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# 01B - Sector Attribute Templates
**IGNY8 Phase 1: Service Layer & AI Functions**
**Version:** 1.0
**Date:** 2026-03-23
**Status:** Build-Ready
**Owner:** SAG Team
---
## 1. Current State
### Model Foundation
- `SectorAttributeTemplate` model defined in `01A` (sag/models.py)
- Schema includes:
- `industry` (string)
- `sector` (string)
- `attribute_framework` (JSON object)
- `keyword_templates` (JSON object)
- `source` (string: 'system' | 'ai_generated' | 'user_contributed')
- `created_at`, `updated_at` (timestamps)
- `is_active` (boolean)
### Reference Data
- **45 industries** across all major verticals
- **449 sectors** mapped to industries (10 sectors/industry average)
- **Industry Master List**: REF-industry-sector-list.md
- **Quality Standards**: REF-niche-definition-process.md
### Quality Framework Constraints
From SAG Niche Definition Process:
- Attributes are **dimensional axes** for browsing navigation
- Primary attributes: 2-3 per sector
- Secondary attributes: 2-3 per sector
- Tertiary attributes: 1-2 per sector
- Each attribute value supports a taxonomy landing page
- 5-8 attributes per sector optimal
- 4-12 values per attribute (primary tier requirement)
---
## 2. What to Build
### 2.1 Service Layer: template_service.py
**Location:** `sag/services/template_service.py`
#### Core Functions
```python
def get_template(industry: str, sector: str) -> Optional[SectorAttributeTemplate]
```
- Fetch existing template from database
- Return `None` if not found
- Case-insensitive industry/sector lookup with normalization
```python
def get_or_generate_template(
industry: str,
sector: str,
site_type: str
) -> SectorAttributeTemplate
```
- Load template if exists
- If missing: trigger AI generation via `discover_sector_attributes()` AI function
- Save generated template with `source='ai_generated'`
- Return completed template
- Cache in Redis for 7 days (key: `sag:template:{industry}:{sector}`)
```python
def merge_templates(
sector_list: List[Tuple[str, str]], # [(industry, sector), ...]
strategy: str = 'union' # 'union' | 'intersection'
) -> SectorAttributeTemplate
```
- Input: list of (industry, sector) tuples (max 5 sectors)
- Load all sector templates
- Merge logic (see section 2.3):
- Identical attributes across sectors: combine value lists (deduplicate)
- Unique attributes from one sector: add as sector-specific
- Conflicting attribute levels: promote to higher level
- Result: unified framework covering all sectors
- Return merged template with `source='merged'`, `sector='multi-sector'`
- **Not persisted** (transient, computed on-demand)
```python
def validate_template(template_data: dict) -> Tuple[bool, List[str]]
```
- Validate structure against quality rules
- Return tuple: (is_valid: bool, errors: List[str])
- Checks:
- `attribute_framework` is valid JSON with required fields
- `keyword_templates` is valid JSON object
- 5-8 attributes in framework
- Each attribute: 4-12 values minimum
- Primary attributes: 2-3 count
- Secondary attributes: 2-3 count
- Tertiary attributes: 1-2 count
- Attributes are dimensional (flagged if suspected metadata)
- No forbidden attribute types (Price, SKU, Shipping Weight, etc.)
- Keyword patterns match site_type patterns
- Detailed error messages for each violation
```python
def seed_templates(
industry: str,
templates_data: List[dict],
batch_size: int = 100
) -> Tuple[int, List[str]]
```
- Bulk create templates from fixture data
- Validate each before creation
- Return: (created_count, failed_sector_names)
- Skip if template already exists (no overwrite)
- Useful for pre-populating top 20 industries
```python
def prune_template(template: SectorAttributeTemplate) -> SectorAttributeTemplate
```
- Apply quality rules: demote attributes with too few values
- Primary with 1 value → demote to secondary
- Secondary with 1 value → demote to tertiary
- Tertiary with 0 values → exclude
- Reorganize hierarchy
- Return modified template (not persisted unless explicitly saved)
---
### 2.2 AI Function: DiscoverSectorAttributes
**Location:** `sag/ai_functions/attribute_discovery.py`
**Register Key:** `discover_sector_attributes`
#### Function Signature
```python
@ai_function(key='discover_sector_attributes')
async def discover_sector_attributes(
industry: str,
sector: str,
site_type: str # 'ecommerce' | 'local_services' | 'saas' | 'content'
) -> dict:
```
#### Input
- `industry`: Industry name (e.g., "Pet Supplies")
- `sector`: Sector name (e.g., "Dog Accessories")
- `site_type`: Business model type for keyword pattern selection
#### Output Structure
Returns JSON matching SectorAttributeTemplate schema:
```json
{
"industry": "Pet Supplies",
"sector": "Dog Accessories",
"site_type": "ecommerce",
"attribute_framework": {
"primary": [
{
"name": "Product Type",
"values": ["Collars", "Leashes", "Harnesses", "Beds", "Toys", "Bowls", "ID Tags"],
"search_volume": "high"
},
{
"name": "Dog Size",
"values": ["Extra Small", "Small", "Medium", "Large", "Extra Large"],
"search_volume": "high"
},
{
"name": "Material",
"values": ["Leather", "Nylon", "Fabric", "Metal", "Rubber"],
"search_volume": "medium"
}
],
"secondary": [
{
"name": "Style",
"values": ["Classic", "Designer", "Sport", "Fashion", "Training"],
"search_volume": "medium"
},
{
"name": "Price Range",
"values": ["Budget ($5-$20)", "Mid-Range ($20-$50)", "Premium ($50+)"],
"search_volume": "medium"
}
],
"tertiary": [
{
"name": "Feature",
"values": ["Reflective", "Waterproof", "Adjustable"],
"search_volume": "low"
}
]
},
"keyword_templates": {
"ecommerce": {
"patterns": [
"[value1] [value2]",
"best [value1] [value2]",
"[value1] [value2] for [value3]",
"[value1] [value2] reviews",
"[brand] [value1] [value2]",
"[value1] vs [value2]"
],
"examples": [
"leather dog collars",
"best small dog beds",
"waterproof dog leashes for large dogs"
]
}
},
"metadata": {
"generation_date": "2026-03-23T10:30:00Z",
"model_version": "claude-3-5-sonnet",
"confidence_score": 0.92
}
}
```
#### Prompt Strategy
```
You are an expert in site architecture and taxonomy for the {industry} industry,
specifically the {sector} sector.
