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This commit is contained in:
alorig
2025-11-09 19:34:54 +05:00
parent c04c688aa0
commit b7d9fd43c7
9 changed files with 686 additions and 52 deletions
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17
backend/igny8_core/ai/__init__.py
View File

@@ -25,6 +25,14 @@ from igny8_core.ai.constants import (
DEFAULT_AI_MODEL,
JSON_MODE_MODELS,
)
from igny8_core.ai.prompts import PromptRegistry, get_prompt
from igny8_core.ai.settings import (
MODEL_CONFIG,
get_model_config,
get_model,
get_max_tokens,
get_temperature,
)
# Don't auto-import functions here - let apps.py handle it lazily
# This prevents circular import issues during Django startup
@@ -52,5 +60,14 @@ __all__ = [
'VALID_SIZES_BY_MODEL',
'DEFAULT_AI_MODEL',
'JSON_MODE_MODELS',
# Prompts
'PromptRegistry',
'get_prompt',
# Settings
'MODEL_CONFIG',
'get_model_config',
'get_model',
'get_max_tokens',
'get_temperature',
]

14
backend/igny8_core/ai/engine.py
View File

@@ -6,6 +6,7 @@ from typing import Dict, Any, Optional
from igny8_core.ai.base import BaseAIFunction
from igny8_core.ai.tracker import StepTracker, ProgressTracker, CostTracker
from igny8_core.ai.ai_core import AICore
from igny8_core.ai.settings import get_model_config
logger = logging.getLogger(__name__)
@@ -62,7 +63,11 @@ class AIEngine:
# Phase 3: AI_CALL - Provider API Call (25-70%)
ai_core = AICore(account=self.account)
model = fn.get_model(self.account)
function_name = fn.get_name()
# Get model config from settings
model_config = get_model_config(function_name)
model = model_config.get('model')
# Track AI call start
self.step_tracker.add_response_step("AI_CALL", "success", f"Calling {model or 'default'} model...")
@@ -73,9 +78,10 @@ class AIEngine:
raw_response = ai_core.run_ai_request(
prompt=prompt,
model=model,
max_tokens=4000,
temperature=0.7,
function_name=fn.get_name()
max_tokens=model_config.get('max_tokens'),
temperature=model_config.get('temperature'),
response_format=model_config.get('response_format'),
function_name=function_name
)
except Exception as e:
error_msg = f"AI call failed: {str(e)}"

21
backend/igny8_core/ai/functions/auto_cluster.py
View File

@@ -6,8 +6,9 @@ from typing import Dict, List, Any
from django.db import transaction
from igny8_core.ai.base import BaseAIFunction
from igny8_core.modules.planner.models import Keywords, Clusters
from igny8_core.modules.system.utils import get_prompt_value
from igny8_core.ai.ai_core import AICore
from igny8_core.ai.prompts import PromptRegistry
from igny8_core.ai.settings import get_model_config
logger = logging.getLogger(__name__)
@@ -90,20 +91,18 @@ class AutoClusterFunction(BaseAIFunction):
}
def build_prompt(self, data: Dict, account=None) -> str:
"""Build clustering prompt"""
"""Build clustering prompt using registry"""
keyword_data = data['keyword_data']
sector_id = data.get('sector_id')
# Get prompt template
prompt_template = get_prompt_value(account, 'clustering')
# Format keywords
keywords_text = '\n'.join([
f"- {kw['keyword']} (Volume: {kw['volume']}, Difficulty: {kw['difficulty']}, Intent: {kw['intent']})"
for kw in keyword_data
])
prompt = prompt_template.replace('[IGNY8_KEYWORDS]', keywords_text)
# Build context
context = {'KEYWORDS': keywords_text}
# Add sector context if available
if sector_id:
@@ -111,14 +110,20 @@ class AutoClusterFunction(BaseAIFunction):
from igny8_core.auth.models import Sector
sector = Sector.objects.get(id=sector_id)
if sector:
prompt += f"\n\nNote: These keywords are for the '{sector.name}' sector."
context['SECTOR'] = sector.name
except Exception:
pass
# Get prompt from registry
prompt = PromptRegistry.get_prompt(
function_name='auto_cluster',
account=account,
context=context
)
# IMPORTANT: When using JSON mode, OpenAI requires explicit JSON instruction
# The prompt template already includes "Format the output as a JSON object"
# but we need to ensure it's explicit for JSON mode compliance
# Check if prompt already explicitly requests JSON (case-insensitive)
prompt_lower = prompt.lower()
has_json_request = (
'json' in prompt_lower and

