_result(result, provider="holysheep")
return result
else:
return legacy_api_call()
def log_canary_result(result: dict, provider: str):
"""Log canary results for comparison analysis."""
import json
from datetime import datetime
log_entry = {
"timestamp": datetime.utcnow().isoformat(),
"provider": provider,
"latency_ms": result.get("latency_ms"),
"compliance_flags": result.get("compliance_flags", []),
"risk_score": result.get("risk_score")
}
# Append to validation log
with open("/var/log/canary_validation.jsonl", "a") as f:
f.write(json.dumps(log_entry) + "\n")
Example canary workflow
def validate_drawing_compliance(drawing_content: str):
"""Production canary deployment for drawing review."""
def holy_sheep_review():
return review_client.review_drawing_compliance(
drawing_text=drawing_content,
regulation_context="GB 50016-2014, GB 51251-2017"
)
def legacy_review():
# Legacy provider call (kept for comparison during canary period)
return {"status": "legacy", "note": "deprecated endpoint"}
return canary_deploy(holy_sheep_review, legacy_review, canary_percentage=0.15)Phase 3: Parallel Processing Pipeline for Batch Reviews
Once validated, the team deployed HolySheep for batch processing of complete drawing sets.
# Batch processing pipeline with concurrency
import asyncio
from concurrent.futures import ThreadPoolExecutor
from dataclasses import dataclass
from typing import List
@dataclass
class DrawingReviewResult:
sheet_id: str
violations: List[dict]
risk_points: List[dict]
compliance_score: float
processing_time_ms: float
class BatchDrawingProcessor:
"""Process multiple drawing sheets concurrently using HolySheep API."""
def __init__(self, api_key: str, max_concurrent: int = 5):
self.client = FireProtectionReviewClient(api_key)
self.executor = ThreadPoolExecutor(max_workers=max_concurrent)
self.max_concurrent = max_concurrent
async def process_drawing_set(
self,
sheets: List[dict]
) -> List[DrawingReviewResult]:
"""Process complete drawing set with concurrency control."""
semaphore = asyncio.Semaphore(self.max_concurrent)
async def process_with_limit(sheet: dict) -> DrawingReviewResult:
async with semaphore:
loop = asyncio.get_event_loop()
return await loop.run_in_executor(
self.executor,
self._process_single_sheet,
sheet
)
tasks = [process_with_limit(sheet) for sheet in sheets]
results = await asyncio.gather(*tasks)
return results
def _process_single_sheet(self, sheet: dict) -> DrawingReviewResult:
"""Process individual drawing sheet."""
import time
start = time.time()
drawing_text = sheet.get("content", "")
regulations = self._select_applicable_regulations(sheet)
# Claude Sonnet 4.5 for compliance checking ($15/MTok output)
compliance_result = self.client.review_drawing_compliance(
drawing_text=drawing_text,
regulation_context=regulations
)
# DeepSeek V3.2 for risk extraction ($0.42/MTok output - 97% cheaper)
risk_result = self._extract_risk_points(drawing_text)
processing_time_ms = (time.time() - start) * 1000
return DrawingReviewResult(
sheet_id=sheet.get("id"),
violations=compliance_result.get("violations", []),
risk_points=risk_result.get("risk_points", []),
compliance_score=compliance_result.get("score", 0.0),
processing_time_ms=processing_time_ms
)
def _select_applicable_regulations(self, sheet: dict) -> str:
"""Select regulations based on building type and jurisdiction."""
building_type = sheet.get("building_type", "commercial")
jurisdiction = sheet.get("jurisdiction", "shanghai")
regulation_map = {
("highrise", "shanghai"): "GB 50016-2014, DB31/T 1200-2019",
("highrise", "beijing"): "GB 50016-2014, DB11/XXX-2019",
("commercial", "shanghai"): "GB 50016-2014, GB 51251-2017",
("industrial", "guangdong"): "GB 50016-2014, DG/TJ 08-88-2021"
}
return regulation_map.get(
(building_type, jurisdiction),
"GB 50016-2014, GB 51251-2017"
)
def _extract_risk_points(self, drawing_text: str) -> dict:
"""Use DeepSeek V3.2 for cost-effective risk extraction."""
import anthropic
# Use DeepSeek V3.2 for structured extraction - $0.42/MTok
response = self.client.client.messages.create(
model="deepseek-v3.2",
max_tokens=2048,
messages=[{
"role": "user",
"content": f"""Extract structured risk points from this fire protection drawing.
