Taskmaster-AI Automation Framework Cheat Sheet
generieren
Überblick
Taskmaster-AI ist ein fortschrittliches KI-gestütztes Task-Automatisierungs- und Workflow-Management-Framework, das künstliche Intelligenz mit Roboterprozessautomatisierung (RPA)-Fähigkeiten verbindet. Es ermöglicht eine intelligente Automatisierung komplexer Geschäftsprozesse, Entscheidungsprozesse und repetitive Aufgaben durch natürliche Sprachverarbeitung, maschinelles Lernen und adaptive Ausführungsstrategien. Das Framework unterstützt multimodale Interaktionen, dynamische Workflow-Generation und intelligente Fehlerbehandlung.
ZEIT *Authorisierung erforderlich: Stellen Sie sicher, dass Sie eine ordnungsgemäße Autorisierung vor der Implementierung von Automatisierungs-Workflows haben, die mit Geschäftssystemen, Datenbanken oder externen Services interagieren.
Installation und Inbetriebnahme
Python Installation
```bash
Install Taskmaster-AI framework
pip install taskmaster-ai
Install with additional dependencies
pip install taskmaster-ai[full]
Install development version
pip install git+https://github.com/taskmaster-ai/taskmaster-ai.git
Install with specific AI providers
pip install taskmaster-ai[openai,anthropic,azure]
Verify installation
taskmaster-ai --version ```_
Docker Installation
```bash
Pull official Docker image
docker pull taskmasterai/taskmaster-ai:latest
Run with environment variables
docker run -d \ --name taskmaster-ai \ -e OPENAI_API_KEY=your_openai_key \ -e ANTHROPIC_API_KEY=your_anthropic_key \ -v $(pwd)/workflows:/app/workflows \ -p 8080:8080 \ taskmasterai/taskmaster-ai:latest
Run with custom configuration
docker run -d \ --name taskmaster-ai \ -v $(pwd)/config:/app/config \ -v $(pwd)/workflows:/app/workflows \ -v $(pwd)/data:/app/data \ taskmasterai/taskmaster-ai:latest ```_
Konfiguration Setup
```yaml
config/taskmaster.yaml
api_settings: openai: api_key: "$\\{OPENAI_API_KEY\\}" model: "gpt-4" max_tokens: 2000 temperature: 0.1
anthropic: api_key: "$\\{ANTHROPIC_API_KEY\\}" model: "claude-3-sonnet-20240229" max_tokens: 2000
azure: api_key: "$\\{AZURE_OPENAI_KEY\\}" endpoint: "$\\{AZURE_OPENAI_ENDPOINT\\}" deployment: "gpt-4"
automation_settings: max_retries: 3 timeout: 300 parallel_tasks: 5 error_handling: "graceful" logging_level: "INFO"
workflow_settings: auto_save: true backup_enabled: true version_control: true execution_history: 100
security_settings: encryption_enabled: true audit_logging: true access_control: "rbac" secure_storage: true ```_
Kernrahmenkomponenten
Aufgabendefinition und -management
```python
!/usr/bin/env python3
taskmaster_core.py
from taskmaster_ai import TaskmasterAI, Task, Workflow, AIAgent from taskmaster_ai.decorators import task, workflow, retry, timeout from taskmaster_ai.types import TaskResult, WorkflowContext from typing import Dict, List, Any, Optional import asyncio from datetime import datetime, timedelta
class TaskmasterCore: def init(self, config_path: str = "config/taskmaster.yaml"): self.taskmaster = TaskmasterAI(config_path) self.ai_agent = AIAgent(self.taskmaster.config) self.active_workflows = \\{\\} self.task_registry = \\{\\}
@task(name="data_extraction", category="data_processing")
@retry(max_attempts=3, backoff_factor=2)
@timeout(seconds=120)
async def extract_data_from_source(self, source: str,
extraction_rules: Dict) -> TaskResult:
"""Extract data from various sources using AI-powered extraction"""
try:
# AI-powered source analysis
source_analysis = await self.ai_agent.analyze_data_source(
source=source,
rules=extraction_rules
)
# Dynamic extraction strategy
extraction_strategy = await self.ai_agent.generate_extraction_strategy(
analysis=source_analysis,
requirements=extraction_rules
)
# Execute extraction
extracted_data = await self.execute_extraction(
source=source,
strategy=extraction_strategy
)
# Validate and clean data
validated_data = await self.ai_agent.validate_and_clean_data(
data=extracted_data,
validation_rules=extraction_rules.get("validation", \\\\{\\\\})
)
return TaskResult(
success=True,
data=validated_data,
metadata=\\\\{
"source": source,
"extraction_strategy": extraction_strategy,
"records_extracted": len(validated_data),
"timestamp": datetime.now().isoformat()
\\\\}
)
except Exception as e:
return TaskResult(
success=False,
error=str(e),
metadata=\\\\{"source": source, "timestamp": datetime.now().isoformat()\\\\}
)
@task(name="decision_making", category="ai_reasoning")
async def ai_decision_maker(self, context: Dict,
decision_criteria: Dict) -> TaskResult:
"""Make intelligent decisions based on context and criteria"""
try:
# Analyze decision context
context_analysis = await self.ai_agent.analyze_decision_context(
context=context,
criteria=decision_criteria
)
# Generate decision options
decision_options = await self.ai_agent.generate_decision_options(
analysis=context_analysis,
constraints=decision_criteria.get("constraints", \\\\{\\\\})
)
# Evaluate options using AI reasoning
evaluation = await self.ai_agent.evaluate_decision_options(
options=decision_options,
criteria=decision_criteria,
context=context
)
# Make final decision
final_decision = await self.ai_agent.make_final_decision(
evaluation=evaluation,
confidence_threshold=decision_criteria.get("confidence_threshold", 0.8)
)
return TaskResult(
success=True,
data=\\\\{
"decision": final_decision,
"confidence": evaluation.get("confidence", 0),
"reasoning": evaluation.get("reasoning", ""),
"alternatives": decision_options
\\\\},
metadata=\\\\{
"decision_type": decision_criteria.get("type", "general"),
"timestamp": datetime.now().isoformat()
\\\\}
)
except Exception as e:
return TaskResult(
success=False,
error=str(e),
metadata=\\\\{"timestamp": datetime.now().isoformat()\\\\}
)
@task(name="process_automation", category="rpa")
async def automate_business_process(self, process_definition: Dict,
input_data: Dict) -> TaskResult:
"""Automate complex business processes with AI guidance"""
try:
# AI-powered process analysis
process_analysis = await self.ai_agent.analyze_business_process(
definition=process_definition,
input_data=input_data
)
# Generate execution plan
execution_plan = await self.ai_agent.generate_execution_plan(
process=process_definition,
analysis=process_analysis,
data=input_data
)
# Execute process steps
execution_results = []
for step in execution_plan["steps"]:
step_result = await self.execute_process_step(
step=step,
context=input_data,
previous_results=execution_results
)
execution_results.append(step_result)
# AI-powered error handling and adaptation
if not step_result.success:
recovery_action = await self.ai_agent.generate_recovery_action(
failed_step=step,
error=step_result.error,
context=input_data
)
if recovery_action:
recovery_result = await self.execute_recovery_action(
action=recovery_action,
context=input_data
)
execution_results.append(recovery_result)
# Analyze overall execution
execution_summary = await self.ai_agent.analyze_execution_results(
results=execution_results,
original_plan=execution_plan
)
