ADK V1.10.0 Docs: New Features & Updates Guide

by Rajiv Sharma 47 views

#table of contents

Introduction

Hey guys! We've got some exciting updates in the ADK v1.10.0 release, but it looks like our documentation needs a little love to catch up. This article dives deep into the documentation mismatches and outlines the necessary updates to keep you all in the loop. We'll cover everything from the new parallel function calling feature to the refactored agent and tool configurations. So, let’s get started and make sure you have all the right info to leverage these awesome new capabilities! This comprehensive guide ensures that you, our valued developers, have a clear understanding of the changes and can seamlessly integrate them into your projects. We're addressing critical updates, including new features, refactors, and essential modifications to existing tools and configurations. Each section provides a detailed overview, implementation guidance, and the reasoning behind the changes, empowering you to make the most of ADK v1.10.0. Let’s dive in and explore the enhancements that will elevate your development experience. We’ll walk through each change, explaining not just what's new, but why it matters and how it enhances your workflow. Consider this your go-to resource for navigating the latest ADK updates, ensuring you’re equipped with the knowledge to build more robust and efficient applications. We are committed to providing you with the most accurate and up-to-date information, so you can focus on what you do best: creating innovative solutions. So, stick around, and let's get this documentation up to speed together!

1. New Feature: Parallel Function Calling

Parallel function calling is a game-changer in the ADK, and we need to make sure the docs reflect this! The ADK now supports the parallel execution of function calls, which can significantly improve performance when an agent needs to call multiple tools at once. This is especially beneficial when dealing with I/O operations or other time-consuming tasks. Let's break down what this means and how to use it. Documenting this feature accurately is super important because it allows you guys to build more efficient and responsive applications. By leveraging parallel execution, you can reduce the overall execution time and improve the user experience. This feature is particularly useful in complex scenarios where multiple tools need to be invoked simultaneously, such as when processing natural language queries that require fetching data from multiple sources. The ability to execute function calls in parallel streamlines the workflow and ensures that your applications can handle a higher volume of requests without compromising performance. To make the most of this feature, it's essential to understand how to implement it correctly and the benefits it brings to your projects. We're going to walk you through the necessary steps and considerations to ensure you're well-equipped to use parallel function calling effectively. This documentation update is crucial for anyone looking to optimize their ADK applications and take full advantage of the platform's capabilities.

Understanding Parallel Function Calling

So, what exactly is parallel function calling? Simply put, it's the ability to run multiple functions at the same time, instead of one after the other. This is a major upgrade because many agents need to use several tools to complete a task. Imagine an agent that needs to fetch data from a database, perform a web search, and then analyze the results. With parallel function calling, these tasks can happen simultaneously, drastically reducing the overall time it takes to get the job done. This feature is particularly effective when dealing with asynchronous operations, such as network requests or file I/O, where the agent doesn't need to wait for one task to finish before starting another. By leveraging the power of parallel execution, you can significantly enhance the efficiency and responsiveness of your agents. This not only improves the performance of individual tasks but also allows your applications to handle a higher volume of requests with ease. To fully grasp the benefits of parallel function calling, it's crucial to understand the underlying mechanisms and how they differ from traditional sequential execution. We're here to break down the technical details and provide clear explanations, ensuring you have a solid foundation for implementing this powerful feature in your projects. So, let's dive deeper into the mechanics of parallel function calling and explore how it can transform your agent workflows.

Implementing Parallel Function Calling

To enable parallel function calling, you need to use asynchronous tools (defined with async def). The ADK will automatically execute these tools in parallel when they are called by the agent. This means you need to define your tools as asynchronous functions, which allows the ADK to manage their execution concurrently. It's a relatively straightforward process, but it's crucial to get the details right to ensure everything works smoothly. For a detailed example, you can refer to the Parallel Functions sample, which provides a practical demonstration of how to implement this feature. This sample will walk you through the steps of defining asynchronous tools, configuring your agent to use them, and observing the performance benefits of parallel execution. By following the example, you'll gain a hands-on understanding of how to integrate parallel function calling into your projects. It's also important to consider the dependencies between your tools and how they interact with each other when running in parallel. Proper planning and design are essential to avoid conflicts and ensure the stability of your application. We'll provide additional guidance and best practices to help you navigate these considerations and make the most of parallel function calling. So, let's move on to the practical aspects of implementation and see how you can start leveraging this feature in your own projects.

