Troubleshooting VLLM And Llama.cpp Rerank Response Format Issues With OpenAI API

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Introduction

This article delves into troubleshooting rerank response format issues encountered when using VLLM and llama.cpp with the OpenAI API in the Continue.dev environment. Specifically, it addresses the error: Failed to rerank retrieval results Error: Unexpected rerank response format from openai. Expected 'data' array but got: ["id","model","usage","results"]. This issue arises when attempting to use self-hosted reranking models with VLLM or llama.cpp, which are accessed via the OpenAI endpoint. This comprehensive guide will walk you through the problem, its causes, potential solutions, and steps to reproduce the error, ensuring a thorough understanding and resolution of the issue.

Understanding the Problem

Initial Error Encountered

The primary issue discussed in this article revolves around an error encountered while using a self-hosted reranking model through VLLM or llama.cpp, utilizing the OpenAI API endpoint. The error message, “Failed to rerank retrieval results Error: Unexpected rerank response format from openai. Expected ‘data’ array but got: [“id”,“model”,“usage”,“results”]”, indicates a discrepancy in the expected and actual response formats. This typically occurs when the system expects a data array in the response but receives an object containing keys like id, model, usage, and results.

Root Cause Analysis

To diagnose the root cause, it’s essential to understand the underlying code structure. The investigation begins in llms/OpenAI.ts, where it's noted that there isn't a specific reranker method. The reranking functionality is instead defined in the parent BaseLLM class within llms/index.ts. The relevant code snippet from BaseLLM is:

async rerank(query: string, chunks: Chunk[]): Promise<number[]> {
  if (this.shouldUseOpenAIAdapter("rerank") && this.openaiAdapter) {
    const results = await this.openaiAdapter.rerank({
      model: this.model,
      query,
      documents: chunks.map((chunk) => chunk.content),
    });

    // Standard OpenAI format
    if (results.data && Array.isArray(results.data)) {
      return results.data
        .sort((a, b) => a.index - b.index)
        .map((result) => result.relevance_score);
    }

    throw new Error(
      `Unexpected rerank response format from ${this.providerName}. ` +
        `Expected 'data' array but got: ${JSON.stringify(Object.keys(results))}`,    );
  }

  throw new Error(
    `Reranking is not supported for provider type ${this.providerName}`,  );
}

This code block reveals that the system anticipates a results.data array in the response. However, VLLM and llama.cpp, as per their response structures, return the rerank results under results.results. This mismatch triggers the error.

VLLM Documentation Insights

Referring to the VLLM documentation, specifically the VLLM OpenAI rerank RerankResponse section, it's evident that the expected format is indeed results.results. This confirms the discrepancy between the expected format in the BaseLLM class and the actual response format from VLLM.

The Core Issue and Potential Implications

The central issue is the hardcoded expectation of results.data in the BaseLLM class, which doesn't align with the response format provided by VLLM and llama.cpp. This misalignment raises a crucial question: Is this hardcoded expectation in BaseLLM intentional for a specific reason, possibly to maintain compatibility with other providers? Altering this expectation might inadvertently break functionality with other providers that adhere to the results.data format.

Additionally, the placement of this OpenAI-specific logic within the BaseLLM class seems questionable. Ideally, provider-specific logic should reside within the respective provider's class (e.g., OpenAI.ts) to maintain a cleaner and more modular architecture.

Reproducing the Error

To effectively troubleshoot and resolve this issue, reproducing the error in a controlled environment is essential. Here are the step-by-step instructions to replicate the error:

Step-by-Step Reproduction Guide

  1. Configure VLLM as a Reranker:

    • Start by setting up VLLM as a reranker. This can be achieved using the following command, which initiates VLLM on 0.0.0.0:8082 with specific configurations for GPU memory utilization and quantization:

      VLLM_ATTENTION_BACKEND=FLASH_ATTN vllm serve --host 0.0.0.0 --port 8082 --gpu-memory-utilization 0.2 --task score Alibaba-NLP/gte-reranker-modernbert-base --quantization="fp8"

    • This command configures VLLM to use the Alibaba-NLP/gte-reranker-modernbert-base model, which is crucial for the reranking task. The --quantization="fp8" flag enables FP8 quantization, which optimizes performance and memory usage. The VLLM_ATTENTION_BACKEND=FLASH_ATTN setting ensures that FlashAttention is used, further enhancing performance.

  2. Configure an LLM with the Rerank Role:

    • In your config.yaml file, define an LLM configuration that includes the rerank role. This involves specifying the provider (openai), model (Alibaba-NLP/gte-reranker-modernbert-base), API base (http://localhost:8082/v1), and API key (NONE). A sample configuration snippet is provided:

      - name: GTE ModernBert
  provider: openai
  model: Alibaba-NLP/gte-reranker-modernbert-base
  apiBase: http://localhost:8082/v1
  apiKey: NONE
  defaultCompletionOptions:
    contextLength: 2048
    maxTokens: 1024
  roles:
    - rerank

    • This configuration tells the system to use the GTE ModernBert model for reranking tasks, pointing to the VLLM server running locally. The defaultCompletionOptions specify the context length and maximum tokens for the model, while the roles array assigns the rerank role to this configuration.