return TaskResult(
success=execution_summary["overall_success"],
data=\\\\{
"execution_results": execution_results,
"summary": execution_summary,
"process_metrics": execution_summary.get("metrics", \\\\{\\\\})
\\\\},
metadata=\\\\{
"process_id": process_definition.get("id", "unknown"),
"execution_time": execution_summary.get("execution_time", 0),
"timestamp": datetime.now().isoformat()
\\\\}
)
except Exception as e:
return TaskResult(
success=False,
error=str(e),
metadata=\\\\{"timestamp": datetime.now().isoformat()\\\\}
)
async def execute_extraction(self, source: str, strategy: Dict) -> List[Dict]:
"""Execute data extraction based on AI-generated strategy"""
extraction_methods = \\\\{
"web_scraping": self.web_scraping_extraction,
"api_extraction": self.api_extraction,
"database_query": self.database_extraction,
"file_parsing": self.file_parsing_extraction,
"document_processing": self.document_processing_extraction
\\\\}
method = extraction_methods.get(strategy["method"])
if not method:
raise ValueError(f"Unknown extraction method: \\\\{strategy['method']\\\\}")
return await method(source, strategy["parameters"])
async def web_scraping_extraction(self, url: str, parameters: Dict) -> List[Dict]:
"""AI-guided web scraping"""
from selenium import webdriver
from bs4 import BeautifulSoup
import requests
# AI-optimized scraping strategy
scraping_config = await self.ai_agent.optimize_scraping_config(
url=url,
parameters=parameters
)
if scraping_config["method"] == "selenium":
driver = webdriver.Chrome(options=scraping_config["chrome_options"])
try:
driver.get(url)
# AI-guided element selection
elements = await self.ai_agent.find_target_elements(
page_source=driver.page_source,
selectors=parameters["selectors"]
)
extracted_data = []
for element in elements:
data = await self.ai_agent.extract_element_data(
element=element,
extraction_rules=parameters["extraction_rules"]
)
extracted_data.append(data)
return extracted_data
finally:
driver.quit()
else: # requests-based scraping
response = requests.get(url, headers=scraping_config["headers"])
soup = BeautifulSoup(response.content, 'html.parser')
# AI-guided content extraction
extracted_data = await self.ai_agent.extract_content_from_soup(
soup=soup,
extraction_rules=parameters["extraction_rules"]
)
return extracted_data
async def api_extraction(self, endpoint: str, parameters: Dict) -> List[Dict]:
"""AI-optimized API data extraction"""
import aiohttp
# AI-generated API request strategy
request_strategy = await self.ai_agent.generate_api_request_strategy(
endpoint=endpoint,
parameters=parameters
)
async with aiohttp.ClientSession() as session:
extracted_data = []
for request_config in request_strategy["requests"]:
async with session.request(
method=request_config["method"],
url=request_config["url"],
headers=request_config["headers"],
params=request_config.get("params"),
json=request_config.get("json")
) as response:
if response.status == 200:
data = await response.json()
# AI-powered data transformation
transformed_data = await self.ai_agent.transform_api_response(
data=data,
transformation_rules=parameters["transformation_rules"]
)
extracted_data.extend(transformed_data)
return extracted_data
Workflow Definition and Execution
class WorkflowManager: def init(self, taskmaster_core: TaskmasterCore): self.core = taskmaster_core self.workflow_registry = \\{\\}
@workflow(name="intelligent_data_pipeline")
async def intelligent_data_pipeline(self, config: Dict) -> WorkflowContext:
"""AI-powered end-to-end data processing pipeline"""
context = WorkflowContext()
try:
# Step 1: AI-guided source discovery
sources = await self.core.ai_agent.discover_data_sources(
requirements=config["data_requirements"]
)
context.add_step_result("source_discovery", sources)
# Step 2: Parallel data extraction
extraction_tasks = []
for source in sources:
task = self.core.extract_data_from_source(
source=source["location"],
extraction_rules=source["extraction_rules"]
)
extraction_tasks.append(task)
extraction_results = await asyncio.gather(*extraction_tasks)
context.add_step_result("data_extraction", extraction_results)
# Step 3: AI-powered data integration
integrated_data = await self.core.ai_agent.integrate_data_sources(
extraction_results=extraction_results,
integration_rules=config["integration_rules"]
)
context.add_step_result("data_integration", integrated_data)
# Step 4: Intelligent data quality assessment
quality_assessment = await self.core.ai_agent.assess_data_quality(
data=integrated_data,
quality_criteria=config["quality_criteria"]
)
context.add_step_result("quality_assessment", quality_assessment)
# Step 5: AI-driven data transformation
if quality_assessment["requires_transformation"]:
transformed_data = await self.core.ai_agent.transform_data(
data=integrated_data,
transformation_plan=quality_assessment["transformation_plan"]
)
context.add_step_result("data_transformation", transformed_data)
final_data = transformed_data
else:
final_data = integrated_data
# Step 6: Intelligent output generation
output_result = await self.core.ai_agent.generate_output(
data=final_data,
output_config=config["output_config"]
)
context.add_step_result("output_generation", output_result)
context.mark_success()
return context
except Exception as e:
context.mark_failure(str(e))
return context
@workflow(name="automated_decision_workflow")
async def automated_decision_workflow(self, decision_config: Dict) -> WorkflowContext:
"""AI-powered automated decision-making workflow"""
context = WorkflowContext()
try:
# Step 1: Context gathering
context_data = await self.gather_decision_context(
sources=decision_config["context_sources"]
)
context.add_step_result("context_gathering", context_data)
# Step 2: AI decision making
decision_result = await self.core.ai_decision_maker(
context=context_data,
decision_criteria=decision_config["criteria"]
)
context.add_step_result("decision_making", decision_result)
# Step 3: Action execution based on decision
if decision_result.success:
action_result = await self.execute_decision_actions(
decision=decision_result.data["decision"],
action_config=decision_config["actions"]
)
context.add_step_result("action_execution", action_result)
# Step 4: Result monitoring and feedback
monitoring_result = await self.monitor_decision_outcomes(
decision=decision_result.data["decision"],
monitoring_config=decision_config["monitoring"]
)
context.add_step_result("outcome_monitoring", monitoring_result)
context.mark_success()
return context
except Exception as e:
context.mark_failure(str(e))
return context
async def gather_decision_context(self, sources: List[Dict]) -> Dict:
"""Gather context data from multiple sources"""
context_data = \\\\{\\\\}
for source in sources:
if source["type"] == "database":
data = await self.core.database_extraction(
source["connection"],
source["query_parameters"]
)
elif source["type"] == "api":
data = await self.core.api_extraction(
source["endpoint"],
source["parameters"]
)
elif source["type"] == "file":
