Google Search Tool Does Not Return Grounding Metadata In ADK Runner Events

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Introduction

This article addresses a critical issue encountered while utilizing the Google Search tool within the ADK (Agent Development Kit) Runner environment. Specifically, the grounding metadata, which is essential for verifying the source and reliability of information obtained from Google Search, is absent from the event objects emitted by the Runner. This absence significantly hinders the ability to programmatically access and process provenance information, impacting the trustworthiness and transparency of AI agent responses. This comprehensive guide provides a deep dive into the bug, steps to reproduce it, expected behavior, and potential solutions, ensuring developers can effectively leverage the Google Search tool within their ADK-powered applications.

Understanding the Bug: Missing Grounding Metadata

The core of the issue lies in the missing grounding metadata within the event objects generated by the ADK Runner after the execution of the Google Search tool. Grounding metadata plays a vital role in establishing the credibility of information retrieved from external sources, such as Google Search. It provides contextual details about the source of the information, allowing developers and users to assess its reliability and potential biases. Without this metadata, it becomes challenging to trace the origin of the information, which can lead to trust issues and difficulties in debugging agent behavior. While the grounding metadata seems to be logged in debug or information logs, its absence from the Runner's event stream prevents direct programmatic access. This limitation hinders developers from effectively utilizing the provenance information for various tasks, including:

  • Verifying information accuracy: Grounding metadata enables developers to cross-reference information with its original source, ensuring its accuracy and consistency.
  • Identifying potential biases: By examining the source of information, developers can identify potential biases or perspectives that might influence the agent's responses.
  • Improving transparency: Providing grounding metadata to users enhances the transparency of AI agent behavior, allowing them to understand the basis for the agent's conclusions.
  • Debugging agent behavior: Access to grounding metadata is crucial for debugging agent behavior, particularly when dealing with complex scenarios involving multiple tools and agents.

Reproducing the Issue: A Step-by-Step Guide

To effectively address this bug, it's essential to understand how to reproduce it. This section provides a detailed, step-by-step guide that developers can follow to observe the missing grounding metadata issue firsthand.

1. Configure ADK Agents

The first step involves setting up the ADK agents with a specific structure that includes an orchestrator_agent, a chat_agent, and a google_search_agent. The chat_agent is designed to utilize the google_search_agent as a tool. This hierarchical structure allows us to simulate a real-world scenario where an orchestrator agent manages multiple sub-agents, each responsible for specific tasks. The following Python code snippet demonstrates the agent configuration:

from google.adk.agents.llm_agent import LlmAgent
from google.adk.tools import agent_tool
from google.adk.tools import google_search

orchestrator_agent = LlmAgent(
model=_settings.google_llm_model,
name="ai_assistant",
instruction=(prompt.INSTRUCTION_PROMPT),
sub_agents=[
chat_agent,


],
)


chat_agent = LlmAgent(
model=_settings.google_llm_model,
name="chat_ai_assistant",
instruction=(prompt.INSTRUCTION_PROMPT),
global_instruction=(prompt.GLOBAL_PROMPT),
tools=[
agent_tool.AgentTool(agent=google_search_agent, skip_summarization=True),
],
sub_agents=[
faq_agent,



],
)


google_search_agent = LlmAgent(
model=_settings.google_llm_model,
name="google_search_agent",
instruction=prompt.GOOGLE_SEARCH_PROMPT,
tools=[
google_search
],
)


faq_agent = LlmAgent(
model=_settings.google_llm_model,
name="faq_agent",
instruction=prompt.FAQ_PROMPT,
tools=(
[
faq_tool
]
),
)

In this configuration:

  • orchestrator_agent: Acts as the main agent, coordinating the activities of other sub-agents.
  • chat_agent: Handles user interactions and can utilize the google_search_agent to retrieve information.
  • google_search_agent: Specifically designed to interact with the Google Search tool.
  • faq_agent: Another sub-agent that might be used for answering frequently asked questions.

2. Implement the process_message Function

Next, you need to implement a process_message function that utilizes the Runner to interact with the orchestrator_agent. This function takes a user query as input and orchestrates the agent's response. It iterates through the events emitted by the Runner, allowing you to observe the agent's reasoning process and tool interactions. The following code snippet demonstrates the implementation of the process_message function:

async def process_message(
 self, query: str, session_id: str, user_id: str, dtr_token: str
 ) -> str:
 """Process a message and get the agent's response.

Args:
query: The user's query
session_id: The session ID


Returns:
str: The agent's response
"""
try:



self.get_or_create_session(session_id, user_id, dtr_token)



runner = Runner(
agent=orchestrator_agent,
app_name=self.app_name,
session_service=self.session_service,
)



content = types.Content(role="user", parts=[types.Part(text=query)])



final_response_text = "Agent did not produce a final response."



async for event in runner.run_async(
user_id=user_id, session_id=session_id, new_message=content
):



if event.is_final_response():
if event.content and event.content.parts:
if event.content.parts[0].function_response:
final_response_text = event.content.parts[
0
].function_response.response["result"]
elif event.content.parts[0].text:
final_response_text = event.content.parts[0].text
else:
final_response_text = event.content.parts[1].text
elif event.actions and event.actions.escalate:
final_response_text = f"Agent escalated: {event.error_message or 'No specific message.'}"
break


return final_response_text


except Exception as e:
logger.error(f"Error processing message: ") # Consider adding {e} to the log message
raise

Key aspects of the process_message function include:

  • Runner Initialization: A Runner instance is created, configured with the orchestrator_agent and session management services.
  • User Message Preparation: The user's query is encapsulated within a Content object, representing the message sent to the agent.
  • Event Iteration: The runner.run_async() method is used to execute the agent and iterate through the emitted events asynchronously. This is where the observation of event objects occurs.
  • Response Extraction: The function extracts the final response text from the event stream, handling different response types, such as function responses, text responses, and escalation scenarios.

3. Trigger Google Search Tool Usage

To observe the bug, you need to call the process_message function with a query that prompts the chat_agent to utilize the google_search_agent tool. This can be achieved by formulating a query that requires the agent to retrieve information from the web. For example, you could use a query like, "What are the latest advancements in AI?"

4. Examine the Event Objects

The crucial step is to examine the event objects yielded by runner.run_async(). By inspecting the events, you can observe the absence of grounding_metadata in the event pertaining to the google_search tool's result. Specifically, you'll notice that the event object lacks the expected structure for accessing grounding information (e.g., event.content.parts[0].tool_output.grounding_metadata).

Expected Behavior: Grounding Metadata in Event Objects

The expected behavior is that the event object corresponding to the google_search tool's output should include grounding_metadata. This metadata should be directly accessible within the event structure, allowing developers to programmatically retrieve and process it. For instance, the metadata might be located at a path like event.content.parts[0].tool_output.grounding_metadata. The presence of this metadata would enable developers to:

  • Verify the source of information: Identify the specific websites or documents from which the information was extracted.
  • Assess the reliability of information: Evaluate the credibility and trustworthiness of the sources.
  • Provide attribution: Properly attribute the information to its original sources.
  • Debug agent behavior: Trace the reasoning process of the agent and identify potential issues.

Suspected Cause: Missing Intermediary Event

One potential cause of the missing grounding metadata is a missing intermediary event object. It's suspected that an event object containing the grounding metadata might be missing between the AgentTool call event and the eventual result event. This intermediary event should ideally capture the output of the google_search tool, including the grounding metadata, before it's processed further by the agent. The absence of this event prevents the metadata from being propagated to the final event object, resulting in the observed bug.

Impact and Implications

The absence of grounding metadata has significant implications for the trustworthiness and transparency of AI agents powered by ADK. Without this metadata, it becomes challenging to:

  • Ensure factual accuracy: Verifying the accuracy of information retrieved from external sources becomes difficult, potentially leading to the dissemination of misinformation.
  • Build user trust: Users may be hesitant to trust AI agents that cannot provide clear evidence for their claims.
  • Comply with ethical guidelines: Many ethical guidelines for AI development emphasize the importance of transparency and accountability, which are difficult to achieve without grounding metadata.
  • Debug and improve agent performance: Identifying the root cause of errors and improving agent performance becomes more challenging without access to provenance information.

Potential Solutions and Workarounds

Addressing the missing grounding metadata issue is crucial for ensuring the reliability and trustworthiness of ADK-powered AI agents. While a definitive solution might require modifications to the ADK framework itself, several potential solutions and workarounds can be explored:

1. Investigate Logging Mechanisms

As mentioned earlier, the grounding metadata appears to be logged elsewhere (e.g., in debug or info logs). One potential workaround is to investigate these logging mechanisms and extract the metadata from the logs. However, this approach is not ideal, as it relies on parsing log files, which can be cumbersome and prone to errors. A more robust solution would involve making the metadata directly accessible within the event objects.

2. Modify AgentTool Callback Mechanisms

The bug report also mentions that general tool callback mechanisms do not appear to function as expected. Exploring and potentially modifying these callback mechanisms could provide a way to capture the grounding metadata before it's lost. By implementing a callback function that is triggered after the execution of the google_search tool, it might be possible to extract the metadata and store it for later use.

3. Implement a Custom Metadata Propagation Mechanism

A more involved solution would be to implement a custom metadata propagation mechanism. This could involve creating a dedicated data structure for storing grounding metadata and passing it along with the event objects. While this approach requires more effort, it offers greater control over the metadata and ensures its availability throughout the agent's execution.

4. Contribute to the ADK Project

The most effective long-term solution is to contribute to the ADK project by reporting the bug and potentially proposing a fix. By working with the ADK development team, it's possible to address the issue at its source and ensure that grounding metadata is properly handled within the framework. This could involve submitting a pull request with the necessary code changes or providing detailed feedback on potential solutions.

Conclusion

The missing grounding metadata issue in the ADK Runner when using the Google Search tool is a significant concern that impacts the trustworthiness and transparency of AI agents. By understanding the bug, its causes, and potential solutions, developers can take steps to mitigate its impact and contribute to a more reliable and transparent AI ecosystem. This article has provided a comprehensive overview of the issue, empowering developers to effectively address it and build more trustworthy AI applications using the Google Search tool within ADK.

By actively working towards resolving this issue, the ADK community can ensure that AI agents are not only intelligent but also transparent and accountable, fostering greater trust and confidence in AI technology.