Working with Vector Search

Use Vector Search with Full Text Search and Query.

Use Vector Search

To configure a project to use vector search, follow the installation instructions to add the Vector Search extension.

Note

You must install Couchbase Lite to use the Vector Search extension.

Create a Vector Index

This method shows how you can create a vector index using the Couchbase Lite Vector Search extension.

// create the configuration for a vector index named "vector"
// with 3 dimensions, 100 centroids, no encoding, using cosine distance
// with a max training size 5000 and amin training size 2500
// no vector encoding and using COSINE distance measurement
val config = VectorIndexConfiguration("vector", 3L, 100L).apply {
    encoding = VectorEncoding.none()
    metric = VectorIndexConfiguration.DistanceMetric.COSINE
    numProbes = 8L
    minTrainingSize = 2500L
    maxTrainingSize = 5000L
}

First, initialize the config object with the VectorIndexConfiguration() method with the following parameters:

The expression of the data as a vector.
The width or dimensions of the vector index is set to 3.
The amount of centroids is set to 100. This means that there will be one hundred buckets with a single centroid each that gathers together similar vectors.

You can also alter some optional config settings such as encoding. From there, you create an index within a given collection using the previously generated config object.

Note

The number of vectors, the width or dimensions of the vectors and the training size can incur high CPU and memory costs as the size of each variable increases. This is because the training vectors have to be resident on the machine.

Vector Index Configuration

The table below displays the different configurations you can modify within your VectorIndexConfiguration() function. For more information on specific configurations, see Vector Search.

Table 1. Vector Index Configuration Options

Configuration Name	Default Configuration	Further Information
Expression	No default	A SQL++ expression indicating where to get the vectors. A document property for embedded vectors or `prediction()` to call a registered Predictive model.
Number of Dimensions	No default	2-4096
Number of Centroids	No default	1-64000. The general guideline is an approximate square root of the number of documents.
Distance Metric	Squared Euclidean Distance (euclideanSquared)	You can set the following alternates as your Distance Metric: cosine (1 - Cosine similarity) Euclidean dot (negated dot product)
Encoding	Scalar Quantizer(SQ) or SQ-8 bits	There are three possible configurations: None No compression, No data loss Scalar Quantizer (SQ) or SQ-8 bits (Default) Reduces the number of bits per dimension Product Quantizer (PQ) Reduces the number of dimensions and bits per dimension
Training Size	The default values for both the minimum and maximum training size is zero. The training size is calculated based on the number of Centroids and the encoding type.	The guidelines for the minimum and maximum training size are as follows: The minimum training size is set to 25x the number of Centroids or 2^{PQ’s bits} when PQ is used The maximum training size is set to 256x the number of Centroids or 2^{PQ’s bits} when PQ is used
NumProbes	The default value is 0. The number of Probes is calculated based on the number of Centroids.	A guideline for setting a custom number of probes is at least 8 or 0.5% the number of Centroids.
isLazy	False	Setting the value to true will enable lazy mode for the vector index.

Caution

Altering the default training sizes could be detrimental to the accuracy of returned results produced by the model and total computation time.

Generating Vectors

You can use the following methods to generate vectors in Couchbase Lite:

You can call a Machine Learning(ML) model, and embed the generated vectors inside the documents.
You can use the prediction() function to generate vectors to be indexed for each document at the indexing time.
You can use Lazy Vector Index (lazy index) to generate vectors asynchronously from remote ML models that may not always be reachable or functioning, skipping or scheduling retries for those specific cases.

Below are example configurations of the previously mentioned methods.

Create a Vector Index with Embeddings

This method shows you how to create a Vector Index with embeddings.

// Get the collection named "colors" in the default scope.
val collection = database.getCollection("colors")
    ?: throw IllegalStateException("No such collection: colors")

// Create a vector index configuration with a document property named "vector",
// 3 dimensions, and 100 centroids.
val config = VectorIndexConfiguration("vector", 3, 100)
// Create a vector index from the configuration with the name "colors_index".
collection.createIndex("colors_index", config)

First, create the standard configuration, setting up an expression, number of dimensions and number of centroids for the vector embedding.
Next, create a vector index, colors_index, on a collection and pass it the configuration.