data = await self.core.file_parsing_extraction(
source["path"],
source["parsing_parameters"]
)
else:
continue
context_data[source["name"]] = data
return context_data
async def execute_decision_actions(self, decision: Dict,
action_config: Dict) -> TaskResult:
"""Execute actions based on AI decision"""
action_mapping = action_config.get("action_mapping", \\\\{\\\\})
decision_type = decision.get("type", "default")
actions = action_mapping.get(decision_type, [])
execution_results = []
for action in actions:
if action["type"] == "api_call":
result = await self.execute_api_action(action, decision)
elif action["type"] == "database_update":
result = await self.execute_database_action(action, decision)
elif action["type"] == "notification":
result = await self.execute_notification_action(action, decision)
elif action["type"] == "workflow_trigger":
result = await self.execute_workflow_trigger(action, decision)
else:
result = TaskResult(success=False, error=f"Unknown action type: \\\\{action['type']\\\\}")
execution_results.append(result)
overall_success = all(result.success for result in execution_results)
return TaskResult(
success=overall_success,
data=\\\\{"action_results": execution_results\\\\},
metadata=\\\\{"decision": decision, "timestamp": datetime.now().isoformat()\\\\}
)
def main(): # Initialize Taskmaster-AI taskmaster = TaskmasterCore() workflow_manager = WorkflowManager(taskmaster)
# Example: Intelligent data pipeline
pipeline_config = \\\\{
"data_requirements": \\\\{
"domain": "e-commerce",
"data_types": ["product_data", "customer_data", "sales_data"],
"freshness": "daily",
"quality_threshold": 0.95
\\\\},
"integration_rules": \\\\{
"join_keys": ["product_id", "customer_id"],
"conflict_resolution": "ai_guided",
"schema_mapping": "automatic"
\\\\},
"quality_criteria": \\\\{
"completeness": 0.9,
"accuracy": 0.95,
"consistency": 0.9,
"timeliness": 0.8
\\\\},
"output_config": \\\\{
"format": "parquet",
"destination": "data_warehouse",
"partitioning": "date",
"compression": "snappy"
\\\\}
\\\\}
# Execute workflow
async def run_pipeline():
result = await workflow_manager.intelligent_data_pipeline(pipeline_config)
print(f"Pipeline execution: \\\\{'Success' if result.success else 'Failed'\\\\}")
if result.success:
print("Pipeline steps completed:")
for step, result in result.step_results.items():
print(f" \\\\{step\\\\}: \\\\{result\\\\}")
# Run the pipeline
asyncio.run(run_pipeline())
if name == "main": main() ```_
Erweiterte KI-Integration
Natürliche Sprache Workflow Erstellung
```python
!/usr/bin/env python3
natural_language_workflows.py
from taskmaster_ai import NLWorkflowGenerator, WorkflowParser from taskmaster_ai.nlp import IntentRecognizer, EntityExtractor import asyncio
class NaturalLanguageWorkflowCreator: def init(self, taskmaster_core): self.core = taskmaster_core self.nl_generator = NLWorkflowGenerator() self.intent_recognizer = IntentRecognizer() self.entity_extractor = EntityExtractor()
async def create_workflow_from_description(self, description: str) -> Dict:
"""Create executable workflow from natural language description"""
# Step 1: Parse natural language intent
intent_analysis = await self.intent_recognizer.analyze_intent(description)
# Step 2: Extract entities and parameters
entities = await self.entity_extractor.extract_entities(
text=description,
intent=intent_analysis
)
# Step 3: Generate workflow structure
workflow_structure = await self.nl_generator.generate_workflow_structure(
intent=intent_analysis,
entities=entities,
description=description
)
# Step 4: Create executable workflow
executable_workflow = await self.create_executable_workflow(
structure=workflow_structure,
entities=entities
)
return \\\\{
"description": description,
"intent": intent_analysis,
"entities": entities,
"structure": workflow_structure,
"executable": executable_workflow
\\\\}
async def create_executable_workflow(self, structure: Dict, entities: Dict) -> Dict:
"""Convert workflow structure to executable format"""
executable_steps = []
for step in structure["steps"]:
if step["type"] == "data_extraction":
executable_step = await self.create_extraction_step(step, entities)
elif step["type"] == "data_processing":
executable_step = await self.create_processing_step(step, entities)
elif step["type"] == "decision_making":
executable_step = await self.create_decision_step(step, entities)
elif step["type"] == "action_execution":
executable_step = await self.create_action_step(step, entities)
elif step["type"] == "notification":
executable_step = await self.create_notification_step(step, entities)
else:
executable_step = await self.create_generic_step(step, entities)
executable_steps.append(executable_step)
return \\\\{
"name": structure["name"],
"description": structure["description"],
"steps": executable_steps,
"error_handling": structure.get("error_handling", "default"),
"monitoring": structure.get("monitoring", "basic")
\\\\}
async def create_extraction_step(self, step: Dict, entities: Dict) -> Dict:
"""Create data extraction step"""
return \\\\{
"type": "task",
"task_name": "extract_data_from_source",
"parameters": \\\\{
"source": entities.get("data_source", step.get("source")),
"extraction_rules": \\\\{
"format": entities.get("data_format", "auto"),
"fields": entities.get("required_fields", []),
"filters": entities.get("data_filters", \\\\{\\\\}),
"validation": entities.get("validation_rules", \\\\{\\\\})
\\\\}
\\\\},
"retry_config": \\\\{
"max_attempts": 3,
"backoff_factor": 2
\\\\}
\\\\}
async def create_decision_step(self, step: Dict, entities: Dict) -> Dict:
"""Create AI decision-making step"""
return \\\\{
"type": "task",
"task_name": "ai_decision_maker",
"parameters": \\\\{
"decision_criteria": \\\\{
"type": entities.get("decision_type", "classification"),
"criteria": entities.get("decision_criteria", \\\\{\\\\}),
"confidence_threshold": entities.get("confidence_threshold", 0.8),
"constraints": entities.get("decision_constraints", \\\\{\\\\})
\\\\}
\\\\},
"dependencies": step.get("dependencies", [])
\\\\}
Example usage
async def demo_natural_language_workflows(): taskmaster = TaskmasterCore() nl_creator = NaturalLanguageWorkflowCreator(taskmaster)
# Example descriptions
descriptions = [
"Extract customer data from the CRM database, analyze purchase patterns, and send personalized recommendations via email",
"Monitor website performance metrics, detect anomalies, and automatically scale infrastructure if needed",
"Process incoming support tickets, categorize by urgency, assign to appropriate teams, and notify managers of high-priority issues",
"Analyze social media mentions, determine sentiment, generate summary reports, and alert marketing team of negative trends"
]
for description in descriptions:
print(f"\nProcessing: \\\\{description\\\\}")
print("=" * 80)
workflow = await nl_creator.create_workflow_from_description(description)
print(f"Intent: \\\\{workflow['intent']['primary_intent']\\\\}")
print(f"Entities: \\\\{list(workflow['entities'].keys())\\\\}")