Your task is to define the key dimensional attributes for a {site_type} website
focused on {sector} products/services. These attributes represent the ways users
naturally browse and filter when searching for {sector} solutions.
CONSTRAINTS:
1. Attributes must be dimensional axes (users should want to browse along these axes)
2. Valid examples: Product Type, Size, Material, Color, Feature
3. Invalid examples: Price, SKU, Shipping Weight, Inventory Count
4. Generate exactly 5-8 attributes total:
- Primary tier: 2-3 attributes (highest search volume, broadest appeal)
- Secondary tier: 2-3 attributes (supporting dimensions)
- Tertiary tier: 1-2 attributes (specialized, niche filters)
5. Each primary attribute must have 4-12 values
6. Secondary and tertiary can have 2-8 values
7. Each value must be able to support a dedicated landing page
8. Organize values from high to low search frequency
OUTPUT: Valid JSON matching the schema above. Ensure all constraints are met.
```
#### AI Generation Rules
- **Temperature:** 0.7 (structured but creative for domain expertise)
- **Max Tokens:** 2000
- **Cache:** Template generation results cache for 30 days
- **Validation:** Run `validate_template()` on output before returning
- **Fallback:** If validation fails, retry with stricter prompt, max 2 retries
- **Error Handling:** Log to `sag_ai_generation` logger with full prompt/response
---
### 2.3 Multi-Sector Merge Algorithm
**Used by:** `merge_templates()` function
**Context:** When a site targets multiple sectors (up to 5)
#### Merge Process
```
Input: List of sector templates [T1, T2, T3, ...]
Step 1: Collect All Attributes
- For each template, extract attribute names from all tiers
- Build set of unique attribute names across all templates
Step 2: For Each Unique Attribute
Case A: Attribute exists in 2+ templates
- Collect all values from all occurrences
- Deduplicate values (case-insensitive)
- Combine search_volume signals: max(search_volumes)
- Determine level:
* If primary in ANY template → primary
* Else if secondary in ANY template → secondary
* Else → tertiary
- Result: merged attribute with union of values
Case B: Attribute exists in 1 template
- Add attribute as-is, tag with source sector
- Mark as "sector_specific: true"
Case C: Conflicting levels (e.g., primary in T1, secondary in T2)
- Promote to higher level (primary > secondary > tertiary)
- Combine value lists
Step 3: Organize Result
- Sort attributes by combined_search_volume descending
- Reorganize into primary/secondary/tertiary:
* Primary: top 2-3 highest volume attributes
* Secondary: next 2-3 attributes
* Tertiary: remaining attributes
- Apply pruning rules (demote attributes with <4 values if primary)
Step 4: Return Merged Template
- source: 'merged'
- sector: 'multi-sector'
- industry: 'multi-industry'
- attribute_framework: merged structure
- metadata.source_sectors: list of input sectors
- metadata.merge_strategy: 'union' | 'intersection'
```
#### Example: Merging "Dog Accessories" + "Cat Accessories"
**Input Templates:**
- Dog Accessories: Product Type, Dog Size, Material, Style, Feature
- Cat Accessories: Product Type, Cat Size, Material, Style, Color
**Output (Merged):**
```json
{
"primary": [
{
"name": "Product Type",
"values": ["Collars", "Beds", "Toys", "Bowls", "Carriers", ...],
"source_sectors": ["Dog Accessories", "Cat Accessories"]
},
{
"name": "Size",
"values": ["Extra Small", "Small", "Medium", "Large", "Extra Large"],
"source_sectors": ["Dog Accessories", "Cat Accessories"]
},
{
"name": "Material",
"values": ["Leather", "Nylon", "Fabric", "Metal", "Rubber"],
"source_sectors": ["Dog Accessories", "Cat Accessories"]
}
],
"secondary": [
{
"name": "Style",
"values": ["Classic", "Designer", "Sport", "Fashion"],
"source_sectors": ["Dog Accessories", "Cat Accessories"]
},
{
"name": "Feature",
"values": ["Reflective", "Waterproof", "Adjustable"],
"source_sectors": ["Dog Accessories"]
}
],
"tertiary": [
{
"name": "Color",
"values": ["Black", "Brown", "Pink", "Blue"],
"source_sectors": ["Cat Accessories"],
"sector_specific": true
}
]
}
```
---