Create Vector Index Embeddings from a Predictive Model

This method generates vectors to be indexed for each document at the index time by using the prediction() function. The key difference to note is that the config object uses the output of the prediction() function as the expression parameter to generate the vector index.

class ColorModel : PredictiveModel {
    override fun predict(input: Dictionary): Dictionary? {
        // Get the color input from the input dictionary
        val color = input.getString("colorInput")
            ?: throw IllegalStateException("No input color found")

        // Use ML model to get a vector (an array of floats) for the input color.
        val vector = Color.getVector(color) ?: return null

        // Create an output dictionary by setting the vector result to
        // the dictionary key named "vector".
        val output = MutableDictionary()
        output.setValue("vector", vector)
        return output
    }
}

fun createVectorIndexFromPredictiveIndex() {
    // Register the predictive model named "ColorModel".
    Database.prediction.registerModel("ColorModel", ColorModel())

    // Create a vector index configuration with an expression using the prediction
    // function to get the vectors from the registered predictive model.
    val expression = "prediction(ColorModel, {\"colorInput\": color}).vector"
    val config = VectorIndexConfiguration(expression, 3, 100)

    // Create vector index from the configuration
    collection.createIndex("colors_index", config)
}

Note

You can use less storage by using the prediction() function as the encoded vectors will only be stored in the index. However, the index time will be longer as vector embedding generation is occurring at run time.

Create a Lazy Vector Index

Lazy indexing is an alternate approach to using the standard predictive model with regular vector indexes which handle the indexing process automatically. You can use lazy indexing to use a ML model that is not available locally on the device and to create vector indexes without having vector embeddings in the documents.

// Creating a lazy vector index using the document's property named "color".
// The "color" property's value will be used to compute a vector when updating the index.
val config = VectorIndexConfiguration("color", 3, 100).apply { 
    isLazy = true
}

You can enable lazy vector indexing by setting the isLazy property to true in your vector index configuration.

Note

Lazy Vector Indexing is opt-in functionality, the isLazy property is set to false by default.

Updating the Lazy Index

Below is an example of how you can update your lazy index.

val index = collection.getIndex("colors_index")
    ?: throw IllegalStateException("colors_index not found")

while (true) {
    // Start an update on it (in this case, limit to 50 entries at a time)
    index.beginUpdate(50)?.use { updater ->
        for (i in 0..<updater.count) {
            // The value type will depend on the expression you have set in your index.
            // In this example, it is a string property.
            val color = updater.getString(i)

            try {
                val embedding: List<Float>? = Color.getVectorAsync(color)
                // Set the computed vector here. If vector is null, calling setVector
                // will cause the underlying document to NOT be indexed.
                updater.setVector(embedding, i)
            } catch (e: IOException) {
                // Bad connection? Corrupted over the wire? Something bad happened
                // and the vector cannot be generated at the moment: skip it.
                // The next time beginUpdate() is called, we'll try it again.
                updater.skipVector(i)
            }
        }
        // This writes the vectors to the index. You MUST either have set or skipped each
        // of the vectors in the updater or this call will throw an exception.
        updater.finish()
    }
    // loop until there are no more vectors to update
        ?: break
}

You procedurally update the vectors in the index by looping through the vectors in batches until you reach the value of the limit parameter.

The update process follows the following sequence:

Get a value for the updater.
1. If the there is no value for the vector, handle it. In this case, the vector will be skipped and considered the next time beginUpdate() is called.
Note

A key benefit of lazy indexing is that the indexing process continues if a vector fails to generate. For standard vector indexing, this will cause the affected documents to be dropped from the indexing process.
Set the vector from the computed vector derived from the updater value and your ML model.
1. If there is no value for the vector, this will result in the underlying document to not be indexed.
Once all vectors have completed the update loop, finish updating.

Note

updater.finish() will throw an error if any values inside the updater have not been set or skipped.

Vector Search SQL++ Support

Couchbase Lite currently supports Hybrid Vector Search and the APPROX_VECTOR_DISTANCE() function.

Important

Similar to the Full Text Search match() function, the APPROX_VECTOR_DISTANCE() function and Hybrid Vector Search cannot use the OR expression with the other expressions in the related WHERE clause.

Use Hybrid Vector Search

You can use Hybrid Vector Search (Hybrid Search) to perform vector search in conjunction with regular SQL++ queries. With Hybrid Search, you perform vector search on documents that have already been filtered based on criteria specified in the WHERE clause.

Note

A LIMIT clause is required for non-hybrid Vector Search, this avoids a slow, exhaustive unlimited search of all possible vectors.

Hybrid Vector Search with Full Text Match

Below are examples of using Hybrid Search with the Full Text match() function.

// Create a hybrid vector search query with full-text's match() that
// uses the the full-text index named "color_desc_index".
val sql = $$"""
    SELECT meta().id, color
    WHERE MATCH(color_desc_index, $text)
    ORDER BY approx_vector_distance(vector, $vector)
    LIMIT 8
""".trimIndent()

val query = database.createQuery(sql)

// Get a vector, an array of float numbers, for the input color code (e.g. FF000AA).
// Normally, you will get the vector from your ML model.
val vector = Color.getVector("FF00AA")
    ?: throw IllegalStateException("Vector not found")

val parameters = Parameters()
// Set the vector array to the parameter "$vector"
parameters.setValue("vector", vector)
// Set the vector array to the parameter "$text".
parameters.setString("text", "vibrant")
query.parameters = parameters

// Execute the query
query.execute().use { rs ->
    // process results
}

Prediction with Hybrid Vector Search

Below are examples of using Hybrid Search with an array of vectors generated by the Prediction() function at index time.

