Alwrity version 0.5.4
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ajaysi
@@ -65,7 +65,7 @@ class AIServiceManager:
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'temperature': 0.3, # more deterministic for schema-constrained JSON
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'top_p': 0.9,
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'top_k': 40,
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'max_tokens': 2048, # increased from 1024 for larger structured outputs
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'max_tokens': 8192, # increased from 4096 to prevent JSON truncation
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'enable_caching': True,
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'cache_duration_minutes': 60,
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'performance_monitoring': True,
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@@ -439,26 +439,27 @@ Format as structured JSON with detailed assessment and optimization guidance.
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async def _execute_ai_call(self, service_type: AIServiceType, prompt: str, schema: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Execute AI call with performance monitoring.
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Execute AI call with comprehensive error handling and monitoring.
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Args:
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service_type: Type of AI service
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prompt: AI prompt
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schema: JSON schema for response
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service_type: Type of AI service being called
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prompt: The prompt to send to AI
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schema: Expected response schema
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Returns:
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AI response
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Dictionary with AI response or error information
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"""
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start_time = datetime.utcnow()
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success = False
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error_message = None
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result = {}
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try:
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logger.info(f"🤖 Executing AI call for {service_type.value}")
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logger.debug(f"Using gemini provider extended={_GEMINI_EXTENDED}")
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# Execute AI call with timeout (run sync provider in a thread)
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# Emit educational content for frontend
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await self._emit_educational_content(service_type, "start")
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# Execute the AI call
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response = await asyncio.wait_for(
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asyncio.to_thread(
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self._call_gemini_structured,
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@@ -468,87 +469,56 @@ Format as structured JSON with detailed assessment and optimization guidance.
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timeout=self.config['timeout_seconds']
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)
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# Parse response
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if isinstance(response, dict):
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result = response
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elif isinstance(response, str):
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try:
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result = json.loads(response)
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except json.JSONDecodeError:
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# Return raw string if not valid JSON
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result = {"raw_response": response}
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else:
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# Fallback to string conversion
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result = {"raw_response": str(response)}
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# Treat provider-reported errors or empty results as failures
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if isinstance(result, dict) and ('error' in result or not result):
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error_message = result.get('error', 'Empty AI response') if isinstance(result, dict) else 'Empty AI response'
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# record metrics and raise
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response_time = (datetime.utcnow() - start_time).total_seconds()
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metrics = AIServiceMetrics(
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service_type=service_type,
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response_time=response_time,
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success=False,
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error_message=error_message
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)
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self.metrics.append(metrics)
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# Check for errors in response
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if response.get("error"):
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error_message = response["error"]
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logger.error(f"AI call error for {service_type.value}: {error_message}")
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await self._emit_educational_content(service_type, "error", error_message)
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raise Exception(error_message)
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success = True
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logger.info(f"✅ AI call for {service_type.value} completed successfully")
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except asyncio.TimeoutError:
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error_message = f"AI call timeout for {service_type.value}"
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logger.error(error_message)
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# record metrics and raise
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response_time = (datetime.utcnow() - start_time).total_seconds()
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metrics = AIServiceMetrics(
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service_type=service_type,
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response_time=response_time,
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success=False,
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error_message=error_message
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)
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self.metrics.append(metrics)
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raise Exception(error_message)
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except json.JSONDecodeError as e:
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error_message = f"JSON decode error for {service_type.value}: {str(e)}"
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logger.error(error_message)
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response_time = (datetime.utcnow() - start_time).total_seconds()
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metrics = AIServiceMetrics(
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service_type=service_type,
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response_time=response_time,
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success=False,
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error_message=error_message