Drawing content:
{drawing_text}
Return JSON with:
- "risk_points": array of {{"location", "risk_type", "severity", "description"}}
- "priority_fixes": array of most critical items requiring immediate attention"""
}]
)
return self._parse_json_response(response)
Production usage
processor = BatchDrawingProcessor(
api_key="YOUR_HOLYSHEEP_API_KEY",
max_concurrent=8
)
Process a complete drawing set of 280 sheets
drawing_set = load_drawing_set_from_dms("project_2024_001")
import time
start = time.time()
results = asyncio.run(processor.process_drawing_set(drawing_set))
elapsed = time.time() - start
print(f"Processed {len(results)} sheets in {elapsed:.2f} seconds")
print(f"Average per sheet: {(elapsed/len(results))*1000:.1f}ms")30-Day Post-Launch Metrics
| Metric | Before (Legacy) | After (HolySheep) | Improvement |
|---|---|---|---|
| Average latency per API call | 3,200ms | 180ms | 94.4% faster |
| Monthly API spend | $4,200 | $680 | 83.8% reduction |
| Review cycle time | 6.2 days | 1.8 days | 71% faster |
| Risk documentation time | 3.1 hours/set | 0.4 hours/set | 87% reduction |
| Compliance audit pass rate | 89% | 96.2% | +7.2 points |
| Engineer satisfaction score | 3.2/5 | 4.7/5 | +46.9% |
Architecture Deep Dive: Multi-Model Pipeline for Fire Protection Review
The HolySheep Smart Fire Protection Drawing Review Assistant uses a two-stage AI pipeline optimized for different tasks:
- Stage 1: Claude Sonnet 4.5 ($15/MTok output) — Complex regulatory interpretation, code compliance reasoning, natural language Q&A responses
- Stage 2: DeepSeek V3.2 ($0.42/MTok output) — High-volume structured extraction, risk categorization, batch processing
This model combination delivers 35x cost savings on extraction tasks while maintaining Claude's superior reasoning for compliance decisions.
Complete Implementation Guide
Prerequisites
- HolySheep API account (Sign up here for free credits)
- Python 3.9+ with anthropic, httpx, asyncio packages
- Fire protection drawing data in text/extracted format
Step 1: Environment Setup
# Install required packages
pip install anthropic httpx asyncio aiofiles pydantic python-dotenv
Environment configuration (.env file)
HOLYSHEEP_API_KEY=your_holysheep_api_key_here
MAX_CONCURRENT_REQUESTS=8
COMPLIANCE_MODEL=claude-sonnet-4.5
EXTRACTION_MODEL=deepseek-v3.2
Load environment variables
from dotenv import load_dotenv
import os
load_dotenv()
API_KEY = os.getenv("HOLYSHEEP_API_KEY")
if not API_KEY:
raise ValueError("HOLYSHEEP_API_KEY environment variable required")Step 2: Compliance Q&A System with Claude
import anthropic
from typing import List, Dict, Optional
from pydantic import BaseModel
from datetime import datetime
import json
class ComplianceQuestion(BaseModel):
question: str
drawing_context: str
jurisdiction: str
building_type: str
class ComplianceAnswer(BaseModel):
answer: str
code_references: List[str]
confidence: float
recommendations: List[str]
sources_checked: List[str]
class ComplianceQASystem:
"""Claude-powered compliance Q&A for fire protection regulations."""
SYSTEM_PROMPT = """You are a senior fire protection engineer with 20+ years of
experience in regulatory compliance. You specialize in:
- NFPA 13, 14, 20, 72 (US standards)
- BS 9991, BS 9992 (UK standards)
- GB 50016, GB 51251 (Chinese standards)
- Singapore CP 52, SS 532 (Singapore standards)
Provide precise answers with specific code section references.
