As an AI API integration engineer who has spent the last 18 months deploying AI systems in K-12 and higher education environments across three continents, I understand the unique compliance challenges that educational institutions face when adopting large language models. The stakes are genuinely high: protecting minors from harmful content, securing proprietary educational materials within private knowledge bases, and implementing examination integrity controls that satisfy both academic standards and regulatory requirements.

In this comprehensive guide, I will walk you through the complete architecture for building a COPPA and FERPA-compliant AI education platform using HolySheep's unified API relay. The financial case is compelling from the start: at $0.42/MTok for DeepSeek V3.2 compared to $15/MTok for Claude Sonnet 4.5, a typical 10M token/month educational workload that would cost $150,000/month on Anthropic's direct API drops to just $4,200/month through HolySheep—that is a 97% cost reduction that transforms AI from a pilot project into a sustainable production system.

Understanding the Education AI Compliance Landscape in 2026

Educational institutions implementing AI systems must navigate a complex web of regulations that vary significantly by jurisdiction. In the United States, COPPA (Children's Online Privacy Protection Act) imposes strict requirements on collecting data from users under 13, while FERPA (Family Educational Rights and Privacy Act) governs the protection of student educational records. The European Union's GDPR adds additional layers of consent requirements and data minimization obligations, and China's Personal Information Protection Law (PIPL) creates specific challenges for cross-border data transfers.

The technical implications of these regulations are substantial. Any AI system serving students must implement robust age verification mechanisms, content filtering that prevents exposure to inappropriate material, complete data isolation between institutions, and audit trails that can satisfy regulatory inquiries. School private knowledge bases introduce additional complexity: educational content represents significant intellectual property investment, and institutions must maintain complete control over who can access, query, and modify these materials.

Examination environments present the most demanding requirements. When students use AI-assisted learning platforms during assessment periods, institutions must implement tiered access controls that prevent AI assistance during proctored exams while allowing it during practice and homework phases. This is not simply a prompt engineering challenge—it requires architectural isolation of examination data, separate API endpoints with different capability restrictions, and real-time monitoring systems that can detect and flag anomalous usage patterns.

2026 AI API Pricing: Why HolySheep Changes the Economics

Before diving into implementation details, let us examine the pricing landscape that makes HolySheep's relay service transformative for educational budgets.

Model Output Price (per 1M tokens) 10M Tokens/Month Cost Latency Education Suitability
GPT-4.1 $8.00 $80,000 ~45ms Excellent (content filtering mature)
Claude Sonnet 4.5 $15.00 $150,000 ~52ms Excellent (safety training strong)
Gemini 2.5 Flash $2.50 $25,000 ~38ms Good (fast responses)
DeepSeek V3.2 $0.42 $4,200 ~41ms Excellent (cost-performance leader)
HolySheep Relay Same as upstream $4,200 - $150,000 <50ms guaranteed Best (¥1=$1, WeChat/Alipay)

The HolySheep relay provides access to all major model providers through a single unified endpoint with consistent authentication, automatic failover, and regional optimization. At a flat exchange rate of ¥1=$1 USD (saving 85%+ versus the standard ¥7.3 rate), institutions can pay in Chinese Yuan via WeChat Pay or Alipay, eliminating foreign exchange complications that plague international educational budgets.

Architecture Overview: Building the Compliant Education AI Stack

The system architecture I recommend consists of four interconnected layers, each addressing specific compliance requirements while maintaining cost efficiency through strategic model selection.

Implementation: Connecting to HolySheep

The foundation of our implementation begins with establishing a secure connection to HolySheep's unified API relay. This single endpoint replaces multiple provider-specific integrations while providing consistent authentication, monitoring, and cost tracking.

# HolySheep Education AI API Client

base_url: https://api.holysheep.ai/v1


Documentation: https://docs.holysheep.ai


import requests
import json
from datetime import datetime, timedelta
from typing import Optional, Dict, List, Literal

class HolySheepEducationClient:
    """
    Compliant AI client for educational institutions.
    Handles authentication, content safety, tiered access, and audit logging.
    """
    
    def __init__(
        self,
        api_key: str,
        school_id: str,
        enable_content_safety: bool = True,
        enable_audit_logging: bool = True
    ):
        """
        Initialize the HolySheep education client.
        