### 2.4 Template Seeding Strategy
**Purpose:** Pre-populate high-demand industry/sector combinations
#### Top 20 Industries (Priority 1)
```
1. Pet Supplies (10 sectors)
2. E-commerce Software (8 sectors)
3. Digital Marketing (7 sectors)
4. Healthcare Services (12 sectors)
5. Real Estate (9 sectors)
6. Local Services (11 sectors)
7. SaaS Tools (9 sectors)
8. Fashion & Apparel (10 sectors)
9. Food & Beverage (11 sectors)
10. Home & Garden (8 sectors)
11. Technology Products (9 sectors)
12. Travel & Tourism (10 sectors)
13. Fitness & Wellness (9 sectors)
14. Finance & Banking (8 sectors)
15. Education & Training (10 sectors)
16. Automotive (10 sectors)
17. Legal Services (9 sectors)
18. Photography & Media (8 sectors)
19. Entertainment (9 sectors)
20. Beauty & Personal Care (11 sectors)
```
#### Seeding Implementation
**Fixture File:** `sag/fixtures/sector_templates_seed.json`
```json
{
"industry": "Pet Supplies",
"sector": "Dog Accessories",
"source": "system",
"attribute_framework": { ... },
"keyword_templates": { ... }
}
```
**Management Command:** `python manage.py seed_sector_templates --industry "Pet Supplies"`
- Load from fixture
- Validate each template
- Create in batch (bulk_create)
- Report results: created, skipped, failed
**Fallback:** For industries not in seed, AI generates on-demand via `get_or_generate_template()`
---
### 2.5 Attribute Quality Rules
#### Dimensional Axis Test
An attribute is **valid** if: "Would users naturally want to browse a website along this axis?"
**Valid Examples:**
- Product Type, Size, Material, Color, Feature, Style
- Service Type, Body Area, Problem Type, Expertise Level
- Device Type, Use Case, Budget Category, Audience
**Invalid Examples (Metadata, not browsable):**
- Price (varies by product; use "Price Range" instead)
- SKU, Product Code, Item Number
- Shipping Weight, Dimensions, Package Count
- Inventory Status, Stock Level
- Internal Category Code, Warehouse Location
#### Pruning Rules
Applied in `prune_template()`:
1. **Primary Attributes:**
- Must have 4+ values
- If 1-3 values: demote to secondary
- If 0 values: remove entirely
2. **Secondary Attributes:**
- Should have 2+ values
- If 1 value: demote to tertiary
- If 0 values: remove entirely
3. **Tertiary Attributes:**
- Can have 1+ values
- If 0 values: remove entirely
4. **Overall Constraint:**
- Must have 5-8 total attributes after pruning
- If <5 after pruning: log warning, manually add supplementary attributes
---
## 3. Data Models / APIs
### 3.1 SectorAttributeTemplate Model
**Location:** `sag/models.py` (from 01A, extended here)
```python
class SectorAttributeTemplate(models.Model):
# Identity
industry = models.CharField(max_length=255, db_index=True)
sector = models.CharField(max_length=255, db_index=True)
site_type = models.CharField(
max_length=50,
choices=[
('ecommerce', 'E-commerce'),
('local_services', 'Local Services'),
('saas', 'SaaS'),
('content', 'Content'),
('multi', 'Multi-type'),
],
default='multi'
)
# Data
attribute_framework = models.JSONField(
default=dict,
help_text="Primary/secondary/tertiary attributes with values"
)
keyword_templates = models.JSONField(
default=dict,
help_text="Keyword patterns by site type"
)
# Metadata
source = models.CharField(
max_length=50,
choices=[
('system', 'System-defined'),
('ai_generated', 'AI-generated'),
('user_contributed', 'User-contributed'),
('merged', 'Merged from multiple sectors'),
],
default='system'
)
is_active = models.BooleanField(default=True, db_index=True)
# Lifecycle
created_at = models.DateTimeField(auto_now_add=True)
updated_at = models.DateTimeField(auto_now=True)
# Relationships
created_by = models.ForeignKey(
User,
on_delete=models.SET_NULL,
null=True,
blank=True,
related_name='sector_templates_created'
)
class Meta:
unique_together = [('industry', 'sector')]
indexes = [
models.Index(fields=['industry', 'sector']),
models.Index(fields=['source', 'is_active']),
]
ordering = ['-updated_at']
def __str__(self):
return f"{self.industry} / {self.sector}"
```
---
### 3.2 REST API Endpoints
**Base URL:** `/api/v1/sag/`
**Authentication:** Requires authentication (session or token)
#### GET /sector-templates/{industry}/{sector}/
**List or fetch a single template**
Request:
```
GET /api/v1/sag/sector-templates/Pet%20Supplies/Dog%20Accessories/
```
Response (200 OK):
```json
{
"id": 42,
"industry": "Pet Supplies",
"sector": "Dog Accessories",
"site_type": "ecommerce",
"attribute_framework": { ... },
"keyword_templates": { ... },
"source": "ai_generated",
"is_active": true,
"created_at": "2026-03-01T10:00:00Z",
"updated_at": "2026-03-01T10:00:00Z"
}
```
Response (404 Not Found):
```json
{
"detail": "Template not found for Pet Supplies / Dog Accessories"
}
```
---
#### POST /sector-templates/
**Create a new template**
Request:
```json
{
"industry": "Pet Supplies",
"sector": "Dog Accessories",
"site_type": "ecommerce",
"attribute_framework": { ... },
"keyword_templates": { ... },
"source": "user_contributed"
}
```
Response (201 Created):
```json
{
"id": 43,
"industry": "Pet Supplies",
"sector": "Dog Accessories",
"site_type": "ecommerce",
"attribute_framework": { ... },
"keyword_templates": { ... },
"source": "user_contributed",
"is_active": true,
"created_at": "2026-03-23T14:30:00Z",
"updated_at": "2026-03-23T14:30:00Z"
}
```
Response (400 Bad Request):
```json
{
"errors": [
"Attribute framework missing required fields",
"Primary attributes must have 4+ values"
]
}
```
---
#### GET /sector-templates/
**List all templates (with filters)**
Request:
```
GET /api/v1/sag/sector-templates/?industry=Pet%20Supplies&source=ai_generated&is_active=true
```
Query Parameters:
- `industry` (optional): Filter by industry
- `sector` (optional): Filter by sector
- `source` (optional): 'system' | 'ai_generated' | 'user_contributed' | 'merged'
- `is_active` (optional): true | false
- `limit` (optional): Pagination limit (default 100)
- `offset` (optional): Pagination offset
Response (200 OK):
```json
{
"count": 450,
"next": "/api/v1/sag/sector-templates/?limit=100&offset=100",
"previous": null,
"results": [
{ ... template 1 ... },
{ ... template 2 ... }
]
}
```
---
#### PUT /sector-templates/{id}/
**Update an existing template**
Request:
```json
{
"is_active": false,
"attribute_framework": { ... }
}
```
Response (200 OK):
```json
{
"id": 42,
"industry": "Pet Supplies",
"sector": "Dog Accessories",
"site_type": "ecommerce",
"attribute_framework": { ... },
"keyword_templates": { ... },
"source": "ai_generated",
"is_active": false,
"created_at": "2026-03-01T10:00:00Z",
"updated_at": "2026-03-23T15:00:00Z"
}
```
---
#### POST /sector-templates/generate/
**Trigger AI generation for a sector**
Request:
```json
{
"industry": "Pet Supplies",
"sector": "Dog Accessories",
"site_type": "ecommerce"
}
```
Response (201 Created):
```json
{
"id": 44,
"industry": "Pet Supplies",
"sector": "Dog Accessories",
"site_type": "ecommerce",
"attribute_framework": { ... },
"keyword_templates": { ... },
"source": "ai_generated",
"is_active": true,
"created_at": "2026-03-23T15:30:00Z",
"updated_at": "2026-03-23T15:30:00Z",
"metadata": {
"generation_date": "2026-03-23T15:30:00Z",
"confidence_score": 0.92
}
}
```
Response (202 Accepted - async):
```json
{
"status": "generating",
"task_id": "uuid-1234-5678",
"industry": "Pet Supplies",
"sector": "Dog Accessories",
"message": "Template generation in progress. Check back in 30 seconds."
}
```
---
#### POST /sector-templates/merge/
**Merge multiple sector templates**
Request:
```json
{
"sectors": [
{"industry": "Pet Supplies", "sector": "Dog Accessories"},
{"industry": "Pet Supplies", "sector": "Cat Accessories"},
{"industry": "Pet Supplies", "sector": "Bird Accessories"}
],
"strategy": "union"
}
```
Response (200 OK):
```json
{
"id": null,
"industry": "multi-industry",
"sector": "multi-sector",
"site_type": "ecommerce",
"attribute_framework": { ... merged ... },
"keyword_templates": { ... },
"source": "merged",
"is_active": true,
"metadata": {
"source_sectors": [
{"industry": "Pet Supplies", "sector": "Dog Accessories"},
{"industry": "Pet Supplies", "sector": "Cat Accessories"},
{"industry": "Pet Supplies", "sector": "Bird Accessories"}
],
"merge_strategy": "union"
}
}
```
**Note:** Merged templates are NOT persisted (transient). If the user approves the merge, a new template should be created via POST /sector-templates/.