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)
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self.metrics.append(metrics)
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# Don't raise JSON decode errors as fatal - let the calling code handle them
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# The Gemini provider should have already attempted to repair malformed JSON
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result = {"error": error_message, "raw_response": str(e)}
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success = False
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# Validate response structure
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if not response or not isinstance(response, dict):
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error_message = "Invalid response structure from AI service"
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logger.error(f"AI call error for {service_type.value}: {error_message}")
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await self._emit_educational_content(service_type, "error", error_message)
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raise Exception(error_message)
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success = True
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processing_time = (datetime.utcnow() - start_time).total_seconds()
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# Emit success educational content
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await self._emit_educational_content(service_type, "success", processing_time=processing_time)
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# Record metrics
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self._record_metrics(service_type, processing_time, success, error_message)
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logger.info(f"✅ AI call for {service_type.value} completed successfully in {processing_time:.2f}s")
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return {
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"data": response,
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"processing_time": processing_time,
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"service_type": service_type.value,
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"success": True
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}
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except Exception as e:
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error_message = f"AI call error for {service_type.value}: {str(e)}"
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logger.error(error_message)
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response_time = (datetime.utcnow() - start_time).total_seconds()
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metrics = AIServiceMetrics(
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service_type=service_type,
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response_time=response_time,
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success=False,
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error_message=error_message
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)
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self.metrics.append(metrics)
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raise
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# Calculate response time and record metrics for successful calls
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response_time = (datetime.utcnow() - start_time).total_seconds()
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metrics = AIServiceMetrics(
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service_type=service_type,
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response_time=response_time,
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success=success,
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error_message=None
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)
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self.metrics.append(metrics)
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return result
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processing_time = (datetime.utcnow() - start_time).total_seconds()
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error_message = str(e)
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# Emit error educational content
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await self._emit_educational_content(service_type, "error", error_message)
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# Record metrics
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self._record_metrics(service_type, processing_time, success, error_message)
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logger.error(f"❌ AI call error for {service_type.value}: {error_message}")
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return {
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"error": error_message,
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"processing_time": processing_time,
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"service_type": service_type.value,
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"success": False
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}
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def _call_gemini_structured(self, prompt: str, schema: Dict[str, Any]):
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"""Call gemini structured JSON with flexible signature support.
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@@ -907,4 +877,161 @@ Format as structured JSON with detailed assessment and optimization guidance.
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'status': 'unhealthy',
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'error': str(e),
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'timestamp': datetime.utcnow().isoformat()
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}
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}
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async def _emit_educational_content(self, service_type: AIServiceType, status: str, error_message: str = None, processing_time: float = None):
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"""
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Emit educational content for frontend during AI calls.
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Args:
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service_type: Type of AI service being called
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status: Current status (start, success, error)
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error_message: Error message if applicable
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processing_time: Processing time if applicable
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"""
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try:
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educational_content = self._get_educational_content(service_type, status, error_message, processing_time)
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# Emit to any connected SSE clients
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# This would integrate with your SSE system
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logger.info(f"📚 Emitting educational content for {service_type.value}: {status}")
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# For now, just log the educational content
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# In a real implementation, this would be sent to connected SSE clients
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logger.debug(f"Educational content: {educational_content}")
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except Exception as e:
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logger.error(f"Error emitting educational content: {e}")
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def _get_educational_content(self, service_type: AIServiceType, status: str, error_message: str = None, processing_time: float = None) -> Dict[str, Any]:
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"""
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Generate educational content based on service type and status.
