Mastering Google ADK: Building AI Agents with Review Gemini AI – Top 10 Skills

Google’s Agent Development Kit (ADK) offers a powerful framework for building sophisticated AI agents capable of performing complex tasks and interacting with the world through various tools and APIs. Leveraging the strengths of Google’s large language models (LLMs), especially Gemini AI, the ADK empowers developers to create intelligent agents that can automate processes, provide personalized experiences, and solve intricate problems. To truly master the ADK and build high-performing AI agents with review capabilities powered by Gemini AI, focusing on these ten key skills is crucial:

1. Deep Understanding of the ADK Architecture & Core Components:

The ADK is built upon a modular architecture that requires a solid grasp of its core components. This includes understanding the roles and functionalities of:

• Agent: The central orchestrator, responsible for deciding which tools to use, when to use them, and how to interpret their results. A thorough understanding of the agent’s lifecycle, including planning, execution, and observation, is paramount. Mastery involves knowing how to configure the agent’s behavior through parameters like max_iterations 和 max_duration, optimizing its decision-making process for specific tasks. Furthermore, comprehending the different agent types, such as ReAct agents (Reasoning and Acting), and when to utilize each type based on the complexity of the problem is critical. For example, a ReAct agent is well-suited for tasks requiring multiple steps of reasoning and action, while a simpler agent might suffice for straightforward tasks.

• 工具 The building blocks that agents use to interact with the outside world. This could include search engines, databases, APIs, or even custom-built tools. Mastering tool integration means understanding how to define tools using the ADK’s schema, ensuring they’re properly documented and easy for the agent to understand. This includes crafting clear descriptions for the tool’s purpose and input parameters, as well as handling potential errors or exceptions that might arise during tool execution. Developing custom tools often involves wrapping existing functionalities within a well-defined API, allowing the agent to seamlessly access and utilize them. Understanding the concept of tool grounding is also crucial – ensuring the agent understands the capabilities and limitations of each tool to prevent misuse or unrealistic expectations.

• 内存 Agents need a persistent memory system to store and retrieve information, allowing them to learn from past experiences and adapt to changing environments. The ADK offers various memory options, including vector databases and relational databases. Mastery involves selecting the appropriate memory type for the agent’s specific needs, understanding how to index and query the memory effectively, and managing the memory’s capacity and cost. This includes techniques like summarization to reduce the size of stored information and relevance filtering to prioritize the most important data. For AI agents dealing with review analysis, the memory system would ideally store past reviews, relevant product information, and any learned patterns or sentiment tendencies.

• Message Queues: Enabling communication and data flow between different components of the system, including the agent, tools, and memory. Understanding message queue mechanisms is vital for building scalable and resilient AI agent systems.

2. Gemini AI Integration and Prompt Engineering:

Gemini AI provides the intelligence behind the ADK agents. Mastering prompt engineering is crucial for effectively guiding Gemini’s behavior. This involves crafting clear, concise, and informative prompts that accurately convey the agent’s task and desired outcome. For review analysis, prompts might include instructions for identifying sentiment, extracting key features, comparing reviews, or generating summaries. Techniques like few-shot learning (providing examples of desired outputs) and chain-of-thought prompting (encouraging the agent to explain its reasoning) can significantly improve the quality of the agent’s responses. Understanding Gemini AI’s specific strengths and weaknesses is also important. For example, knowing its capabilities in handling nuanced language and its potential biases can help developers create more robust and reliable review analysis agents. Furthermore, understanding the parameters that control Gemini AI’s output, such as temperature and top_p, is important for fine-tuning the agent’s creativity and coherence.

3. Review Data Acquisition and Preprocessing:

AI agents for review analysis are only as good as the data they consume. Mastering review data acquisition involves collecting data from various sources, such as e-commerce websites, social media platforms, and app stores. This often requires web scraping techniques or utilizing APIs to access review data. Data preprocessing is equally important, as raw review data often contains noise and inconsistencies. This includes cleaning the data by removing irrelevant characters, correcting spelling errors, and standardizing formatting. Sentiment analysis often benefits from stemming or lemmatization, which reduces words to their root form. Furthermore, techniques like tokenization (splitting text into individual words or phrases) and vectorization (converting text into numerical representations) are essential for preparing the data for Gemini AI. Understanding different data formats (e.g., JSON, CSV) and efficiently parsing and processing them is also crucial.