33
backend/igny8_core/ai/functions/generate_content.py
View File

@@ -8,9 +8,10 @@ from typing import Dict, List, Any
from django.db import transaction
from igny8_core.ai.base import BaseAIFunction
from igny8_core.modules.writer.models import Tasks
from igny8_core.modules.system.utils import get_prompt_value, get_default_prompt
from igny8_core.ai.ai_core import AICore
from igny8_core.ai.validators import validate_tasks_exist
from igny8_core.ai.prompts import PromptRegistry
from igny8_core.ai.settings import get_model_config
logger = logging.getLogger(__name__)
@@ -72,7 +73,7 @@ class GenerateContentFunction(BaseAIFunction):
return tasks
def build_prompt(self, data: Any, account=None) -> str:
"""Build content generation prompt for a single task"""
"""Build content generation prompt for a single task using registry"""
if isinstance(data, list):
# For now, handle single task (will be called per task)
if not data:
@@ -81,10 +82,7 @@ class GenerateContentFunction(BaseAIFunction):
else:
task = data
# Get prompt template
prompt_template = get_prompt_value(account or task.account, 'content_generation')
if not prompt_template:
prompt_template = get_default_prompt('content_generation')
account = account or task.account
# Build idea data string
idea_data = f"Title: {task.title or 'Untitled'}\n"
@@ -132,10 +130,17 @@ class GenerateContentFunction(BaseAIFunction):
if not keywords_data and task.idea:
keywords_data = task.idea.target_keywords or ''
# Replace placeholders
prompt = prompt_template.replace('[IGNY8_IDEA]', idea_data)
prompt = prompt.replace('[IGNY8_CLUSTER]', cluster_data)
prompt = prompt.replace('[IGNY8_KEYWORDS]', keywords_data)
# Get prompt from registry with context
prompt = PromptRegistry.get_prompt(
function_name='generate_content',
account=account,
task=task,
context={
'IDEA': idea_data,
'CLUSTER': cluster_data,
'KEYWORDS': keywords_data,
}
)
return prompt
@@ -228,11 +233,17 @@ def generate_content_core(task_ids: List[int], account_id: int = None, progress_
# Build prompt for this task
prompt = fn.build_prompt([task], account)
# Get model config from settings
model_config = get_model_config('generate_content')
# Call AI using centralized request handler
ai_core = AICore(account=account)
result = ai_core.run_ai_request(
prompt=prompt,
max_tokens=4000,
model=model_config.get('model'),
max_tokens=model_config.get('max_tokens'),
temperature=model_config.get('temperature'),
response_format=model_config.get('response_format'),
function_name='generate_content'
)

