Understanding the Subtle Impacts of Punctuation on Vector Search in Azure Cognitive Search

Understanding the Subtle Impacts of Punctuation on Vector Search in Azure Cognitive Search

In the realm of text search and retrieval, minute details can sometimes make a significant difference. One such detail is the presence or absence of punctuation marks in query strings. This post explores a specific scenario involving Microsoft Azure Cognitive Search, a cloud-based search-as-a-service solution, and how a simple question mark (?) may affect vector search results when using this platform.

Problem Description:

Users employing Azure Cognitive Search noted variations in search results based on whether a question mark was included at the end of a query or not. For instance, the queries “What is Love?”, “What is Love ?”, and “What is Love” yielded different results despite their semantic similarity.

Traditional Tackling Approaches:

Typically, vector search is conducted by converting text into vector embeddings which are then mapped into a continuous vector space, facilitating the comparison of semantic similarity between various text snippets. This process isn’t influenced on a character-by-character basis, but rather, it’s the overall semantic understanding that guides the vector representation.

The Underlying Issue:

The observed discrepancies in search results were tied back to how the embeddings are generated. Even a small alteration like the addition of a question mark alters the vector, thereby potentially altering the search results. Though the vectors for semantically similar queries should ideally be close, they won’t be identical due to these minute differences.

Deep Dive into the Issue:

Investigation into this issue revealed that different tokens are generated for queries with and without a question mark, or with varying spacing around the question mark. This tokenization process affects the resultant embeddings, and subsequently, the search results. For instance, a direct comparison of the embeddings for different query variations showed slight differences in cosine similarity, indicating that the vectors, while close, were not identical.

Solutions and Recommendations:

  1. Comparing Embeddings: By utilizing APIs to directly compare embeddings, users can better understand the subtle differences that arise from minor text alterations.
  2. Understanding Tokenization: Being cognizant of how tokenization works, and how different tokens are generated for varying text, can help in anticipating and mitigating the impacts on vector search.
  3. Normalization: Implementing text normalization procedures, such as removing or standardizing punctuation and casing, can be a prudent step to ensure more consistent search results.

Concluding Remarks:

While well-trained embedding models should ideally be robust to minor text variations, real-world scenarios like the one discussed highlight that there’s room for nuanced behaviors. Awareness and understanding of these nuances, along with the adoption of mitigating strategies like text normalization, can significantly enhance the accuracy and reliability of vector search operations.