Benefits of Parallel Function Calling

The benefits of parallel function calling are huge! Faster execution times, improved agent responsiveness, and better resource utilization are just a few of the perks. By allowing multiple tools to run simultaneously, you can significantly reduce the time it takes for your agent to complete complex tasks. This is especially important in real-time applications where quick responses are crucial. Imagine an agent that needs to analyze customer sentiment from multiple sources, such as social media feeds and customer reviews. With parallel function calling, the agent can process these data streams concurrently, providing timely insights and improving the overall customer experience. Beyond speed, parallel function calling also leads to better resource utilization. By spreading the workload across multiple threads or processes, you can make more efficient use of your system's resources and handle a higher volume of requests. This is particularly beneficial in high-traffic environments where performance bottlenecks can impact the overall stability and scalability of your application. To fully appreciate the advantages of parallel function calling, it's essential to consider the impact on your specific use cases and how it can help you achieve your goals. We're here to highlight the key benefits and provide real-world examples to illustrate the transformative power of this feature. So, let's delve into the advantages of parallel function calling and see how it can elevate your agent workflows to the next level.

2. New Feature: Live Session Resumption

Next up, we have live session resumption, another fantastic addition to the ADK! This feature allows a live agent to reconnect to a session after a disconnection, preserving the conversation history and state. This is incredibly useful for mobile applications or any environment where network connectivity can be unreliable. Imagine a user chatting with an agent on their phone, and they briefly lose internet connection. With live session resumption, the conversation can pick up right where it left off, creating a seamless experience. This is a game-changer for user satisfaction and the overall reliability of your applications. Documenting this feature is vital because it addresses a common pain point in real-time interactions and provides a robust solution for maintaining continuity. By implementing live session resumption, you can ensure that your users have a smooth and uninterrupted experience, even in challenging network conditions. This not only enhances user engagement but also builds trust and credibility in your application. To make the most of this feature, it's essential to understand how it works and the steps required to integrate it into your projects. We're going to guide you through the implementation process and highlight the best practices for ensuring a seamless session resumption experience. So, let's dive into the details of live session resumption and see how it can transform the way you build and deploy real-time applications.

Understanding Live Session Resumption

So, how does live session resumption actually work? The key is the live_session_resumption_handle from the InvocationContext. You need to store this handle and use it to reconnect to the session. This handle acts as a unique identifier for the session, allowing the ADK to restore the conversation history and state. When a disconnection occurs, you can use the stored handle to initiate a new connection and seamlessly resume the session. This mechanism ensures that the user's context is preserved, and they can continue the conversation without losing any information. The ability to maintain session continuity is crucial for creating a positive user experience, especially in scenarios where network connectivity is unreliable. By leveraging live session resumption, you can minimize disruptions and provide a seamless interaction, regardless of the underlying network conditions. To fully appreciate the technical nuances of this feature, it's essential to understand the role of the InvocationContext and how the live_session_resumption_handle is generated and managed. We're here to provide a clear and concise explanation of these concepts, ensuring you have a solid understanding of the underlying mechanics. So, let's explore the inner workings of live session resumption and see how it empowers you to build more robust and user-friendly applications.

Implementing Live Session Resumption

To implement live session resumption, you'll need to dive into docs/streaming/dev-guide/part1.md and docs/streaming/custom-streaming-ws.md. These documents will guide you through the process of storing and using the live_session_resumption_handle. The general steps involve capturing the handle from the InvocationContext when a session is established, storing it securely (e.g., in a database or local storage), and then using it to reconnect to the session when needed. It's important to consider the security implications of storing the handle and ensure that it is protected from unauthorized access. You may also want to implement mechanisms for invalidating or rotating the handle to further enhance security. Additionally, you'll need to handle the reconnection logic in your application, including gracefully handling any errors that may occur during the resumption process. This may involve implementing retry mechanisms or providing feedback to the user if the session cannot be resumed. To make the implementation process smoother, it's helpful to have a clear understanding of the underlying streaming architecture and how the ADK manages sessions. We're here to provide guidance and best practices to help you navigate these technical considerations and ensure a successful implementation. So, let's delve into the practical aspects of implementing live session resumption and see how you can seamlessly integrate it into your streaming applications.

