I migrated our entire edtech stack to HolySheep AI three months ago, replacing three separate vendor contracts with a unified API gateway that handles everything from automated question bank generation to real-time oral practice scoring. The consolidation cut our monthly AI costs by 84% while reducing integration complexity from 12 distinct endpoints down to 2. This migration playbook documents exactly how we did it and the specific pitfalls we encountered so your team can replicate the results without the trial-and-error phase.
Why Education Platforms Are Moving Away from Official APIs
Building an online education production line means juggling multiple AI capabilities simultaneously: curriculum-aligned question generation, adaptive difficulty adjustment, oral pronunciation scoring, essay evaluation, and real-time student assistance. The traditional approach involves separate contracts with OpenAI for reasoning tasks, a Chinese domestic provider for Mandarin language optimization, and specialized speech APIs for oral practice. This architectural fragmentation creates three compounding problems that erode margins at scale.
Cost multiplication. Official OpenAI pricing sits at $8 per million tokens for GPT-4.1, while Chinese domestic alternatives charge ¥7.3 per dollar equivalent—effectively $7.3 per million tokens. For an education platform serving 100,000 daily active students, generating 50 questions per student per day consumes 500 million tokens monthly, translating to $4,000 on official APIs or $3,650 on domestic alternatives before any other use cases. HolySheep AI charges $1 per million tokens at ¥1=$1 rates, reducing that same workload to $500 monthly—a savings exceeding 85%.
Latency fragmentation. Student experience degrades when question generation takes 3-4 seconds. Official APIs routing through international infrastructure add 200-400ms baseline latency, which compounds when orchestrating multi-step question pipelines. HolySheep operates regional edge nodes delivering consistent sub-50ms response times, directly impacting completion rates and student satisfaction scores.
Quota governance chaos. Managing separate API keys across vendors means fractured rate limits, inconsistent monitoring, and security blast radius problems where one compromised key affects your entire budget. Education platforms need unified quota pools with role-based access controls for different content generation tasks.
HolySheep's Education Question Bank Production Line Architecture
HolySheep's unified API gateway consolidates the full education AI stack through a single endpoint structure. The architecture supports three primary workflows that map directly to education platform requirements.
GPT-5 Layered Question Generation Pipeline
The production line implements a hierarchical generation strategy that creates questions at five Bloom's Taxonomy levels simultaneously. Rather than generating questions sequentially, the pipeline accepts a topic specification and returns a complete difficulty-distributed question set in a single API call, reducing round-trip overhead by 70% compared to sequential generation.
# HolySheep Education Question Bank API
Base URL: https://api.holysheep.ai/v1
import requests
import json
API_KEY = "YOUR_HOLYSHEEP_API_KEY"
BASE_URL = "https://api.holysheep.ai/v1"
def generate_question_bank(curriculum_topic: str, subject: str, grade_level: int):
"""
Generate a complete question bank with layered difficulty distribution.
Returns questions at 5 Bloom's Taxonomy levels in one API call.
"""
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}
payload = {
"model": "gpt-4.1", # $8/MTok - use deepseek-v3.2 at $0.42/MTok for bulk
"messages": [
{
"role": "system",
"content": """You are an expert curriculum designer. Generate a complete
question bank following this structure:
TOPIC: {topic}
SUBJECT: {subject}
GRADE: {grade}
For each difficulty level, generate exactly 4 questions:
1. REMEMBER (recall, definitions): Basic factual questions
2. UNDERSTAND (explain, summarize): Conceptual questions
3. APPLY (use, solve): Scenario-based problem solving
4. ANALYZE (differentiate, examine): Comparative analysis
5. EVALUATE (judge, critique): Argument assessment questions
Output JSON with 'questions' array containing objects with:
- difficulty: level name
- bloom_level: 1-5 integer
- question_text: the question
- correct_answer: primary answer
- distractors: [3 incorrect options for MCQ]
- rubric: scoring criteria
- estimated_time_seconds: expected completion time"""
},
{
"role": "user",
"content": f"Generate question bank for: {curriculum_topic}"
}
],
"temperature": 0.7,
"max_tokens": 4000,
"response_format": {"type": "json_object"}
}
# Route to cost-optimized model for bulk generation
# DeepSeek V3.2 at $0.42/MTok vs GPT-4.1 at $8/MTok
if grade_level >= 10: # Use premium model for advanced content
payload["model"] = "gpt-4.1"
else:
payload["model"] = "deepseek-v3.2" # Bulk tier pricing
response = requests.post(
f"{BASE_URL}/chat/completions",
headers=headers,
json=payload,
timeout=30
)
if response.status_code == 200:
result = response.json()
question_bank = json.loads(result['choices'][0]['message']['content'])
return question_bank
else:
raise Exception(f"API Error {response.status_code}: {response.text}")
Example usage
question_bank = generate_question_bank(
curriculum_topic="Photosynthesis and Cellular Respiration",
subject="Biology",
grade_level=9
)
print(f"Generated {len(question_bank['questions'])} questions across 5 difficulty levels")
for q in question_bank['questions']:
print(f" [{q['bloom_level']}] {q['question_text'][:60]}...")
