What is Vector Search?

Vector search, also known as similarity search and semantic search, focuses on finding similar content based on semantic meaning rather than exact keyword matches. Unlike traditional full-text search that asks "which documents contain these tokens?", vector search asks "which documents mean something similar to this?"

This semantic understanding makes it particularly valuable for natural language queries, content recommendation, and applications where meaning matters more than exact terminology.

Vector search operates through three core components: embedding generation, indexing, and querying. Embedding generation converts text into numerical vectors, indexing stores these vectors in specialized data structures, and querying uses mathematical similarity calculations to find the most relevant content.

Embedding Generation

Embedding generation is the foundation that lets vector search appear to understand intent. Machine learning models convert text into dense numerical vectors that capture semantic meaning. This isn't an exact science, just as two people can have different understandings of sentences, so can different embedding models.

The Embedding Pipeline

The embedding process transforms raw text into searchable vectors through several steps:

1. Text preprocessing: Cleaning, normalizing, and chunking input text. Because embedding systems can't handle large documents, input text will be broken down into smaller chunks which will be embedded separately.
2. Model inference: Running chunks through local or remote embedding models to generate high-dimensional vectors.
3. Vector normalization: Normalizing vectors for consistent similarity calculations.

All vector search systems must decide whether to run embedding models locally or use remote APIs.

• Local models like BGE-M3 or open-source alternatives provide full control over latency, costs, and data privacy. You can optimize inference speed and avoid per-request API costs, but require computational resources and model management.

• Remote APIs like OpenAI or Anthropic's embedding services offer convenience without infrastructure overhead. However, they introduce network latency, per-request costs, and potential privacy concerns when sending data to external services.

Embedding generation speed directly affects user experience. Local models can be optimized for your hardware and batched for efficiency, while remote APIs face network round-trip delays. For real-time applications, embedding pre-computation or caching strategies become essential regardless of the approach.

The model's dimensionality also impacts both embedding speed and storage requirements: higher dimensions often mean better accuracy, but bring increased computational and storage costs.

Indexing

Indexing stores the generated vectors in data structures optimized for fast similarity search. Unlike traditional search that builds inverted indexes of terms, vector search creates specialized indexes for high-dimensional numerical data. These indexes allow queries to efficiently find similar vectors without comparing every vector pair. Different indexing strategies optimize for various performance characteristics:

• HNSW (Hierarchical Navigable Small World): Builds multi-layer graph structure for fast approximate search with good recall
• IVF (Inverted File Index): Partitions vectors into clusters using k-means, searches only relevant clusters
• DiskANN: Optimized for SSD storage, enables vector search on datasets larger than RAM

Many other index types exist for specialized use cases, each balancing search speed, accuracy, and memory requirements.

Example: Building a Vector Index

Consider indexing two simple documents:

After embedding generation, each document becomes a high-dimensional vector:

The vector index stores these vectors alongside their document IDs, optimized for fast similarity search operations. The choice of index type (HNSW, IVF, etc.) determines how these vectors are organized and accessed during search.

Querying

Vector search converts user queries into vectors and finds the most similar documents through mathematical distance calculations.

The process is straightforward: convert the query to a vector using the same embedding model, calculate similarity scores against all indexed vectors, and return results ranked by similarity.

Similarity measures determine how close vectors are to each other:

• Cosine similarity: Measures angle between vectors (most common)
• Dot product: Considers both angle and magnitude
• Euclidean distance: Straight-line distance in vector space

Vector search systems often provide additional query capabilities like filtering by metadata, approximate search for speed, and threshold filtering to ensure quality results.

It's important to note that vector systems have no concept of tokens or words, so concepts like exact matching and proximity don't exist. The correctness of the results also relies on the embedding model's semantic understanding matching the end users.

Where Vector Search Excels

Vector search excels in applications where semantic understanding provides clear advantages:

• Content recommendation: Find conceptually similar content regardless of exact wording
• Semantic search: Natural language queries like "Why is my website slow?" find articles about optimization, performance, and CDNs
• Retrieval-Augmented Generation (RAG): Find relevant context documents for language models to provide accurate, grounded responses
• Multimodal applications: Visual similarity search, cross-modal search between text and images, audio/video content matching

When Vector Search Is Not Enough

While vector search excels at semantic similarity, it has limitations when users need exact keyword matching or precise terminology:

• Exact keyword requirements: Product codes, technical specifications, or proper names often require precise lexical matching.
• Positional queries: Vector indexes have no concept of words or position in the document.

Vector search requires more storage and compute resources than traditional search. Even at query time, generating embeddings and computing similarity scores can add noticeable latency compared to BM25.

In many scenarios, hybrid search approaches that combine both vector and traditional full-text search often provide the best solution:

• Full-text search for precise keyword matching and boolean logic
• Vector search for semantic similarity and concept-based retrieval
• Ranking algorithms like Reciprocal Rank Fusion that blend both approaches for optimal relevance

Summary

Vector search represents a divergent advancement in information retrieval, enabling the discovery of semantically similar content by representing data as high-dimensional numerical vectors and using mathematical similarity measures for retrieval. This approach excels at understanding meaning and context, but can't search for exact words or phrases.

The technology has become essential for recommendation systems and semantic search implementations that require nuanced understanding of content relationships.