Use Cases for Live Session Resumption

The use cases for live session resumption are vast and varied. Think about mobile applications, where users frequently experience intermittent connectivity. This feature ensures that conversations aren't abruptly cut off, providing a much smoother user experience. Another great use case is in customer service applications, where agents can reconnect to a customer's session even if the connection drops. This prevents the need for customers to repeat themselves, saving time and frustration. Live session resumption is also invaluable in environments with unstable network conditions, such as remote locations or areas with poor cellular coverage. By maintaining session continuity, you can ensure that your applications remain usable and reliable, regardless of the network environment. Beyond these specific scenarios, live session resumption can also enhance the overall user experience by providing a sense of persistence and reliability. Users appreciate applications that can gracefully handle interruptions and seamlessly resume their activities. To fully leverage the benefits of this feature, it's essential to consider the specific needs of your users and the environments in which they will be using your application. We're here to highlight the key use cases and provide real-world examples to inspire you and help you identify opportunities to integrate live session resumption into your projects. So, let's explore the diverse applications of this feature and see how it can transform the way you build and deploy real-time applications.

3. New Feature: Enterprise Web Search Tool

We've also got a cool new tool to talk about: the enterprise web search tool! This built-in tool, enterprise_web_search_tool, allows agents to perform searches using a custom enterprise web search engine. This is a game-changer for organizations that need to integrate their internal knowledge bases and data sources into their agents. Imagine an agent that can seamlessly search through your company's intranet, documentation, and other internal resources to answer user queries. This not only enhances the agent's capabilities but also improves the overall efficiency of your organization. Documenting this tool is essential because it opens up new possibilities for building intelligent agents that can leverage enterprise-specific information. By integrating the enterprise web search tool, you can create agents that are more knowledgeable, responsive, and tailored to your organization's needs. This not only improves the quality of interactions but also reduces the reliance on manual search and information retrieval processes. To make the most of this tool, it's essential to understand how it works and the steps required to configure it for your specific environment. We're going to guide you through the implementation process and highlight the best practices for integrating the enterprise web search tool into your agent workflows. So, let's dive into the details of this exciting new feature and see how it can transform the way your organization uses intelligent agents.

Understanding the Enterprise Web Search Tool

So, what makes the enterprise web search tool so special? It's all about connecting your agents to your organization's unique knowledge ecosystem. Unlike generic web search tools, this tool allows you to integrate your internal search engine, ensuring that your agents can access the most relevant and up-to-date information specific to your organization. This is crucial for building agents that can provide accurate and context-aware responses. Imagine an agent that can answer questions about your company's products, policies, or procedures by seamlessly searching through your internal documentation and knowledge bases. This not only improves the agent's performance but also enhances the overall user experience. The enterprise web search tool is designed to be highly configurable, allowing you to tailor it to your specific needs and environment. You can integrate it with a variety of search engines and customize the search parameters to optimize the results. To fully appreciate the capabilities of this tool, it's essential to understand its architecture and how it interacts with your enterprise search infrastructure. We're here to provide a clear and concise explanation of these concepts, ensuring you have a solid understanding of the underlying mechanisms. So, let's explore the inner workings of the enterprise web search tool and see how it empowers you to build more knowledgeable and effective agents.

Utilizing the Enterprise Web Search Tool

To utilize the enterprise web search tool, you'll need to add a new section in docs/tools/built-in-tools.md that describes its functionality and how to configure it. This documentation should cover the necessary steps for integrating the tool with your enterprise search engine, including any required API keys or credentials. It should also provide guidance on how to customize the search parameters to optimize the results for your specific use case. Additionally, you may want to include examples of how the tool can be used in different scenarios, such as answering questions about company policies or retrieving product information. Providing clear and concise documentation is crucial for ensuring that users can easily integrate the enterprise web search tool into their agents. This includes explaining the tool's inputs and outputs, as well as any limitations or known issues. It's also helpful to provide troubleshooting tips and best practices for using the tool effectively. To make the documentation more accessible, consider using code snippets and diagrams to illustrate the configuration process and the tool's architecture. We're here to provide guidance and best practices to help you create comprehensive and user-friendly documentation for the enterprise web search tool. So, let's delve into the practical aspects of utilizing this tool and see how you can empower your agents to access enterprise-specific information.