4. Sentiment Analysis and Emotion Detection:

The ability to accurately identify and classify sentiment (positive, negative, neutral) is fundamental for review analysis. Mastering sentiment analysis involves understanding different techniques, such as lexicon-based approaches, machine learning models, and deep learning models. Gemini AI can be used to perform sentiment analysis directly through prompt engineering, or it can be integrated with existing sentiment analysis libraries. Emotion detection goes beyond sentiment analysis by identifying specific emotions expressed in the text, such as joy, sadness, anger, and fear. Mastering emotion detection can provide deeper insights into customer opinions and help identify potential areas of concern. Training custom sentiment analysis models on domain-specific review data can significantly improve accuracy.

5. Aspect-Based Sentiment Analysis (ABSA):

Going beyond overall sentiment, ABSA focuses on identifying the sentiment associated with specific aspects or features of a product or service. For example, a review might express positive sentiment towards the "battery life" of a phone but negative sentiment towards the "camera quality." Mastering ABSA involves identifying the relevant aspects, extracting the sentiment associated with each aspect, and aggregating the results to provide a comprehensive understanding of customer opinions. Techniques like dependency parsing and entity recognition can be used to identify aspects and their corresponding sentiment. ABSA is crucial for identifying specific areas where a product or service excels or needs improvement.

6. Text Summarization and Key Information Extraction:

The ability to summarize long reviews or sets of reviews is essential for providing concise overviews of customer opinions. Mastering text summarization involves understanding different techniques, such as extractive summarization (selecting the most important sentences) and abstractive summarization (generating new sentences that convey the main points). Gemini AI excels at abstractive summarization and can be used to generate high-quality summaries of reviews. Key information extraction involves identifying and extracting specific pieces of information from reviews, such as product features, prices, and contact information. Named entity recognition (NER) and regular expressions can be used to extract this information.

7. Bias Detection and Mitigation:

AI models can inherit biases from the data they are trained on, leading to unfair or discriminatory outcomes. Mastering bias detection involves identifying potential biases in review data and Gemini AI’s responses. This includes biases related to gender, race, ethnicity, and other sensitive attributes. Techniques like adversarial training and data augmentation can be used to mitigate biases in the training data. It’s also important to carefully evaluate the model’s performance across different demographic groups to ensure fairness. Understanding the ethical implications of using AI for review analysis is paramount.

8. Agent Orchestration and Workflow Management:

Building complex AI agents often involves orchestrating multiple tools and tasks. Mastering agent orchestration involves designing and implementing workflows that efficiently guide the agent through the desired process. This includes defining the sequence of steps, handling errors, and managing dependencies. Tools like Apache Airflow can be used to manage complex workflows. For example, an agent might first retrieve reviews, then perform sentiment analysis, then extract aspects, and finally generate a summary report. Efficient workflow management is crucial for building scalable and reliable AI agent systems.

9. Monitoring, Logging, and Debugging:

Developing robust AI agents requires effective monitoring, logging, and debugging capabilities. Mastering these skills involves setting up monitoring systems to track the agent’s performance, logging key events and data, and using debugging tools to identify and fix errors. Tools like Prometheus and Grafana can be used to monitor the agent’s performance. Comprehensive logging is essential for understanding the agent’s behavior and diagnosing issues. Debugging tools like debuggers and log analyzers can help identify and resolve errors quickly. Continuously monitoring the agent’s performance and identifying areas for improvement is crucial for maintaining its effectiveness.

10. Continuous Learning and Adaptation:

The field of AI is constantly evolving. Mastering Google ADK requires a commitment to continuous learning and adaptation. This involves staying up-to-date with the latest research and developments in AI, experimenting with new techniques and tools, and adapting the agent’s design and implementation based on feedback and new data. Continuously retraining the model on new review data can help improve its accuracy and adapt to changing customer preferences. Participating in online communities and attending conferences can help stay informed about the latest trends in AI. Embracing a growth mindset and actively seeking opportunities to learn and improve are essential for long-term success in building AI agents with Google ADK.

By focusing on these ten skills, developers can effectively leverage Google ADK and Gemini AI to build powerful AI agents that can analyze reviews, extract insights, and provide valuable information for businesses and consumers alike. The combination of a strong foundational understanding of the ADK, proficiency in prompt engineering, and expertise in review data analysis will pave the way for creating intelligent agents that can truly master the art of understanding and interpreting customer opinions.