// Create a hybrid vector search query that uses prediction() for computing vectors.
val sql = $$"""
    SELECT meta().id, color
    WHERE saturation > 0.5
    ORDER BY approx_vector_distance(prediction(ColorModel, {"colorInput": color}).vector, $vector)
    LIMIT 8
""".trimIndent()

val query = database.createQuery(sql)

// Get a vector, an array of float numbers, for the input color code (e.g. FF000AA).
// Normally, you will get the vector from your ML model.
val vector = Color.getVector("FF00AA")
    ?: throw IllegalStateException("Vector not found")

// Set the vector array to the parameter "$vector"
val parameters = Parameters()
parameters.setValue("vector", vector)
query.parameters = parameters

// Execute the query
query.execute().use { rs ->
    // process results
}

`APPROX_VECTOR_DISTANCE(vector-expr, target-vector, [metric], [nprobes], [accurate])`

Warning

If you use a different distance metric in the APPROX_VECTOR_DISTANCE() function from the one configured in the index, you will receive an error when compiling the query.

Parameter	Is Required	Description
vector-expr		The expression returning a vector (NOT Index Name). Must match the expression specified in the vector index exactly.
target-vector		The target vector.
metric		Values : "EUCLIDEAN_SQUARED", “L2_SQUARED”, “EUCLIDEAN”, “L2”, ”COSINE”, “DOT”. If not specified, the metric set in the vector index is used. If specified, the metric must match with the metric set in the vector index. This optional parameter allows multiple indexes to be attached to the same field in a document.
nprobes		Number of buckets to search for the nearby vectors. If not specified, the nprobes set in the vector index is used.
accurate		If not present, false will be used, which means that the quantized/encoded vectors in the index will be used for calculating the distance. IMPORTANT: Only accurate = false is supported

Use `APPROX_VECTOR_DISTANCE()`

// Create a vector search query by using the approx_vector_distance() in WHERE clause.
val sql = $$"""
    SELECT meta().id, color
    FROM _default.colors
    WHERE approx_vector_distance(vector, $vector) < 0.5
    LIMIT 8
""".trimIndent()

val query = database.createQuery(sql)

// Get a vector, an array of float numbers, for the input color code (e.g. FF000AA).
// Normally, you will get the vector from your ML model.
val vector = Color.getVector("FF00AA")
    ?: throw IllegalStateException("Vector not found")

// Set the vector array to the parameter "$vector"
val parameters = Parameters()
parameters.setValue("vector", vector)
query.parameters = parameters

// Execute the query
query.execute().use { rs ->
    // process results
}

This function returns the approximate distance between a given vector, typically generated from your ML model, and an array of vectors with size equal to the LIMIT parameter, collected by a SQL++ query using APPROX_VECTOR_DISTANCE().

Prediction with `APPROX_VECTOR_DISTANCE()`

Below are examples of using APPROX_VECTOR_DISTANCE() with an array of vectors generated by the Prediction() function at index time.

// Create a vector search query that uses prediction() for computing vectors.
val sql = $$"""
    SELECT meta().id, color
    FROM _default.colors
    ORDER BY APPROX_VECTOR_DISTANCE(prediction(ColorModel, {"colorInput": color}).vector, $vector)
    LIMIT 8
""".trimIndent()

val query = database.createQuery(sql)

// Get a vector, an array of float numbers, for the input color code (e.g. FF000AA).
// Normally, you will get the vector from your ML model.
val vector = Color.getVector("FF00AA")
    ?: throw IllegalStateException("Vector not found")

// Set the vector array to the parameter "$vector"
val parameters = Parameters()
parameters.setValue("vector", vector)
query.parameters = parameters

// Execute the query
query.execute().use { rs ->
    // process results
}

Working with Vector Search

Use Vector Search

Create a Vector Index

Vector Index Configuration

Generating Vectors

Create a Vector Index with Embeddings

Create Vector Index Embeddings from a Predictive Model

Create a Lazy Vector Index

Updating the Lazy Index

Vector Search SQL++ Support

Use Hybrid Vector Search

Hybrid Vector Search with Full Text Match

Prediction with Hybrid Vector Search

APPROX_VECTOR_DISTANCE(vector-expr, target-vector, [metric], [nprobes], [accurate])

Use APPROX_VECTOR_DISTANCE()

Prediction with APPROX_VECTOR_DISTANCE()

`APPROX_VECTOR_DISTANCE(vector-expr, target-vector, [metric], [nprobes], [accurate])`

Use `APPROX_VECTOR_DISTANCE()`

Prediction with `APPROX_VECTOR_DISTANCE()`