pgvector support for Swift
Supports PostgresNIO and PostgresClientKit
Follow the instructions for your database library:
Or check out an example:
- Embeddings with OpenAI
- Binary embeddings with Cohere
- Hybrid search with Ollama
- Sparse search with Text Embeddings Inference
Add to your application’s Package.swift
dependencies: [
+ .package(url: "https://github.com/pgvector/pgvector-swift", from: "0.1.0")
],
targets: [
.executableTarget(name: "App", dependencies: [
+ .product(name: "Pgvector", package: "pgvector-swift"),
+ .product(name: "PgvectorNIO", package: "pgvector-swift")
])
]Import the packages
import Pgvector
import PgvectorNIOEnable the extension
try await client.query("CREATE EXTENSION IF NOT EXISTS vector")Register the types
try await PgvectorNIO.registerTypes(client)Create a table
try await client.query("CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3))")Insert vectors
let embedding1 = Vector([1, 1, 1])
let embedding2 = Vector([2, 2, 2])
let embedding3 = Vector([1, 1, 2])
try await client.query("INSERT INTO items (embedding) VALUES (\(embedding1)), (\(embedding2)), (\(embedding3))")Get the nearest neighbors
let embedding = Vector([1, 1, 1])
let rows = try await client.query("SELECT id, embedding::text FROM items ORDER BY embedding <-> \(embedding) LIMIT 5")
for try await row in rows {
print(row)
}Add an approximate index
try await client.query("CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)")
// or
try await client.query("CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100)")Use vector_ip_ops for inner product and vector_cosine_ops for cosine distance
See a full example
Add to your application’s Package.swift
dependencies: [
+ .package(url: "https://github.com/pgvector/pgvector-swift", from: "0.1.0")
],
targets: [
.executableTarget(name: "App", dependencies: [
+ .product(name: "Pgvector", package: "pgvector-swift"),
+ .product(name: "PgvectorClientKit", package: "pgvector-swift")
])
]Import the packages
import Pgvector
import PgvectorClientKitEnable the extension
let text = "CREATE EXTENSION IF NOT EXISTS vector"
let statement = try connection.prepareStatement(text: text)
try statement.execute()Create a table
let text = "CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3))"
let statement = try connection.prepareStatement(text: text)
try statement.execute()Insert vectors
let text = "INSERT INTO items (embedding) VALUES ($1), ($2), ($3)"
let statement = try connection.prepareStatement(text: text)
try statement.execute(parameterValues: [Vector([1, 1, 1]), Vector([2, 2, 2]), Vector([1, 1, 2])])Get the nearest neighbors
let text = "SELECT * FROM items ORDER BY embedding <-> $1 LIMIT 5"
let statement = try connection.prepareStatement(text: text)
let cursor = try statement.execute(parameterValues: [Vector([1, 1, 1])])
for row in cursor {
let columns = try row.get().columns
let id = try columns[0].int()
let embedding = try columns[1].vector()
print(id, embedding)
}Add an approximate index
let text = "CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)"
// or
let text = "CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100)"
let statement = try connection.prepareStatement(text: text)
try statement.execute()Use vector_ip_ops for inner product and vector_cosine_ops for cosine distance
See a full example
Create a vector from an array
let vec = Vector([1, 2, 3])Create a half vector from an array
let vec = HalfVector([1, 2, 3])Create a sparse vector from an array
let vec = SparseVector([1, 0, 2, 0, 3, 0])Or a dictionary of non-zero elements
let vec = SparseVector([0: 1, 2: 2, 4: 3], dim: 6)!Note: Indices start at 0
Get the number of dimensions
let dim = vec.dimGet the indices and values of non-zero elements
let indices = vec.indices
let values = vec.valuesView the changelog
Everyone is encouraged to help improve this project. Here are a few ways you can help:
- Report bugs
- Fix bugs and submit pull requests
- Write, clarify, or fix documentation
- Suggest or add new features
To get started with development:
git clone https://github.com/pgvector/pgvector-swift.git
cd pgvector-swift
createdb pgvector_swift_test
swift testTo run an example:
cd Examples/Ollama
createdb pgvector_example
swift run