print(f"Steps: \\\\{len(workflow['structure']['steps'])\\\\}")
# Execute the workflow
try:
result = await execute_generated_workflow(workflow['executable'])
print(f"Execution: \\\\{'Success' if result.success else 'Failed'\\\\}")
except Exception as e:
print(f"Execution error: \\\\{e\\\\}")
if name == "main": asyncio.run(demo_natural_language_workflows()) ```_
Multi-Modal AI Integration
```python
!/usr/bin/env python3
multimodal_ai_integration.py
from taskmaster_ai import MultiModalAI, VisionProcessor, AudioProcessor, TextProcessor import asyncio from typing import Union, List, Dict
class MultiModalTaskProcessor: def init(self, taskmaster_core): self.core = taskmaster_core self.vision_processor = VisionProcessor() self.audio_processor = AudioProcessor() self.text_processor = TextProcessor() self.multimodal_ai = MultiModalAI()
async def process_multimodal_input(self, inputs: List[Dict]) -> Dict:
"""Process multiple types of input (text, image, audio, video)"""
processed_inputs = \\\\{\\\\}
for input_item in inputs:
input_type = input_item["type"]
input_data = input_item["data"]
if input_type == "text":
processed = await self.text_processor.process_text(input_data)
elif input_type == "image":
processed = await self.vision_processor.process_image(input_data)
elif input_type == "audio":
processed = await self.audio_processor.process_audio(input_data)
elif input_type == "video":
processed = await self.vision_processor.process_video(input_data)
elif input_type == "document":
processed = await self.process_document(input_data)
else:
processed = \\\\{"error": f"Unsupported input type: \\\\{input_type\\\\}"\\\\}
processed_inputs[input_item.get("name", input_type)] = processed
# Combine insights from all modalities
combined_analysis = await self.multimodal_ai.combine_modalities(processed_inputs)
return \\\\{
"individual_analyses": processed_inputs,
"combined_analysis": combined_analysis,
"recommendations": combined_analysis.get("recommendations", []),
"confidence": combined_analysis.get("confidence", 0)
\\\\}
async def process_document(self, document_path: str) -> Dict:
"""Process documents with OCR and content analysis"""
# Extract text using OCR
extracted_text = await self.vision_processor.extract_text_from_document(document_path)
# Analyze document structure
document_structure = await self.vision_processor.analyze_document_structure(document_path)
# Process extracted text
text_analysis = await self.text_processor.analyze_text(extracted_text)
# Extract key information
key_information = await self.text_processor.extract_key_information(
text=extracted_text,
document_type=document_structure.get("type", "unknown")
)
return \\\\{
"extracted_text": extracted_text,
"document_structure": document_structure,
"text_analysis": text_analysis,
"key_information": key_information
\\\\}
async def generate_multimodal_response(self, analysis: Dict,
response_config: Dict) -> Dict:
"""Generate responses in multiple formats based on analysis"""
responses = \\\\{\\\\}
for format_type in response_config["formats"]:
if format_type == "text_summary":
response = await self.generate_text_summary(analysis)
elif format_type == "visual_report":
response = await self.generate_visual_report(analysis)
elif format_type == "audio_briefing":
response = await self.generate_audio_briefing(analysis)
elif format_type == "interactive_dashboard":
response = await self.generate_interactive_dashboard(analysis)
else:
response = \\\\{"error": f"Unsupported response format: \\\\{format_type\\\\}"\\\\}
responses[format_type] = response
return responses
async def generate_text_summary(self, analysis: Dict) -> str:
"""Generate comprehensive text summary"""
summary_prompt = f"""
Based on the following multimodal analysis, generate a comprehensive summary:
Analysis: \\\\{analysis\\\\}
Include:
1. Key findings from each modality
2. Combined insights
3. Actionable recommendations
4. Confidence levels and limitations
"""
summary = await self.core.ai_agent.generate_text(
prompt=summary_prompt,
max_tokens=1000,
temperature=0.1
)
return summary
async def generate_visual_report(self, analysis: Dict) -> Dict:
"""Generate visual report with charts and graphs"""
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from io import BytesIO
import base64
# Extract quantitative data for visualization
quantitative_data = self.extract_quantitative_data(analysis)
visualizations = \\\\{\\\\}
# Create various visualizations
for viz_type, data in quantitative_data.items():
fig, ax = plt.subplots(figsize=(10, 6))
if viz_type == "confidence_scores":
ax.bar(data.keys(), data.values())
ax.set_title("Confidence Scores by Modality")
ax.set_ylabel("Confidence")
elif viz_type == "sentiment_analysis":
ax.pie(data.values(), labels=data.keys(), autopct='%1.1f%%')
ax.set_title("Sentiment Distribution")
elif viz_type == "key_metrics":
df = pd.DataFrame(list(data.items()), columns=['Metric', 'Value'])
sns.barplot(data=df, x='Metric', y='Value', ax=ax)
ax.set_title("Key Metrics")
# Convert to base64 for embedding
buffer = BytesIO()
plt.savefig(buffer, format='png', bbox_inches='tight', dpi=300)
buffer.seek(0)
image_base64 = base64.b64encode(buffer.getvalue()).decode()
plt.close()
visualizations[viz_type] = \\\\{
"image_data": image_base64,
"format": "png",
"description": f"Visualization of \\\\{viz_type\\\\}"
\\\\}
return \\\\{
"visualizations": visualizations,
"report_metadata": \\\\{
"generated_at": datetime.now().isoformat(),
"analysis_summary": analysis.get("combined_analysis", \\\\{\\\\})
\\\\}
\\\\}
def extract_quantitative_data(self, analysis: Dict) -> Dict:
"""Extract quantitative data for visualization"""
quantitative_data = \\\\{\\\\}
# Extract confidence scores
confidence_scores = \\\\{\\\\}
for modality, data in analysis.get("individual_analyses", \\\\{\\\\}).items():
if isinstance(data, dict) and "confidence" in data:
confidence_scores[modality] = data["confidence"]
if confidence_scores:
quantitative_data["confidence_scores"] = confidence_scores
# Extract sentiment data
sentiment_data = \\\\{\\\\}
for modality, data in analysis.get("individual_analyses", \\\\{\\\\}).items():
if isinstance(data, dict) and "sentiment" in data:
sentiment = data["sentiment"]
if isinstance(sentiment, dict):
for sentiment_type, score in sentiment.items():
sentiment_data[sentiment_type] = sentiment_data.get(sentiment_type, 0) + score
if sentiment_data:
quantitative_data["sentiment_analysis"] = sentiment_data
# Extract key metrics
combined_analysis = analysis.get("combined_analysis", \\\\{\\\\})
if "metrics" in combined_analysis:
quantitative_data["key_metrics"] = combined_analysis["metrics"]
return quantitative_data
Example usage
async def demo_multimodal_processing(): taskmaster = TaskmasterCore() multimodal_processor = MultiModalTaskProcessor(taskmaster)
# Example multimodal inputs
inputs = [
\\\\{
"type": "text",
"name": "customer_feedback",
"data": "The product quality has improved significantly, but delivery times are still too long."
