Vectors

Create and manage vector embeddings for semantic search, recommendations, and AI-powered graph queries. Use ai.setprovider() to set the active embedding AI provider.

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

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

Vector Creation

ai.vector()

Converts a list of numbers to a VECTOR type. Values are stored as 32-bit floats, so minor precision differences may occur (e.g., 0.1 becomes 0.10000000149011612). This is useful when you need to explicitly create a VECTOR value for storing in a VECTOR property or passing to similarity functions.

GQL
RETURN ai.vector([0.1, 0.2, 0.3])

Result:

JSON
{
  "values": [
    0.10000000149011612,
    0.20000000298023224,
    0.30000001192092896
  ]
}

ai.embed()

Generates an embedding vector from text using the configured AI provider.

An AI provider must be configured with ai.setapikey() before using this function.

GQL
LET embedding = ai.embed("Introduction to graph databases")
RETURN embedding, ai.dimension(embedding) AS dimensions

Result:

JSON
{
  "embedding": {
    "values": [
      -0.0258026123046875,
      -0.0126800537109375,
      …
      0.0162200927734375,
      -0.017486572265625
    ]
  },
  "dimensions": 1536
}

ai.embed_batch()

Generates embedding vectors for multiple texts in a single batched call. Supports up to 2048 inputs, internally chunked for efficiency. Null or non-string elements produce null vectors at the same index.

GQL
LET texts = ["graph databases", "machine learning", "data science"]
RETURN ai.embed_batch(texts)

Result:

JSON
[
  {
    "values": [
      -0.000690460205078125,
      0.034271240234375,
      …
      0.033294677734375,
      -0.00782012939453125
    ]
  },
  {
    "values": [
      -0.0121917724609375,
      -0.0113372802734375,
     …
      -0.01312255859375,
      -0.0019989013671875
    ]
  },
  {
    "values": [
      0.0034503936767578125,
      -0.010650634765625,
      …      
      0.00691986083984375,
      0.02203369140625
    ]
  }
]