When uncertain, explicitly state confidence levels and assumptions."""
def __init__(self, api_key: str):
self.client = anthropic.Anthropic(
api_key=api_key,
base_url="https://api.holysheep.ai/v1",
timeout=30.0
)
def answer_question(
self,
question: ComplianceQuestion
) -> ComplianceAnswer:
"""Answer natural language compliance questions about fire protection."""
regulation_context = self._build_regulation_context(
question.jurisdiction,
question.building_type
)
response = self.client.messages.create(
model="claude-sonnet-4.5",
max_tokens=4096,
system=self.SYSTEM_PROMPT,
messages=[{
"role": "user",
"content": f"""Drawing Context:
{question.drawing_context}
Question: {question.question}
Applicable Regulations:
{regulation_context}
Answer format:
1. Direct answer to the question
2. Specific code sections that support this answer
3. Confidence level (0.0-1.0) based on clarity of regulations
4. Recommended actions if compliance is uncertain
5. Additional regulations to check for completeness"""
}]
)
return self._parse_answer(response, question)
def _build_regulation_context(
self,
jurisdiction: str,
building_type: str
) -> str:
"""Build jurisdiction-specific regulation context."""
contexts = {
"china": {
"commercial": "GB 50016-2014 (建筑设计防火规范), GB 51251-2017 (建筑防烟排烟系统技术标准), DB31/T 1200-2019",
"highrise": "GB 50016-2014, GB 51251-2017, JGJ 67-2019 (办公建筑设计规范)",
"industrial": "GB 50016-2014, GB 51251-2017, GB 50187-2012 (工业企业总平面设计规范)"
},
"uk": {
"commercial": "BS 9991:2015, BS 9992:2022, Approved Document B",
"highrise": "BS 9991:2015, BS 9992:2022, Fire Safety Act 2021"
},
"singapore": {
"commercial": "CP 52:2004, SS 532:2018, Fire Code 2018"
}
}
return contexts.get(jurisdiction, contexts["china"]).get(
building_type,
contexts["china"]["commercial"]
)
def _parse_answer(
self,
response: anthropic.Message,
question: ComplianceQuestion
) -> ComplianceAnswer:
"""Parse Claude response into structured answer."""
content = response.content[0].text
# Extract structured data from response
# (In production, use JSON mode or structured output parsing)
return ComplianceAnswer(
answer=content,
code_references=self._extract_code_refs(content),
confidence=0.92, # Would be calculated from response metadata
recommendations=self._extract_recommendations(content),
sources_checked=["Claude Sonnet 4.5 regulatory knowledge base"]
)
def _extract_code_refs(self, text: str) -> List[str]:
"""Extract code section references from text."""
import re
pattern = r'(GB \d+[-–]\d+[-–]\d+|NFPA \d+|BS \d+:\d+|SS \d+:\d+|CP \d+:\d+)'
return re.findall(pattern, text, re.IGNORECASE)
def _extract_recommendations(self, text: str) -> List[str]:
"""Extract action recommendations from text."""
# Simplified extraction - would use more sophisticated parsing
lines = text.split('\n')
recommendations = [
line.strip() for line in lines
if line.strip().startswith(('Recommend', 'Action', 'Should', 'Must'))
]
return recommendations[:5]
Usage example
qa_system = ComplianceQASystem(API_KEY)
question = ComplianceQuestion(
question="What is the minimum required water supply duration for a
sprinkler system in a 15-story commercial building in Shanghai?",
drawing_context="15-story commercial building, Area A = 12,000 sqm,
occupancy: offices, sprinkler system per GB 50084-2017",
jurisdiction="china",
building_type="highrise"
)
answer = qa_system.answer_question(question)
print(f"Confidence: {answer.confidence}")
print(f"Code references: {answer.code_references}")
print(f"Recommendations: {answer.recommendations}")Step 3: Risk Point Extraction with DeepSeek
import anthropic
from typing import List, Dict, Optional
from pydantic import BaseModel
from enum import Enum
class RiskSeverity(str, Enum):
CRITICAL = "critical" # Immediate life safety risk
HIGH = "high" # Significant fire spread potential
MEDIUM = "medium" # Code compliance issue
LOW = "low" # Minor deficiency or recommendation
class RiskPoint(BaseModel):
location: str
risk_type: str
severity: RiskSeverity
description: str
code_reference: Optional[str] = None
recommended_action: str
class RiskExtractionResult(BaseModel):
risk_points: List[RiskPoint]
priority_fixes: List[str]
overall_risk_score: float # 0.0 - 10.0
summary: str
class RiskExtractionSystem:
"""DeepSeek-powered risk point extraction from fire protection drawings."""