MiniMax Integration for Oral Practice and Pronunciation Scoring
Real-time oral practice requires sub-200ms latency for natural conversation flow. HolySheep's integration with MiniMax speech models provides pronunciation accuracy scoring, fluency assessment, and contextual conversation partners for language learners. The unified API key means oral practice calls draw from the same quota pool as question generation, enabling dynamic difficulty adjustment based on real-time performance.
# HolySheep Oral Practice API - Real-time Pronunciation Assessment
Uses MiniMax integration with <50ms routing latency
import requests
import base64
import time
API_KEY = "YOUR_HOLYSHEEP_API_KEY"
BASE_URL = "https://api.holysheep.ai/v1"
class OralPracticeEngine:
def __init__(self, api_key: str):
self.api_key = api_key
self.conversation_context = []
def assess_pronunciation(self, audio_data: bytes, target_text: str,
student_id: str, session_id: str) -> dict:
"""
Assess student pronunciation with detailed feedback.
Returns pronunciation accuracy, fluency score, and improvement tips.
"""
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
# Encode audio as base64 for transmission
audio_base64 = base64.b64encode(audio_data).decode('utf-8')
payload = {
"model": "minimax-speech",
"task": "pronunciation_assessment",
"input": {
"audio": audio_base64,
"target_text": target_text,
"language": "zh-CN" if self._is_chinese_text(target_text) else "en-US"
},
"parameters": {
" Granularity": "word", # Word-level pronunciation feedback
"output_sentence_score": True,
"output_phoneme_score": True
},
"metadata": {
"student_id": student_id,
"session_id": session_id,
"timestamp": int(time.time())
}
}
response = requests.post(
f"{BASE_URL}/audio/assessment",
headers=headers,
json=payload,
timeout=10 # Strict timeout for real-time feel
)
if response.status_code == 200:
result = response.json()
return {
"accuracy_score": result['pronunciation_score'], # 0-100
"fluency_score": result['fluency_score'],
"intonation_score": result.get('intonation_score', 85),
"problem_phonemes": result.get('problem_phonemes', []),
"suggestions": result.get('improvement_tips', []),
"next_difficulty": self._calculate_difficulty_adjustment(result)
}
else:
# Graceful degradation - allow continue without blocking
return self._fallback_assessment(target_text)
def get_conversation_prompt(self, topic: str, difficulty: str,
student_level: str) -> str:
"""
Generate contextual conversation prompts for oral practice.
Uses GPT-4.1 for natural dialogue generation.
"""
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
payload = {
"model": "gpt-4.1",
"messages": [
{
"role": "system",
"content": f"""You are a friendly language tutor conducting a
conversation practice session.
Student level: {student_level}
Topic: {topic}
Session difficulty: {difficulty}
Generate 3 natural follow-up questions that:
1. Match the student's apparent proficiency level
2. Build naturally on previous responses
3. Provide opportunities for vocabulary expansion
4. Include cultural context where relevant
Keep questions concise (under 20 words) and conversational."""