\\\\},
\\\\{
"type": "image",
"name": "product_photo",
"data": "path/to/product_image.jpg"
\\\\},
\\\\{
"type": "audio",
"name": "customer_call",
"data": "path/to/customer_call.wav"
\\\\},
\\\\{
"type": "document",
"name": "support_ticket",
"data": "path/to/support_ticket.pdf"
\\\\}
]
# Process multimodal inputs
analysis = await multimodal_processor.process_multimodal_input(inputs)
print("Multimodal Analysis Results:")
print("=" * 50)
print(f"Combined Analysis: \\\\{analysis['combined_analysis']\\\\}")
print(f"Recommendations: \\\\{analysis['recommendations']\\\\}")
print(f"Overall Confidence: \\\\{analysis['confidence']\\\\}")
# Generate multimodal responses
response_config = \\\\{
"formats": ["text_summary", "visual_report", "audio_briefing"]
\\\\}
responses = await multimodal_processor.generate_multimodal_response(
analysis, response_config
)
print("\nGenerated Responses:")
print("=" * 50)
for format_type, response in responses.items():
print(f"\\\\{format_type\\\\}: Generated successfully")
if name == "main": asyncio.run(demo_multimodal_processing()) ```_
Beispiele für Workflow Automation
E-Commerce Automation
```python
!/usr/bin/env python3
ecommerce_automation.py
from taskmaster_ai import EcommerceAutomation, InventoryManager, CustomerAnalytics import asyncio
class EcommerceWorkflowAutomation: def init(self, taskmaster_core): self.core = taskmaster_core self.inventory_manager = InventoryManager() self.customer_analytics = CustomerAnalytics()
async def automated_inventory_management(self, config: Dict) -> Dict:
"""Automated inventory management with AI predictions"""
# Step 1: Analyze current inventory levels
current_inventory = await self.inventory_manager.get_current_inventory()
# Step 2: AI-powered demand forecasting
demand_forecast = await self.core.ai_agent.forecast_demand(
historical_data=current_inventory["historical_sales"],
external_factors=config["external_factors"],
forecast_horizon=config["forecast_days"]
)
# Step 3: Optimize inventory levels
optimization_result = await self.core.ai_agent.optimize_inventory(
current_inventory=current_inventory,
demand_forecast=demand_forecast,
constraints=config["inventory_constraints"]
)
# Step 4: Generate purchase orders
purchase_orders = []
for item in optimization_result["reorder_items"]:
po = await self.generate_purchase_order(
item=item,
supplier_preferences=config["supplier_preferences"]
)
purchase_orders.append(po)
# Step 5: Update inventory system
update_result = await self.inventory_manager.update_inventory_levels(
optimization_result["new_levels"]
)
return \\\\{
"current_inventory": current_inventory,
"demand_forecast": demand_forecast,
"optimization_result": optimization_result,
"purchase_orders": purchase_orders,
"update_result": update_result
\\\\}
async def personalized_marketing_automation(self, config: Dict) -> Dict:
"""AI-driven personalized marketing campaigns"""
# Step 1: Customer segmentation
customer_segments = await self.customer_analytics.segment_customers(
criteria=config["segmentation_criteria"]
)
# Step 2: Generate personalized content
personalized_campaigns = \\\\{\\\\}
for segment_id, segment_data in customer_segments.items():
campaign_content = await self.core.ai_agent.generate_marketing_content(
segment_profile=segment_data["profile"],
campaign_objectives=config["campaign_objectives"],
brand_guidelines=config["brand_guidelines"]
)
personalized_campaigns[segment_id] = \\\\{
"segment_data": segment_data,
"campaign_content": campaign_content,
"delivery_schedule": await self.optimize_delivery_schedule(
segment_data["preferences"]
)
\\\\}
# Step 3: A/B testing setup
ab_test_config = await self.core.ai_agent.design_ab_tests(
campaigns=personalized_campaigns,
test_objectives=config["test_objectives"]
)
# Step 4: Campaign execution
execution_results = \\\\{\\\\}
for segment_id, campaign in personalized_campaigns.items():
result = await self.execute_marketing_campaign(
campaign=campaign,
ab_config=ab_test_config.get(segment_id, \\\\{\\\\})
)
execution_results[segment_id] = result
return \\\\{
"customer_segments": customer_segments,
"personalized_campaigns": personalized_campaigns,
"ab_test_config": ab_test_config,
"execution_results": execution_results
\\\\}
async def automated_customer_service(self, config: Dict) -> Dict:
"""AI-powered customer service automation"""
# Step 1: Ticket classification and routing
incoming_tickets = await self.get_incoming_tickets()
classified_tickets = []
for ticket in incoming_tickets:
classification = await self.core.ai_agent.classify_support_ticket(
ticket_content=ticket["content"],
customer_history=ticket["customer_history"],
classification_rules=config["classification_rules"]
)
routing_decision = await self.core.ai_agent.route_ticket(
classification=classification,
agent_availability=config["agent_availability"],
routing_rules=config["routing_rules"]
)
classified_tickets.append(\\\\{
"ticket": ticket,
"classification": classification,
"routing": routing_decision
\\\\})
# Step 2: Automated response generation
automated_responses = []
for classified_ticket in classified_tickets:
if classified_ticket["classification"]["automation_eligible"]:
response = await self.core.ai_agent.generate_customer_response(
ticket=classified_ticket["ticket"],
classification=classified_ticket["classification"],
response_templates=config["response_templates"]
)
automated_responses.append(\\\\{
"ticket_id": classified_ticket["ticket"]["id"],
"response": response,
"confidence": response["confidence"]
\\\\})
# Step 3: Quality assurance
qa_results = []
for response in automated_responses:
if response["confidence"] < config["qa_threshold"]:
qa_result = await self.core.ai_agent.quality_check_response(
response=response,
quality_criteria=config["quality_criteria"]
)
qa_results.append(qa_result)
return \\\\{
"classified_tickets": classified_tickets,
"automated_responses": automated_responses,
"qa_results": qa_results,
"metrics": \\\\{
"total_tickets": len(incoming_tickets),
"automated_responses": len(automated_responses),
"qa_flagged": len(qa_results)
\\\\}
\\\\}
Financial Services Automation
class FinancialServicesAutomation: def init(self, taskmaster_core): self.core = taskmaster_core
async def fraud_detection_workflow(self, config: Dict) -> Dict:
"""AI-powered fraud detection and prevention"""
# Step 1: Real-time transaction monitoring
transactions = await self.get_real_time_transactions()
# Step 2: AI fraud scoring
fraud_scores = []
for transaction in transactions:
score = await self.core.ai_agent.calculate_fraud_score(
