Vector search

Vector search in Amazon OpenSearch Service enables you to search for semantically similar content using machine learning embeddings rather than traditional keyword matching. Vector search converts your data (text, images, audio, etc.) into high-dimensional numerical vectors (embeddings) that capture the semantic meaning of the content. When you perform a search, OpenSearch compares the vector representation of your query against the stored vectors to find the most similar items.

Vector search includes the following key components.

Vector fields

OpenSearch supports the knn_vector field type to store dense vectors with configurable dimensions (up to 16,000).

Search methods

• k-NN (k-nearest neighbors): Finds the k most similar vectors

• Approximate k-NN: Uses algorithms like HNSW (Hierarchical Navigable Small World) for faster searches on large datasets

Distance metrics

Supports various similarity calculations including:

• Euclidean distance

• Cosine similarity

• Dot product

Common use cases

Vector search supports the following common use cases.

• Semantic search: Find documents with similar meaning, not just matching keywords

• Recommendation systems: Suggest similar products, content, or users

• Image search: Find visually similar images

• Anomaly detection: Identify outliers in data patterns

• RAG (Retrieval Augmented Generation): Enhance LLM responses with relevant context

Integration with machine learning

OpenSearch integrates with the following machine learning services and models:

• Amazon Bedrock: For generating embeddings using foundation models

• Amazon SageMaker AI: For custom ML model deployment

• Hugging Face models: Pre-trained embedding models

• Custom models: Your own trained embedding models

Vector search enables you to build sophisticated AI-powered applications that understand context and meaning, going far beyond traditional text matching capabilities.