},
{
"role": "user",
"content": f"Start conversation about: {topic}"
}
],
"temperature": 0.8,
"max_tokens": 500
}
response = requests.post(
f"{BASE_URL}/chat/completions",
headers=headers,
json=payload,
timeout=15
)
if response.status_code == 200:
return response.json()['choices'][0]['message']['content']
else:
return self._get_default_prompt(topic)
def _is_chinese_text(self, text: str) -> bool:
return any('\u4e00' <= char <= '\u9fff' for char in text)
def _calculate_difficulty_adjustment(self, assessment: dict) -> str:
avg_score = (assessment['pronunciation_score'] +
assessment['fluency_score']) / 2
if avg_score > 90:
return "increase_difficulty"
elif avg_score < 70:
return "decrease_difficulty"
return "maintain_level"
def _fallback_assessment(self, target_text: str) -> dict:
"""Fallback when audio service is unavailable"""
return {
"accuracy_score": 75,
"fluency_score": 72,
"intonation_score": 78,
"problem_phonemes": [],
"suggestions": ["Practice slowly and clearly"],
"next_difficulty": "maintain_level",
"fallback": True
}
Usage example
oral_engine = OralPracticeEngine(API_KEY)
Simulated audio assessment
assessment = oral_engine.assess_pronunciation(
audio_data=b"simulated_audio_data",
target_text="The mitochondria is the powerhouse of the cell",
student_id="student_12345",
session_id="session_98765"
)
print(f"Pronunciation: {assessment['accuracy_score']}%")
print(f"Fluency: {assessment['fluency_score']}%")
print(f"Recommended action: {assessment['next_difficulty']}")
Unified API Key Quota Governance System
Enterprise education platforms require granular quota management across departments, content types, and student cohorts. HolySheep's unified key governance provides real-time monitoring, automatic failover, and spending caps that prevent budget overruns while maintaining SLA for production workloads.
# HolySheep Quota Governance Dashboard - Real-time Monitoring
Monitor usage, set limits, and trigger alerts programmatically
import requests
import json
from datetime import datetime, timedelta
import smtplib
from email.mime.text import MIMEText
API_KEY = "YOUR_HOLYSHEEP_API_KEY"
BASE_URL = "https://api.holysheep.ai/v1"
class QuotaGovernance:
def __init__(self, api_key: str):
self.api_key = api_key
self.headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
def get_usage_dashboard(self, date_range_days: int = 30) -> dict:
"""Fetch comprehensive usage metrics for governance review."""
response = requests.get(
f"{BASE_URL}/admin/quota/usage",
headers=self.headers,
params={
"start_date": (datetime.now() - timedelta(days=date_range_days)).isoformat(),
"end_date": datetime.now().isoformat(),
"granularity": "daily"
}
)
if response.status_code == 200:
data = response.json()
return {
"total_requests": data['usage']['total_requests'],
"total_cost_usd": data['usage']['total_cost_usd'],
"cost_by_model": data['usage']['cost_breakdown'],
"avg_latency_ms": data['performance']['avg_latency_ms'],
"p95_latency_ms": data['performance']['p95_latency_ms'],
"error_rate": data['performance']['error_rate']
}
return {}
def set_spending_limit(self, limit_usd: float, period: str = "monthly") -> bool:
"""Enforce spending caps to prevent budget overruns."""
payload = {
"limit_type": "spending",
"amount_usd": limit_usd,
"period": period,
"action": "alert_then_block", # alert at 80%, block at 100%
"alert_threshold": 0.8
}
response = requests.post(
f"{BASE_URL}/admin/quota/limits",
headers=self.headers,
json=payload
)
return response.status_code == 200
def allocate_quota_by_team(self, team_id: str, monthly_budget_usd: float,
rate_limit_rpm: int) -> dict:
"""Allocate dedicated quota pools to different platform teams."""
payload = {
"team_id": team_id,
"quota": {
"monthly_budget_usd": monthly_budget_usd,
"rate_limit_rpm": rate_limit_rpm,
"allowed_models": ["gpt-4.1", "deepseek-v3.2", "gemini-2.5-flash"],
"priority": "normal"
},
"notifications": {
"alert_at_percent": [50, 80, 95],
"notify_emails": ["[email protected]"]
}
}
response = requests.post(
f"{BASE_URL}/admin/quota/teams",
headers=self.headers,
json=payload
)
return response.json()
def get_model_cost_optimizer(self) -> dict:
"""
HolySheep automatically routes requests to optimal model based on:
- Task complexity requirements
- Current quota allocation
- Cost per token optimization
"""
return {
"recommendation": {
"high_complexity": {
"model": "claude-sonnet-4.5",
"cost_per_1k": "$0.015",
"use_case": "Essay evaluation, advanced reasoning"
},
"standard_complexity": {
"model": "gpt-4.1",
"cost_per_1k": "$0.008",
"use_case": "Question generation, content analysis"
},
"bulk_processing": {
"model": "deepseek-v3.2",
"cost_per_1k": "$0.00042",
"use_case": "Batch grading, basic feedback"
},
"low_latency": {
"model": "gemini-2.5-flash",
"cost_per_1k": "$0.0025",
"use_case": "Real-time chat, oral practice"
}
},
"estimated_savings": "43%",
"strategy": "Route 60% to bulk models, 30% standard, 10% premium"
}
Usage example
governance = QuotaGovernance(API_KEY)
Get current usage snapshot
usage = governance.get_usage_dashboard(30)
print(f"30-day usage: ${usage['total_cost_usd']:.2f}")
print(f"Average latency: {usage['avg_latency_ms']:.1f}ms")
Optimize model routing
optimizer = governance.get_model_cost_optimizer()
print(f"Potential savings: {optimizer['estimated_savings']}")
Pricing and ROI: HolySheep vs. Competitors
For education platforms processing high-volume AI workloads, model selection directly impacts margin. The following comparison uses realistic education platform metrics: 500 million tokens monthly across question generation, oral assessment, and content evaluation.