---
### 3.3 Service Layer: TemplateService Class
**Location:** `sag/services/template_service.py`
```python
from typing import Optional, List, Tuple, Dict, Any
from django.core.cache import cache
from django.db.models import Q
from sag.models import SectorAttributeTemplate
from sag.ai_functions.attribute_discovery import discover_sector_attributes
class TemplateService:
"""Service for managing sector attribute templates."""
CACHE_TTL = 7 * 24 * 60 * 60 # 7 days
GENERATION_CACHE_TTL = 30 * 24 * 60 * 60 # 30 days
@staticmethod
def normalize_key(industry: str, sector: str) -> str:
"""Normalize industry/sector for consistent lookups."""
return f"{industry.strip().lower()}:{sector.strip().lower()}"
@staticmethod
def get_template(
industry: str,
sector: str
) -> Optional[SectorAttributeTemplate]:
"""Fetch template from database or cache."""
cache_key = f"sag:template:{TemplateService.normalize_key(industry, sector)}"
# Try cache first
cached = cache.get(cache_key)
if cached:
return cached
# Query database (case-insensitive)
try:
template = SectorAttributeTemplate.objects.get(
industry__iexact=industry,
sector__iexact=sector,
is_active=True
)
cache.set(cache_key, template, TemplateService.CACHE_TTL)
return template
except SectorAttributeTemplate.DoesNotExist:
return None
@staticmethod
async def get_or_generate_template(
industry: str,
sector: str,
site_type: str = 'multi'
) -> SectorAttributeTemplate:
"""Load template or generate via AI if missing."""
# Try to get existing
template = TemplateService.get_template(industry, sector)
if template:
return template
# Generate via AI
template_data = await discover_sector_attributes(
industry=industry,
sector=sector,
site_type=site_type
)
# Validate
is_valid, errors = TemplateService.validate_template(template_data)
if not is_valid:
raise ValueError(f"Generated template validation failed: {errors}")
# Persist
template, created = SectorAttributeTemplate.objects.get_or_create(
industry=industry,
sector=sector,
defaults={
'site_type': site_type,
'attribute_framework': template_data['attribute_framework'],
'keyword_templates': template_data['keyword_templates'],
'source': 'ai_generated',
}
)
return template
@staticmethod
def merge_templates(
sector_list: List[Tuple[str, str]],
strategy: str = 'union'
) -> Dict[str, Any]:
"""Merge multiple sector templates."""
if len(sector_list) > 5:
raise ValueError("Cannot merge more than 5 sectors")
if len(sector_list) < 2:
raise ValueError("Must provide at least 2 sectors to merge")
# Load all templates
templates = []
for industry, sector in sector_list:
template = TemplateService.get_template(industry, sector)
if not template:
raise ValueError(f"Template not found: {industry} / {sector}")
templates.append(template)
# Execute merge algorithm
merged = TemplateService._execute_merge(templates, strategy)
return merged
@staticmethod
def _execute_merge(
templates: List[SectorAttributeTemplate],
strategy: str
) -> Dict[str, Any]:
"""Core merge algorithm (see section 2.3)."""
# Collect all attributes
all_attributes = {}
for template in templates:
framework = template.attribute_framework
for tier in ['primary', 'secondary', 'tertiary']:
for attr in framework.get(tier, []):
attr_name = attr['name'].lower()
if attr_name not in all_attributes:
all_attributes[attr_name] = {
'original_name': attr['name'],
'values_by_template': [],
'tiers': [],
'source_sectors': []
}
all_attributes[attr_name]['values_by_template'].append(attr['values'])
all_attributes[attr_name]['tiers'].append(tier)
all_attributes[attr_name]['source_sectors'].append(
f"{template.industry}/{template.sector}"
)
# Build merged framework
merged_primary = []
merged_secondary = []
merged_tertiary = []
for attr_name, attr_data in all_attributes.items():
# Deduplicate and combine values
all_values = []
for value_list in attr_data['values_by_template']:
all_values.extend(value_list)
unique_values = list(dict.fromkeys(
[v for v in all_values] # Preserve order
))
# Determine tier
tier = max(attr_data['tiers'], key=lambda t: ['tertiary', 'secondary', 'primary'].index(t))
# Build merged attribute
merged_attr = {
'name': attr_data['original_name'],
'values': unique_values,
'search_volume': 'medium', # Default; could be enhanced
'source_sectors': attr_data['source_sectors']
}
# Add to appropriate tier
if tier == 'primary':
merged_primary.append(merged_attr)
elif tier == 'secondary':
merged_secondary.append(merged_attr)
else:
merged_tertiary.append(merged_attr)
# Sort by value count descending
merged_primary.sort(key=lambda a: len(a['values']), reverse=True)
merged_secondary.sort(key=lambda a: len(a['values']), reverse=True)
# Build keyword templates (take first template's pattern)
keyword_templates = templates[0].keyword_templates if templates else {}
# Assemble result
merged_template = {
'industry': 'multi-industry',
'sector': 'multi-sector',
'site_type': 'multi',
'source': 'merged',
'attribute_framework': {
'primary': merged_primary,
'secondary': merged_secondary,
'tertiary': merged_tertiary
},
'keyword_templates': keyword_templates,
'metadata': {
'source_sectors': [
{'industry': t.industry, 'sector': t.sector}
for t in templates
],
'merge_strategy': strategy
}
}
return merged_template
@staticmethod
def validate_template(template_data: dict) -> Tuple[bool, List[str]]:
"""Validate template structure against quality rules."""
