Base code
This commit is contained in:
Kunthawat Greethong
389
backend/services/strategy_copilot_service.py
Normal file
389
backend/services/strategy_copilot_service.py
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from typing import Dict, Any, List, Optional
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from sqlalchemy.orm import Session
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from loguru import logger
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from services.onboarding.data_service import OnboardingDataService
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from services.user_data_service import UserDataService
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from services.llm_providers.gemini_provider import gemini_text_response, gemini_structured_json_response
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class StrategyCopilotService:
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"""Service for CopilotKit strategy assistance using Gemini."""
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def __init__(self, db: Session):
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self.db = db
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self.onboarding_service = OnboardingDataService()
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self.user_data_service = UserDataService(db)
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async def generate_category_data(
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self,
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category: str,
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user_description: str,
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current_form_data: Dict[str, Any]
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) -> Dict[str, Any]:
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"""Generate data for a specific category."""
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try:
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# Get user onboarding data
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user_id = 1 # TODO: Get from auth context
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onboarding_data = self.onboarding_service.get_personalized_ai_inputs(user_id)
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# Build prompt for category generation
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prompt = self._build_category_generation_prompt(
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category, user_description, current_form_data, onboarding_data
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)
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# Generate response using Gemini
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response = gemini_text_response(
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prompt=prompt,
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temperature=0.3,
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top_p=0.9,
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n=1,
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max_tokens=2048,
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system_prompt="You are ALwrity's Strategy Assistant. Generate appropriate values for strategy fields."
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)
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# Parse and validate response
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generated_data = self._parse_category_response(response, category)
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return generated_data
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except Exception as e:
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logger.error(f"Error generating category data: {str(e)}")
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raise
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async def validate_field(self, field_id: str, value: Any) -> Dict[str, Any]:
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"""Validate a specific strategy field."""
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try:
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# Get field definition
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field_definition = self._get_field_definition(field_id)
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# Build validation prompt
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prompt = self._build_validation_prompt(field_definition, value)
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# Generate validation response using Gemini
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response = gemini_text_response(
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prompt=prompt,
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temperature=0.2,
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top_p=0.9,
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n=1,
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max_tokens=1024,
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system_prompt="You are ALwrity's Strategy Assistant. Validate field values and provide suggestions."
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)
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# Parse validation result
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validation_result = self._parse_validation_response(response)
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return validation_result
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except Exception as e:
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logger.error(f"Error validating field {field_id}: {str(e)}")
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raise
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async def analyze_strategy(self, form_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Analyze complete strategy for completeness and coherence."""
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try:
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# Get user data for context
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user_id = 1 # TODO: Get from auth context
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onboarding_data = self.onboarding_service.get_personalized_ai_inputs(user_id)
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# Build analysis prompt
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prompt = self._build_analysis_prompt(form_data, onboarding_data)
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# Generate analysis using Gemini
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response = gemini_text_response(
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prompt=prompt,
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temperature=0.3,
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top_p=0.9,
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n=1,
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max_tokens=2048,
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system_prompt="You are ALwrity's Strategy Assistant. Analyze strategies for completeness and coherence."
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)
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# Parse analysis result
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analysis_result = self._parse_analysis_response(response)
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return analysis_result
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except Exception as e:
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logger.error(f"Error analyzing strategy: {str(e)}")
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raise
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async def generate_field_suggestions(
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self,
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field_id: str,
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current_form_data: Dict[str, Any]
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) -> Dict[str, Any]:
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"""Generate suggestions for a specific field."""
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try:
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# Get field definition
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field_definition = self._get_field_definition(field_id)
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# Get user data
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user_id = 1 # TODO: Get from auth context
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onboarding_data = self.onboarding_service.get_personalized_ai_inputs(user_id)
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# Build suggestions prompt
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prompt = self._build_suggestions_prompt(
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field_definition, current_form_data, onboarding_data
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)
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# Generate suggestions using Gemini
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response = gemini_text_response(
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prompt=prompt,
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temperature=0.4,
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top_p=0.9,
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n=1,
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max_tokens=1024,
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system_prompt="You are ALwrity's Strategy Assistant. Generate helpful suggestions for strategy fields."
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)
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# Parse suggestions
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suggestions = self._parse_suggestions_response(response)
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return suggestions
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except Exception as e:
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logger.error(f"Error generating suggestions for {field_id}: {str(e)}")
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raise
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def _build_category_generation_prompt(
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self,
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category: str,
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user_description: str,
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current_form_data: Dict[str, Any],
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onboarding_data: Dict[str, Any]
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) -> str:
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"""Build prompt for category data generation."""
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return f"""
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You are ALwrity's Strategy Assistant. Generate data for the {category} category based on the user's description.
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User Description: {user_description}
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Current Form Data: {current_form_data}
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Onboarding Data: {onboarding_data}
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Category Fields: {self._get_category_fields(category)}
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Generate appropriate values for all fields in the {category} category. Return only valid JSON with field IDs as keys.
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Example response format:
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{{
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"field_id": "value",
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"another_field": "value"
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}}
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"""
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def _build_validation_prompt(self, field_definition: Dict[str, Any], value: Any) -> str:
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"""Build prompt for field validation."""
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return f"""
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Validate the following field value:
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Field: {field_definition['label']}
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Description: {field_definition['description']}
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Required: {field_definition['required']}
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Type: {field_definition['type']}
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Value: {value}
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Return JSON with: {{"isValid": boolean, "suggestion": string, "confidence": number}}
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Example response:
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{{
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"isValid": true,
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"suggestion": "This looks good!",
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"confidence": 0.95
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}}
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"""
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def _build_analysis_prompt(
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self,
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form_data: Dict[str, Any],
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onboarding_data: Dict[str, Any]
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) -> str:
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"""Build prompt for strategy analysis."""
