Create vector index

Vector indexes allow Neo4j to run similarity queries across the whole database. The example below shows how to create a vector index for movie embeddings, under the name of moviePlots.

CREATE VECTOR INDEX moviePlots
FOR (m:Movie)
ON m.embedding
OPTIONS {indexConfig: {
    `vector.dimensions`: 384,  (1)
    `vector.similarity_function`: 'cosine'  (2)
}}

1	For embeddings generated with the SentenceTransformers model `all-MiniLM-L6-v2`. A different model may require a different dimension.
2	The cosine similarity function is the most common choice. For more details and other options, see Vector indexes → Cosine and Euclidean similarity functions.

CREATE VECTOR INDEX moviePlots
FOR (m:Movie)
ON m.embedding
OPTIONS {indexConfig: {
    `vector.dimensions`: 1536,  (1)
    `vector.similarity_function`: 'cosine'  (2)
}}

1	For embeddings generated with the OpenAI model `text-embedding-ada-002`. A different model may require a different dimension.
2	The cosine similarity function is the most common choice. For more details and other options, see Vector indexes → Cosine and Euclidean similarity functions.

For more information on vector indexes, see Cypher^® → Indexes → Vector indexes.