Create a Vector Index

Movie posters

GraphAcademy has loaded a sample of 1000 movie poster embeddings into the sandbox. Each movie has a URL to a poster image:

MATCH (m:Movie {title: "Toy Story"})
RETURN m.title, m.poster

The data also contains embeddings for each poster:

MATCH (m:Movie {title: "Toy Story"})
RETURN m.title, m.posterEmbedding

Create a vector index

To search the movie poster embeddings, you must create a vector index. Review the following Cypher to create the vector index before running it:

CREATE VECTOR INDEX moviePosters IF NOT EXISTS
FOR (m:Movie)
ON m.posterEmbedding
OPTIONS {indexConfig: {
 `vector.dimensions`: 512,
 `vector.similarity_function`: 'cosine'
}}

You should note the following about the index:

Run the Cypher to create the vector index.

Check that you created the index successfully using the SHOW INDEXES command.

SHOW VECTOR INDEXES

You should see a result similar to the following:

Once the state is listed as "ONLINE", the index will be ready to query.

The populationPercentage field indicates the proportion of node and property pairing. When the populationPercentage is 100.0, all the movie embeddings have been indexed.

Similar posters

You can use the db.index.vector.queryNodes procedure to find similar movie posters.

MATCH (m:Movie{title: "Babe"})

CALL db.index.vector.queryNodes('moviePosters', 6, m.posterEmbedding)
YIELD node, score

RETURN node.title, node.poster, score;

Pick a different movie and write a similar Cypher query to find similar posters.

You can view the movies that have a poster embedding using this Cypher:

MATCH (m:Movie)
WHERE m.posterEmbedding IS NOT NULL
RETURN m.title, m.poster

Continue

When you are ready, you can move on to the next task.