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Args:
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service_type: Type of AI service being called
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status: Current status (start, success, error)
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error_message: Error message if applicable
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processing_time: Processing time if applicable
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Returns:
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Dictionary with educational content
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"""
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base_content = {
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"service_type": service_type.value,
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"status": status,
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"timestamp": datetime.utcnow().isoformat()
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}
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if status == "start":
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content_map = {
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AIServiceType.STRATEGIC_INTELLIGENCE: {
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"title": "🧠 Strategic Intelligence Analysis",
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"description": "AI is analyzing your market position and identifying strategic opportunities.",
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"details": [
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"🎯 Market positioning analysis",
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"💡 Opportunity identification",
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"📈 Growth potential assessment",
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"🎪 Competitive advantage mapping"
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],
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"insight": "Strategic insights help you understand where you stand in the market and how to differentiate.",
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"ai_prompt_preview": "Analyzing market position, identifying strategic opportunities, assessing growth potential, and mapping competitive advantages...",
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"estimated_time": "15-20 seconds"
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},
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AIServiceType.MARKET_POSITION_ANALYSIS: {
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"title": "🔍 Competitive Intelligence Analysis",
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"description": "AI is analyzing your competitors to identify gaps and opportunities.",
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"details": [
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"🏢 Competitor content strategies",
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"📊 Market gap analysis",
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"🎯 Differentiation opportunities",
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"📈 Industry trend analysis"
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],
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"insight": "Understanding your competitors helps you find unique angles and underserved market segments.",
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"ai_prompt_preview": "Analyzing competitor content strategies, identifying market gaps, finding differentiation opportunities, and assessing industry trends...",
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"estimated_time": "20-25 seconds"
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},
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AIServiceType.PERFORMANCE_PREDICTION: {
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"title": "📊 Performance Forecasting",
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"description": "AI is predicting content performance and ROI based on industry data.",
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"details": [
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"📈 Traffic growth projections",
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"💰 ROI predictions",
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"🎯 Conversion rate estimates",
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"📊 Engagement metrics forecasting"
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],
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"insight": "Performance predictions help you set realistic expectations and optimize resource allocation.",
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"ai_prompt_preview": "Analyzing industry benchmarks, predicting traffic growth, estimating ROI, forecasting conversion rates, and projecting engagement metrics...",
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"estimated_time": "15-20 seconds"
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},
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AIServiceType.CONTENT_SCHEDULE_GENERATION: {
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"title": "📅 Content Calendar Creation",
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"description": "AI is building a comprehensive content schedule optimized for your audience.",
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"details": [
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"📝 Content piece generation",
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"📅 Optimal publishing schedule",
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"🎯 Audience engagement timing",
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"🔄 Content repurposing strategy"
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],
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"insight": "A well-planned content calendar ensures consistent engagement and maximizes content ROI.",
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"ai_prompt_preview": "Generating content pieces, optimizing publishing schedule, determining audience engagement timing, and planning content repurposing...",
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"estimated_time": "25-30 seconds"
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}
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}
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content = content_map.get(service_type, {
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"title": "🤖 AI Analysis in Progress",
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"description": "AI is processing your data and generating insights.",
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"details": ["Processing data", "Analyzing patterns", "Generating insights"],
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"insight": "AI analysis provides data-driven insights to improve your strategy.",
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"estimated_time": "15-20 seconds"
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})
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return {**base_content, **content}
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elif status == "success":
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return {
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**base_content,
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"title": f"✅ {service_type.value.replace('_', ' ').title()} Complete",
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"description": f"Successfully completed {service_type.value.replace('_', ' ')} analysis.",
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"achievement": f"Completed in {processing_time:.1f} seconds",
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"next_step": "Moving to next analysis component..."
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}
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elif status == "error":
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return {
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**base_content,
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"title": f"⚠️ {service_type.value.replace('_', ' ').title()} Issue",
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"description": f"We encountered an issue with {service_type.value.replace('_', ' ')} analysis.",
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"error": error_message,
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"fallback": "Will use industry best practices for this component."
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}
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return base_content
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def _record_metrics(self, service_type: AIServiceType, processing_time: float, success: bool, error_message: str = None):
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"""
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Record metrics for AI service calls.
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Args:
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service_type: Type of AI service being called
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processing_time: Time taken for the call
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success: Whether the call was successful
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error_message: Error message if applicable
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"""
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try:
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metrics = AIServiceMetrics(
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service_type=service_type,
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response_time=processing_time,
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success=success,
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error_message=error_message
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)
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self.metrics.append(metrics)
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# Log metrics for monitoring
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if success:
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logger.debug(f"📊 AI metrics recorded for {service_type.value}: {processing_time:.2f}s")
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else:
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logger.warning(f"📊 AI metrics recorded for {service_type.value}: {processing_time:.2f}s (failed)")
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except Exception as e:
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logger.error(f"Error recording AI metrics: {e}")
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