29
backend/igny8_core/ai/functions/generate_ideas.py
View File

@@ -8,10 +8,11 @@ from typing import Dict, List, Any
from django.db import transaction
from igny8_core.ai.base import BaseAIFunction
from igny8_core.modules.planner.models import Clusters, ContentIdeas
from igny8_core.modules.system.utils import get_prompt_value
from igny8_core.ai.ai_core import AICore
from igny8_core.ai.validators import validate_cluster_exists, validate_cluster_limits
from igny8_core.ai.tracker import ConsoleStepTracker
from igny8_core.ai.prompts import PromptRegistry
from igny8_core.ai.settings import get_model_config
logger = logging.getLogger(__name__)
@@ -96,11 +97,9 @@ class GenerateIdeasFunction(BaseAIFunction):
}
def build_prompt(self, data: Dict, account=None) -> str:
"""Build ideas generation prompt"""
"""Build ideas generation prompt using registry"""
cluster_data = data['cluster_data']
# Get prompt template
prompt_template = get_prompt_value(account or data.get('account'), 'ideas')
account = account or data.get('account')
# Format clusters text
clusters_text = '\n'.join([
@@ -114,9 +113,15 @@ class GenerateIdeasFunction(BaseAIFunction):
for c in cluster_data
])
# Replace placeholders
prompt = prompt_template.replace('[IGNY8_CLUSTERS]', clusters_text)
prompt = prompt.replace('[IGNY8_CLUSTER_KEYWORDS]', cluster_keywords_text)
# Get prompt from registry with context
prompt = PromptRegistry.get_prompt(
function_name='generate_ideas',
account=account,
context={
'CLUSTERS': clusters_text,
'CLUSTER_KEYWORDS': cluster_keywords_text,
}
)
return prompt
@@ -231,11 +236,17 @@ def generate_ideas_core(cluster_id: int, account_id: int = None, progress_callba
tracker.prep("Building prompt...")
prompt = fn.build_prompt(data, account)
# Get model config from settings
model_config = get_model_config('generate_ideas')
# Call AI using centralized request handler
ai_core = AICore(account=account)
result = ai_core.run_ai_request(
prompt=prompt,
max_tokens=4000,
model=model_config.get('model'),
max_tokens=model_config.get('max_tokens'),
temperature=model_config.get('temperature'),
response_format=model_config.get('response_format'),
function_name='generate_ideas',
tracker=tracker
)

42
backend/igny8_core/ai/functions/generate_images.py
View File

@@ -7,9 +7,10 @@ from typing import Dict, List, Any
from django.db import transaction
from igny8_core.ai.base import BaseAIFunction
from igny8_core.modules.writer.models import Tasks, Images
from igny8_core.modules.system.utils import get_prompt_value, get_default_prompt
from igny8_core.ai.ai_core import AICore
from igny8_core.ai.validators import validate_tasks_exist
from igny8_core.ai.prompts import PromptRegistry
from igny8_core.ai.settings import get_model_config
logger = logging.getLogger(__name__)
@@ -107,23 +108,29 @@ class GenerateImagesFunction(BaseAIFunction):
# Use AI to extract image prompts
ai_core = AICore(account=account or data.get('account'))
account_obj = account or data.get('account')
# Get prompt template
prompt_template = get_prompt_value(account or data.get('account'), 'image_prompt_extraction')
if not prompt_template:
prompt_template = get_default_prompt('image_prompt_extraction')
# Format prompt
prompt = prompt_template.format(
title=task.title,
content=task.content[:5000], # Limit content length
max_images=max_images
# Get prompt from registry
prompt = PromptRegistry.get_prompt(
function_name='extract_image_prompts',
account=account_obj,
context={
'title': task.title,
'content': task.content[:5000], # Limit content length
'max_images': max_images
}
)
# Get model config
model_config = get_model_config('extract_image_prompts')
# Call AI to extract prompts using centralized request handler
result = ai_core.run_ai_request(
prompt=prompt,
max_tokens=1000,
model=model_config.get('model'),
max_tokens=model_config.get('max_tokens'),
temperature=model_config.get('temperature'),
response_format=model_config.get('response_format'),
function_name='extract_image_prompts'
)
@@ -214,14 +221,9 @@ def generate_images_core(task_ids: List[int], account_id: int = None, progress_c
data = fn.prepare(payload, account)
tasks = data['tasks']
# Get prompts
image_prompt_template = get_prompt_value(account, 'image_prompt_template')
if not image_prompt_template:
image_prompt_template = get_default_prompt('image_prompt_template')
negative_prompt = get_prompt_value(account, 'negative_prompt')
if not negative_prompt:
negative_prompt = get_default_prompt('negative_prompt')
# Get prompts from registry
image_prompt_template = PromptRegistry.get_image_prompt_template(account)
negative_prompt = PromptRegistry.get_negative_prompt(account)
ai_core = AICore(account=account)
images_created = 0