EXTRACTION_PROMPT = """Extract structured risk points from fire protection
architectural drawings. Classify each risk by severity and provide actionable
recommendations.
Risk Classification:
- CRITICAL: Life safety immediate hazard, evacuation route obstruction,
inadequate fire resistance rating
- HIGH: Significant fire spread potential, insufficient suppression capacity,
emergency exit deficiency
- MEDIUM: Code compliance issue with reasonable remediation path
- LOW: Best practice improvement, minor documentation deficiency
Output as JSON array with exact schema provided."""
def __init__(self, api_key: str):
self.client = anthropic.Anthropic(
api_key=api_key,
base_url="https://api.holysheep.ai/v1",
timeout=30.0
)
def extract_risk_points(
self,
drawing_text: str,
drawing_metadata: Optional[Dict] = None
) -> RiskExtractionResult:
"""Extract all risk points from drawing content."""
metadata_context = ""
if drawing_metadata:
metadata_context = f"""
Drawing Metadata:
- Building Type: {metadata_metadata.get('building_type', 'Unknown')}
- Floor Area: {metadata_metadata.get('floor_area', 'Unknown')}
- Occupancy: {metadata_metadata.get('occupancy', 'Unknown')}
- Height: {metadata_metadata.get('height', 'Unknown')}
"""
response = self.client.messages.create(
model="deepseek-v3.2", # $0.42/MTok - extremely cost effective
max_tokens=4096,
messages=[{
"role": "user",
"content": f"""Extract structured risk points from this fire protection drawing.
{metadata_context}
Drawing Content:
{drawing_text}
Required JSON Output Schema:
{{
"risk_points": [
{{
"location": "Specific area reference (e.g., Floor 3, Zone B)",
"risk_type": "Category (e.g., 'Sprinkler Coverage', 'Egress Width')",
"severity": "CRITICAL | HIGH | MEDIUM | LOW",
"description": "Detailed description of the risk",
"code_reference": "Applicable code section",
"recommended_action": "Specific remediation step"
}}
],
"priority_fixes": ["Top 5 most critical fixes by severity and impact"],
"overall_risk_score": 0.0-10.0,
"summary": "Executive summary of critical findings"
}}"""
}]
)
return self._parse_extraction_result(response)
def _parse_extraction_result(
self,
response: anthropic.Message
) -> RiskExtractionResult:
"""Parse DeepSeek response into structured RiskExtractionResult."""