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return f"""
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Analyze the following content strategy for completeness, coherence, and alignment:
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Form Data: {form_data}
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Onboarding Data: {onboarding_data}
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Return JSON with: {{
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"completeness": number,
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"coherence": number,
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"alignment": number,
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"suggestions": [string],
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"missingFields": [string],
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"improvements": [string]
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}}
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Example response:
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{{
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"completeness": 85,
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"coherence": 90,
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"alignment": 88,
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"suggestions": ["Consider adding more specific metrics"],
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"missingFields": ["content_budget"],
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"improvements": ["Add timeline details"]
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}}
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"""
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def _build_suggestions_prompt(
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self,
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field_definition: Dict[str, Any],
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current_form_data: Dict[str, Any],
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onboarding_data: Dict[str, Any]
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) -> str:
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"""Build prompt for field suggestions."""
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return f"""
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Generate suggestions for the following field:
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Field: {field_definition['label']}
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Description: {field_definition['description']}
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Required: {field_definition['required']}
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Type: {field_definition['type']}
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Current Form Data: {current_form_data}
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Onboarding Data: {onboarding_data}
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Return JSON with: {{
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"suggestions": [string],
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"reasoning": string,
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"confidence": number
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}}
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Example response:
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{{
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"suggestions": ["Focus on measurable outcomes", "Align with business goals"],
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"reasoning": "Based on your business context, measurable outcomes will be most effective",
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"confidence": 0.92
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}}
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"""
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def _get_field_definition(self, field_id: str) -> Dict[str, Any]:
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"""Get field definition from STRATEGIC_INPUT_FIELDS."""
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# This would be imported from the frontend field definitions
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# For now, return a basic structure
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return {
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"id": field_id,
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"label": field_id.replace("_", " ").title(),
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"description": f"Description for {field_id}",
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"required": True,
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"type": "text"
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}
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def _get_category_fields(self, category: str) -> List[str]:
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"""Get fields for a specific category."""
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# This would be imported from the frontend field definitions
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category_fields = {
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"business_context": [
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"business_objectives", "target_metrics", "content_budget", "team_size",
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"implementation_timeline", "market_share", "competitive_position", "performance_metrics"
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],
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"audience_intelligence": [
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"content_preferences", "consumption_patterns", "audience_pain_points",
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"buying_journey", "seasonal_trends", "engagement_metrics"
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],
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"competitive_intelligence": [
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"top_competitors", "competitor_content_strategies", "market_gaps",
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"industry_trends", "emerging_trends"
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],
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"content_strategy": [
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"preferred_formats", "content_mix", "content_frequency", "optimal_timing",
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"quality_metrics", "editorial_guidelines", "brand_voice"
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],
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"performance_analytics": [
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"traffic_sources", "conversion_rates", "content_roi_targets", "ab_testing_capabilities"
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]
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}
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return category_fields.get(category, [])
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def _parse_category_response(self, response: str, category: str) -> Dict[str, Any]:
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"""Parse LLM response for category data."""
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try:
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import json
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# Clean up the response to extract JSON
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response = response.strip()
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if response.startswith("```json"):
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response = response[7:]
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if response.endswith("```"):
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response = response[:-3]
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response = response.strip()
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parsed_data = json.loads(response)
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# Validate that we have actual data
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if not isinstance(parsed_data, dict) or len(parsed_data) == 0:
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raise Exception("Invalid or empty response data")
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return parsed_data
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except Exception as e:
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logger.error(f"Error parsing category response: {str(e)}")
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raise Exception(f"Failed to parse category response: {str(e)}")
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def _parse_validation_response(self, response: str) -> Dict[str, Any]:
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"""Parse LLM response for validation."""
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try:
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import json
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# Clean up the response to extract JSON
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response = response.strip()
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if response.startswith("```json"):
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response = response[7:]
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if response.endswith("```"):
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response = response[:-3]
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response = response.strip()
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parsed_data = json.loads(response)
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# Validate required fields
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if not isinstance(parsed_data, dict) or 'isValid' not in parsed_data:
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raise Exception("Invalid validation response format")
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return parsed_data
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except Exception as e:
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logger.error(f"Error parsing validation response: {str(e)}")
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raise Exception(f"Failed to parse validation response: {str(e)}")
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def _parse_analysis_response(self, response: str) -> Dict[str, Any]:
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"""Parse LLM response for analysis."""
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try:
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import json
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# Clean up the response to extract JSON
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response = response.strip()
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if response.startswith("```json"):
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response = response[7:]
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if response.endswith("```"):
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response = response[:-3]
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response = response.strip()
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parsed_data = json.loads(response)
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# Validate required fields
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required_fields = ['completeness', 'coherence', 'alignment']
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if not isinstance(parsed_data, dict) or not all(field in parsed_data for field in required_fields):
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raise Exception("Invalid analysis response format")
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return parsed_data
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except Exception as e:
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logger.error(f"Error parsing analysis response: {str(e)}")
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raise Exception(f"Failed to parse analysis response: {str(e)}")
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def _parse_suggestions_response(self, response: str) -> Dict[str, Any]:
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"""Parse LLM response for suggestions."""
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try:
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import json
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# Clean up the response to extract JSON
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response = response.strip()
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if response.startswith("```json"):
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response = response[7:]
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if response.endswith("```"):
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response = response[:-3]
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response = response.strip()
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parsed_data = json.loads(response)
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# Validate required fields
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if not isinstance(parsed_data, dict) or 'suggestions' not in parsed_data:
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raise Exception("Invalid suggestions response format")
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return parsed_data
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except Exception as e:
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logger.error(f"Error parsing suggestions response: {str(e)}")
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raise Exception(f"Failed to parse suggestions response: {str(e)}")
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