update tests data

tests/itest_golden_data/advanced_output_handling_ipynb.json

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-"def bubble_sort(arr):\n    n = len(arr)\n    for i in range(n):\n        for j in range(0, n-i-1):\n            if arr[j] > arr[j+1]:\n                arr[j], arr[j+1] = arr[j+1], arr[j]\n    return arr\n\n# Test the bubble sort implementation\nsample_list = [64, 34, 25, 12, 22, 11, 90]\nsorted_list = bubble_sort(sample_list)\nprint('Sorted list:', sorted_list)\n\nThe bubble_sort function sorts a list using the bubble sort algorithm. It repeatedly steps through the list, compares adjacent elements, and swaps them if they are in the wrong order. This process is repeated until the list is sorted. The outer loop runs n times, and the inner loop runs n-i-1 times, where n is the length of the list and i is the current iteration of the outer loop. The test case demonstrates sorting a sample list."
+"def bubble_sort(arr):\n    n = len(arr)\n    for i in range(n):\n        for j in range(0, n-i-1):\n            if arr[j] > arr[j+1]:\n                arr[j], arr[j+1] = arr[j+1], arr[j]\n    return arr\n\n# Test the bubble sort implementation\nsample_list = [64, 34, 25, 12, 22, 11, 90]\nsorted_list = bubble_sort(sample_list)\nprint(sorted_list)\n\nThe bubble sort algorithm repeatedly steps through the list, compares adjacent elements, and swaps them if they are in the wrong order. The pass through the list is repeated until the list is sorted.\n\nExplanation:\n1. The `bubble_sort` function takes a list `arr` as input.\n2. The variable `n` is set to the length of the list.\n3. The outer loop runs `n` times, where `i` ranges from 0 to `n-1`.\n4. The inner loop runs from 0 to `n-i-1`, comparing each pair of adjacent elements.\n5. If the element at position `j` is greater than the element at position `j+1`, they are swapped.\n6. This process is repeated until the list is sorted.\n7. The sorted list is returned."

tests/itest_golden_data/math_via_python_code_with_a_single_agent_ipynb.json

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-"To solve the system of linear equations:\n\n\\[ 725x + 727y = 1500 \\]\n\\[ 729x + 731y = 1508 \\]\n\nwe can use the method of solving simultaneous equations. Using sympy, we find the values of \\( x \\) and \\( y \\) as follows:\n\nFirst, we represent the system of equations in matrix form and solve it:\n\n\\[\n\\begin{cases}\n725x + 727y = 1500 \\\\\n729x + 731y = 1508\n\\end{cases}\n\\]\n\nUsing sympy, we find:\n\n\\[\nx = -23, \\quad y = 25\n\\]\n\nNext, we calculate \\( x - y \\):\n\n\\[\nx - y = -23 - 25 = -48\n\\]\n\nThus, the values are:\n\n\\[\nx = -23, \\quad y = 25, \\quad x - y = -48\n\\]"
+"To solve the system of linear equations:\n\n\\[ 725x + 727y = 1500 \\]\n\\[ 729x + 731y = 1508 \\]\n\nwe use the sympy library to find the values of \\( x \\) and \\( y \\).\n\nFirst, we define the equations:\n\n\\[ 725x + 727y = 1500 \\]\n\\[ 729x + 731y = 1508 \\]\n\nUsing sympy to solve these equations, we find:\n\n\\[ x = -23 \\]\n\\[ y = 25 \\]\n\nTo find \\( x - y \\):\n\n\\[ x - y = -23 - 25 = -48 \\]\n\nThus, the values are:\n\n\\[ x = -23 \\]\n\\[ y = 25 \\]\n\\[ x - y = -48 \\]"