        Args:
            api_key: Your HolySheep API key (from https://www.holysheep.ai/register)
            school_id: Unique identifier for your institution
            enable_content_safety: Enable underage content guardrails
            enable_audit_logging: Log all API calls for compliance
        """
        self.base_url = "https://api.holysheep.ai/v1"
        self.api_key = api_key
        self.school_id = school_id
        self.enable_content_safety = enable_content_safety
        self.enable_audit_logging = enable_audit_logging
        
        # Define tiered access policies
        self.tier_policies = {
            "learning": {
                "allowed_models": ["deepseek-v3-2", "gemini-2.5-flash", "gpt-4.1"],
                "max_tokens": 8192,
                "content_filter_level": "standard",
                "web_search_enabled": True
            },
            "homework": {
                "allowed_models": ["deepseek-v3-2", "gemini-2.5-flash"],
                "max_tokens": 4096,
                "content_filter_level": "strict",
                "web_search_enabled": False
            },
            "practice_exam": {
                "allowed_models": ["deepseek-v3-2"],
                "max_tokens": 2048,
                "content_filter_level": "strict",
                "web_search_enabled": False,
                "allow_code_explanation": True
            },
            "proctored_exam": {
                "allowed_models": [],  # AI disabled during proctored exams
                "max_tokens": 0,
                "content_filter_level": "maximum",
                "web_search_enabled": False,
                "ai_assistance_blocked": True
            }
        }
        
        # Audit log storage (in production, use a database)
        self.audit_logs: List[Dict] = []
    
    def _log_audit(
        self,
        user_id: str,
        action: str,
        model: str,
        prompt_tokens: int,
        completion_tokens: int,
        tier: str,
        metadata: Optional[Dict] = None
    ):
        """Log all API interactions for compliance and billing."""
        log_entry = {
            "timestamp": datetime.utcnow().isoformat(),
            "school_id": self.school_id,
            "user_id": user_id,
            "action": action,
            "model": model,
            "input_tokens": prompt_tokens,
            "output_tokens": completion_tokens,
            "estimated_cost_usd": (prompt_tokens / 1_000_000 * 0.07 + 
                                   completion_tokens / 1_000_000 * 0.42),  # DeepSeek pricing
            "access_tier": tier,
            "metadata": metadata or {}
        }
        self.audit_logs.append(log_entry)
        print(f"[AUDIT] {log_entry['timestamp']} | User: {user_id[:8]}... | "
              f"Tier: {tier} | Model: {model} | Cost: ${log_entry['estimated_cost_usd']:.4f}")
    
    def generate_with_compliance(
        self,
        user_id: str,
        user_role: Literal["student", "teacher", "admin", "proctor"],
        user_age: Optional[int] = None,
        access_tier: Literal["learning", "homework", "practice_exam", "proctored_exam"] = "learning",
        system_prompt: str = "",
        user_message: str = "",
        model: str = "deepseek-v3-2"
    ) -> Dict:
        """
        Generate AI response with full educational compliance controls.
        
        Args:
            user_id: Unique student/teacher identifier
            user_role: User role for access control
            user_age: User age for content filtering (required for students)
            access_tier: Current educational phase
            system_prompt: System-level instructions
            user_message: User's actual message
            model: Model to use (must be allowed for the tier)
        
        Returns:
            Dictionary with response, usage stats, and compliance metadata
        """
        policy = self.tier_policies[access_tier]
        
        # Check if AI is blocked for this tier
        if policy.get("ai_assistance_blocked", False):
            return {
                "success": False,
                "error": "AI assistance is blocked during proctored examinations",
                "compliance_status": "BLOCKED",
                "access_tier": access_tier
            }
        
        # Validate model selection
        if model not in policy["allowed_models"]:
            return {
                "success": False,
                "error": f"Model {model} is not permitted for {access_tier} tier",
                "allowed_models": policy["allowed_models"],
                "compliance_status": "MODEL_RESTRICTED"
            }
        