价格 $18.00 - $22.39
(as of Aug 28, 2025 04:48:21 UTC – 详细信息)

Mastering Google ADK: Build AI Agents with Review Gemini Ai

The world of Artificial Intelligence is rapidly evolving, moving beyond simple tasks to complex problem-solving and creative endeavors. At the forefront of this revolution is Google’s AI development kit, enabling developers and enthusiasts alike to craft intelligent agents that can interact with the world in meaningful ways. This article delves into the intricacies of leveraging the Google ADK to build sophisticated AI agents, with a particular focus on integrating and utilizing the power of Review Gemini Ai for enhanced performance and functionalities.

Unlocking the Potential of the Google AI Development Kit (ADK)

The Google AI Development Kit (ADK) isn’t just another set of libraries; it’s an ecosystem designed to empower developers to bring their AI visions to life. Think of it as a comprehensive toolbox filled with pre-trained models, APIs, and development tools that streamline the AI agent creation process. It provides a foundation for building agents capable of perception, reasoning, and action. The Google ADK encompasses a wide array of capabilities, from natural language processing (NLP) to computer vision, allowing you to create agents that can understand human language, analyze images, and make informed decisions based on their surroundings. Essentially, the ADK allows you to build an AI’s brain, senses, and motor skills.

One of the key advantages of the Google ADK is its accessibility. It’s designed to be relatively easy to learn and use, even for developers with limited prior experience in AI. The comprehensive documentation, tutorials, and code samples make it easier to get started and quickly prototype your ideas. Moreover, the ADK integrates seamlessly with other Google Cloud services, offering a scalable and robust infrastructure for deploying your AI agents. For example, you can easily deploy your AI agents on Google Cloud Platform (GCP) and leverage its compute resources, storage, and networking capabilities. This scalability is crucial as your AI agents become more complex and handle larger amounts of data. Using the Google ADK, you can build anything from simple chatbots to complex autonomous systems.

The core of the Google ADK lies in its powerful AI models. These models are pre-trained on vast datasets and can be fine-tuned for specific tasks. This eliminates the need to train models from scratch, saving you significant time and resources. For instance, if you are building an AI agent that needs to understand customer reviews, you can leverage pre-trained sentiment analysis models to quickly analyze the text and identify the overall sentiment (positive, negative, or neutral). This ability to rapidly prototype and deploy AI agents makes the Google ADK a valuable tool for developers across various industries.

Delving Deeper: Key Components of the Google ADK

To truly master the Google ADK, it’s essential to understand its constituent parts. The ADK is not a monolithic entity; rather, it’s a collection of tools and services that work together to facilitate AI development. Let’s break down some of the most important components:

• TensorFlow: This is the bedrock of the Google ADK. TensorFlow is an open-source machine learning framework that provides a comprehensive set of tools for building and training AI models. It supports a wide range of programming languages, including Python, C++, and Java.
• Keras: A high-level API built on top of TensorFlow, Keras simplifies the process of building and training neural networks. It provides a more intuitive and user-friendly interface for working with TensorFlow, making it accessible to developers with varying levels of experience.
• Cloud TPUs: These are custom-designed AI accelerator chips optimized for TensorFlow. Cloud TPUs provide significantly faster training times compared to traditional GPUs, enabling you to build and train more complex AI models in a fraction of the time.
• Vertex AI: This is Google Cloud’s unified machine learning platform. Vertex AI provides a comprehensive set of tools for managing the entire machine learning lifecycle, from data preparation to model deployment and monitoring. It simplifies the process of building, training, and deploying AI models at scale.
• AI Platform Notebooks: Cloud-based Jupyter notebooks pre-configured with the necessary tools and libraries for AI development. They offer a convenient and collaborative environment for building and experimenting with AI models.

Understanding these components and how they interact is crucial for effectively utilizing the Google ADK. It allows you to choose the right tools for the job and optimize your AI development workflow. For example, if you are working on a computationally intensive AI model, you might consider using Cloud TPUs to accelerate the training process. Similarly, if you need to manage the entire machine learning lifecycle, Vertex AI can provide a unified platform for doing so.