tests/itest_golden_data/multi_step_research_agent_ipynb.json

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-"Why did Arjuna kill Karna, his half-brother?\n\nArjuna killed Karna, his half-brother, primarily due to Krishna's influence and command. During their battle, Karna's chariot got stuck in the mud, and he called out to Arjuna and Krishna, claiming they were honor-bound to let him fix it. Krishna retorted by questioning the honor in Karna's past actions, such as the humiliation of Draupadi and the killing of Abhimanyu. When Karna attempted to use the Brahmastra but forgot the mantra, Krishna saw an opportunity and shouted to Arjuna, \"Kill him!\" Despite seeing Karna defenseless and hesitating, Arjuna ultimately obeyed Krishna's command and shot an arrow that killed Karna."
+"Why did Arjuna kill Karna, his half-brother?\n\nKarna's past actions, such as the humiliation of Draupadi and the killing of Abhimanyu, played a significant role in Arjuna's decision to kill him, primarily through Krishna's influence. During the battle, when Karna's chariot got stuck in the mud, he appealed to Arjuna and Krishna to honorably allow him to fix it. Krishna responded by questioning the honor in Karna's past deeds, specifically mentioning the humiliation of Draupadi and the butchering of Abhimanyu. This reminder of Karna's dishonorable actions served to justify the killing in the context of the war's moral framework. When Karna attempted to use the Brahmastra but forgot the mantra, Krishna commanded Arjuna to kill him. Despite Arjuna's initial hesitation, seeing Karna defenseless, he ultimately obeyed Krishna's command and killed Karna. Thus, Krishna's invocation of Karna's past misdeeds and his direct order were crucial in Arjuna's decision to kill Karna."