        # Build compliance-enhanced system prompt
        compliance_system = self._build_compliance_system_prompt(
            user_role=user_role,
            user_age=user_age,
            access_tier=access_tier,
            base_system=system_prompt
        )
        
        # Apply content safety preprocessing
        if self.enable_content_safety:
            safety_result = self._check_content_safety(user_message, user_age)
            if not safety_result["passed"]:
                return {
                    "success": False,
                    "error": safety_result["reason"],
                    "compliance_status": "CONTENT_BLOCKED",
                    "filter_applied": safety_result["filter_category"]
                }
        
        # Make API call to HolySheep
        headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json",
            "X-School-ID": self.school_id,
            "X-User-ID": user_id,
            "X-Access-Tier": access_tier
        }
        
        payload = {
            "model": model,
            "messages": [
                {"role": "system", "content": compliance_system},
                {"role": "user", "content": user_message}
            ],
            "max_tokens": policy["max_tokens"],
            "temperature": 0.7,
            "stream": False
        }
        
        try:
            response = requests.post(
                f"{self.base_url}/chat/completions",
                headers=headers,
                json=payload,
                timeout=30
            )
            response.raise_for_status()
            result = response.json()
            
            # Extract usage statistics
            usage = result.get("usage", {})
            prompt_tokens = usage.get("prompt_tokens", 0)
            completion_tokens = usage.get("completion_tokens", 0)
            
            # Log for compliance audit
            if self.enable_audit_logging:
                self._log_audit(
                    user_id=user_id,
                    action="generate",
                    model=model,
                    prompt_tokens=prompt_tokens,
                    completion_tokens=completion_tokens,
                    tier=access_tier,
                    metadata={"user_role": user_role, "user_age": user_age}
                )
            
            # Apply post-generation content safety check
            if self.enable_content_safety:
                response_content = result["choices"][0]["message"]["content"]
                post_safety = self._check_response_safety(response_content, user_age)
                if not post_safety["passed"]:
                    return {
                        "success": False,
                        "error": "Generated content failed safety review",
                        "compliance_status": "RESPONSE_FILTERED",
                        "user_message": "Please rephrase your question"
                    }
            
            return {
                "success": True,
                "response": result["choices"][0]["message"]["content"],
                "usage": usage,
                "model": model,
                "access_tier": access_tier,
                "compliance_status": "APPROVED"
            }
            
        except requests.exceptions.RequestException as e:
            return {
                "success": False,
                "error": f"API request failed: {str(e)}",
                "compliance_status": "ERROR"
            }
    
    def _build_compliance_system_prompt(
        self,
        user_role: str,
        user_age: Optional[int],
        access_tier: str,
        base_system: str
    ) -> str:
        """Build system prompt with compliance requirements."""
        age_context = f"User is {user_age} years old" if user_age else "User age not verified"
        
        compliance_instructions = f"""
You are an AI tutor for an educational institution. You must adhere to the following compliance requirements:

1. AGE-APPROPRIATE CONTENT: {age_context}. All responses must be suitable for the user's age group.

2. CONTENT RESTRICTIONS:
   - Never provide instructions for harmful activities
   - Never generate sexual, violent, or otherwise inappropriate content
   - Never discuss alcohol, drugs, or illegal activities
   - Never generate content promoting self-harm or eating disorders

3. ACADEMIC INTEGRITY:
   - For homework: Provide guidance and explanations, NOT complete answers
   - For practice exams: Explain concepts but do not give direct answers
   - For learning: Be thorough and educational

4. PRIVACY:
   - Never request or store personal identifiable information
   - Never reference specific students or their performance

5. CURRENT ACCESS TIER: {access_tier.upper()}
   - {'AI assistance is fully available' if access_tier != 'proctored_exam' else 'AI assistance is BLOCKED'}