Review Gemini Ai: Augmenting AI Agents with Review Analysis Capabilities

Now, let’s introduce Review Gemini Ai, a specialized AI model designed to analyze and understand text-based reviews. Imagine an AI agent that can not only process and understand language but can also extract valuable insights from customer reviews, product feedback, and social media mentions. Review Gemini Ai empowers your AI agents to do just that. It’s more than just sentiment analysis; it delves deeper into the nuances of language to identify key themes, extract specific opinions, and understand the underlying reasons behind customer satisfaction or dissatisfaction. By incorporating Review Gemini Ai into your AI agent, you can unlock a wealth of valuable information that can be used to improve products, services, and customer experiences.

Consider a scenario where you’re building an AI agent for a restaurant chain. By integrating Review Gemini Ai, the agent can automatically analyze online reviews from platforms like Yelp and Google Reviews. It can identify common themes, such as slow service, delicious food, or unclean restrooms. This information can then be used to identify areas where the restaurant chain needs to improve. For example, if the agent detects a recurring theme of slow service, it can alert the restaurant management to investigate and address the issue. This proactive approach can help the restaurant chain improve customer satisfaction and retain customers. 人工智能机器人评论 can also benefit from the use of Review Gemini AI, providing more detailed and insightful summaries of user experiences.

Review Gemini Ai goes beyond simple sentiment analysis by offering features such as aspect-based sentiment analysis. This allows you to identify the sentiment expressed towards specific aspects of a product or service. For example, in the restaurant scenario, you can identify the sentiment towards the food, the service, the ambiance, and the price. This granular level of detail provides a more comprehensive understanding of customer opinions and allows you to target specific areas for improvement. It can also be used to identify your strengths and leverage them to attract more customers.

Another key advantage of Review Gemini Ai is its ability to handle different languages. It supports a wide range of languages, allowing you to analyze reviews from customers all over the world. This is particularly important for businesses that operate in multiple countries or cater to a diverse customer base. By understanding customer opinions in different languages, you can tailor your products and services to meet the specific needs of each market. You can also identify emerging trends and adapt your business strategy accordingly. The capacity for Review Gemini Ai to be used in different languages gives AI Robots a wider reach.

Integrating Review Gemini Ai with Your Google ADK Agents

The integration of Review Gemini Ai with Google ADK agents is relatively straightforward. The first step is to obtain an API key from Review Gemini Ai. This API key will be used to authenticate your requests and access the Review Gemini Ai service. Once you have your API key, you can use it to send text-based reviews to the Review Gemini Ai API. The API will then analyze the text and return a JSON response containing the sentiment score, key themes, and other relevant information.

You can use the Google ADK’s NLP capabilities to preprocess the text before sending it to the Review Gemini Ai API. This can involve tasks such as tokenization, stemming, and lemmatization. Preprocessing the text can improve the accuracy and efficiency of the Review Gemini Ai analysis. For example, you can remove stop words (e.g., “the,” “a,” “an”) and convert all words to lowercase to reduce the dimensionality of the text. This can also help to normalize the text and make it more consistent.

Once you have received the JSON response from the Review Gemini Ai API, you can use the Google ADK’s data analysis tools to extract and analyze the relevant information. This can involve tasks such as filtering, sorting, and aggregating the data. You can also use the Google ADK’s visualization tools to create charts and graphs that summarize the key findings. For example, you can create a bar chart that shows the overall sentiment score for each product or service. You can also create a word cloud that highlights the most frequently mentioned themes in the reviews.

Here’s a simplified Python code snippet showcasing the integration:

蟒蛇
进口请求

def analyze_review(review_text, api_key):
url = “https://api.reviewgeminiai.com/analyze” # Replace with the actual API endpoint
headers = {“Content-Type”: “application/json”, “Authorization”: f”Bearer {api_key}”}
data = {“text”: review_text}

response = requests.post(url, headers=headers, json=data)

if response.status_code == 200:
return response.json()
否则
print(f”Error: {response.status_code} – {response.text}”)
return None

# Example usage
api_key = “YOUR_REVIEW_GEMINI_AI_API_KEY” # Replace with your actual API key
review_text = “This restaurant has amazing food, but the service was slow.”
analysis_result = analyze_review(review_text, api_key)

if analysis_result:
print(f”Sentiment: {analysis_result[‘sentiment’]}”)
print(f”Key themes: {analysis_result[‘themes’]}”)

This code provides a basic framework for integrating Review Gemini Ai into your Google ADK agents. You can customize this code to fit your specific needs and requirements. Remember to replace `”YOUR_REVIEW_GEMINI_AI_API_KEY”` with your actual API key.

