Vector Search

Vector search is a search method that uses vector embeddings to find results based on semantic similarity rather than exact keyword matches. It converts a search query and indexed data into numerical vectors, then compares those vectors to retrieve the most relevant results. Vector search can be used to search text, images, audio, and other types of unstructured data.

How does vector search work?

Vector search typically works in four steps:

1. Data is converted into embeddings. An embedding model converts data, such as text, images, or audio, into numerical vectors that represent meaningful characteristics and relationships within the data.
2. The vectors are stored and indexed. The resulting embeddings are stored in a vector database or another system that supports vector search.
3. The search query is converted into a vector. When a user enters a query, an embedding model converts the query into a numerical vector.
4. Similar vectors are retrieved. The search system compares the query vector with stored vectors and returns the results with the highest level of similarity.

Why is vector search important?

Vector search enables more intelligent retrieval because it can understand semantic relationships and context. This can help return relevant results when a query and a result use different words but have similar meanings. Vector search is commonly used for semantic search, recommendation systems, retrieval-augmented generation (RAG), and other applications that need to find similar or related information.

Vector search vs. keyword search

Traditional keyword search looks for matches between the words in a query and the words in indexed content. Vector search compares the semantic similarity between numerical representations of the query and indexed content.

Keyword Search: Matches exact keywords and terms represented as words or tokens. It has more limited handling of synonyms and related concepts, can be less effective for natural-language queries, and is best suited for exact searches and lookups.

Vector Search: Matches results based on semantic similarity using numerical vector embeddings. It effectively captures synonyms, related concepts, and natural-language queries, making it ideal for semantic search, recommendations, and retrieval-augmented generation (RAG).

Keyword search can still be useful when users need exact matches, such as searching for a specific name, product ID, or phrase. Vector search is useful when the meaning or relationship between the query and the result is more important than an exact word match.

What are vector embeddings?

A vector embedding is a numerical representation of data, such as text, images, or audio. Embeddings represent meaningful characteristics and relationships within the data, allowing a search system to compare different pieces of content based on their similarity.

What is a vector database?

A vector database is a database designed to store, index, and search vector embeddings. Vector databases allow applications to efficiently find vectors that are similar to a given query vector, which makes them commonly used with vector search systems.

What is vector search used for?

Vector search can be used for a range of applications, including:

• Semantic search: Finding content based on its meaning rather than exact keywords.
• Recommendation systems: Finding products, content, or other items that are similar to something a user has viewed or selected.
• Retrieval-augmented generation (RAG): Retrieving relevant information from a knowledge base to provide context to an AI model.
• Image search: Finding visually or semantically similar images.
• Content discovery: Helping users find related documents, articles, products, or other content.
• Similarity detection: Identifying similar or potentially duplicate pieces of content.