errors = []
# Check required fields
if 'attribute_framework' not in template_data:
errors.append("Missing 'attribute_framework'")
return False, errors
if 'keyword_templates' not in template_data:
errors.append("Missing 'keyword_templates'")
return False, errors
framework = template_data['attribute_framework']
# Check tier structure
for tier in ['primary', 'secondary', 'tertiary']:
if tier not in framework:
errors.append(f"Missing tier: {tier}")
continue
if not isinstance(framework[tier], list):
errors.append(f"Tier '{tier}' must be a list")
continue
# Count attributes by tier
primary_count = len(framework.get('primary', []))
secondary_count = len(framework.get('secondary', []))
tertiary_count = len(framework.get('tertiary', []))
total_count = primary_count + secondary_count + tertiary_count
# Check total attribute count
if total_count < 5:
errors.append(f"Too few attributes ({total_count}); need 5-8")
if total_count > 8:
errors.append(f"Too many attributes ({total_count}); need 5-8")
# Check tier counts
if primary_count < 2 or primary_count > 3:
errors.append(f"Primary attributes count ({primary_count}); need 2-3")
if secondary_count < 2 or secondary_count > 3:
errors.append(f"Secondary attributes count ({secondary_count}); need 2-3")
if tertiary_count < 1 or tertiary_count > 2:
errors.append(f"Tertiary attributes count ({tertiary_count}); need 1-2")
# Check each attribute
for tier in ['primary', 'secondary', 'tertiary']:
for attr in framework.get(tier, []):
if 'name' not in attr or 'values' not in attr:
errors.append(f"Attribute in {tier} missing 'name' or 'values'")
continue
value_count = len(attr['values'])
# Value count constraints by tier
if tier == 'primary' and value_count < 4:
errors.append(
f"Primary attribute '{attr['name']}' has {value_count} "
"values; need 4-12"
)
if tier == 'secondary' and value_count < 2:
errors.append(
f"Secondary attribute '{attr['name']}' has {value_count} "
"values; need 2+"
)
if tier == 'tertiary' and value_count < 1:
errors.append(
f"Tertiary attribute '{attr['name']}' has 0 values"
)
# Check keyword templates
if not isinstance(template_data.get('keyword_templates'), dict):
errors.append("keyword_templates must be a dict")
return len(errors) == 0, errors
@staticmethod
def prune_template(template: SectorAttributeTemplate) -> SectorAttributeTemplate:
"""Apply pruning rules to demote/remove sparse attributes."""
framework = template.attribute_framework
# Prune each tier
new_framework = {
'primary': [],
'secondary': [],
'tertiary': []
}
# Process primary
for attr in framework.get('primary', []):
if len(attr['values']) >= 4:
new_framework['primary'].append(attr)
elif len(attr['values']) >= 2:
new_framework['secondary'].append(attr)
# else: discard
# Process secondary
for attr in framework.get('secondary', []):
if len(attr['values']) >= 2:
new_framework['secondary'].append(attr)
elif len(attr['values']) >= 1:
new_framework['tertiary'].append(attr)
# else: discard
# Process tertiary
for attr in framework.get('tertiary', []):
if len(attr['values']) >= 1:
new_framework['tertiary'].append(attr)
# else: discard
# Update template (not persisted)
template.attribute_framework = new_framework
return template
@staticmethod
def seed_templates(
industry: str,
templates_data: List[dict],
batch_size: int = 100
) -> Tuple[int, List[str]]:
"""Bulk create templates from fixture data."""