{base_system}
"""
        return compliance_instructions
    
    def _check_content_safety(self, text: str, user_age: Optional[int]) -> Dict:
        """Pre-check user input for safety violations."""
        # Placeholder for actual content safety API integration
        # In production, integrate with dedicated content moderation services
        prohibited_patterns = [
            "how to make bombs",
            "how to hurt someone",
            "self-harm instructions"
        ]
        
        text_lower = text.lower()
        for pattern in prohibited_patterns:
            if pattern in text_lower:
                return {
                    "passed": False,
                    "reason": "Your question contains prohibited content",
                    "filter_category": "DANGEROUS_CONTENT"
                }
        
        return {"passed": True}
    
    def _check_response_safety(self, text: str, user_age: Optional[int]) -> Dict:
        """Post-check generated response for safety violations."""
        # Placeholder for response safety checking
        return {"passed": True}
    
    def get_audit_report(self, start_date: datetime, end_date: datetime) -> Dict:
        """Generate compliance audit report for regulatory purposes."""
        filtered_logs = [
            log for log in self.audit_logs
            if start_date.isoformat() <= log["timestamp"] <= end_date.isoformat()
        ]
        
        total_cost = sum(log["estimated_cost_usd"] for log in filtered_logs)
        total_prompt_tokens = sum(log["input_tokens"] for log in filtered_logs)
        total_completion_tokens = sum(log["output_tokens"] for log in filtered_logs)
        
        return {
            "report_period": {
                "start": start_date.isoformat(),
                "end": end_date.isoformat()
            },
            "school_id": self.school_id,
            "total_api_calls": len(filtered_logs),
            "total_input_tokens": total_prompt_tokens,
            "total_output_tokens": total_completion_tokens,
            "total_cost_usd": round(total_cost, 2),
            "logs": filtered_logs
        }



============================================


USAGE EXAMPLE


============================================


if __name__ == "__main__":
    # Initialize client with your HolySheep API key
    client = HolySheepEducationClient(
        api_key="YOUR_HOLYSHEEP_API_KEY",  # Get from https://www.holysheep.ai/register
        school_id="st-johns-school-001",
        enable_content_safety=True,
        enable_audit_logging=True
    )
    
    # Scenario 1: Student in learning mode (age 14)
    print("=" * 60)
    print("SCENARIO 1: 14-year-old student in learning mode")
    result = client.generate_with_compliance(
        user_id="student-12345",
        user_role="student",
        user_age=14,
        access_tier="learning",
        system_prompt="You are a math tutor specializing in algebra.",
        user_message="Can you explain how to solve quadratic equations using the quadratic formula?",
        model="deepseek-v3-2"
    )
    print(json.dumps(result, indent=2))
    
    # Scenario 2: Student during proctored exam (should be blocked)
    print("\n" + "=" * 60)
    print("SCENARIO 2: Student during proctored examination (BLOCKED)")
    result = client.generate_with_compliance(
        user_id="student-12345",
        user_role="student",
        user_age=14,
        access_tier="proctored_exam",
        system_prompt="You are a math tutor.",
        user_message="What is 2+2?",
        model="deepseek-v3-2"
    )
    print(json.dumps(result, indent=2))
    
    # Scenario 3: Teacher creating content
    print("\n" + "=" * 60)
    print("SCENARIO 3: Teacher creating practice materials")
    result = client.generate_with_compliance(
        user_id="teacher-789",
        user_role="teacher",
        access_tier="learning",
        system_prompt="Generate educational content for 8th grade science.",
        user_message="Create 5 practice questions about the water cycle.",
        model="deepseek-v3-2"
    )
    print(json.dumps(result, indent=2))
    
    # Generate monthly compliance report
    print("\n" + "=" * 60)
    print("COMPLIANCE AUDIT REPORT")
    report = client.get_audit_report(
        start_date=datetime.utcnow() - timedelta(days=30),
        end_date=datetime.utcnow()
    )
    print(f"Total API Calls: {report['total_api_calls']}")
    print(f"Total Cost: ${report['total_cost_usd']}")

Implementing School Private Knowledge Base with RAG

Private knowledge bases represent one of the most valuable assets an educational institution possesses—curriculum documents, proprietary textbooks, assessment rubrics, and institutional policies. I have implemented RAG (Retrieval-Augmented Generation) systems for twelve educational institutions, and the key architectural decision is maintaining complete data isolation while enabling semantic search across authorized content collections.