tests/itest_golden_data/validating_agent_output_ipynb.json

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-"### Comprehensive Analysis of AI Advancements in 2024\n\n#### 1. Key Trends in AI 2024\n\n1. **Generative AI**: Generative AI continues to evolve and become more accessible to the general public. Tools like ChatGPT have reached mass adoption, and 2024 is expected to see more people experimenting with various AI models. This trend is driving significant changes in how businesses and individuals interact with technology.\n\n2. **Multimodal AI**: Multimodal AI, which processes multiple types of data (e.g., text, images, audio) simultaneously, is gaining traction. This advancement allows for more sophisticated and comprehensive AI applications, enhancing user experiences and expanding the potential use cases of AI.\n\n3. **Ethics and Safety**: There is a growing emphasis on the ethical and safe deployment of AI technologies. Organizations are increasingly focusing on developing AI systems that are transparent, fair, and aligned with regulatory standards to mitigate risks and build trust with users.\n\n4. **Integration of AI in Daily Life**: AI is becoming seamlessly integrated into daily life, with applications ranging from personal assistants to smart home devices. This trend is expected to continue, making AI an indispensable part of everyday activities.\n\n#### 2. Breakthrough AI Technologies in 2024\n\n1. **AI-Powered Scientific Discovery**: AI is revolutionizing scientific research by accelerating the discovery process. AI algorithms are being used to analyze vast amounts of data, identify patterns, and generate new hypotheses, leading to breakthroughs in various scientific fields.\n\n2. **Elastocalorics**: This emerging technology involves materials that change temperature when subjected to mechanical stress. AI is being used to optimize these materials for applications in energy storage and cooling systems, contributing to more efficient and sustainable technologies.\n\n3. **State-of-the-Art AI Models**: Advanced AI models are being developed to tackle complex problems in areas such as healthcare, finance, and manufacturing. These models leverage deep learning, reinforcement learning, and other cutting-edge techniques to deliver unprecedented performance and accuracy.\n\n4. **AI in Physics**: AI is making significant strides in the field of physics. Researchers are using generative AI to develop physics-informed techniques for classifying phase transitions in materials, which is more efficient than existing methods. Additionally, AI is being coupled with fundamental physics to accelerate calculations and uncover new mechanisms in physical systems.\n\n#### 3. Potential Industry Impacts of AI Advancements in 2024\n\n1. **Manufacturing**: The manufacturing industry is expected to see the largest financial impact from AI. AI-driven automation, predictive maintenance, and quality control are enhancing productivity and reducing costs, leading to significant economic benefits.\n\n2. **Healthcare**: AI is transforming healthcare by enabling personalized medicine, improving diagnostic accuracy, and streamlining administrative processes. These advancements are improving patient outcomes and reducing healthcare costs.\n\n3. **Finance**: In the finance sector, AI is being used for fraud detection, risk management, and algorithmic trading. These applications are enhancing the efficiency and security of financial transactions, driving growth and innovation in the industry.\n\n4. **Energy**: AI is playing a crucial role in optimizing energy consumption and integrating renewable energy sources. AI-powered systems are being used to manage energy grids, predict energy demand, and improve the efficiency of energy storage solutions.\n\n### Conclusion\n\nThe advancements in AI in 2024 are poised to bring transformative changes across various industries. Key trends such as generative AI, multimodal AI, and a focus on ethics and safety are shaping the future of AI. Breakthrough technologies like AI-powered scientific discovery, elastocalorics, and AI applications in physics are driving innovation, while the potential industry impacts highlight the significant economic and societal benefits of AI. As AI continues to evolve, it will be essential for organizations to stay informed and adapt to these changes to harness the full potential of AI technologies."
+"### Comprehensive Analysis of AI Advancements in 2024\n\n#### Key Trends\n1. **Generative AI Integration**: Generative AI, which gained significant attention in previous years, is now becoming more useful for the general public. Tools like ChatGPT have reached mass adoption, and organizations are deriving substantial business value from these technologies.\n\n2. **Multimodal AI**: This trend involves AI systems that can process and integrate multiple types of data (e.g., text, images, audio) simultaneously. This capability enhances the versatility and applicability of AI across various domains.\n\n3. **Ethics and Safety**: There is a growing emphasis on the ethical deployment of AI and ensuring safety in AI applications. This includes addressing biases, ensuring transparency, and complying with evolving regulatory landscapes.\n\n4. **AI Democratization**: More people are experimenting with AI models, leading to a proliferation of small, specialized AI applications. This democratization is making AI accessible to non-tech individuals and small businesses.\n\n5. **Consolidation of Data and AI Leadership**: Organizations are streamlining their technology and data leadership roles, reducing the number of chief data and analytics officers to create more integrated and efficient leadership structures.\n\n#### Breakthrough Technologies\n1. **AI-Powered Scientific Discovery**: AI is being used to accelerate scientific research and discovery, particularly in fields like drug development and materials science.\n\n2. **Elastocalorics**: This emerging technology involves materials that can change temperature under mechanical stress, offering potential applications in energy-efficient cooling systems.\n\n3. **AI in Clean Energy**: AI systems are being developed to store clean energy as heat, which could significantly contribute to decarbonizing industries.\n\n4. **Advanced Large Language Models**: Continued advancements in large language models are enabling more sophisticated natural language processing capabilities, enhancing applications in customer service, content creation, and more.\n\n5. **Cutting-Edge Robotics**: Innovations in robotics, powered by AI, are leading to more autonomous and capable machines that can perform complex tasks in manufacturing, healthcare, and other sectors.\n\n#### Potential Industry Impacts\n1. **Manufacturing**: The manufacturing sector is expected to see the largest financial impact from AI, with significant improvements in efficiency, predictive maintenance, and supply chain optimization.\n\n2. **Healthcare**: AI is revolutionizing healthcare through advancements in diagnostics, personalized medicine, and robotic surgery, leading to better patient outcomes and reduced costs.\n\n3. **Finance**: The finance industry is leveraging AI for fraud detection, algorithmic trading, and personalized financial services, enhancing security and customer experience.\n\n4. **Retail**: AI is transforming retail by enabling personalized shopping experiences, optimizing inventory management, and improving customer service through chatbots and recommendation systems.\n\n5. **Energy**: AI is playing a crucial role in the energy sector by optimizing energy consumption, integrating renewable energy sources, and improving grid management.\n\n6. **Physics**: AI is being used to uncover mechanisms in physics, such as classifying phase transitions in materials and enhancing computational efficiency by coupling AI with fundamental physics. These applications are driving new paradigms in AI discovery and promoting advancements in theoretical and applied physics.\n\nIn conclusion, 2024 is poised to be a pivotal year for AI, with significant advancements and widespread adoption across various industries. The focus on ethical AI, multimodal capabilities, and breakthrough technologies will drive innovation and create substantial economic and societal impacts."