Vector Fundamentals

Overview

Understanding vectors and embeddings in graph databases.

What are Vectors?

Vectors (or embeddings) are arrays of numbers that represent the semantic meaning of data. They enable:

• Semantic Search - Find content by meaning, not just keywords
• Recommendations - Suggest similar items based on features
• Clustering - Group related items together
• RAG (Retrieval Augmented Generation) - Enhance AI with your data

How they work:

• Text, images, or other data is converted to a vector
• Similar items have vectors that are "close" in vector space
• Distance/similarity measures find related items

GQL
// Example: Vector representation
// "Introduction to Graph Databases" might become:
// [0.12, -0.45, 0.78, 0.23, -0.89, 0.56, ...]
// (typically 384 to 1536 dimensions)

Use Cases

Common use cases for vectors in graph databases:

Semantic Search

Find documents by meaning, not just keywords

Recommendation Systems

"Users who liked X also liked Y"

Knowledge Graph Enhancement

Connect entities based on semantic similarity

Duplicate Detection

Find near-duplicate content

Multi-modal Search

Search across text, images, and other media

Semantic search example:

GQL
// Find documents about graph databases
// even if they don't contain those exact words
LET query = AI.embed('how do nodes connect in a network')
MATCH (d:Document)
WHERE AI.COSINE(d.embedding, query) > 0.8
RETURN d.title

Product recommendation:

GQL
MATCH (p:Product {id: 'PROD-123'})
MATCH (similar:Product)
WHERE similar <> p AND AI.COSINE(p.embedding, similar.embedding) > 0.85
RETURN similar.name, similar.price
LIMIT 5

Creating Vectors

Two ways to create vectors in GQL:

Create vector from array:

GQL
LET v = AI.VECTOR([0.1, 0.2, 0.3, 0.4, 0.5])
RETURN AI.DIMENSION(v) AS dimensions

Generate embedding from text:

GQL
LET embedding = AI.embed('Introduction to graph databases')
RETURN AI.DIMENSION(embedding) AS dimensions

Store document with embedding:

GQL
INSERT (:Document {
  title: 'Graph Database Tutorial',
  content: 'Graphs are powerful data structures...',
  embedding: AI.embed('Graph Database Tutorial: Graphs are powerful data structures...')
})

Batch generate embeddings for existing documents:

GQL
MATCH (d:Document)
WHERE d.embedding IS NULL
SET d.embedding = AI.embed(d.title + ' ' + d.content)

Use specific embedding model:

GQL
MATCH (d:Document)
SET d.embedding = AI.embed(d.content, 'text-embedding-3-small')