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Dgraph automatically generates two GraphQL similarity queries for each type that have at least one vector predicate with @search directive. For example 
type User {
  id: ID!
  name: String!
  name_v: [Float!]
    @embedding
    @search(by: ["hnsw(metric: euclidean, exponent: 4)"])
}
With this schema, the auto-generated querySimilar<Object>ByEmbedding query allows us to run similarity search using the vector index specified in our schema. 
getSimilar<Object>ByEmbedding(
    by: vector_predicate,
    topK: n,
    vector: searchVector): [User]
For example, to find top 3 users with names similar to a given user name embedding the following query function can be used. 
querySimilarUserByEmbedding(by: name_v, topK: 3, vector: [0.1, 0.2, 0.3, 0.4, 0.5]) {
        id
        name
        vector_distance
     }
The results obtained for this query includes the 3 closest Users ordered by vector_distance. The vector_distance is the Euclidean distance between the name_v embedding vector and the input vector used in our query. Note: you can omit vector_distance predicate in the query, the result is still ordered by vector_distance. The distance metric used is specified in the index creation. Similarly, the auto-generated querySimilar<Object>ById query allows us to search for similar objects to an existing object, given it’s Id. using the function. 
getSimilar<Object>ById(
    by: vector_predicate,
    topK: n,
    id: userID):  [User]
For example the following query searches for top 3 users whose names are most similar to the name of the user with id 0xef7. 
querySimilarUserById(by: name_v, topK: 3, id: "0xef7") {
    id
    name
    vector_distance
}