Benefits of the Enterprise Web Search Tool

The benefits of the enterprise web search tool are clear: access to custom, internal knowledge, improved agent accuracy, and enhanced efficiency. By integrating this tool, your agents can tap into your organization's unique data sources, providing more relevant and accurate responses. This is especially important for complex queries that require access to internal documentation, policies, or procedures. Imagine an agent that can quickly retrieve the latest information about a company product or service, ensuring that users receive up-to-date and accurate answers. Beyond accuracy, the enterprise web search tool also enhances efficiency by automating the process of information retrieval. Agents can quickly search through vast amounts of data without the need for manual intervention, saving time and resources. This is particularly beneficial in high-volume environments where agents need to handle a large number of queries. The enterprise web search tool also improves the overall user experience by providing faster and more comprehensive responses. Users can get the information they need quickly and easily, without having to navigate through multiple systems or resources. To fully appreciate the advantages of this tool, it's essential to consider the impact on your specific use cases and how it can help you achieve your goals. We're here to highlight the key benefits and provide real-world examples to illustrate the transformative power of this feature. So, let's delve into the advantages of the enterprise web search tool and see how it can elevate your agent workflows to the next level.

4. Refactor: Agent and Tool Configuration

Alright, let's talk about the agent and tool configuration refactor. This is a significant change in how agents and tools are configured, making the process more declarative and config-driven. Instead of defining agents and tools programmatically, you can now use configuration files to specify their behavior and settings. This approach offers several advantages, including improved readability, maintainability, and flexibility. Imagine being able to define your agents and tools in a clear and concise configuration file, rather than wading through complex code. This not only makes it easier to understand the configuration but also simplifies the process of modifying and updating it. Documenting this refactor is crucial because it impacts the fundamental way users interact with the ADK. By providing clear and comprehensive documentation, we can ensure that users can seamlessly transition to the new configuration approach and take full advantage of its benefits. This includes explaining the new configuration format, the available options and settings, and the best practices for structuring your configuration files. We're going to guide you through the refactoring process and highlight the key changes and considerations. So, let's dive into the details of this important update and see how it can transform the way you configure your agents and tools.

Understanding the Refactor

So, what does it mean for the configuration to be more declarative and config-driven? In essence, it shifts the focus from how things are configured to what should be configured. Instead of writing code to create and configure agents and tools, you define their properties and behavior in a configuration file. The ADK then uses this configuration to create and manage the agents and tools automatically. This approach offers several advantages. First, it makes the configuration more readable and easier to understand. Configuration files are typically written in a structured format, such as YAML or JSON, which makes it easy to see the different settings and their values. Second, it improves maintainability. Changes to the configuration can be made directly in the configuration file, without the need to modify the underlying code. This reduces the risk of introducing errors and makes it easier to keep the configuration up to date. Third, it enhances flexibility. You can easily create different configurations for different environments or use cases, simply by changing the configuration file. To fully appreciate the benefits of this refactor, it's essential to understand the underlying principles of declarative configuration and how they differ from imperative configuration. We're here to provide a clear and concise explanation of these concepts, ensuring you have a solid understanding of the rationale behind the refactor. So, let's delve deeper into the principles of declarative configuration and see how they empower you to build more flexible and maintainable applications.

Implementing the New Configuration

To implement the new configuration, you'll need to update the docs/tutorials/agent-team.md tutorial. This involves showcasing the new from_config method and the associated config classes. The from_config method allows you to create agents and tools directly from a configuration file, eliminating the need to write code for manual instantiation and configuration. The config classes provide a structured way to define the properties and settings of your agents and tools in the configuration file. To update the tutorial, you'll need to replace the existing code snippets with examples that demonstrate the use of from_config and the config classes. This includes showing how to load the configuration file, how to create agents and tools from the configuration, and how to customize the settings using the available options. It's also important to explain the structure of the configuration file and the different sections that are used to define agents and tools. Providing clear and concise examples is crucial for ensuring that users can easily transition to the new configuration approach. This includes demonstrating how to define different types of agents and tools, how to configure their behavior, and how to integrate them into your application. We're here to provide guidance and best practices to help you create a comprehensive and user-friendly tutorial that showcases the new configuration capabilities. So, let's delve into the practical aspects of implementing the new configuration and see how you can simplify the way you manage your agents and tools.