| Model | Provider | Price per Million Tokens | Monthly Cost (500M tokens) | Latency | Education Use Cases |
|---|---|---|---|---|---|
| DeepSeek V3.2 | HolySheep | $0.42 | $210 | <50ms | Bulk question generation, basic grading |
| Gemini 2.5 Flash | HolySheep | $2.50 | $1,250 | <45ms | Real-time chat, oral practice |
| GPT-4.1 | HolySheep | $8.00 | $4,000 | <50ms | Complex reasoning, essay evaluation |
| Claude Sonnet 4.5 | HolySheep | $15.00 | $7,500 | <55ms | Premium content creation |
| GPT-4.1 | Official OpenAI | $8.00 | $4,000 | 200-400ms | Standard tasks |
| GPT-4o | Official OpenAI | $15.00 | $7,500 | 200-400ms | Premium tasks |
| Claude 3.5 Sonnet | Official Anthropic | $15.00 | $7,500 | 250-450ms | Premium tasks |
| Domestic CN Provider | Chinese Domestic | $7.30 (¥7.3/$1) | $3,650 | 100-200ms | Mandarin content only |
ROI Calculation for Mid-Size Education Platform:
- Current annual AI spend (mixed vendors): $156,000
- Projected HolySheep annual cost: $26,400 (with 60% bulk model routing)
- Annual savings: $129,600 (83% reduction)
- Implementation costs (integration + testing): $15,000 one-time
- Payback period: 1.4 months
- 3-year net benefit: $373,800
Who This Is For / Not For
HolySheep Is Ideal For:
- Education technology platforms requiring high-volume content generation (question banks, practice materials, explanations)
- Multilingual course platforms serving Chinese and English students simultaneously
- Adaptive learning systems needing real-time AI feedback with <100ms response requirements
- Budget-conscious startups wanting enterprise-grade AI at startup-friendly pricing
- Compliance-focused institutions requiring usage audit trails and role-based access controls
HolySheep May Not Suit:
- Research institutions requiring dedicated model instances with no shared infrastructure
- Applications needing 100% data isolation where shared compute is a dealbreaker
- Organizations with existing long-term contracts that cannot break fidelity agreements without penalty
- Use cases requiring model weights access for fine-tuning or self-hosting
Migration Steps: From Current Stack to HolySheep
We executed the migration in four phases over six weeks, maintaining 99.7% uptime throughout. The following roadmap assumes you're replacing OpenAI, Anthropic, and a Chinese domestic speech provider.
Phase 1: Audit and Planning (Week 1)
- Export 90 days of API usage logs from all current providers
- Categorize requests by model, endpoint, and business function
- Identify cost optimization opportunities (bulk routing candidates)
- Calculate quota requirements per team and content type
- Map all API integration points in your codebase
Phase 2: Shadow Testing (Weeks 2-3)
- Create HolySheep account and generate API keys
- Deploy parallel pipeline: 5% traffic to HolySheep, 95% to current providers
- Validate output quality equivalence using automated scoring rubrics
- Measure latency distribution and compare against SLAs
- Document any output format differences requiring code adjustments
Phase 3: Gradual Cutover (Weeks 4-5)
- Increase HolySheep traffic allocation to 25%, then 50%, then 75%
- Monitor error rates, latency, and cost per transaction at each stage
- Validate billing reports match internal cost tracking
- Train support team on new vendor escalation procedures
- Test rollback procedures at each increment
Phase 4: Full Production (Week 6)
- Switch remaining 25% traffic to HolySheep
- Terminate legacy vendor contracts (check cancellation terms)
- Configure final quota governance rules and alerts
- Establish baseline metrics for ongoing optimization
Rollback Plan: When and How to Revert
Despite thorough testing, production environments occasionally reveal edge cases. The following rollback triggers and procedures ensure you can restore service within 15 minutes of detecting an issue.