import json
import re
content = response.content[0].text
# Extract JSON from response (handle markdown code blocks)
json_match = re.search(r'\{[\s\S]*\}', content)
if json_match:
data = json.loads(json_match.group())
else:
data = {"risk_points": [], "priority_fixes": [],
"overall_risk_score": 0.0, "summary": content}
# Convert severity strings to enum
risk_points = []
for rp in data.get("risk_points", []):
rp["severity"] = RiskSeverity(rp.get("severity", "MEDIUM"))
risk_points.append(RiskPoint(**rp))
return RiskExtractionResult(
risk_points=risk_points,
priority_fixes=data.get("priority_fixes", []),
overall_risk_score=data.get("overall_risk_score", 0.0),
summary=data.get("summary", "")
)
Usage example
extractor = RiskExtractionSystem(API_KEY)
drawing_content = """
FIRE PROTECTION PLAN - FLOOR 3
Building: Commercial Tower A, 20 Stories
Scale: 1:100
SPRINKLER SYSTEM:
- Area of coverage: Zone A (800 sqm), Zone B (650 sqm)
- Sprinkler type: Standard response, 68°C rating
- Spacing: 3.0m x 3.0m in office areas
- Water supply: Urban mains, 0.8 MPa static pressure
EMERGENCY EGRESS:
- Exit 1: Stairwell A (width: 1.2m) - serves 45 occupants
- Exit 2: Stairwell B (width: 1.0m) - serves 38 occupants
- Travel distance to nearest exit: 35m (Zone A), 28m (Zone B)
FIRE RESISTANCE:
- Structural elements: 2-hour rating
- Compartment walls: 1-hour rating
- Shaft enclosures: 2-hour rating
"""
metadata = {
"building_type": "commercial_highrise",
"floor_area": 1450,
"occupancy": "office",
"height": "20 stories"
}
result = extractor.extract_risk_points(drawing_content, metadata)
print(f"Overall Risk Score: {result.overall_risk_score}/10")
print(f"Risk Points Identified: {len(result.risk_points)}")
for rp in result.risk_points:
print(f" [{rp.severity.value.upper()}] {rp.location}: {rp.risk_type}")Step 4: Compliance Logging and Audit Trail
import json
import hashlib
import hmac
from datetime import datetime, timezone
from typing import List, Dict, Any, Optional
from dataclasses import dataclass, field, asdict
from pathlib import Path
import sqlite3
from cryptography.hazmat.primitives import hashes
from cryptography.hazmat.primitives.asymmetric import padding
from cryptography.hazmat.primitives import serialization
from cryptography.hazmat.backends import default_backend
@dataclass
class ComplianceRecord:
"""Immutable compliance review record with cryptographic signature."""
record_id: str
project_id: str
drawing_id: str
review_timestamp: datetime
reviewer_type: str # "AI-Claude", "AI-DeepSeek", "Human"
input_hash: str # SHA-256 of input data
output_data: Dict[str, Any]
signature: str # HMAC signature of record integrity
metadata: Dict[str, Any] = field(default_factory=dict)
def to_dict(self) -> Dict[str, Any]:
return {
"record_id": self.record_id,
"project_id": self.project_id,
"drawing_id": self.drawing_id,
"review_timestamp": self.review_timestamp.isoformat(),
"reviewer_type": self.reviewer_type,
"input_hash": self.input_hash,
"output_data": self.output_data,
"signature": self.signature,
"metadata": self.metadata
}
class ComplianceLogger:
"""Maintain tamper-evident compliance audit trail using HolySheep API."""
def __init__(self, api_key: str, db_path: str = "compliance_audit.db"):
self.api_key = api_key
self.db_path = db_path
self._init_database()
self._load_signing_key()
def _init_database(self):
"""Initialize SQLite database for compliance records."""
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
cursor.execute("""
CREATE TABLE IF NOT EXISTS compliance_records (
record_id TEXT PRIMARY KEY,
project_id TEXT NOT NULL,
drawing_id TEXT NOT NULL,
review_timestamp TEXT NOT NULL,
reviewer_type TEXT NOT NULL,
input_hash TEXT NOT NULL,
output_data TEXT NOT NULL,
signature TEXT NOT NULL,
metadata TEXT,
created_at TEXT DEFAULT CURRENT_TIMESTAMP
)
""")
cursor.execute("""
CREATE INDEX IF NOT EXISTS idx_project
ON compliance_records(project_id, drawing_id)
""")
cursor.execute("""
CREATE INDEX IF NOT EXISTS idx_timestamp
ON compliance_records(review_timestamp)
""")
conn.commit()
conn.close()
def _load_signing_key(self):
"""Load or generate HMAC signing key for record integrity."""
key_path = Path(self.db_path).parent / ".signing_key"
if key_path.exists():
self.signing_key = key_path.read_bytes()
else:
import os
self.signing_key = os.urandom(32)
key_path.write_bytes(self.signing_key)
key_path.chmod(0o600) # Restrict permissions
def _compute_input_hash(self, data: Any) -> str:
"""Compute SHA-256 hash of input data."""
content = json.dumps(data, sort_keys=True, default=str)
return hashlib.sha256(content.encode()).hexdigest()
def _sign_record(self, record_data: Dict[str, Any]) -> str:
"""Generate HMAC-SHA256 signature for record integrity."""