Advantages of the Refactor

The advantages of the refactor are numerous. A more declarative approach, better maintainability, and increased flexibility are just the tip of the iceberg. By moving to a configuration-driven model, you can simplify the process of defining and managing your agents and tools. This reduces the amount of code you need to write and makes it easier to understand and modify the configuration. Improved maintainability is another key benefit. Configuration files are typically easier to manage and version control than code, making it simpler to track changes and roll back to previous versions if needed. This reduces the risk of introducing errors and makes it easier to keep your configuration up to date. Increased flexibility is also a major advantage. You can easily create different configurations for different environments or use cases, simply by changing the configuration file. This allows you to adapt your agents and tools to different situations without having to modify the underlying code. The refactor also promotes better code reuse. By defining your agents and tools in a configuration file, you can easily reuse the same configuration across multiple applications or environments. This reduces duplication and ensures consistency across your projects. To fully appreciate the benefits of this refactor, it's essential to consider the impact on your specific use cases and how it can help you achieve your goals. We're here to highlight the key advantages and provide real-world examples to illustrate the transformative power of this change. So, let's delve into the advantages of the refactor and see how it can elevate your agent development workflows to the next level.

5. Update: BigQuery Tools

Heads up! There's an update to the BigQuery tools: the ask_data_insights tool has been removed. This means we need to update the documentation to reflect this change. It's crucial to keep the documentation accurate to avoid confusion and ensure that users are using the correct tools. This update is important because the ask_data_insights tool is no longer available, and any documentation that references it is now outdated. Removing the tool from the documentation ensures that users are not misled and that they can focus on the available and supported tools. Documenting this change is essential for maintaining the integrity of the ADK documentation and providing a clear and accurate picture of the available functionality. By removing the reference to the ask_data_insights tool, we can prevent users from wasting time trying to use a tool that no longer exists. We're going to walk you through the necessary steps to update the documentation and highlight the implications of this change. So, let's dive into the details of the BigQuery tools update and see how it impacts your projects.

Understanding the BigQuery Tools Update

So, why was the ask_data_insights tool removed? While the specifics might vary, it's common for tools to be removed due to deprecation, redundancy, or changes in the underlying architecture. In this case, the removal of the ask_data_insights tool means that any functionality it provided is no longer available through that specific tool. This may be because the functionality has been integrated into another tool, or it may be that the functionality is no longer supported. It's important to understand the reasons behind the removal to ensure that you can adapt your code and workflows accordingly. This may involve migrating to a different tool or finding an alternative way to achieve the same result. The update also highlights the importance of staying up to date with the latest changes and announcements in the ADK. By keeping track of updates and deprecations, you can proactively adjust your code and avoid potential issues. We're here to provide a clear and concise explanation of the reasons behind the removal and to offer guidance on how to adapt your projects to this change. So, let's delve deeper into the implications of the BigQuery tools update and see how it impacts your development workflow.

Implications of the Update

The implications of the update are straightforward: you can no longer use the ask_data_insights tool. If you were using this tool in your projects, you'll need to find an alternative solution. This might involve using a different BigQuery tool, or it might require a more manual approach. It's important to assess the impact of this change on your existing code and workflows. Identify any places where you were using the ask_data_insights tool and determine the best way to replace it. This may involve rewriting code, reconfiguring your agents, or exploring alternative approaches. The update also highlights the importance of testing your code after making changes. By thoroughly testing your code, you can ensure that it continues to function correctly after the removal of the ask_data_insights tool. This includes testing the functionality that was previously provided by the tool, as well as any related code or workflows. We're here to provide guidance and best practices to help you navigate these implications and ensure a smooth transition. So, let's delve deeper into the implications of the BigQuery tools update and see how you can adapt your projects to this change.

Best Practices After the Update

Following best practices after the update is crucial to ensure a smooth transition. First, remove any references to ask_data_insights in your code. This includes removing the tool from your agent configurations and updating any code that uses the tool. Next, test your code thoroughly to ensure that it continues to function correctly. Pay particular attention to the functionality that was previously provided by the ask_data_insights tool. If you need to replace the functionality, consider using a different BigQuery tool or implementing a manual approach. Evaluate the available alternatives and choose the solution that best meets your needs. It's also important to update your documentation to reflect the removal of the ask_data_insights tool. This includes removing any references to the tool from your internal documentation and updating any tutorials or guides that you have created. By following these best practices, you can minimize the impact of the update and ensure that your projects continue to function smoothly. We're here to provide guidance and support to help you navigate this transition and make the most of the available BigQuery tools. So, let's delve into the best practices after the update and see how you can ensure a smooth transition.