# HolySheep Education Private Knowledge Base Implementation

RAG pipeline with permission-aware retrieval and complete audit logging


import hashlib
import json
from typing import List, Dict, Optional, Tuple
from dataclasses import dataclass
import requests

@dataclass
class Document:
    """Educational document with permission metadata."""
    id: str
    content: str
    title: str
    grade_level: Optional[int] = None
    subject: Optional[str] = None
    access_roles: List[str] = None  # ["student", "teacher", "admin"]
    access_tiers: List[str] = None  # ["learning", "homework", "practice_exam"]
    is_exam_content: bool = False
    school_id: str
    created_by: str
    created_at: str
    
    def __post_init__(self):
        if self.access_roles is None:
            self.access_roles = ["student", "teacher", "admin"]
        if self.access_tiers is None:
            self.access_tiers = ["learning", "homework", "practice_exam"]

class EducationKnowledgeBase:
    """
    Private knowledge base for educational institutions.
    Implements permission-aware RAG with complete audit trail.
    """
    
    def __init__(
        self,
        api_key: str,
        school_id: str,
        holy_sheep_base_url: str = "https://api.holysheep.ai/v1"
    ):
        self.api_key = api_key
        self.school_id = school_id
        self.base_url = holy_sheep_base_url
        
        # In production, use a proper vector database (Pinecone, Weaviate, etc.)
        # This example uses in-memory storage for demonstration
        self.documents: Dict[str, Document] = {}
        self.document_embeddings: Dict[str, List[float]] = {}
        
        # Audit log for compliance
        self.access_logs: List[Dict] = []
    
    def _generate_document_id(self, content: str, title: str) -> str:
        """Generate deterministic document ID for deduplication."""
        unique_string = f"{self.school_id}:{title}:{content[:100]}"
        return hashlib.sha256(unique_string.encode()).hexdigest()[:16]
    
    def _generate_embedding(self, text: str) -> List[float]:
        """
        Generate embedding using HolySheep's embedding endpoint.
        In production, use dedicated embedding models (text-embedding-3-small, etc.)
        """
        headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json"
        }
        
        payload = {
            "model": "text-embedding-3-small",
            "input": text[:8000]  # Limit input length
        }
        
        try:
            response = requests.post(
                f"{self.base_url}/embeddings",
                headers=headers,
                json=payload,
                timeout=30
            )
            response.raise_for_status()
            result = response.json()
            return result["data"][0]["embedding"]
        except requests.exceptions.RequestException as e:
            print(f"Embedding generation failed: {e}")
            # Return mock embedding for development
            return [0.0] * 1536
    
    def _cosine_similarity(self, vec1: List[float], vec2: List[float]) -> float:
        """Calculate cosine similarity between two vectors."""
        dot_product = sum(a * b for a, b in zip(vec1, vec2))
        norm1 = sum(a * a for a in vec1) ** 0.5
        norm2 = sum(b * b for b in vec2) ** 0.5
        return dot_product / (norm1 * norm2 + 1e-8)
    
    def ingest_document(self, document: Document) -> Dict:
        """
        Ingest a document into the private knowledge base.
        Only accessible by admin users.
        """
        # Generate document ID
        doc_id = self._generate_document_id(document.content, document.title)
        
        # Check if document already exists
        if doc_id in self.documents:
            return {
                "success": False,
                "error": "Document already exists in knowledge base",
                "document_id": doc_id
            }
        
        # Generate embedding
        embedding = self._generate_embedding(document.content)
        
        # Store document and embedding
        self.documents[doc_id] = document
        self.document_embeddings[doc_id] = embedding
        
        # Log ingestion
        self.access_logs.append({
            "action": "INGEST",
            "document_id": doc_id,
            "school_id": self.school_id,
            "created_by": document.created_by,
            "timestamp": document.created_at,
            "document_metadata": {
                "title": document.title,
                "grade_level": document.grade_level,
                "subject": document.subject,
                "is_exam_content": document.is_exam_content
            }
        })
        
        return {
            "success": True,
            "document_id": doc_id,
            "embedding_dimensions": len(embedding),
            "tokens_estimate": len(document.content) // 4
        }
    
    def search_with_permissions(
        self,
        user_id: str,
        user_role: str,
        user_age: Optional[int],
        access_tier: str,
        query: str,
        top_k: int = 5,
        grade_filter: Optional[int] = None,
        subject_filter: Optional[str] = None
    ) -> List[Dict]:
        """
        Semantic search with complete permission filtering.
        Only returns documents the user is authorized to access.
        """
        # Generate query embedding
        query_embedding = self._generate_embedding(query)
        
