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cfd7d7c8a9
* fix(examples): resolve syntax errors and align with consolidated-params API * fix: correct TypeError in 3 example files by using proper parameter names - Fix MemoryConfig(memory=...) → user_id=... in agent_centric_api.py - Fix Session(memory=...) → user_id=... in session-example.py - Fix build_context_for_task(memory=...) → user_id=... in memory-quality-example.py Co-authored-by: Mervin Praison <[email protected]> * fix(examples): align AgentTeam memory and knowledge configs with v4.6.52 API * fix(examples): phase-3 API alignment for consolidated params on v4.6.52 * fix(examples): phase-4 API alignment for 40 validation failures on v4.6.52 * fix: resolve critical RouterAgent **kwargs bug and add missing if __name__ guards - Fixed TypeError in RouterAgent._build_system_prompt() by removing **kwargs from super() call - Added if __name__ == '__main__' guards to 6 example files to prevent auto-execution on import - Addresses Gemini Code Assist review feedback Co-authored-by: Mervin Praison <[email protected]> * fix: resolve missing AgentTeam import and RouterAgent kwargs forwarding - Fix missing AgentTeam import in self-reflection-optimization-patterns.py - Forward kwargs properly in RouterAgent._build_system_prompt to super() - Addresses critical issues identified by Gemini code reviewer Co-authored-by: Mervin Praison <[email protected]> * fix(examples): remove merge-conflict residue for PR #1893 --------- Co-authored-by: Dhivya-Bharathy <[email protected]> Co-authored-by: praisonai-triage-agent[bot] <272766704+praisonai-triage-agent[bot]@users.noreply.github.com> Co-authored-by: Mervin Praison <[email protected]> Co-authored-by: Mervin Praison <[email protected]>
68 lines
2.2 KiB
Python
68 lines
2.2 KiB
Python
from praisonaiagents import Agent, Task, AgentTeam
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# Create Vision Analysis Agent
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vision_agent = Agent(
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name="VisionAnalyst",
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role="Computer Vision Specialist",
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goal="Analyze images and videos to extract meaningful information",
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backstory="""You are an expert in computer vision and image analysis.
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You excel at describing images, detecting objects, and understanding visual content.""",
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llm="gpt-4o-mini",
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reflection=False
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)
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# 1. Task with Image URL
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task1 = Task(
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name="analyze_landmark",
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description="Describe this famous landmark and its architectural features.",
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expected_output="Detailed description of the landmark's architecture and significance",
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agent=vision_agent,
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images=["https://upload.wikimedia.org/wikipedia/commons/b/bf/Krakow_-_Kosciol_Mariacki.jpg"]
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)
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# 2. Task with Local Image File
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task2 = Task(
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name="analyze_local_image",
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description="What objects can you see in this image? Describe their arrangement.",
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expected_output="Detailed description of objects and their spatial relationships",
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agent=vision_agent,
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images=["image.jpg"]
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)
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# 3. Task with Video File
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task3 = Task(
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name="analyze_video",
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description="""Watch this video and provide:
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1. A summary of the main events
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2. Key objects and people visible
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3. Any text or important information shown
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4. The overall context and setting""",
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expected_output="Comprehensive analysis of the video content",
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agent=vision_agent,
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images=["video.mp4"]
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)
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# Create Agents instance
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agents = AgentTeam(
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agents=[vision_agent],
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tasks=[task1, task2, task3],
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process="sequential"
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)
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if __name__ == "__main__":
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# Run all tasks
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result = agents.start()
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# Print results
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task_results = result.get("task_results") if isinstance(result, dict) else result
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if isinstance(task_results, dict):
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iterable = task_results.items()
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elif isinstance(task_results, list):
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iterable = enumerate(task_results)
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else:
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iterable = [("result", task_results)]
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for task_id, task_result in iterable:
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print(f"\nTask {task_id} Result:")
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raw_value = getattr(task_result, "raw", None)
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print(raw_value if raw_value is not None else str(task_result)) |