6. Update: Claude Model Configuration

Good news for Claude fans! The Claude model wrapper now supports customizing max_tokens. This means you can control the maximum number of tokens generated by the Claude model, giving you more fine-grained control over its output. This is a valuable addition because it allows you to optimize the model's performance and resource usage. By setting the max_tokens parameter, you can limit the amount of text generated by the model, which can reduce the cost of running the model and improve its response time. It also allows you to prevent the model from generating overly long or rambling responses. Documenting this update is essential because it empowers users to take full advantage of the Claude model's capabilities. By providing clear and concise documentation on how to configure the max_tokens parameter, we can ensure that users can effectively control the model's output and optimize its performance. We're going to walk you through the steps of implementing this update and highlight the benefits of customizing the max_tokens parameter. So, let's dive into the details of the Claude model configuration update and see how it impacts your projects.

Understanding the Claude Model Update

So, what does the max_tokens parameter actually do? It sets a limit on the number of tokens that the Claude model can generate in its response. A token is a basic unit of text, typically a word or a part of a word. By controlling the number of tokens, you can influence the length and complexity of the model's output. This is particularly useful when you want to ensure that the model's responses are concise and focused. For example, if you're using the Claude model to generate short answers to questions, you might want to set a relatively low max_tokens value. On the other hand, if you're using the model to generate longer pieces of text, such as articles or stories, you might need to set a higher value. The update also highlights the importance of understanding the relationship between the max_tokens parameter and the cost of running the Claude model. The more tokens the model generates, the more resources it consumes, and the higher the cost. By carefully setting the max_tokens value, you can optimize the model's performance and cost-effectiveness. We're here to provide a clear and concise explanation of the max_tokens parameter and how it impacts the Claude model's behavior. So, let's delve deeper into the Claude model update and see how it empowers you to control the model's output.

Implementing the max_tokens Parameter

To implement the max_tokens parameter, you'll need to add it to the Claude model configuration example in docs/agents/models.md. This involves updating the configuration code snippet to include the max_tokens setting and providing a brief explanation of its purpose. The configuration example should clearly show how to set the max_tokens value and the range of values that are supported. It's also important to explain the relationship between the max_tokens value and the model's output. For example, you might want to mention that setting a low value can result in truncated responses, while setting a high value can lead to more verbose responses. Additionally, you may want to provide guidance on how to choose the appropriate max_tokens value for different use cases. This might involve recommending specific values for generating short answers, longer pieces of text, or code snippets. Providing clear and concise documentation is crucial for ensuring that users can easily implement the max_tokens parameter and take full advantage of its benefits. We're here to provide guidance and best practices to help you create a comprehensive and user-friendly documentation update. So, let's delve into the practical aspects of implementing the max_tokens parameter and see how you can control the Claude model's output.

Benefits of Customizing max_tokens

The benefits of customizing max_tokens are significant: you get more control over output length, cost savings, and improved performance. By setting the max_tokens parameter, you can ensure that the Claude model's responses are tailored to your specific needs. This is particularly useful when you want to generate concise answers or limit the amount of text generated by the model. Cost savings are another major benefit. By limiting the number of tokens generated, you can reduce the cost of running the Claude model. This is especially important if you're using the model in a high-volume environment. Improved performance is also a key advantage. By preventing the model from generating overly long responses, you can improve its response time and reduce the risk of generating irrelevant or nonsensical text. The ability to customize the max_tokens parameter also allows you to optimize the model for different use cases. For example, you might want to set a low value for generating short answers and a higher value for generating longer pieces of text. To fully appreciate the benefits of customizing the max_tokens parameter, it's essential to consider the impact on your specific use cases and how it can help you achieve your goals. We're here to highlight the key advantages and provide real-world examples to illustrate the transformative power of this feature. So, let's delve into the benefits of customizing max_tokens and see how it can elevate your Claude model workflows to the next level.