transaction=transaction,
customer_profile=transaction["customer_profile"],
historical_patterns=transaction["historical_patterns"],
fraud_models=config["fraud_models"]
)
fraud_scores.append(score)
# Step 3: Risk-based decision making
risk_decisions = []
for i, score in enumerate(fraud_scores):
decision = await self.core.ai_decision_maker(
context=\\\\{
"transaction": transactions[i],
"fraud_score": score,
"customer_risk_profile": transactions[i]["customer_profile"]
\\\\},
decision_criteria=config["risk_criteria"]
)
risk_decisions.append(decision)
# Step 4: Automated actions
action_results = []
for i, decision in enumerate(risk_decisions):
if decision.data["decision"]["action"] != "approve":
action_result = await self.execute_fraud_prevention_action(
transaction=transactions[i],
decision=decision.data["decision"],
action_config=config["fraud_actions"]
)
action_results.append(action_result)
return \\\\{
"transactions_processed": len(transactions),
"fraud_scores": fraud_scores,
"risk_decisions": risk_decisions,
"actions_taken": action_results,
"summary": \\\\{
"approved": sum(1 for d in risk_decisions if d.data["decision"]["action"] == "approve"),
"flagged": sum(1 for d in risk_decisions if d.data["decision"]["action"] == "flag"),
"blocked": sum(1 for d in risk_decisions if d.data["decision"]["action"] == "block")
\\\\}
\\\\}
async def automated_compliance_monitoring(self, config: Dict) -> Dict:
"""Automated regulatory compliance monitoring"""
# Step 1: Data collection from multiple sources
compliance_data = await self.collect_compliance_data(
sources=config["data_sources"]
)
# Step 2: AI-powered compliance analysis
compliance_analysis = await self.core.ai_agent.analyze_compliance(
data=compliance_data,
regulations=config["applicable_regulations"],
compliance_rules=config["compliance_rules"]
)
# Step 3: Risk assessment
risk_assessment = await self.core.ai_agent.assess_compliance_risk(
analysis=compliance_analysis,
risk_framework=config["risk_framework"]
)
# Step 4: Generate compliance reports
compliance_reports = await self.generate_compliance_reports(
analysis=compliance_analysis,
risk_assessment=risk_assessment,
report_templates=config["report_templates"]
)
# Step 5: Automated remediation
if risk_assessment["requires_action"]:
remediation_actions = await self.execute_compliance_remediation(
risk_assessment=risk_assessment,
remediation_config=config["remediation_config"]
)
else:
remediation_actions = []
return \\\\{
"compliance_data": compliance_data,
"compliance_analysis": compliance_analysis,
"risk_assessment": risk_assessment,
"compliance_reports": compliance_reports,
"remediation_actions": remediation_actions
\\\\}
Example usage
async def demo_ecommerce_automation(): taskmaster = TaskmasterCore() ecommerce_automation = EcommerceWorkflowAutomation(taskmaster)
# Inventory management configuration
inventory_config = \\\\{
"external_factors": \\\\{
"seasonality": "high",
"market_trends": "growing",
"economic_indicators": "stable"
\\\\},
"forecast_days": 30,
"inventory_constraints": \\\\{
"max_storage_capacity": 10000,
"budget_limit": 50000,
"supplier_lead_times": \\\\{"supplier_a": 7, "supplier_b": 14\\\\}
\\\\},
"supplier_preferences": \\\\{
"preferred_suppliers": ["supplier_a", "supplier_b"],
"quality_requirements": "high",
"delivery_requirements": "fast"
\\\\}
\\\\}
# Execute inventory management workflow
inventory_result = await ecommerce_automation.automated_inventory_management(inventory_config)
print("Inventory Management Results:")
print("=" * 50)
print(f"Items to reorder: \\\\{len(inventory_result['optimization_result']['reorder_items'])\\\\}")
print(f"Purchase orders generated: \\\\{len(inventory_result['purchase_orders'])\\\\}")
# Marketing automation configuration
marketing_config = \\\\{
"segmentation_criteria": \\\\{
"behavioral": ["purchase_frequency", "average_order_value"],
"demographic": ["age_group", "location"],
"psychographic": ["interests", "lifestyle"]
\\\\},
"campaign_objectives": \\\\{
"primary": "increase_sales",
"secondary": "improve_retention",
"kpis": ["conversion_rate", "customer_lifetime_value"]
\\\\},
"brand_guidelines": \\\\{
"tone": "friendly_professional",
"style": "modern_minimalist",
"values": ["quality", "sustainability", "innovation"]
\\\\},
"test_objectives": ["subject_line_optimization", "content_personalization"]
\\\\}
# Execute marketing automation workflow
marketing_result = await ecommerce_automation.personalized_marketing_automation(marketing_config)
print("\nMarketing Automation Results:")
print("=" * 50)
print(f"Customer segments: \\\\{len(marketing_result['customer_segments'])\\\\}")
print(f"Personalized campaigns: \\\\{len(marketing_result['personalized_campaigns'])\\\\}")
if name == "main": asyncio.run(demo_ecommerce_automation()) ```_
Monitoring und Analytics
Leistungsüberwachung
```python
!/usr/bin/env python3
performance_monitoring.py
from taskmaster_ai import PerformanceMonitor, MetricsCollector, AlertManager import asyncio from datetime import datetime, timedelta import json
class TaskmasterPerformanceMonitor: def init(self, taskmaster_core): self.core = taskmaster_core self.metrics_collector = MetricsCollector() self.alert_manager = AlertManager() self.performance_thresholds = \\{\\}
async def monitor_workflow_performance(self, workflow_id: str,
monitoring_config: Dict) -> Dict:
"""Monitor workflow performance in real-time"""
# Collect performance metrics
metrics = await self.metrics_collector.collect_workflow_metrics(
workflow_id=workflow_id,
metrics_config=monitoring_config["metrics"]
)
# Analyze performance trends
performance_analysis = await self.analyze_performance_trends(
metrics=metrics,
historical_data=monitoring_config.get("historical_data", \\\\{\\\\}),
analysis_config=monitoring_config["analysis"]
)
# Check for performance issues
issues = await self.detect_performance_issues(
metrics=metrics,
thresholds=monitoring_config["thresholds"],
analysis=performance_analysis
)
# Generate alerts if needed
alerts = []
if issues:
for issue in issues:
alert = await self.alert_manager.create_alert(
issue=issue,
alert_config=monitoring_config["alerting"]
)
alerts.append(alert)
# Generate performance recommendations
recommendations = await self.generate_performance_recommendations(
metrics=metrics,
issues=issues,
analysis=performance_analysis
)
return \\\\{
"workflow_id": workflow_id,
"metrics": metrics,
"performance_analysis": performance_analysis,
"issues": issues,
"alerts": alerts,
"recommendations": recommendations,
"timestamp": datetime.now().isoformat()