270
backend/igny8_core/ai/prompts.py Normal file
View File

@@ -0,0 +1,270 @@
"""
Prompt Registry - Centralized prompt management with override hierarchy
Supports: task-level overrides → DB prompts → default fallbacks
"""
import logging
from typing import Dict, Any, Optional
from django.db import models
logger = logging.getLogger(__name__)
class PromptRegistry:
"""
Centralized prompt registry with hierarchical resolution:
1. Task-level prompt_override (if exists)
2. DB prompt for (account, function)
3. Default fallback from registry
"""
# Default prompts stored in registry
DEFAULT_PROMPTS = {
'clustering': """Analyze the following keywords and group them into topic clusters.
Each cluster should include:
- "name": A clear, descriptive topic name
- "description": A brief explanation of what the cluster covers
- "keywords": A list of related keywords that belong to this cluster
Format the output as a JSON object with a "clusters" array.
IMPORTANT: In the "keywords" array, you MUST use the EXACT keyword strings from the input list below. Do not modify, paraphrase, or create variations of the keywords. Only use the exact keywords as they appear in the input list.
Clustering rules:
- Group keywords based on strong semantic or topical relationships (intent, use-case, function, audience, etc.)
- Clusters should reflect how people actually search — problem ➝ solution, general ➝ specific, product ➝ benefit, etc.
- Avoid grouping keywords just because they share similar words — focus on meaning
- Include 3–10 keywords per cluster where appropriate
- Skip unrelated or outlier keywords that don't fit a clear theme
- CRITICAL: Only return keywords that exactly match the input keywords (case-insensitive matching is acceptable)
Keywords to process:
[IGNY8_KEYWORDS]""",
'ideas': """Generate SEO-optimized, high-quality content ideas and detailed outlines for each of the following keyword clusters.
Clusters to analyze:
[IGNY8_CLUSTERS]
Keywords in each cluster:
[IGNY8_CLUSTER_KEYWORDS]
Return your response as JSON with an "ideas" array.
For each cluster, generate 1-3 content ideas.
Each idea must include:
- "title": compelling blog/article title that naturally includes a primary keyword
- "description": detailed content outline with H2/H3 structure (as plain text or structured JSON)
- "content_type": the type of content (blog_post, article, guide, tutorial)
- "content_structure": the editorial structure (cluster_hub, landing_page, pillar_page, supporting_page)
- "estimated_word_count": estimated total word count (1500-2200 words)
- "target_keywords": comma-separated list of keywords that will be covered (or "covered_keywords")
- "cluster_name": name of the cluster this idea belongs to (REQUIRED)
- "cluster_id": ID of the cluster this idea belongs to (REQUIRED - use the exact cluster ID from the input)
IMPORTANT: You MUST include the exact "cluster_id" from the cluster data provided. Match the cluster name to find the correct cluster_id.
Return only valid JSON with an "ideas" array.""",
'content_generation': """You are an editorial content strategist. Generate a complete blog post/article based on the provided content idea.
CONTENT IDEA DETAILS:
[IGNY8_IDEA]
KEYWORD CLUSTER:
[IGNY8_CLUSTER]
ASSOCIATED KEYWORDS:
[IGNY8_KEYWORDS]
Generate well-structured, SEO-optimized content with:
- Engaging introduction
- 5-8 H2 sections with H3 subsections
- Natural keyword integration
- 1500-2000 words total
- Proper HTML formatting (h2, h3, p, ul, ol, table tags)
Return the content as plain text with HTML tags.""",
'image_prompt_extraction': """Extract image prompts from the following article content.
ARTICLE TITLE: {title}
ARTICLE CONTENT:
{content}
Extract image prompts for:
1. Featured Image: One main image that represents the article topic
2. In-Article Images: Up to {max_images} images that would be useful within the article content
Return a JSON object with this structure:
{{
"featured_prompt": "Detailed description of the featured image",
"in_article_prompts": [
"Description of first in-article image",
"Description of second in-article image",
...
]
}}
Make sure each prompt is detailed enough for image generation, describing the visual elements, style, mood, and composition.""",
'image_prompt_template': 'Create a high-quality {image_type} image to use as a featured photo for a blog post titled "{post_title}". The image should visually represent the theme, mood, and subject implied by the image prompt: {image_prompt}. Focus on a realistic, well-composed scene that naturally communicates the topic without text or logos. Use balanced lighting, pleasing composition, and photographic detail suitable for lifestyle or editorial web content. Avoid adding any visible or readable text, brand names, or illustrative effects. **And make sure image is not blurry.**',