7. New Sample: Parallel Functions

Last but not least, we have a new sample for parallel functions! This sample, parallel_functions, is a fantastic resource for learning how to use the new parallel function calling feature. It demonstrates how to define asynchronous tools and how to call them in parallel, giving you a practical understanding of how this feature works. This is a valuable addition because it provides a hands-on learning experience that can help you quickly grasp the concepts and techniques involved in parallel function calling. By working through the sample, you can see how the code is structured, how the tools are defined, and how the parallel execution is managed. Documenting this sample is essential because it serves as a valuable learning resource for users who want to master the parallel function calling feature. By providing clear and concise documentation, we can ensure that users can easily understand the sample and use it to build their own parallel function applications. We're going to walk you through the key aspects of the sample and highlight the benefits of using it as a learning tool. So, let's dive into the details of the parallel functions sample and see how it can enhance your understanding of this powerful feature.

Understanding the Parallel Functions Sample

So, what's included in the parallel functions sample? It's based on the contributing/samples/parallel_functions/README.md file from the adk-python repository. This README provides a detailed overview of the sample, including its purpose, how it works, and how to run it. The sample typically includes a code example that demonstrates how to define asynchronous tools and how to call them in parallel. It may also include additional resources, such as documentation, tutorials, or test cases. The purpose of the sample is to provide a practical demonstration of the parallel function calling feature, allowing users to see how it works in a real-world scenario. By examining the code and running the sample, you can gain a better understanding of the concepts and techniques involved in parallel execution. The sample also serves as a starting point for building your own parallel function applications. You can modify the code, experiment with different configurations, and adapt it to your specific needs. We're here to provide a clear and concise explanation of the key aspects of the parallel functions sample and how it can help you learn about parallel execution. So, let's delve deeper into the sample and see how it empowers you to build more efficient and responsive applications.

Utilizing the Parallel Functions Sample

To utilize the parallel functions sample, you'll need to create a new tutorial page under docs/tutorials/. This page should be based on the content of the contributing/samples/parallel_functions/README.md file. The tutorial should provide a step-by-step guide on how to run the sample, how to understand the code, and how to modify it for your own purposes. It should also explain the key concepts and techniques involved in parallel function calling, such as asynchronous programming, coroutines, and concurrency. Additionally, the tutorial may want to include exercises or challenges that encourage users to experiment with the sample and apply their knowledge. Providing clear and concise instructions is crucial for ensuring that users can easily utilize the sample and learn from it. This includes explaining the prerequisites for running the sample, such as the required software and libraries. It's also helpful to provide troubleshooting tips and best practices for using the sample effectively. We're here to provide guidance and best practices to help you create a comprehensive and user-friendly tutorial that showcases the parallel functions sample. So, let's delve into the practical aspects of utilizing the sample and see how it can enhance your understanding of parallel execution.

Benefits of the Parallel Functions Sample

The benefits of the parallel functions sample are clear: hands-on learning, practical examples, and a starting point for your own projects. By working through the sample, you can gain a practical understanding of how parallel function calling works in a real-world scenario. This is much more effective than simply reading about the concepts and techniques involved. The sample provides a concrete example that you can examine, modify, and experiment with, allowing you to learn by doing. The sample also serves as a starting point for building your own parallel function applications. You can use the code as a template and adapt it to your specific needs. This can save you a significant amount of time and effort compared to starting from scratch. Additionally, the sample provides a valuable resource for troubleshooting issues and understanding best practices. By examining the code and running the sample, you can gain insights into common problems and how to avoid them. To fully appreciate the benefits of the parallel functions sample, it's essential to consider the impact on your learning and development process. We're here to highlight the key advantages and provide real-world examples to illustrate the transformative power of this learning resource. So, let's delve into the benefits of the parallel functions sample and see how it can elevate your understanding of parallel execution.

Conclusion

Alright, guys! We've covered a lot in this guide, from parallel function calling to the BigQuery tools update. Ensuring our documentation stays up-to-date is super important for your success with ADK v1.10.0. By addressing these mismatches, we're making sure you have the right information to build awesome applications. Remember, these updates are designed to enhance your development experience and empower you to create more efficient, reliable, and intelligent applications. We're committed to providing you with the best possible resources, so you can focus on what you do best: innovating and building. So, let's all pitch in to keep the documentation accurate and make the ADK even better! We hope this comprehensive guide has been helpful in navigating the changes and updates in ADK v1.10.0. By staying informed and leveraging these new features and improvements, you can significantly enhance your development workflows and build more powerful and effective applications. We encourage you to explore the documentation, experiment with the samples, and take full advantage of the capabilities that ADK v1.10.0 offers. And as always, we appreciate your feedback and contributions in making the ADK community even stronger. Happy coding!