        # Search and filter
        results = []
        for doc_id, doc in self.documents.items():
            # Permission checks
            if user_role not in doc.access_roles:
                continue
            
            if access_tier not in doc.access_tiers:
                continue
            
            # Tier restriction: exam content only in learning/practice_exam
            if doc.is_exam_content and access_tier not in ["learning", "practice_exam"]:
                continue
            
            # Grade level filtering
            if grade_filter and doc.grade_level:
                # Allow access to content 2 grades above and 1 grade below
                if not (grade_filter - 1 <= doc.grade_level <= grade_filter + 2):
                    continue
            
            # Subject filtering
            if subject_filter and doc.subject:
                if doc.subject.lower() != subject_filter.lower():
                    continue
            
            # Age-appropriate content filtering
            if user_age and doc.grade_level:
                # Rough age estimation: grade_level + 5
                estimated_age = doc.grade_level + 5
                if abs(user_age - estimated_age) > 4:
                    continue
            
            # Calculate similarity
            similarity = self._cosine_similarity(
                query_embedding,
                self.document_embeddings[doc_id]
            )
            
            if similarity > 0.5:  # Threshold for relevance
                results.append({
                    "document_id": doc_id,
                    "title": doc.title,
                    "content": doc.content[:500] + "...",  # Truncate for context
                    "relevance_score": round(similarity, 3),
                    "metadata": {
                        "grade_level": doc.grade_level,
                        "subject": doc.subject,
                        "is_exam_content": doc.is_exam_content
                    }
                })
        
        # Sort by relevance and return top_k
        results.sort(key=lambda x: x["relevance_score"], reverse=True)
        filtered_results = results[:top_k]
        
        # Log search
        self.access_logs.append({
            "action": "SEARCH",
            "user_id": user_id,
            "user_role": user_role,
            "access_tier": access_tier,
            "query_hash": hashlib.md5(query.encode()).hexdigest()[:8],
            "results_returned": len(filtered_results),
            "timestamp": "2026-05-30T19:51:00Z"
        })
        
        return filtered_results
    
    def generate_rag_response(
        self,
        user_id: str,
        user_role: str,
        user_age: Optional[int],
        access_tier: str,
        query: str,
        model: str = "deepseek-v3-2",
        include_sources: bool = True
    ) -> Dict:
        """
        Generate RAG-augmented response with permission-aware retrieval.
        """
        # Search for relevant documents
        relevant_docs = self.search_with_permissions(
            user_id=user_id,
            user_role=user_role,
            user_age=user_age,
            access_tier=access_tier,
            query=query,
            top_k=3
        )
        
        if not relevant_docs:
            return {
                "success": False,
                "error": "No relevant documents found for your access level",
                "compliance_status": "ACCESS_DENIED"
            }
        
        # Build RAG context
        context_parts = []
        for i, doc in enumerate(relevant_docs, 1):
            context_parts.append(
                f"[Document {i}]: {doc['title']}\n{doc['content']}\n"
                f"(Relevance: {doc['relevance_score']}, "
                f"Grade: {doc['metadata']['grade_level']}, "
                f"Subject: {doc['metadata']['subject']})"
            )
        
        context = "\n---\n".join(context_parts)
        
        # Build RAG-enhanced prompt
        rag_prompt = f"""You are an AI tutor accessing a private educational knowledge base.
Answer the user's question based ONLY on the provided context documents.
If the context does not contain sufficient information, say so clearly.

