Refactor multimodal agent execution and enhance documentation structure

- Simplified the execution of the multimodal agent by removing unnecessary result printing, allowing for a cleaner start method call.
- Updated `mint.json` to include new tabs for "Documentation" and "Use Cases," improving navigation and accessibility of relevant information.
- Added a new "Usecases" group in the navigation section of `mint.json`, detailing various use cases for better user guidance.

This commit streamlines the multimodal agent's functionality and enhances the documentation for improved user experience.

agents/code_review.py

@@ -0,0 +1,108 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import time
+from typing import List, Dict
+
+def analyze_code_changes():
+    """Simulates code analysis"""
+    issues = [
+        {"type": "style", "severity": "low", "file": "main.py"},
+        {"type": "security", "severity": "high", "file": "auth.py"},
+        {"type": "performance", "severity": "medium", "file": "data.py"}
+    ]
+    return issues[int(time.time()) % 3]
+
+def suggest_fixes(issue: Dict):
+    """Simulates fix suggestions"""
+    fixes = {
+        "style": "Apply PEP 8 formatting",
+        "security": "Implement input validation",
+        "performance": "Use list comprehension"
+    }
+    return fixes.get(issue["type"], "Review manually")
+
+def apply_automated_fix(fix: str):
+    """Simulates applying automated fixes"""
+    success = int(time.time()) % 2 == 0
+    return "fixed" if success else "manual_review"
+
+# Create specialized agents
+analyzer = Agent(
+    name="Code Analyzer",
+    role="Code analysis",
+    goal="Analyze code changes and identify issues",
+    instructions="Review code changes and report issues",
+    tools=[analyze_code_changes]
+)
+
+fix_suggester = Agent(
+    name="Fix Suggester",
+    role="Solution provider",
+    goal="Suggest fixes for identified issues",
+    instructions="Provide appropriate fix suggestions",
+    tools=[suggest_fixes]
+)
+
+fix_applier = Agent(
+    name="Fix Applier",
+    role="Fix implementation",
+    goal="Apply suggested fixes automatically when possible",
+    instructions="Implement suggested fixes and report results",
+    tools=[apply_automated_fix]
+)
+
+# Create workflow tasks
+analysis_task = Task(
+    name="analyze_code",
+    description="Analyze code changes for issues",
+    expected_output="Identified code issues",
+    agent=analyzer,
+    is_start=True,
+    next_tasks=["suggest_fixes"]
+)
+
+suggestion_task = Task(
+    name="suggest_fixes",
+    description="Suggest fixes for identified issues",
+    expected_output="Fix suggestions",
+    agent=fix_suggester,
+    next_tasks=["apply_fixes"]
+)
+
+fix_task = Task(
+    name="apply_fixes",
+    description="Apply suggested fixes",
+    expected_output="Fix application status",
+    agent=fix_applier,
+    task_type="decision",
+    condition={
+        "fixed": "",
+        "manual_review": ["suggest_fixes"]  # Loop back for manual review
+    }
+)
+
+# Create workflow
+workflow = PraisonAIAgents(
+    agents=[analyzer, fix_suggester, fix_applier],
+    tasks=[analysis_task, suggestion_task, fix_task],
+    process="workflow",
+    verbose=True
+)
+
+def main():
+    print("\nStarting Code Review Workflow...")
+    print("=" * 50)
+
+    # Run workflow
+    results = workflow.start()
+
+    # Print results
+    print("\nCode Review Results:")
+    print("=" * 50)
+    for task_id, result in results["task_results"].items():
+        if result:
+            print(f"\nTask: {task_id}")
+            print(f"Result: {result.raw}")
+            print("-" * 50)
+
+if __name__ == "__main__":
+    main()

agents/multimodal.py

@@ -51,9 +51,4 @@
 )
 
 # Run all tasks
-result = agents.start()
-
-# Print results
-for task_id, task_result in result["task_results"].items():
-    print(f"\nTask {task_id} Result:")
-    print(task_result.raw)
+agents.start()