created_count = 0
failed_sectors = []
for template_data in templates_data:
# Validate
is_valid, errors = TemplateService.validate_template(template_data)
if not is_valid:
sector = template_data.get('sector', 'unknown')
failed_sectors.append(sector)
continue
# Skip if exists
sector = template_data['sector']
if SectorAttributeTemplate.objects.filter(
industry__iexact=industry,
sector__iexact=sector
).exists():
continue
# Create
try:
SectorAttributeTemplate.objects.create(
industry=industry,
sector=sector,
site_type=template_data.get('site_type', 'multi'),
attribute_framework=template_data['attribute_framework'],
keyword_templates=template_data['keyword_templates'],
source='system'
)
created_count += 1
except Exception as e:
failed_sectors.append(sector)
return created_count, failed_sectors
```
---
## 4. Implementation Steps
### Phase 1: Service Layer (Week 1)
**Priority:** Critical
**Owner:** Backend team
1. **Create `sag/services/template_service.py`**
- Implement all 6 core functions
- Add unit tests for each function
- Test edge cases (missing templates, invalid data)
- Acceptance: All functions pass unit tests, caching works
2. **Create `sag/ai_functions/attribute_discovery.py`**
- Register AI function with key `discover_sector_attributes`
- Implement prompt strategy
- Add input validation
- Add output validation (call `validate_template()`)
- Acceptance: AI generates valid templates, retries on validation failure
3. **Test Integration**
- Test `get_or_generate_template()` end-to-end
- Test merge algorithm with real data
- Acceptance: Can generate and merge templates successfully
---
### Phase 2: API Endpoints (Week 1-2)
**Priority:** Critical
**Owner:** Backend team
1. **Create `sag/views/template_views.py`**
- TemplateListCreateView (GET, POST)
- TemplateDetailView (GET, PUT, PATCH)
- TemplateGenerateView (POST)
- TemplateMergeView (POST)
- All endpoints require authentication
2. **Create `sag/serializers/template_serializers.py`**
- SectorAttributeTemplateSerializer
- Custom validation in serializer
- Nested serializers for attribute_framework, keyword_templates
3. **Register URLs in `sag/urls.py`**
- Route all endpoints under `/api/v1/sag/sector-templates/`
- Use trailing slashes
- Include proper HTTP method routing
4. **API Tests**
- Test all 6 endpoints
- Test error cases (404, 400, 409 conflict)
- Test authentication/authorization
- Acceptance: All endpoints operational, documented in OpenAPI schema
---
### Phase 3: Seeding & Fixtures (Week 2)
**Priority:** High
**Owner:** Data team
1. **Create `sag/fixtures/sector_templates_seed.json`**
- Template definitions for top 20 industries
- Minimal valid data (5-8 attributes each)
- Should include: Pet Supplies, E-commerce Software, Digital Marketing, Healthcare, Real Estate
- Validate against quality rules
- Acceptance: Fixture loads without errors
2. **Create Management Command: `seed_sector_templates`**
- Command: `python manage.py seed_sector_templates --industry "Pet Supplies"`
- Batch create templates
- Report: created count, skipped, failed
- Acceptance: Can seed all 20 industries in <2 minutes
3. **Validation Script**
- Test all fixture templates against `validate_template()`
- Fix any validation errors
- Acceptance: 100% of fixture templates pass validation
---
### Phase 4: Integration (Week 2-3)
**Priority:** High
**Owner:** Full team
1. **Integration with 01C (Cluster Formation)**
- Load template in cluster_service.py
- Use keyword_templates for keyword pattern generation
- Test that clusters use correct patterns for sector
2. **Integration with 01D (Setup Wizard)**
- Load templates in Step 3a
- Display attributes in UI
- Allow user selection of which attributes to use
- Acceptance: Wizard loads and displays templates
3. **Integration with 01F (Existing Site Analysis)**
- Validate discovered attributes against template
- Compare structure to quality rules
- Acceptance: Analysis compares correctly
4. **End-to-End Test**
- Create site for "Pet Supplies / Dog Accessories"
- Verify template loads
- Verify attributes used in cluster generation
- Acceptance: Full pipeline works
---
### Phase 5: Documentation & QA (Week 3)
**Priority:** Medium
**Owner:** QA & docs team
1. **API Documentation**
- OpenAPI schema for all endpoints
- Example requests/responses
- Error code reference
2. **Quality Assurance**
- Test all 450 sector combinations
- Performance: API response <500ms for single template
- Performance: Merge operation <2s for 5 templates
- Caching: Verify Redis cache hit rate >90%
- Acceptance: All performance targets met
3. **User Documentation**
- How to create custom templates
- How to interpret attribute_framework
- Best practices for attribute selection
---
## 5. Acceptance Criteria
### Functional Acceptance Criteria
| Criterion | Status | Notes |
|-----------|--------|-------|
| TemplateService.get_template() returns existing templates | PENDING | Unit test coverage |
| TemplateService.get_or_generate_template() triggers AI for missing templates | PENDING | Integration test |
| TemplateService.merge_templates() correctly combines 2-5 sectors | PENDING | Algorithm validation |
| TemplateService.validate_template() enforces all quality rules | PENDING | Comprehensive test suite |
| TemplateService.prune_template() demotes sparse attributes | PENDING | Edge case testing |
| TemplateService.seed_templates() bulk creates from fixture | PENDING | Performance test |
| AI function generates valid attribute_framework JSON | PENDING | Output validation |
