Neuron AI: GetUsage() Null When Using RAG - Bug Report

Nov 9, 2025 by Admin 55 views

Hey guys, let's dive into a peculiar issue reported in Neuron AI version 2.8 concerning the getUsage() method when used in conjunction with the RAGDiscussion category. This article aims to break down the bug, reproduce the scenario, explore the expected behavior, and discuss potential solutions or workarounds. If you're encountering a similar problem or are just curious about the intricacies of Neuron AI's RAG implementation, stick around!

Understanding the Bug: getUsage() and RAG

In essence, the bug manifests when the NeuronAI\RAG component is integrated into an agent builder. Specifically, the $chunk->getUsage() method, which is supposed to return data about token usage (input, output, and total tokens), starts returning null. This behavior is in stark contrast to the norm, where usage statistics are accurately provided for each streamed chunk when RAG is not in play.

To put it simply, when RAG is enabled, the usage statistics seem to vanish into thin air. This can be a significant hurdle for developers who rely on these metrics for monitoring, cost analysis, or performance optimization. The absence of usage data makes it challenging to understand the resource consumption of the AI agents, potentially leading to unexpected costs or performance bottlenecks.

This issue highlights the importance of diligent debugging and comprehensive testing when integrating new features or components into an existing system. While RAG undoubtedly enhances the conversational capabilities of AI agents, its interaction with other core functionalities, such as usage tracking, needs to be carefully scrutinized to ensure seamless operation.

The problem likely stems from how RAG modifies the response processing or wraps the response objects, potentially overwriting or bypassing the mechanism that normally captures usage information. It's also possible that a configuration step is being overlooked, or that the RAG implementation inherently alters the way responses are structured, thereby impacting the availability of usage statistics.

Reproducing the Issue: A Step-by-Step Guide

To truly grasp the scope of the problem, let's walk through the steps to reproduce it. This will not only help you confirm if you're experiencing the same issue but also provide a clear scenario for developers to investigate and fix.

Set up an Agent Builder and Include NeuronAI\RAG: First, you need to create an agent builder instance within your Neuron AI environment. This is the foundation upon which you'll construct your AI agent. Crucially, ensure that you include the NeuronAI\RAG component in this setup. This is the key ingredient that triggers the bug.
Run a Streamed Chat Response: Next, initiate a streamed chat response using your agent. Streaming is a common technique for handling long-form conversations, where the response is delivered in chunks rather than all at once. This is important because the bug manifests specifically within the context of streamed responses.
Access $chunk->getUsage() Inside the Stream Loop: Within the loop that processes the streamed chunks, attempt to access the $chunk->getUsage() method. This is where you'll try to retrieve the token usage information for each chunk of the response.
Observe the Output: null: Finally, observe the output of $chunk->getUsage(). If you're encountering the bug, you'll consistently see null being returned for all chunks. This confirms that the usage statistics are not being properly captured or made available when RAG is active.

This straightforward reproduction scenario clearly demonstrates the issue and provides a solid starting point for further investigation and debugging. By following these steps, developers can reliably replicate the bug and begin to pinpoint the root cause.

Expected Behavior: Usage Stats with RAG

Now, let's clarify what the expected behavior should be. The core of the issue is that $chunk->getUsage() should always return token usage information, regardless of whether RAG is enabled or not. After all, RAG is meant to enhance the agent's capabilities, not break fundamental functionalities like usage tracking.

Ideally, when RAG is integrated, the $chunk->getUsage() method should continue to provide accurate statistics on:

Input Tokens: The number of tokens in the user's input that triggered the current response chunk.
Output Tokens: The number of tokens generated by the AI model in the current response chunk.
Total Tokens: The sum of input and output tokens for the current chunk, representing the total token consumption.

These metrics are essential for understanding the cost and efficiency of the AI agent's interactions. Without them, it's difficult to optimize prompts, manage expenses, and ensure that the agent is performing as expected. The absence of usage statistics undermines the value proposition of Neuron AI, particularly for users who rely on these metrics for business-critical applications.

In a well-functioning system, RAG should seamlessly integrate with the existing usage tracking mechanisms, ensuring that developers have a comprehensive view of the agent's resource consumption. The bug, therefore, represents a deviation from this expected behavior and highlights a potential disconnect between the RAG implementation and the core usage tracking functionality.

Diving Deeper: Potential Causes and Solutions

So, what could be causing this issue, and what are the potential solutions? Let's explore some hypotheses:

Response Wrapper Overwrite: One possibility is that RAG replaces the default response wrapper, which is responsible for collecting and providing usage information. If the RAG implementation uses a custom response wrapper that doesn't include the necessary logic for tracking tokens, $chunk->getUsage() would naturally return null.
- Solution: The fix here would involve ensuring that the RAG response wrapper either inherits the usage tracking functionality from the default wrapper or implements its own mechanism for capturing token counts. This might involve modifying the RAG component to properly track tokens during response generation.
Configuration Missing: It's also conceivable that there's a configuration step that's being overlooked when RAG is enabled. Perhaps a specific setting needs to be activated to ensure that usage statistics are collected in conjunction with RAG.
- Solution: Review the Neuron AI documentation and RAG configuration options to identify any missing or incorrect settings. Ensure that all necessary parameters for usage tracking are properly configured when using RAG.
Incompatibility or Bug in RAG: The issue might stem from an incompatibility between RAG and the core usage tracking functionality or a bug within the RAG implementation itself. RAG might be inadvertently interfering with the process of collecting or exposing usage statistics.
- Solution: This would require a deeper dive into the RAG codebase to identify the source of the conflict. Debugging and code analysis might be necessary to pinpoint the exact location where the usage tracking is being disrupted. If a bug is identified, a patch or update to RAG would be needed.
Asynchronous Token Counting Issues: In streamed responses, token counting might be handled asynchronously. RAG's operations could be interfering with the timing or synchronization of this asynchronous process, leading to the $chunk->getUsage() method being called before the token counts are available.
- Solution: Implement a mechanism to ensure that token counts are fully computed and available before $chunk->getUsage() is called. This might involve using promises, callbacks, or other asynchronous programming techniques to synchronize the token counting process with the response streaming.

Workarounds and Next Steps

While the root cause is being investigated, are there any temporary workarounds? Unfortunately, without access to the underlying token usage data, it's challenging to provide a direct workaround. However, you could potentially explore these avenues:

Manual Token Counting: As a last resort, you could attempt to implement manual token counting using a tokenizer library. This would involve analyzing the input and output text to estimate token usage. However, this approach is prone to inaccuracies and may not perfectly match the tokenization used by the AI model.
Disable RAG for Usage Tracking: If usage tracking is critical, you could temporarily disable RAG for specific scenarios where accurate statistics are needed. This would allow you to gather usage data without the interference of RAG, although it would sacrifice the benefits of RAG in those instances.

In the meantime, the best course of action is to report this bug to the Neuron AI team, providing detailed information about your setup, the steps to reproduce the issue, and any observations you've made. This will help the developers prioritize the bug and work towards a fix.

Conclusion: Addressing the getUsage() Null Issue

The getUsage() returning null issue when using RAG in Neuron AI is a significant problem that needs to be addressed. It hinders the ability to monitor and optimize AI agent performance, potentially leading to unexpected costs and inefficiencies. By understanding the bug, reproducing the scenario, and exploring potential causes and solutions, we can collectively work towards resolving this issue and ensuring the seamless integration of RAG with core Neuron AI functionalities.

Hopefully, this article has shed some light on the problem and provided you with valuable insights. Stay tuned for updates and fixes from the Neuron AI team, and don't hesitate to share your own experiences and findings in the comments below. Let's work together to make Neuron AI even better!