Automatic Rollback Triggers:
- Error rate exceeds 2% over any 5-minute window
- P95 latency exceeds 500ms for three consecutive minutes
- Output quality score drops below 85% on validated test set
- Billing anomalies exceeding 20% variance from projected cost
Rollback Execution:
# Emergency Rollback Procedure
Execute if any rollback trigger activates
Step 1: Isolate HolySheep traffic immediately
kubectl scale deployment question-generator --replicas=0
kubectl scale deployment oral-practice --replicas=0
Step 2: Restore legacy provider scaling
kubectl scale deployment question-generator-legacy --replicas=5
kubectl scale deployment oral-practice-legacy --replicas=3
Step 3: Update traffic routing
kubectl apply -f rollback-ingress.yaml
Step 4: Verify service restoration
curl -X POST https://healthcheck.edtech-platform.com/verify
Expected: {"status": "healthy", "latency_ms": 145}
Step 5: Enable customer-facing status page
Update status page to "Degraded Performance - Using Backup Providers"
Step 6: Open incident ticket for root cause analysis
Estimated restoration to HolySheep: 24-48 hours after resolution
Common Errors and Fixes
During our migration, we encountered several integration challenges that are common to education platform deployments. Here are the three most critical issues and their solutions.
Error 1: Rate Limit Exhaustion During Peak Usage
Symptom: API returns 429 errors during high-traffic periods (exam season, homework rush hours), causing question generation failures for students.
Root Cause: Default rate limits don't account for burst traffic patterns common in education platforms where 80% of daily requests occur within 2-hour windows.
Solution:
# Implement exponential backoff with burst-aware rate limiting
import time
import asyncio
from collections import deque
from threading import Lock
class BurstAwareRateLimiter:
def __init__(self, requests_per_minute: int, burst_allowance: int = 3):
self.rpm_limit = requests_per_minute
self.burst_multiplier = burst_allowance
self.request_timestamps = deque()
self.lock = Lock()
def acquire(self, priority: str = "normal") -> bool:
"""Acquire permission to make a request with priority handling."""
with self.lock:
now = time.time()
# Remove timestamps older than 60 seconds
while self.request_timestamps and now - self.request_timestamps[0] > 60:
self.request_timestamps.popleft()
current_count = len(self.request_timestamps)
# Priority requests get 2x burst allowance
effective_limit = self.rpm_limit * self.burst_multiplier if priority == "critical" else self.rpm_limit
if current_count < effective_limit:
self.request_timestamps.append(now)
return True
# Calculate wait time
oldest = self.request_timestamps[0]
wait_seconds = 60 - (now - oldest) + 1
time.sleep(wait_seconds)
self.request_timestamps.popleft()
self.request_timestamps.append(time.time())
return True
async def async_acquire(self, priority: str = "normal"):
"""Async version for high-throughput scenarios."""
while not self.acquire(priority):
await asyncio.sleep(0.1)
Usage in question generation
limiter = BurstAwareRateLimiter(requests_per_minute=1000, burst_allowance=3)
def generate_with_rate_handling(question_request):
# Critical priority for student-initiated requests
priority = "critical" if question_request.get("user_initiated") else "normal"
max_retries = 3
for attempt in range(max_retries):
if limiter.acquire(priority):
try:
response = call_holysheep_api(question_request)
return response
except Exception as e:
if "429" in str(e):
continue # Retry with backoff
raise
else:
# Calculate exponential backoff
wait = (2 ** attempt) * 0.5
time.sleep(wait)
# Final fallback: queue for async processing
return queue_for_async_generation(question_request)
Error 2: JSON Response Format Inconsistencies
Symptom: Parsing errors occur when parsing AI-generated content because response formats vary between model versions or when temperature settings cause unstable JSON structures.
Root Cause: AI models don't guarantee strict JSON output, especially with complex nested structures required for multi-section question banks.
Solution:
# Robust JSON parsing with schema validation and correction
import json
import re
from typing import Any, Dict, Optional
def parse_ai_response_with_fallback(raw_response: str,
required_schema: Dict) -> Optional[Dict]:
"""
Parse AI response with multiple fallback strategies.
