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Conduct Similarity Search

This page provides details of how to execute similarity searches. For more advanced search filters, see Customize Filters.

Similarity searches in KDB.AI are based on Approximate Nearest Neighbor (ANN) algorithms.

Setup

Before you start, make sure you have:

To perform a search, specify the name of the table in which the relevant vector embeddings are stored. Using Python Client, you can create a table object from the session: 

documents = session.table("documents")

Now that you have a vector embedding, you can perform a search for the nearest neighbors. Python Client uses the table object, whereas REST Client uses the table name as above. In this example, the embeddings are assumed to be eight dimensional and the number of nearest neighbours is set to three.

Use the following command to search for the nearest neighbours:

index_name = 'vector_index'
documents.search(vectors={index_name: [[1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0]]}, n=3)

curl -s -H "Content-Type: application/json" localhost:8082/api/v2/databases/default/tables/documents/search \
-d '{"n": 3, "vectors" {"vector_index":[[1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0]]}}'

// gw is a handler to the gateway
vectors: enlist[`vector_index]!enlist enlist 1.0 0.0 1.0 1.0 0.0 1.0 1.0 0.0 1.0 1.0 0.0 1.0;
gw(`search;`database`table`n`vectors!(`default;`documents;3;vectors))

Batch searches

For larger workloads you can send multiple query vectors at once as seen in the following command.

index_name = 'vector_index'
documents.search(vectors={index_name: [[1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0],[1.0,7.0,1.0,1.0,7.0,1.0,1.0,7.0,1.0,1.0,7.0,1.0]]}, n=3)

curl -s -H "Content-Type: application/json" localhost:8082/api/v2/databases/default/tables/documents/search \ 
-d '{"n":3,
"vectors": {"vector_index": [[1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0],[1.0,7.0,1.0,1.0,7.0,1.0,1.0,7.0,1.0,1.0,7.0,1.0]]}}' 

// gw is a handler to the gateway
vectors: enlist[`vector_index]!enlist (1.0 0.0 1.0 1.0 0.0 1.0 1.0 0.0 1.0 1.0 0.0 1.0; 1.0 7.0 1.0 1.0 7.0 1.0 1.0 7.0 1.0 1.0 7.0 1.0);
gw(`search;`database`table`n`vectors!(`default;`documents;3;vectors))

Customize searches using Range

Use the keyword range to find all vectors within a distance you define. This applies to the qFlat index only.

index_name = 'qFlat_index'
documents.search(vectors={qFlat_index: [[1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0],[1.0,7.0,1.0,1.0,7.0,1.0,1.0,7.0,1.0,1.0,7.0,1.0]]}, range=5.5)

curl -s -H "Content-Type: application/json" localhost:8082/api/v2/databases/default/tables/documents/search \ 
-d '{"vectors": {"vector_index": [[1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0],[1.0,7.0,1.0,1.0,7.0,1.0,1.0,7.0,1.0,1.0,7.0,1.0]]}, "range":5.5}' 

// gw is a handler to the gateway
vectors: enlist[`vector_index]!enlist (1.0 0.0 1.0 1.0 0.0 1.0 1.0 0.0 1.0 1.0 0.0 1.0; 1.0 7.0 1.0 1.0 7.0 1.0 1.0 7.0 1.0 1.0 7.0 1.0);
gw(`search;`database`table`vectors`range!(`default;`documents;vectors;5.5e))

Processing results

You can return a subset of the columns in the table, reducing the amount to data sent back to the client: 

index_name = "vector_index"
documents.search(vectors={index_name: [[1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0]]}, n=3, aggs=[["author", "content"]])

curl -s -H "Content-Type: application/json" localhost:8082/api/v2/databases/default/tables/documents/search \ 
-d '{"n":3,
"vectors":{"vector_index":[[1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0]]}, aggs:[["author","content"]]}'

// gw is a handler to the gateway
vectors: enlist[`vector_index]!enlist enlist 1.0 0.0 1.0 1.0 0.0 1.0 1.0 0.0 1.0 1.0 0.0 1.0;
gw(`search;`database`table`vectors`n`aggs!(`default;`documents;vectors;3;{x!x}(`author;`content)))

In addition to returning a subset of the columns, you can return aggregated results, grouped by categorical variables, and sorted by column name:

index_name = "vector_index"
documents.search(vectors={index_name:[[1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0]]}, n=3, aggs={"sumLength":["sum","length"]}, group_by=["author"], sort_columns=["sumLength"])

curl -s -H "Content-Type: application/json" localhost:8082/api/v2/databases/default/tables/documents/search \ 
-d '{"n":3,
"vectors":{"vector_index":[[1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0]]}, aggs:{"sumLength":["sum","length"]},
groupBy:["author"],sort_columns:["sumLength"]}'

// gw is a handler to the gateway
vectors: enlist[`vector_index]!enlist enlist 1.0 0.0 1.0 1.0 0.0 1.0 1.0 0.0 1.0 1.0 0.0 1.0;
gw(`search;`database`table`vectors`n`aggs`groupBy`sortColumns!(`default;`documents;vectors;3;enlist[`sumLength]!enlist (`sum;`length);`author;`sumLength))

You can find all supported aggregations listed here.

Next steps

Now that you're familiar with similarity searches, you can do the following: