AnythingLLM Bug: 'resultVariable' Lost After LLM Instruction

by Alex Johnson 61 views

Introduction

This article delves into a specific bug encountered within the AnythingLLM application, particularly concerning the behavior of resultVariable after the execution of the executeLLMInstruction function. The user reports an issue where the resultVariable, intended to store the output of an LLM instruction, is unexpectedly removed or set to NULL after the function's execution. This leads to the loss of the generated response and prevents the variable from being used in subsequent steps within the agent flow. The user provides detailed steps to reproduce the issue, including the specific configuration of their Agentflow, and offers a potential code fix. This analysis will explore the problem, evaluate the user's proposed solution, and offer insights into the underlying cause and potential resolutions.

The Problem: resultVariable Vanishing Act

The core of the problem lies in the interaction between the executeLLMInstruction function and how it handles the returned results from the language model. When using the Tencent Hunyuan-lite model (or potentially other models), the completion object, which is expected to contain the LLM's response, does not always include a result member as anticipated by the AnythingLLM code. Instead, the response is stored in a textResponse member. This discrepancy leads to the resultVariable not being populated correctly, or being set to NULL as the code attempts to access a non-existent result property. Consequently, the information generated by the LLM is lost, and the user is unable to use it in the following steps of their agent flow. This is a critical issue because it breaks the expected flow of information within the application, preventing users from building complex and dynamic workflows that rely on the output of LLM instructions.

To exacerbate the issue, the user highlights that in the next step, they are unable to use the variable ResponseText. This situation effectively renders the output of the first instruction useless. The agent flow is designed to pass information between steps, and when a variable's value disappears, the whole process breaks down. This bug severely limits the functionality and usability of AnythingLLM, as it directly impacts the core feature of chaining LLM instructions and acting upon their results.

User's Proposed Solution: A Quick Fix

The user identified the issue within the llm-instruction.js file, specifically at the line where the completion object is processed. They observed that the completion object contains a textResponse property instead of the expected result property. To address this, they propose adding the following code snippet before the return statement:

if (completion["textResponse"]){ completion["result"] =completion.textResponse; }

This code checks if the textResponse property exists within the completion object. If it does, it assigns the value of textResponse to a new result property within the same object. This effectively creates the result property that the rest of the code expects, allowing the value generated by the language model to be stored in the resultVariable and passed to subsequent steps. The user's solution is a practical workaround, addressing the immediate problem and enabling users to continue using the application with their desired agent flow.

However, the user acknowledges that they are unsure whether this is the correct solution. While it solves the immediate problem, it is essential to consider the underlying cause and whether this is the only place where result is assumed to exist. A more comprehensive fix would identify all locations that expect a result property and ensure that the code handles situations where textResponse is used instead.

Reproduction Steps and Agentflow Configuration

The user provides clear steps to reproduce the bug. They involve creating an Agentflow in the AnythingLLM Desktop App (version 1.9.0) with a specific configuration. The Agentflow is designed with two llmInstruction steps. The first step uses a start configuration to set up two variables: keywords and ResponseText, initializing ResponseText to "1". The first llmInstruction then uses the ResponseText variable in its instruction and sets resultVariable to ResponseText. The second llmInstruction then tries to use the ResponseText variable in its instruction, but it is not replaced, demonstrating that the variable from the first step is not correctly passed on. This configuration effectively illustrates how the loss of the resultVariable breaks the expected flow and prevents the second instruction from accessing the result of the first instruction. This demonstrates how a simple agent flow can expose this issue.

This test case is crucial because it helps developers understand the bug and confirms the issues with passing variables across the steps. The user's thoroughness in providing this reproduction helps developers quickly identify the root cause of the bug.

Deeper Dive: Analyzing the Code

To fully understand and resolve this issue, a more thorough examination of the code is needed. Specifically, the following areas require careful scrutiny:

  • llm-instruction.js: This file is the primary focus, as it contains the logic for executing LLM instructions and handling the responses. The code needs to correctly interpret the LLM's response, regardless of the property name used (result or textResponse).
  • Model-Specific Adaptations: The code might need to be adapted to handle different LLM providers and their response formats. Each provider may return the response in a different way, and the code should handle this variability. If the application is designed to be compatible with multiple language models, it must make allowances for the various response formats of different LLMs.
  • Variable Handling: The mechanism for passing variables between steps in the Agentflow needs to be robust and reliable. The system should correctly store and retrieve variables, ensuring that they are available when needed. The mechanism for passing variables between steps must also be thoroughly checked to guarantee the smooth transition of data between the various stages of the agent flow.
  • Error Handling: The code should include robust error handling to deal with cases where the LLM does not return a response or returns an unexpected response format. It should log any errors and gracefully handle them to prevent the application from crashing or behaving unexpectedly. Appropriate error logging is essential for diagnosing problems when dealing with language models.

Conclusion: Navigating the Bug and Enhancing AnythingLLM

The bug identified by the user highlights a crucial issue in AnythingLLM: the inconsistent handling of LLM responses, particularly in how it interprets the output from different language models. The user's proposed solution offers a temporary fix by mapping textResponse to result. However, a comprehensive solution necessitates deeper investigation to accommodate all LLM providers and correctly manage variables between steps within Agentflows. This involves making the application more flexible to different response structures and ensuring that the data is correctly passed through all steps of the process.

To improve AnythingLLM and enhance user experience, developers should focus on the following:

  • Robustness: Making the code more adaptable to various LLM response formats. This could involve creating a response handler that detects the format and processes the response accordingly.
  • Error Prevention: Implement checks to ensure the existence of result or textResponse properties before accessing them. This proactive approach will help mitigate future issues.
  • Comprehensive Testing: Perform rigorous testing with different LLM providers and complex Agentflow configurations to catch potential bugs early.

By addressing these concerns, AnythingLLM can provide a smoother and more reliable experience, allowing users to leverage the power of LLMs effectively. The user's report is crucial for the continuous improvement of the application and demonstrates the importance of actively engaging with the user base to identify and resolve issues promptly.

For more information on Agentflows and related topics, please check the official documentation on AnythingLLM's Github