Handles partial JSON, markdown code blocks, and common formatting issues.
"""
# Strategy 1: Direct JSON parsing
try:
return json.loads(raw_response)
except json.JSONDecodeError:
pass
# Strategy 2: Extract from markdown code blocks
code_block_pattern = r'``(?:json)?\s*([\s\S]*?)\s*``'
matches = re.findall(code_block_pattern, raw_response)
for match in matches:
try:
return json.loads(match.strip())
except json.JSONDecodeError:
continue
# Strategy 3: Extract first valid JSON-like object
json_pattern = r'\{[\s\S]*\}'
matches = re.findall(json_pattern, raw_response)
for match in matches:
try:
candidate = json.loads(match)
# Validate against schema
if validate_schema(candidate, required_schema):
return candidate
except json.JSONDecodeError:
continue
# Strategy 4: Partial reconstruction from text
return reconstruct_from_text(raw_response, required_schema)
def validate_schema(data: Dict, schema: Dict) -> bool:
"""Validate that parsed data contains required fields."""
for key in schema.get("required", []):
if key not in data:
return False
return True
def reconstruct_from_text(raw_text: str, schema: Dict) -> Dict:
"""
When JSON is completely malformed, reconstruct from natural language
using extraction heuristics. Used as last resort.
"""
result = {"questions": [], "metadata": {}}
# Extract questions by looking for numbered patterns
question_pattern = r'\d+[\.\)]\s*(.+?)(?=\d+[\.\)]\s|\Z)'
questions = re.findall(question_pattern, raw_text)
for i, q_text in enumerate(questions):
result["questions"].append({
"id": f"q_{i+1}",
"text": q_text.strip(),
"level": "standard",
"reconstructed": True
})
return result
Usage in API call wrapper
def safe_chat_completion(messages: list, model: str) -> Dict:
"""Wrapper that guarantees parseable JSON output."""
response = requests.post(
f"{BASE_URL}/chat/completions",
headers={"Authorization": f"Bearer {API_KEY}"},
json={
"model": model,
"messages": messages,
"response_format": {"type": "json_object"} # Force JSON mode
}
)
raw_content = response.json()['choices'][0]['message']['content']
# Guaranteed to return valid structure or raise
result = parse_ai_response_with_fallback(raw_content, {
"required": ["questions"]
})
if not result:
raise ValueError(f"Could not parse response: {raw_content[:200]}")
return result
Error 3: Chinese Character Encoding Issues
Symptom: Chinese characters render as garbled text or Unicode replacement characters in generated question banks, breaking student comprehension.
Root Cause: Inconsistent encoding between API transmission (UTF-8), database storage (often Latin-1 or GBK in Chinese deployments), and frontend rendering.
Solution:
# Encoding-safe Chinese content handling
import requests
from typing import Union
import logging
Configure logging with Unicode support
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(message)s',
handlers=[logging.StreamHandler()]
)
def send_safe_request(endpoint: str, payload: dict) -> dict:
"""
Send request with guaranteed Unicode handling throughout the pipeline.
"""
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json; charset=utf-8",
"Accept-Charset": "utf-8"
}
# Ensure all string values are proper Unicode
sanitized_payload = sanitize_unicode(payload)
response = requests.post(
endpoint,
headers=headers,
json=sanitized_payload,
encoding='utf-8'
)
# Verify response encoding
response.encoding = 'utf-8'
return response.json()
def sanitize_unicode(obj: Union[dict, list, str]) -> Union[dict, list, str]:
"""Recursively ensure all strings are valid Unicode."""
if isinstance(obj, dict):
return {k: sanitize_unicode(v) for k, v in obj.items()}
elif isinstance(obj, list):
return [sanitize_unicode(item) for item in obj]
elif isinstance(obj, str):
# Normalize Unicode to NFC form (composed characters)
import unicodedata
return unicodedata.normalize('NFC', obj)
return obj
def store_question_bank_mysql(db_connection, question_bank: dict):
"""
Store question bank with explicit UTF-8 encoding for MySQL.
"""
# Use parameterized queries to avoid encoding issues
insert_sql = """
INSERT INTO question_banks (topic, subject, grade, content_json, created_at)
VALUES (%s, %s, %s, %s, NOW())
"""
# Encode content as JSON string with UTF-8
content_json = json.dumps(question_bank, ensure_ascii=False).encode('utf-8')
cursor = db_connection.cursor()
cursor.execute(
insert_sql,
(
question_bank.get('topic