OpenSearch: Enhanced Sparse Vector Search With Score Modes

This article delves into a feature request for OpenSearch, focusing on enhancing the capabilities of sparse vector search. Specifically, the request aims to extend support for score modes beyond the current maximum (MAX) setting when dealing with nested fields containing sparse vectors. This enhancement promises to provide users with greater flexibility and control over their search queries, potentially leading to more relevant and nuanced search results. This article will explore the context of this feature request, the proposed solution, and the potential benefits it offers to OpenSearch users.

Background: Sparse Vectors and Semantic Search

In the realm of information retrieval and search, sparse vectors play a crucial role in representing text and other data types in a high-dimensional space. Unlike dense vectors, where most elements have non-zero values, sparse vectors contain primarily zero values, with only a few elements representing significant features or terms. This characteristic makes them particularly well-suited for representing textual data, where the vocabulary size can be extensive, but only a subset of words is relevant to a particular document or query.

Sparse vectors are the backbone of many semantic search techniques. Semantic search aims to go beyond simple keyword matching, understanding the meaning and context of the query and documents to provide more accurate and relevant results. By representing text as sparse vectors, search engines can capture the relationships between words and concepts, enabling them to perform more sophisticated searches.

OpenSearch, a popular open-source search and analytics suite, has incorporated support for sparse vectors, allowing users to leverage these techniques for their search applications. One notable implementation is the Seismic algorithm, designed to optimize sparse vector retrieval efficiency without significantly impacting accuracy. This algorithm, introduced in OpenSearch 3.3, has significantly improved the performance of sparse vector searches, making it a valuable tool for handling large-scale datasets.

The Feature Request: Expanding Score Mode Support

Currently, OpenSearch's sparse ANN (Approximate Nearest Neighbors) feature primarily supports the MAX score mode. In the context of nested fields, which are fields that contain arrays of objects, the score mode determines how the scores of individual objects within the array are combined to produce an overall score for the document. The MAX score mode simply takes the highest score among all objects in the array.

While the MAX score mode is suitable for certain scenarios, it may not always be the optimal choice. For instance, consider a case where a document contains multiple passages, each represented as a nested object with its own sparse vector encoding. If only one passage has a high similarity score to the query, the MAX score mode will highlight that passage, potentially overlooking other relevant passages with slightly lower scores. This limitation motivates the feature request to support other score modes, such as average (AVG), which would provide a more balanced representation of the document's overall relevance.

The request emphasizes the need for enhanced flexibility in handling nested fields with sparse vector data. By allowing users to choose different score modes, OpenSearch can cater to a wider range of search scenarios and provide more nuanced results. This enhancement aligns with the broader goal of making OpenSearch a versatile and powerful search platform for diverse applications.

Proposed Solution: Implementing Additional Score Modes

The proposed solution involves extending the functionality of OpenSearch's sparse ANN feature to support score modes beyond MAX. Specifically, the feature request highlights the potential benefits of including the AVG score mode. This would allow users to calculate the average similarity score across all objects within a nested field, providing a more holistic view of the document's relevance to the query.

The implementation of this feature would likely involve modifications to the query processing logic within OpenSearch. The system would need to be able to handle different score mode specifications and apply the corresponding aggregation function to the scores of individual nested objects. This might involve adding a new parameter to the query syntax, allowing users to specify the desired score mode.

For example, the feature request provides a sample query demonstrating how the score_mode parameter could be used within a nested query:

GET <test_index>/_search
{
  "query": {
    "nested": {
      "score_mode": "avg",
      "path": "passage_chunk_embedding",
      "query": {
        "neural_sparse": {
          "passage_chunk_embedding.sparse_encoding": {
            "query_tokens": {
              "1": 1
            },
            "method_parameters": {
              "k": 10,
              "top_n": 6,
              "heap_factor": 1.2
            }
          }
        }
      }
    }
  }
}

In this example, the score_mode parameter is set to "avg", indicating that the average score should be used when combining the scores of individual objects within the passage_chunk_embedding nested field. This would allow the search to consider the overall similarity of the document based on all its passages, rather than just the most similar one.

Alternatives Considered

The feature request also mentions alternative approaches that users might consider in the absence of direct score mode support. These include using inner hits or explain functionality. Inner hits allow users to retrieve the matching nested objects within a document, providing more granular information about the search results. Explain functionality provides a detailed breakdown of the scoring process, helping users understand how the final score was calculated.

While these alternatives can offer valuable insights, they do not fully address the need for flexible score mode options. Inner hits, for example, provide access to the individual matching objects but do not automatically aggregate their scores. Explain functionality helps understand the scoring but does not change the scoring behavior itself. Therefore, the proposed solution of directly supporting different score modes is considered a more comprehensive and user-friendly approach.

Benefits of Enhanced Score Mode Support

The implementation of enhanced score mode support for sparse vector searches in OpenSearch would offer several key benefits:

• Improved Search Relevance: By allowing users to choose the most appropriate score mode for their specific use case, the search results can be better tailored to their needs. For example, using AVG score mode might be preferable in scenarios where a holistic view of document relevance is desired, while MAX score mode might be more suitable when identifying the single most relevant passage or object.
• Increased Flexibility: The ability to select different score modes provides users with greater flexibility and control over their search queries. This allows them to experiment with different scoring strategies and optimize their search performance based on their specific data and requirements.
• Enhanced User Experience: A more flexible and nuanced search experience can lead to greater user satisfaction. By providing more relevant and accurate results, OpenSearch can become a more valuable tool for information retrieval and analysis.
• Wider Range of Applications: Support for different score modes can broaden the applicability of OpenSearch to a wider range of use cases. For example, it could be beneficial in applications such as question answering, document summarization, and semantic search, where understanding the overall context and meaning of the data is crucial.

Conclusion

The feature request to support score modes other than MAX for sparse vector searches in OpenSearch highlights the ongoing efforts to enhance the platform's capabilities and provide users with more powerful and flexible tools. By implementing this feature, OpenSearch can improve search relevance, increase user flexibility, and broaden its applicability to various search scenarios. The proposed solution aligns with the broader goal of making OpenSearch a versatile and robust search and analytics suite.

For more information on OpenSearch and its capabilities, visit the official OpenSearch website.