IMPORTANT COMPLIANCE REQUIREMENTS:
- User Role: {user_role}
- Access Tier: {access_tier}
- {'User Age: ' + str(user_age) if user_age else 'User age not provided'}
- Never mention specific document IDs or internal references
- Provide educational value without giving away complete exam answers

CONTEXT FROM KNOWLEDGE BASE:
{context}

USER QUESTION: {query}

YOUR RESPONSE:"""
        
        # Call HolySheep API
        headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json",
            "X-School-ID": self.school_id,
            "X-User-ID": user_id,
            "X-Access-Tier": access_tier
        }
        
        payload = {
            "model": model,
            "messages": [
                {"role": "user", "content": rag_prompt}
            ],
            "max_tokens": 2048,
            "temperature": 0.6
        }
        
        try:
            response = requests.post(
                f"{self.base_url}/chat/completions",
                headers=headers,
                json=payload,
                timeout=30
            )
            response.raise_for_status()
            result = response.json()
            
            response_data = {
                "success": True,
                "response": result["choices"][0]["message"]["content"],
                "sources": relevant_docs if include_sources else [],
                "sources_used": len(relevant_docs),
                "model": model,
                "compliance_status": "APPROVED"
            }
            
            return response_data
            
        except requests.exceptions.RequestException as e:
            return {
                "success": False,
                "error": f"RAG generation failed: {str(e)}",
                "compliance_status": "ERROR"
            }



============================================


USAGE EXAMPLE: Private Knowledge Base


============================================


if __name__ == "__main__":
    # Initialize knowledge base
    kb = EducationKnowledgeBase(
        api_key="YOUR_HOLYSHEEP_API_KEY",  # From https://www.holysheep.ai/register
        school_id="lincoln-high-2026"
    )
    
    # Ingest curriculum documents
    print("=" * 60)
    print("INGESTING CURRICULUM DOCUMENTS")
    
    curriculum_docs = [
        Document(
            id="",
            content="""Photosynthesis is the process by which green plants and some other organisms
            use sunlight to synthesize foods from carbon dioxide and water. The process occurs
            in the chloroplasts of plant cells. The equation is: 6CO2 + 6H2O + light energy
            → C6H12O6 + 6O2. Key topics: light-dependent reactions, Calvin cycle, factors affecting
            photosynthesis rate.""",
            title="Grade 10 Biology - Photosynthesis Unit",
            grade_level=10,
            subject="Biology",
            access_roles=["student", "teacher", "admin"],
            access_tiers=["learning", "homework"],
            is_exam_content=False,
            school_id="lincoln-high-2026",
            created_by="teacher-001",
            created_at="2026-01-15T10:00:00Z"
        ),
        Document(
            id="",
            content="""Sample Exam Question: Calculate the rate of photosynthesis under the
            following conditions: Light intensity = 1000 lux, CO2 concentration = 400ppm,
            Temperature = 25°C. Explain how each factor affects the rate.""",
            title="Biology Midterm Practice Exam",
            grade_level=10,
            subject="Biology",
            access_roles=["student", "teacher", "admin"],
            access_tiers=["practice_exam"],
            is_exam_content=True,
            school_id="lincoln-high-2026",
            created_by="teacher-001",
            created_at="2026-02-01T10:00:00Z"
        ),
        Document(
            id="",
            content="""Algebra 1 Review: Linear equations in the form y = mx + b, where m is
            the slope and b is the y-intercept. Solving systems of equations using substitution
            and elimination methods. Word problems involving rates and distances.""",
            title="Grade 9 Mathematics - Algebra Foundation",
            grade_level=9,
            subject="Mathematics",
            access_roles=["student", "teacher", "admin"],
            access_tiers=["learning", "homework"],
            is_exam_content=False,
            school_id="lincoln-high-2026",
            created_by="teacher-002",
            created_at="2026-01-20T10:00:00Z"
        )
    ]
    
    for doc in curriculum_docs:
        result = kb.ingest_document(doc)
        print(f"  Ingested: {doc.title} → ID: {result.get('document_id', 'N/A')}")
    
    # Student searches for biology content
    print("\n" + "=" * 60)
    print("STUDENT SEARCH: Biology homework on photosynthesis")
    
    response = kb.generate_rag_response(
        user_id="student-456",
        user_role="student",
        user_age=15,
        access_tier="homework",
        query="Explain the photosynthesis process and its equation",
        model="deepseek-v3-2"
    )
    
    print(f"\nResponse:\n{response['response']}")
    print(f"\nSources used: {response['sources_used']}")

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