真实世界的应用和使用案例

The combination of the Google ADK and Review Gemini Ai unlocks a wide range of possibilities across various industries. Let’s explore some concrete examples:

• E-commerce: AI agents can analyze product reviews to identify areas for improvement, personalize product recommendations, and detect fraudulent reviews. This can help e-commerce businesses improve product quality, increase sales, and protect their brand reputation.
• Hospitality: AI agents can analyze hotel and restaurant reviews to identify areas for improvement in customer service, food quality, and ambiance. This can help hospitality businesses improve customer satisfaction, attract more customers, and increase revenue.
• 医疗保健： AI agents can analyze patient feedback to identify areas for improvement in patient care, communication, and overall experience. This can help healthcare providers improve patient outcomes, reduce readmission rates, and enhance their reputation.
• Financial Services: AI agents can analyze customer reviews and social media mentions to identify areas for improvement in customer service, product offerings, and overall brand perception. This can help financial services companies improve customer loyalty, attract new customers, and mitigate risks.
• Market Research: AI agents can analyze customer reviews, social media posts, and survey responses to identify trends, understand customer preferences, and gain insights into market dynamics. This can help businesses make informed decisions about product development, marketing strategies, and overall business strategy.

These are just a few examples of the many ways in which the Google ADK and Review Gemini Ai can be used to solve real-world problems. The possibilities are endless, and as AI technology continues to evolve, we can expect to see even more innovative applications emerge in the future. 桌面机器人助手 could even use these tools to provide better, more personalized assistance based on user feedback and online sentiment.

Addressing Common Challenges and Best Practices

While the Google ADK and Review Gemini Ai offer powerful capabilities, it’s important to be aware of some common challenges and best practices to ensure successful implementation:

• Data Quality: The accuracy and reliability of your AI agent depend heavily on the quality of the data used to train and evaluate it. Ensure that your data is clean, relevant, and representative of the real-world scenarios that your agent will encounter.
• 减少偏差： AI models can inherit biases from the data they are trained on. It’s crucial to identify and mitigate these biases to ensure that your AI agent makes fair and unbiased decisions. Use techniques such as data augmentation, adversarial training, and bias detection tools to address this issue.
• 伦理方面的考虑： As AI technology becomes more powerful, it’s important to consider the ethical implications of its use. Ensure that your AI agent is used responsibly and ethically, and that it does not perpetuate harmful stereotypes or discriminate against certain groups.
• 可扩展性： As your AI agent handles larger amounts of data and more complex tasks, it’s important to ensure that it can scale efficiently. Use cloud-based infrastructure and optimization techniques to improve the performance and scalability of your AI agent.
• 安全： Protect your AI agent from malicious attacks and unauthorized access. Implement security measures such as authentication, authorization, and encryption to protect your data and models.

By addressing these challenges and following best practices, you can increase the likelihood of success and ensure that your AI agents are reliable, ethical, and scalable. Remember that AI development is an iterative process, so be prepared to experiment, learn, and adapt your approach as you go.

Comparison Table for AI Development Platforms:

FAQ: Your Questions Answered

Here are some frequently asked questions about using the Google ADK and Review Gemini Ai to build AI agents:

What are the prerequisites for using the Google ADK?

The primary prerequisites for using the Google ADK involve having a Google Cloud Platform (GCP) account. This account will provide you with access to the necessary resources and services, including virtual machines, storage, and the AI Platform. You’ll also need a basic understanding of programming, particularly Python, as it’s the most commonly used language for AI development. Familiarity with machine learning concepts, such as neural networks and model training, is also beneficial. However, the Google ADK offers a variety of tools and resources that can help beginners get started, so you don’t necessarily need to be an expert in machine learning to begin experimenting. Setting up your environment with the necessary SDKs and libraries is also essential, and Google provides comprehensive documentation to guide you through the process.

How does Review Gemini Ai differ from standard sentiment analysis tools?

Review Gemini Ai distinguishes itself from standard sentiment analysis tools through its advanced capabilities and focus on granular insights. While traditional sentiment analysis typically provides a general positive, negative, or neutral sentiment score, Review Gemini Ai delves deeper to identify specific themes, aspects, and emotions expressed within text. For instance, instead of simply classifying a restaurant review as negative, Review Gemini Ai can pinpoint that the food was praised (positive sentiment) while the service was criticized (negative sentiment). This level of detail enables businesses to understand the nuanced reasons behind customer satisfaction or dissatisfaction. Furthermore, Review Gemini Ai often incorporates advanced techniques like aspect-based sentiment analysis, which allows users to analyze sentiment towards specific features or attributes of a product or service, providing a more comprehensive understanding of customer feedback.