'negative_prompt': 'text, watermark, logo, overlay, title, caption, writing on walls, writing on objects, UI, infographic elements, post title',
}
# Mapping from function names to prompt types
FUNCTION_TO_PROMPT_TYPE = {
'auto_cluster': 'clustering',
'generate_ideas': 'ideas',
'generate_content': 'content_generation',
'generate_images': 'image_prompt_extraction',
'extract_image_prompts': 'image_prompt_extraction',
}
@classmethod
def get_prompt(
cls,
function_name: str,
account: Optional[Any] = None,
task: Optional[Any] = None,
context: Optional[Dict[str, Any]] = None
) -> str:
"""
Get prompt for a function with hierarchical resolution.
Priority:
1. task.prompt_override (if task provided and has override)
2. DB prompt for (account, function)
3. Default fallback from registry
Args:
function_name: AI function name (e.g., 'auto_cluster', 'generate_ideas')
account: Account object (optional)
task: Task object with optional prompt_override (optional)
context: Additional context for prompt rendering (optional)
Returns:
Prompt string ready for formatting
"""
# Step 1: Check task-level override
if task and hasattr(task, 'prompt_override') and task.prompt_override:
logger.info(f"Using task-level prompt override for {function_name}")
prompt = task.prompt_override
return cls._render_prompt(prompt, context or {})
# Step 2: Get prompt type
prompt_type = cls.FUNCTION_TO_PROMPT_TYPE.get(function_name, function_name)
# Step 3: Try DB prompt
if account:
try:
from igny8_core.modules.system.models import AIPrompt
db_prompt = AIPrompt.objects.get(
account=account,
prompt_type=prompt_type,
is_active=True
)
logger.info(f"Using DB prompt for {function_name} (account {account.id})")
prompt = db_prompt.prompt_value
return cls._render_prompt(prompt, context or {})
except Exception as e:
logger.debug(f"No DB prompt found for {function_name}: {e}")
# Step 4: Use default fallback
prompt = cls.DEFAULT_PROMPTS.get(prompt_type, '')
if not prompt:
logger.warning(f"No default prompt found for {prompt_type}, using empty string")
return cls._render_prompt(prompt, context or {})
@classmethod
def _render_prompt(cls, prompt_template: str, context: Dict[str, Any]) -> str:
"""
Render prompt template with context variables.
Supports both .format() style ({variable}) and placeholder replacement ([IGNY8_*]).
Args:
prompt_template: Prompt template string
context: Context variables for rendering
Returns:
Rendered prompt string
"""
if not context:
return prompt_template
# Normalize context keys - convert UPPER to lowercase for .format()
normalized_context = {}
for key, value in context.items():
# Try both original key and lowercase version
normalized_context[key] = value
normalized_context[key.lower()] = value
# Try .format() style first (for {variable} placeholders)
try:
return prompt_template.format(**normalized_context)
except (KeyError, ValueError):
# Fall back to simple string replacement for [IGNY8_*] placeholders
rendered = prompt_template
for key, value in context.items():
placeholder = f'[IGNY8_{key.upper()}]'
if placeholder in rendered:
rendered = rendered.replace(placeholder, str(value))
return rendered
@classmethod
def get_image_prompt_template(cls, account: Optional[Any] = None) -> str:
"""
Get image prompt template.
Returns template string (not rendered) - caller should format with .format()
"""
prompt_type = 'image_prompt_template'
# Try DB prompt
if account:
try:
from igny8_core.modules.system.models import AIPrompt
db_prompt = AIPrompt.objects.get(
account=account,
prompt_type=prompt_type,
is_active=True
)
return db_prompt.prompt_value
except Exception:
pass
# Use default
return cls.DEFAULT_PROMPTS.get(prompt_type, '')
@classmethod
def get_negative_prompt(cls, account: Optional[Any] = None) -> str:
"""
Get negative prompt.
Returns template string (not rendered).
"""
prompt_type = 'negative_prompt'
# Try DB prompt
if account:
try:
from igny8_core.modules.system.models import AIPrompt
db_prompt = AIPrompt.objects.get(
account=account,
prompt_type=prompt_type,
is_active=True
)
return db_prompt.prompt_value
except Exception:
pass
# Use default
return cls.DEFAULT_PROMPTS.get(prompt_type, '')
# Convenience function for backward compatibility
def get_prompt(function_name: str, account=None, task=None, context=None) -> str:
"""Get prompt using registry"""
return PromptRegistry.get_prompt(function_name, account=account, task=task, context=context)