\\\\}
async def analyze_performance_trends(self, metrics: Dict,
historical_data: Dict,
analysis_config: Dict) -> Dict:
"""Analyze performance trends using AI"""
trend_analysis = await self.core.ai_agent.analyze_performance_trends(
current_metrics=metrics,
historical_metrics=historical_data,
analysis_parameters=analysis_config
)
return \\\\{
"trends": trend_analysis["trends"],
"anomalies": trend_analysis["anomalies"],
"predictions": trend_analysis["predictions"],
"insights": trend_analysis["insights"]
\\\\}
async def detect_performance_issues(self, metrics: Dict,
thresholds: Dict,
analysis: Dict) -> List[Dict]:
"""Detect performance issues based on metrics and AI analysis"""
issues = []
# Threshold-based detection
for metric_name, metric_value in metrics.items():
if metric_name in thresholds:
threshold = thresholds[metric_name]
if isinstance(threshold, dict):
if "max" in threshold and metric_value > threshold["max"]:
issues.append(\\\\{
"type": "threshold_exceeded",
"metric": metric_name,
"value": metric_value,
"threshold": threshold["max"],
"severity": threshold.get("severity", "medium")
\\\\})
if "min" in threshold and metric_value < threshold["min"]:
issues.append(\\\\{
"type": "threshold_below",
"metric": metric_name,
"value": metric_value,
"threshold": threshold["min"],
"severity": threshold.get("severity", "medium")
\\\\})
# AI-based anomaly detection
for anomaly in analysis.get("anomalies", []):
issues.append(\\\\{
"type": "anomaly_detected",
"description": anomaly["description"],
"confidence": anomaly["confidence"],
"severity": anomaly.get("severity", "medium"),
"affected_metrics": anomaly.get("affected_metrics", [])
\\\\})
return issues
async def generate_performance_recommendations(self, metrics: Dict,
issues: List[Dict],
analysis: Dict) -> List[Dict]:
"""Generate AI-powered performance improvement recommendations"""
recommendations = await self.core.ai_agent.generate_performance_recommendations(
current_metrics=metrics,
identified_issues=issues,
trend_analysis=analysis,
optimization_objectives=["efficiency", "reliability", "cost"]
)
return recommendations
async def setup_automated_monitoring(self, workflows: List[str],
monitoring_config: Dict) -> Dict:
"""Setup automated monitoring for multiple workflows"""
monitoring_tasks = []
for workflow_id in workflows:
# Create monitoring task
task = asyncio.create_task(
self.continuous_workflow_monitoring(
workflow_id=workflow_id,
config=monitoring_config,
interval=monitoring_config.get("monitoring_interval", 60)
)
)
monitoring_tasks.append(task)
return \\\\{
"monitored_workflows": workflows,
"monitoring_tasks": len(monitoring_tasks),
"configuration": monitoring_config
\\\\}
async def continuous_workflow_monitoring(self, workflow_id: str,
config: Dict,
interval: int):
"""Continuously monitor a workflow"""
while True:
try:
monitoring_result = await self.monitor_workflow_performance(
workflow_id=workflow_id,
monitoring_config=config
)
# Store monitoring results
await self.store_monitoring_results(workflow_id, monitoring_result)
# Check for critical issues
critical_issues = [
issue for issue in monitoring_result["issues"]
if issue.get("severity") == "critical"
]
if critical_issues:
await self.handle_critical_issues(workflow_id, critical_issues)
# Wait for next monitoring cycle
await asyncio.sleep(interval)
except Exception as e:
print(f"Monitoring error for workflow \\\\{workflow_id\\\\}: \\\\{e\\\\}")
await asyncio.sleep(interval)
Analytics and Reporting
class TaskmasterAnalytics: def init(self, taskmaster_core): self.core = taskmaster_core
async def generate_workflow_analytics(self, timeframe: Dict,
analytics_config: Dict) -> Dict:
"""Generate comprehensive workflow analytics"""
# Collect workflow execution data
execution_data = await self.collect_execution_data(
timeframe=timeframe,
filters=analytics_config.get("filters", \\\\{\\\\})
)
# Analyze workflow performance
performance_analytics = await self.analyze_workflow_performance(
execution_data=execution_data,
analysis_config=analytics_config["performance_analysis"]
)
# Analyze resource utilization
resource_analytics = await self.analyze_resource_utilization(
execution_data=execution_data,
resource_config=analytics_config["resource_analysis"]
)
# Generate business impact analysis
business_impact = await self.analyze_business_impact(
execution_data=execution_data,
performance_data=performance_analytics,
impact_config=analytics_config["business_impact"]
)
# Create visualizations
visualizations = await self.create_analytics_visualizations(
performance_analytics=performance_analytics,
resource_analytics=resource_analytics,
business_impact=business_impact,
viz_config=analytics_config["visualizations"]
)
return \\\\{
"timeframe": timeframe,
"execution_summary": \\\\{
"total_workflows": len(execution_data),
"successful_executions": sum(1 for w in execution_data if w["success"]),
"failed_executions": sum(1 for w in execution_data if not w["success"]),
"average_execution_time": sum(w["execution_time"] for w in execution_data) / len(execution_data)
\\\\},
"performance_analytics": performance_analytics,
"resource_analytics": resource_analytics,
"business_impact": business_impact,
"visualizations": visualizations
\\\\}
async def analyze_workflow_performance(self, execution_data: List[Dict],
analysis_config: Dict) -> Dict:
"""Analyze workflow performance metrics"""
performance_metrics = \\\\{\\\\}
# Calculate basic performance metrics
execution_times = [w["execution_time"] for w in execution_data]
success_rates = [w["success"] for w in execution_data]
performance_metrics["execution_time"] = \\\\{
"average": sum(execution_times) / len(execution_times),
"median": sorted(execution_times)[len(execution_times) // 2],
"min": min(execution_times),
"max": max(execution_times),
"percentile_95": sorted(execution_times)[int(len(execution_times) * 0.95)]
\\\\}
performance_metrics["success_rate"] = \\\\{
"overall": sum(success_rates) / len(success_rates),
"by_workflow_type": self.calculate_success_by_type(execution_data)
\\\\}
# AI-powered performance insights
performance_insights = await self.core.ai_agent.analyze_performance_patterns(
execution_data=execution_data,
metrics=performance_metrics,
analysis_parameters=analysis_config
)
return \\\\{
"metrics": performance_metrics,
"insights": performance_insights,
"trends": performance_insights.get("trends", []),
"bottlenecks": performance_insights.get("bottlenecks", []),
"optimization_opportunities": performance_insights.get("optimizations", [])
\\\\}
