Vector fields

Vector types are used for storing high-dimensional vectors, which are especially useful for similarity search operations.

When specifying vector types, the dimension is required between parentheses (e.g., Vector(1536)). Common dimensions include:

• 1536 (OpenAI embeddings)
• 768 (many sentence transformers)
• 384 (smaller models)

All vector types support specialized distance operations for similarity search and filtering. See the Vector distance operators section for details.

To ensure optimal performance with vector similarity searches, consider creating specialized vector indexes on your vector fields. See the Vector indexes section for more details.

info

The usage of Vector fields requires the pgvector PostgreSQL extension to be installed, which comes by default on new Serverpod projects. To upgrade an existing project, see the Upgrading to pgvector support guide.

Vector

The Vector type stores full-precision floating-point vectors for general-purpose embeddings.

class: Document
table: document
fields:
  ### The category of the document (e.g., article, tutorial).
  category: String

  ### The contents of the document.
  content: String

  ### A vector field for storing document embeddings
  embedding: Vector(1536)

HalfVector

The HalfVector type uses half-precision (16-bit) floating-point numbers, providing memory savings with acceptable precision loss for several applications.

class: Document
table: document
fields:
  content: String
  ### Half-precision embedding for memory efficiency
  embedding: HalfVector(1536)

SparseVector

The SparseVector type efficiently stores sparse vectors where most values are zero, which is ideal for high-dimensional data with few non-zero elements.

class: Document
table: document
fields:
  content: String
  ### Sparse vector for keyword-based embeddings
  keywords: SparseVector(10000)

Bit

The Bit type stores binary vectors where each element is 0 or 1, offering maximum memory efficiency for binary embeddings.

class: Document
table: document
fields:
  content: String
  ### Binary vector for semantic hashing
  hash: Bit(256)