  3. Configure @Codebase for Context Retrieval:

    • Set up the @Codebase provider to retrieve sufficient context that necessitates reranking. This involves adjusting parameters such as nRetrieve (number of chunks to retrieve) and nFinal (number of final chunks after reranking). Also, ensure that useReranking is set to true.

      - provider: codebase
  params:
    nRetrieve: 128
    nFinal: 32
    useReranking: true

    • The nRetrieve parameter is set to 128, indicating that the system will initially retrieve a large number of context chunks. The nFinal parameter is set to 32, specifying the number of chunks to retain after reranking. The useReranking: true setting ensures that the reranking process is enabled.

  4. Trigger Reranking in Chat:

    • In your chat interface, formulate a query that utilizes the @Codebase context. This triggers the context retrieval and reranking process.
    • For example, you might ask a question about a specific part of your codebase, ensuring that the system needs to retrieve and rerank context chunks to provide an accurate answer.

By following these steps, you should be able to reproduce the error consistently, providing a solid foundation for further investigation and resolution.

Proposed Solutions and Workarounds

Addressing the rerank response format issue requires a multifaceted approach, considering both immediate workarounds and long-term solutions. Here are several strategies to consider:

Immediate Workarounds

  1. Conditional Logic in BaseLLM:

    • One immediate workaround involves adding conditional logic within the BaseLLM class to handle the different response formats from VLLM and standard OpenAI. This can be achieved by checking the provider type and adjusting the response parsing accordingly.
    • For instance, you can modify the rerank method in BaseLLM to check if the provider is VLLM or llama.cpp. If so, it would access results.results; otherwise, it would default to results.data.
    • This approach ensures that the system correctly parses the response based on the provider, resolving the immediate error.
    • However, this workaround adds complexity to the BaseLLM class and might not be the most scalable solution in the long run.

  2. Adapter Pattern:

    • Another potential workaround is to implement an adapter pattern. This involves creating an intermediary layer that translates the VLLM/llama.cpp response format into the format expected by the BaseLLM.
    • The adapter would receive the response from VLLM/llama.cpp and transform it into a structure containing the data array as expected by the existing code.
    • This approach provides a cleaner separation of concerns and avoids modifying the core BaseLLM class directly.
    • However, it introduces additional complexity and requires creating and maintaining the adapter components.

Long-Term Solutions

  1. Provider-Specific Logic:

    • The most sustainable long-term solution is to move the provider-specific logic out of the BaseLLM class and into the respective provider classes (e.g., OpenAI.ts, VLLM.ts, LlamaCpp.ts).
    • This involves creating a rerank method within each provider class that handles the response format specific to that provider.
    • The BaseLLM class would then call the appropriate rerank method based on the provider type, ensuring that each provider's response is handled correctly.
    • This approach promotes modularity, maintainability, and scalability, making it easier to add support for new providers in the future.

  2. Standardized Response Format:

    • Another approach is to standardize the response format across all providers. This involves defining a common format for rerank responses and ensuring that all providers adhere to this format.
    • For example, you could decide that all providers should return a data array containing the rerank results.
    • This approach simplifies the parsing logic in the BaseLLM class and reduces the need for provider-specific handling.
    • However, it requires coordination across all providers and might involve changes to the VLLM and llama.cpp APIs.

Code Implementation Example (Conditional Logic Workaround)

Here’s an example of how you might implement the conditional logic workaround within the BaseLLM class:

async rerank(query: string, chunks: Chunk[]): Promise<number[]> {
  if (this.shouldUseOpenAIAdapter("rerank") && this.openaiAdapter) {
    const results = await this.openaiAdapter.rerank({
      model: this.model,
      query,
      documents: chunks.map((chunk) => chunk.content),
    });

    let rerankResults;
    if (this.providerName === "vllm" || this.providerName === "llama.cpp") {
      rerankResults = results.results; // Access results.results for VLLM/llama.cpp
    } else {
      rerankResults = results.data; // Standard OpenAI format
    }

    if (rerankResults && Array.isArray(rerankResults)) {
      return rerankResults
        .sort((a, b) => a.index - b.index)
        .map((result) => result.relevance_score);
    }

    throw new Error(
      `Unexpected rerank response format from ${this.providerName}. ` +
        `Expected 'data' or 'results' array but got: ${JSON.stringify(Object.keys(results))}`,    );
  }

  throw new Error(
    `Reranking is not supported for provider type ${this.providerName}`,  );
}

This code snippet demonstrates how to conditionally access the rerank results based on the provider name. If the provider is VLLM or llama.cpp, it accesses results.results; otherwise, it defaults to results.data. This ensures that the system correctly parses the response from different providers.

Conclusion

In conclusion, the rerank response format issue encountered when using VLLM and llama.cpp with the OpenAI API in Continue.dev stems from a mismatch between the expected response format in the BaseLLM class and the actual response format provided by these providers. The immediate workaround involves adding conditional logic to handle different response formats, while long-term solutions include moving provider-specific logic to respective provider classes or standardizing the response format across all providers. By understanding the problem, reproducing the error, and implementing the proposed solutions, you can effectively resolve this issue and ensure the smooth functioning of your reranking system. Addressing this issue not only enhances the reliability of your current setup but also lays the groundwork for a more scalable and maintainable architecture in the future.