Can I use the Google ADK and Review Gemini Ai for languages other than English?

Yes, both the Google ADK and Review Gemini Ai offer support for multiple languages. The Google ADK leverages its robust natural language processing (NLP) capabilities to handle various languages, enabling you to build AI agents that can understand and interact with users in their native tongues. Review Gemini Ai is also designed to be multilingual, allowing you to analyze reviews and extract insights from text written in different languages. This is particularly useful for businesses that operate globally or cater to a diverse customer base. However, it’s important to note that the accuracy and performance of language models may vary depending on the specific language and the availability of training data. It’s always recommended to test your AI agents with data in the target languages to ensure optimal performance.

What are the cost implications of using the Google ADK and Review Gemini Ai?

The cost of using the Google ADK and Review Gemini Ai can vary depending on several factors, including the scale of your project, the amount of data you process, and the specific services you utilize. The Google ADK operates on a pay-as-you-go model, meaning you only pay for the resources you consume. This includes costs associated with compute instances, storage, and API usage. Review Gemini Ai typically offers different pricing tiers based on the number of API calls or the volume of text processed. It’s essential to carefully evaluate your project requirements and estimate your usage to determine the potential costs. Google Cloud Platform also offers a free tier that provides limited access to certain services, which can be useful for experimenting and prototyping.

How do I ensure the ethical use of AI agents built with the Google ADK and Review Gemini Ai?

Ensuring the ethical use of AI agents built with the Google ADK and Review Gemini Ai is a crucial responsibility. This involves carefully considering the potential biases in your data and algorithms, and taking steps to mitigate them. For instance, if your training data is skewed towards a particular demographic, your AI agent may exhibit discriminatory behavior. It’s also important to be transparent about how your AI agent works and how it makes decisions. Explainable AI (XAI) techniques can help you understand the reasoning behind your AI agent’s predictions and identify potential issues. Additionally, you should establish clear guidelines and policies for the use of your AI agent, and regularly audit its performance to ensure that it’s behaving ethically and responsibly. Prioritizing fairness, transparency, and accountability is essential for building trustworthy AI systems. 情感人工智能机器人, in particular, require careful consideration of ethical implications.

What level of coding experience is required to use the Google ADK effectively?

While the Google ADK is designed to be accessible to a wide range of developers, having some coding experience is beneficial for using it effectively. A basic understanding of programming concepts, such as variables, data structures, and control flow, is essential. Familiarity with Python is particularly helpful, as it’s the most commonly used language for AI development with the Google ADK. However, you don’t need to be an expert coder to get started. The Google ADK provides a wealth of documentation, tutorials, and code samples that can help you learn as you go. Additionally, there are many online resources and communities that can provide support and guidance. Even with limited coding experience, you can leverage the Google ADK to build and deploy powerful AI agents, as long as you’re willing to learn and experiment.

🔥 赞助广告

Eilik - 适合儿童和成人的可爱机器人宠物

现在的价格 $139.99

$149.00 6% 关闭

Miko 3：人工智能儿童智能机器人

现在的价格 $199.00

$249.00 20% 关闭

eufy 机器人真空吸尘器 11S MAX - 超薄、强劲、安静

现在的价格 $159.99

$279.99 43% 关闭

Ruko 1088 儿童智能机器人 - 可编程 STEM 玩具

现在的价格 $79.96

$129.96 38% 关闭

披露： didiar.com上的某些链接可能会为我们带来少量佣金，您无需支付额外费用。所有产品均通过第三方商家销售，并非由 didiar.com 直接销售。价格、供货情况和产品细节可能会有变化，请查看商家网站了解最新信息。

所有商标、产品名称和品牌标识均属于其各自所有者。didiar.com 是一个提供评论、比较和推荐的独立平台。我们与这些品牌没有任何关联，也没有得到任何品牌的认可，我们不负责产品的销售或履行。

didiar.com上的某些内容可能是由品牌赞助或与品牌合作创建的。为了与我们的独立评论和推荐区分开来，赞助内容会被明确标注。

更多详情，请参阅我们的 条款和条件.

：人工智能机器人技术中心 " Top 10 Mastering Google ADK: Build AI Agents with Review Gemini Ai