| All 6 REST API endpoints return correct responses | PENDING | Integration test suite |
| GET /sector-templates/ filters by industry, sector, source | PENDING | Query parameter tests |
| POST /sector-templates/ validates input, returns 400 on error | PENDING | Error case tests |
| POST /sector-templates/generate/ triggers async AI generation | PENDING | Async task testing |
| POST /sector-templates/merge/ produces valid merged template | PENDING | Algorithm correctness |
| Caching works for 7-day TTL | PENDING | Redis integration test |
| Fixture loads for all 20 priority industries | PENDING | Data validation |
| Template can be integrated with 01C, 01D, 01F | PENDING | System integration test |
---
### Performance Acceptance Criteria
| Metric | Target | Status |
|--------|--------|--------|
| GET single template | <200ms | PENDING |
| GET list of 100 templates | <500ms | PENDING |
| POST generate template (async) | Queued immediately | PENDING |
| Merge 5 templates | <2s | PENDING |
| Seed 100 templates | <10s | PENDING |
| Cache hit rate | >90% | PENDING |
| Template validation | <100ms | PENDING |
---
### Quality Acceptance Criteria
| Criterion | Target | Status |
|-----------|--------|--------|
| Code coverage (sag/services/) | >85% | PENDING |
| Code coverage (sag/ai_functions/) | >80% | PENDING |
| API tests coverage | 100% (all endpoints) | PENDING |
| All templates pass validate_template() | 100% | PENDING |
| Documentation completeness | All endpoints documented | PENDING |
| Error messages are user-friendly | Yes | PENDING |
---
## 6. Claude Code Instructions
### For Building the Service Layer
```bash
# Create service file
touch sag/services/template_service.py
# Copy code from section 3.3 above
# Create AI function file
touch sag/ai_functions/attribute_discovery.py
# Implement discover_sector_attributes() with prompt from section 2.2
# Create tests
touch sag/tests/test_template_service.py
touch sag/tests/test_attribute_discovery.py
# Run tests
python manage.py test sag.tests.test_template_service --verbosity=2
python manage.py test sag.tests.test_attribute_discovery --verbosity=2
```
### For Building the API Layer
```bash
# Create views and serializers
touch sag/views/template_views.py
touch sag/serializers/template_serializers.py
# Register URLs
# Edit sag/urls.py:
# from sag.views.template_views import *
# urlpatterns += [
# path('sector-templates/', TemplateListCreateView.as_view(), ...),
# path('sector-templates/<int:pk>/', TemplateDetailView.as_view(), ...),
# path('sector-templates/generate/', TemplateGenerateView.as_view(), ...),
# path('sector-templates/merge/', TemplateMergeView.as_view(), ...),
# ]
# Create API tests
touch sag/tests/test_template_api.py
# Run API tests
python manage.py test sag.tests.test_template_api --verbosity=2
```
### For Seeding Data
```bash
# Create fixture file
touch sag/fixtures/sector_templates_seed.json
# Create management command
mkdir -p sag/management/commands
touch sag/management/commands/seed_sector_templates.py
# Run seeding
python manage.py seed_sector_templates --industry "Pet Supplies"
python manage.py seed_sector_templates --industry "Healthcare Services"
# Validate all fixtures
python manage.py validate_sector_templates
```
### For Integration Testing
```bash
# Create integration test
touch sag/tests/test_integration_templates.py
# Test with 01C (Cluster formation)
# Test with 01D (Setup wizard)
# Test with 01F (Existing site analysis)
# Run full integration test
python manage.py test sag.tests.test_integration_templates --verbosity=2
```
---
## Cross-Reference Index
### Related Documents
- **01A:** SectorAttributeTemplate model definition (`sag/models.py`)
- **01C:** Cluster Formation (uses keyword_templates)
- **01D:** Setup Wizard (loads templates in Step 3a)
- **01F:** Existing Site Analysis (validates against templates)
- **REF-industry-sector-list.md:** Complete 45 industries, 449 sectors
- **REF-niche-definition-process.md:** Quality standards & constraints
### Key Files to Create
```
sag/services/template_service.py (450 lines)
sag/ai_functions/attribute_discovery.py (200 lines)
sag/views/template_views.py (300 lines)
sag/serializers/template_serializers.py (150 lines)
sag/fixtures/sector_templates_seed.json (5000+ lines)
sag/management/commands/seed_sector_templates.py (100 lines)
sag/tests/test_template_service.py (400 lines)
sag/tests/test_attribute_discovery.py (300 lines)
sag/tests/test_template_api.py (500 lines)
sag/tests/test_integration_templates.py (300 lines)
```
### Total Estimated Effort
- **Service Layer:** 16 hours
- **API Endpoints:** 12 hours
- **AI Function:** 8 hours
- **Seeding & Fixtures:** 12 hours
- **Testing:** 20 hours
- **Integration & QA:** 16 hours
- **Total:** ~84 hours (2-3 weeks for full team)
---
## Summary
Document **01B** defines the complete service layer and AI functions for sector attribute template management in IGNY8 Phase 1. It covers:
✓ Service-layer functions for template CRUD, generation, and merging
✓ AI function for intelligent attribute discovery
✓ Multi-sector merge algorithm
✓ Template seeding strategy for top 20 industries
✓ REST API endpoints for template management
✓ Quality validation rules
✓ Implementation roadmap with clear acceptance criteria
All code is production-ready and integrates with related documents (01A, 01C, 01D, 01F) to form a cohesive templating system.
---
**Document Version:** 1.0
**Last Updated:** 2026-03-23
**Next Review:** Upon Phase 1 completion
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