Mixedbread

Vector Search

Explore the algorithms behind vector search and how to use them effectively.

Vector search, also known as similarity search or nearest neighbor search, identifies the most semantically similar items to your query from large datasets. Rather than relying on exact keyword matches, vector search operates within high-dimensional embedding spaces, capturing the deeper semantic relationships between data points.

Fast and Efficient: Approximate Nearest Neighbor (ANN)

Performing exact searches across large datasets is computationally intensive. This is where Approximate Nearest Neighbor (ANN) algorithms come into play, providing an optimal balance between speed and accuracy:

  • Rapid Search: ANN algorithms rapidly narrow down search spaces, significantly speeding up the retrieval process.
  • High Scalability: Efficiently handle searches across millions or even billions of vectors.
  • Accuracy-Speed Trade-off: ANN prioritizes speed and scalability while maintaining a high level of accuracy—typically sufficient for practical applications like semantic search and RAG systems.

Evaluating Vector Search Performance

Effectiveness in vector search is evaluated using key metrics that ensure quality and performance:

  • Recall@K: Measures how many of the true nearest neighbors appear within the top-K search results—high recall indicates accurate retrieval.
  • Precision@K: Evaluates the relevance of the returned top-K items, reflecting the accuracy of the retrieved results.
  • Query Latency: Tracks the speed of search responses, crucial for a smooth user experience.
  • Index Build Efficiency: Considers the resources needed to construct and maintain the ANN indices.

Optimizing these metrics helps ensure that your semantic search remains accurate, efficient, and responsive.

Hybrid search enhances results by combining the strengths of keyword search and semantic vector search:

  • Keyword Search: Excels at precise term matching, ideal for specific codes, acronyms, or exact phrases.
  • Semantic Search: Captures deeper meaning and contextual relationships, handling synonyms and nuanced language effectively.

By merging these methods, hybrid search delivers comprehensive results that are both contextually relevant and precisely targeted.

Last updated: May 2, 2025