content = json.dumps(record_data, sort_keys=True, default=str)
signature = hmac.new(
self.signing_key,
content.encode(),
hashlib.sha256
).hexdigest()
return signature
def _verify_record(self, record: ComplianceRecord) -> bool:
"""Verify record integrity using stored signature."""
record_dict = asdict(record)
record_dict.pop("signature") # Exclude signature from verification
expected_signature = self._sign_record(record_dict)
return hmac.compare_digest(record.signature, expected_signature)
def log_review(
self,
project_id: str,
drawing_id: str,
reviewer_type: str,
input_data: Any,
output_data: Dict[str, Any],
metadata: Optional[Dict[str, Any]] = None
) -> ComplianceRecord:
"""Log a compliance review with cryptographic integrity."""
import uuid
record_id = str(uuid.uuid4())
review_timestamp = datetime.now(timezone.utc)
# Compute input hash
input_hash = self._compute_input_hash(input_data)
# Prepare record data for signing
record_dict = {
"record_id": record_id,
"project_id": project_id,
"drawing_id": drawing_id,
"review_timestamp": review_timestamp.isoformat(),
"reviewer_type": reviewer_type,
"input_hash": input_hash,
"output_data": output_data
}
# Generate signature
signature = self._sign_record(record_dict)
# Create record
record = ComplianceRecord(
record_id=record_id,
project_id=project_id,
drawing_id=drawing_id,
review_timestamp=review_timestamp,
reviewer_type=reviewer_type,
input_hash=input_hash,
output_data=output_data,
signature=signature,
metadata=metadata or {}
)
# Persist to database
self._persist_record(record)
return record
def _persist_record(self, record: ComplianceRecord):
"""Store record in SQLite database."""
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
cursor.execute("""
INSERT INTO compliance_records
(record_id, project_id, drawing_id, review_timestamp,
reviewer_type, input_hash, output_data, signature, metadata)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
""", (
record.record_id,
record.project_id,
record.drawing_id,
record.review_timestamp.isoformat(),
record.reviewer_type,
record.input_hash,
json.dumps(record.output_data),
record.signature,
json.dumps(record.metadata)
))
conn.commit()
conn.close()
def generate_audit_report(
self,
project_id: str,
start_date: Optional[datetime] = None,
end_date: Optional[datetime] = None
) -> Dict[str, Any]:
"""Generate compliance audit report with integrity verification."""
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
query = "SELECT * FROM compliance_records WHERE project_id = ?"
params = [project_id]
if start_date:
query += " AND review_timestamp >= ?"
params.append(start_date.isoformat())
if end_date:
query += " AND review_timestamp <= ?"
params.append(end_date.isoformat())
cursor.execute(query, params)
rows = cursor.fetchall()
conn.close()
records = []
all_verified = True
for row in rows:
record_dict = {
"record_id": row[0],
"project_id": row[1],
"drawing_id": row[2],
"review_timestamp": row[3],
"reviewer_type": row[4],
"input_hash": row[5],
"output_data": json.loads(row[6]),
"signature": row[7],
"metadata": json.loads(row[8]) if row[8] else {}
}
record = ComplianceRecord(**record_dict)
verified = self._verify_record(record)
all_verified &= verified
records.append({
"record": record_dict,
"integrity_verified": verified
})
return {
"project_id": project_id,
"report_generated": datetime.now(timezone.utc).isoformat(),
"total_records": len(records),
"all_integrity_verified": all_verified,
"records": records
}
Complete integration example
logger = ComplianceLogger(API_KEY, "compliance_audit.db")
Log Claude compliance review
claude_result = qa_system.answer_question(question)
compliance_record = logger.log_review(
project_id="PROJECT_2024_001",
drawing_id="DWG_3_FLOOR_PLAN",
reviewer_type="AI-Claude-Sonnet-4.5",
input_data={"question": question.question, "context": question.drawing_context},
output_data={
"answer": claude_result.answer,
"code_references": claude_result.code_references,
"confidence": claude_result.confidence
},
metadata={"jurisdiction": "china", "building_type":