92
backend/igny8_core/ai/settings.py Normal file
View File

@@ -0,0 +1,92 @@
"""
AI Settings - Centralized model configurations and limits
"""
from typing import Dict, Any
# Model configurations for each AI function
MODEL_CONFIG = {
"auto_cluster": {
"model": "gpt-4o-mini",
"max_tokens": 3000,
"temperature": 0.7,
"response_format": {"type": "json_object"}, # Auto-enabled for JSON mode models
},
"generate_ideas": {
"model": "gpt-4.1",
"max_tokens": 4000,
"temperature": 0.7,
"response_format": {"type": "json_object"},
},
"generate_content": {
"model": "gpt-4.1",
"max_tokens": 8000,
"temperature": 0.7,
"response_format": None, # Text output
},
"generate_images": {
"model": "dall-e-3",
"size": "1024x1024",
"provider": "openai",
},
"extract_image_prompts": {
"model": "gpt-4o-mini",
"max_tokens": 1000,
"temperature": 0.7,
"response_format": {"type": "json_object"},
},
}
# Function name aliases (for backward compatibility)
FUNCTION_ALIASES = {
"cluster_keywords": "auto_cluster",
"auto_cluster_keywords": "auto_cluster",
"auto_generate_ideas": "generate_ideas",
"auto_generate_content": "generate_content",
"auto_generate_images": "generate_images",
}
def get_model_config(function_name: str) -> Dict[str, Any]:
"""
Get model configuration for an AI function.
Args:
function_name: AI function name (e.g., 'auto_cluster', 'generate_ideas')
Returns:
Dict with model, max_tokens, temperature, etc.
"""
# Check aliases first
actual_name = FUNCTION_ALIASES.get(function_name, function_name)
# Get config or return defaults
config = MODEL_CONFIG.get(actual_name, {})
# Merge with defaults
default_config = {
"model": "gpt-4.1",
"max_tokens": 4000,
"temperature": 0.7,
"response_format": None,
}
return {**default_config, **config}
def get_model(function_name: str) -> str:
"""Get model name for function"""
config = get_model_config(function_name)
return config.get("model", "gpt-4.1")
def get_max_tokens(function_name: str) -> int:
"""Get max tokens for function"""
config = get_model_config(function_name)
return config.get("max_tokens", 4000)
def get_temperature(function_name: str) -> float:
"""Get temperature for function"""
config = get_model_config(function_name)
return config.get("temperature", 0.7)