agents/pdf_agents_example.py

@@ -0,0 +1,41 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Create PDF Analysis Agent
+pdf_agent = Agent(
+    name="PDFAnalyst",
+    role="PDF Document Specialist",
+    goal="Analyze PDF documents to extract meaningful information",
+    backstory="""You are an expert in PDF document analysis and text extraction.
+    You excel at understanding document structure, extracting content, and analyzing textual information.""",
+    llm="gpt-4o-mini",
+    self_reflect=False
+)
+
+# 1. Task with PDF URL
+task1 = Task(
+    name="analyze_pdf_url",
+    description="Extract and analyze content from this PDF document.",
+    expected_output="Detailed analysis of the PDF content and structure",
+    agent=pdf_agent,
+    input=["https://example.com/document.pdf"]
+)
+
+# 2. Task with Local PDF File
+task2 = Task(
+    name="analyze_local_pdf",
+    description="What information can you extract from this PDF? Analyze its content.",
+    expected_output="Detailed analysis of the PDF content and structure",
+    agent=pdf_agent,
+    input=["document.pdf"] 
+)
+
+# Create PraisonAIAgents instance
+agents = PraisonAIAgents(
+    agents=[pdf_agent],
+    tasks=[task1, task2],
+    process="sequential",
+    verbose=1
+)
+
+# Run all tasks
+agents.start()

cookbooks/usecases/adaptive-learning.py

@@ -0,0 +1,132 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import time
+from typing import Dict
+
+def assess_student_level():
+    """Simulates student assessment"""
+    levels = ["beginner", "intermediate", "advanced"]
+    current_time = int(time.time())
+    return levels[current_time % 3]
+
+def generate_content(level: str):
+    """Simulates content generation"""
+    content_types = {
+        "beginner": "basic concepts and examples",
+        "intermediate": "practice problems and applications",
+        "advanced": "complex scenarios and projects"
+    }
+    return content_types.get(level, "basic concepts")
+
+def evaluate_performance():
+    """Simulates performance evaluation"""
+    scores = ["low", "medium", "high"]
+    current_time = int(time.time())
+    return scores[current_time % 3]
+
+def adapt_difficulty(performance: str):
+    """Simulates difficulty adaptation"""
+    adaptations = {
+        "low": "decrease",
+        "medium": "maintain",
+        "high": "increase"
+    }
+    return adaptations.get(performance, "maintain")
+
+# Create specialized agents
+assessor = Agent(
+    name="Student Assessor",
+    role="Level Assessment",
+    goal="Assess student's current level",
+    instructions="Evaluate student's knowledge and skills",
+    tools=[assess_student_level]
+)
+
+generator = Agent(
+    name="Content Generator",
+    role="Content Creation",
+    goal="Generate appropriate learning content",
+    instructions="Create content based on student's level",
+    tools=[generate_content]
+)
+
+evaluator = Agent(
+    name="Performance Evaluator",
+    role="Performance Assessment",
+    goal="Evaluate student's performance",
+    instructions="Assess learning outcomes",
+    tools=[evaluate_performance]
+)
+
+adapter = Agent(
+    name="Difficulty Adapter",
+    role="Content Adaptation",
+    goal="Adapt content difficulty",
+    instructions="Adjust difficulty based on performance",
+    tools=[adapt_difficulty]
+)
+
+# Create workflow tasks
+assessment_task = Task(
+    name="assess_level",
+    description="Assess student's current level",
+    expected_output="Student's proficiency level",
+    agent=assessor,
+    is_start=True,
+    next_tasks=["generate_content"]
+)
+
+generation_task = Task(
+    name="generate_content",
+    description="Generate appropriate content",
+    expected_output="Learning content",
+    agent=generator,
+    next_tasks=["evaluate_performance"]
+)
+
+evaluation_task = Task(
+    name="evaluate_performance",
+    description="Evaluate student's performance",
+    expected_output="Performance assessment",
+    agent=evaluator,
+    next_tasks=["adapt_difficulty"]
+)
+
+adaptation_task = Task(
+    name="adapt_difficulty",
+    description="Adapt content difficulty",
+    expected_output="Difficulty adjustment",
+    agent=adapter,
+    task_type="decision",
+    condition={
+        "decrease": ["generate_content"],
+        "maintain": "",
+        "increase": ["generate_content"]
+    }
+)
+
+# Create workflow
+workflow = PraisonAIAgents(
+    agents=[assessor, generator, evaluator, adapter],
+    tasks=[assessment_task, generation_task, evaluation_task, adaptation_task],
+    process="workflow",
+    verbose=True
+)
+
+def main():
+    print("\nStarting Adaptive Learning Workflow...")
+    print("=" * 50)
+
+    # Run workflow
+    results = workflow.start()
+
+    # Print results
+    print("\nAdaptive Learning Results:")
+    print("=" * 50)
+    for task_id, result in results["task_results"].items():
+        if result:
+            print(f"\nTask: {task_id}")
+            print(f"Result: {result.raw}")
+            print("-" * 50)
+
+if __name__ == "__main__":
+    main()