def calculate_success_by_type(self, execution_data: List[Dict]) -> Dict:
"""Calculate success rates by workflow type"""
type_stats = \\\\{\\\\}
for workflow in execution_data:
workflow_type = workflow.get("type", "unknown")
if workflow_type not in type_stats:
type_stats[workflow_type] = \\\\{"total": 0, "successful": 0\\\\}
type_stats[workflow_type]["total"] += 1
if workflow["success"]:
type_stats[workflow_type]["successful"] += 1
# Calculate success rates
for workflow_type, stats in type_stats.items():
stats["success_rate"] = stats["successful"] / stats["total"]
return type_stats
Example usage
async def demo_monitoring_and_analytics(): taskmaster = TaskmasterCore() monitor = TaskmasterPerformanceMonitor(taskmaster) analytics = TaskmasterAnalytics(taskmaster)
# Setup monitoring configuration
monitoring_config = \\\\{
"metrics": ["execution_time", "memory_usage", "cpu_usage", "success_rate"],
"thresholds": \\\\{
"execution_time": \\\\{"max": 300, "severity": "high"\\\\},
"memory_usage": \\\\{"max": 80, "severity": "medium"\\\\},
"cpu_usage": \\\\{"max": 90, "severity": "high"\\\\},
"success_rate": \\\\{"min": 0.95, "severity": "critical"\\\\}
\\\\},
"analysis": \\\\{
"trend_window": "24h",
"anomaly_detection": True,
"prediction_horizon": "1h"
\\\\},
"alerting": \\\\{
"channels": ["email", "slack"],
"escalation_rules": \\\\{
"critical": \\\\{"immediate": True, "escalate_after": 300\\\\},
"high": \\\\{"immediate": False, "escalate_after": 900\\\\}
\\\\}
\\\\}
\\\\}
# Monitor workflow performance
workflow_id = "intelligent_data_pipeline"
monitoring_result = await monitor.monitor_workflow_performance(
workflow_id=workflow_id,
monitoring_config=monitoring_config
)
print("Monitoring Results:")
print("=" * 50)
print(f"Workflow: \\\\{monitoring_result['workflow_id']\\\\}")
print(f"Issues detected: \\\\{len(monitoring_result['issues'])\\\\}")
print(f"Alerts generated: \\\\{len(monitoring_result['alerts'])\\\\}")
print(f"Recommendations: \\\\{len(monitoring_result['recommendations'])\\\\}")
# Generate analytics
analytics_config = \\\\{
"filters": \\\\{"workflow_type": "data_processing"\\\\},
"performance_analysis": \\\\{
"include_trends": True,
"bottleneck_detection": True,
"optimization_analysis": True
\\\\},
"resource_analysis": \\\\{
"include_cost_analysis": True,
"utilization_patterns": True
\\\\},
"business_impact": \\\\{
"kpis": ["processing_volume", "cost_savings", "time_savings"],
"roi_calculation": True
\\\\},
"visualizations": \\\\{
"performance_charts": True,
"resource_heatmaps": True,
"trend_graphs": True
\\\\}
\\\\}
timeframe = \\\\{
"start": (datetime.now() - timedelta(days=7)).isoformat(),
"end": datetime.now().isoformat()
\\\\}
analytics_result = await analytics.generate_workflow_analytics(
timeframe=timeframe,
analytics_config=analytics_config
)
print("\nAnalytics Results:")
print("=" * 50)
print(f"Total workflows analyzed: \\\\{analytics_result['execution_summary']['total_workflows']\\\\}")
print(f"Success rate: \\\\{analytics_result['execution_summary']['successful_executions'] / analytics_result['execution_summary']['total_workflows'] * 100:.1f\\\\}%")
print(f"Average execution time: \\\\{analytics_result['execution_summary']['average_execution_time']:.2f\\\\}s")
if name == "main": asyncio.run(demo_monitoring_and_analytics()) ```_
Best Practices und Fehlerbehebung
Sicherheit Best Practices
```python
Security configuration for Taskmaster-AI
security_config = \\{ "authentication": \\{ "method": "oauth2", "token_expiry": 3600, "refresh_token_enabled": True, "multi_factor_auth": True \\}, "authorization": \\{ "rbac_enabled": True, "permission_model": "least_privilege", "audit_logging": True \\}, "encryption": \\{ "data_at_rest": "AES-256", "data_in_transit": "TLS-1.3", "key_management": "vault", "key_rotation": "monthly" \\}, "api_security": \\{ "rate_limiting": True, "input_validation": "strict", "output_sanitization": True, "cors_policy": "restrictive" \\}, "workflow_security": \\{ "sandbox_execution": True, "resource_limits": True, "network_isolation": True, "code_signing": True \\} \\} ```_
Leistungsoptimierung
```python
Performance optimization configuration
optimization_config = \\{ "execution": \\{ "parallel_processing": True, "max_concurrent_tasks": 10, "task_queuing": "priority_based", "resource_pooling": True \\}, "caching": \\{ "result_caching": True, "cache_ttl": 3600, "cache_strategy": "lru", "distributed_cache": True \\}, "ai_optimization": \\{ "model_caching": True, "batch_processing": True, "request_batching": True, "response_streaming": True \\}, "resource_management": \\{ "auto_scaling": True, "resource_monitoring": True, "garbage_collection": "optimized", "memory_management": "efficient" \\} \\} ```_
Gemeinsame Themen und Lösungen
```python
Common troubleshooting scenarios
troubleshooting_guide = \\{ "workflow_execution_failures": \\{ "symptoms": ["Tasks timing out", "Unexpected errors", "Resource exhaustion"], "solutions": [ "Check resource limits and increase if necessary", "Review error logs for specific failure points", "Implement retry logic with exponential backoff", "Optimize task dependencies and execution order" ] \\}, "ai_model_performance": \\{ "symptoms": ["Slow response times", "Low accuracy", "High token usage"], "solutions": [ "Optimize prompts for efficiency", "Use appropriate model for task complexity", "Implement result caching", "Consider fine-tuning for specific use cases" ] \\}, "integration_issues": \\{ "symptoms": ["API connection failures", "Data format mismatches", "Authentication errors"], "solutions": [ "Verify API credentials and permissions", "Check network connectivity and firewall rules", "Validate data schemas and transformation rules", "Implement robust error handling and retries" ] \\} \\} ```_
Ressourcen und Dokumentation
Offizielle Mittel
- Taskmaster-AI Dokumentation
- [API Reference](LINK_12 -%20[GitHub%20Repository](_LINK_12 -%20[Gemeinschaftsforum](_LINK_12___
%20Lernressourcen
-%20Workflow%20Design%20Patterns - AI Integration Best Practices - [Performance Optimization Guide](__LINK_12 -%20(LINK_12___)
Gemeinschaft und Unterstützung
- [Discord Community](_LINK_12 -%20[Stack%20Overflow%20Tag](_LINK_12 -%20YouTube%20Tutorials
- [Blog und Fallstudien](__LINK_12___
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*Dieses Betrügereiblatt bietet umfassende Anleitung für die Implementierung von KI-powered Task Automation und Workflow Management mit Taskmaster-AI. Befolgen Sie immer die besten Sicherheitspraktiken und testen Sie Workflows vor der Produktion. *