Improved example

Author: ankane

Package.swift

@@ -5,7 +5,7 @@ import PackageDescription
 let package = Package(
     name: "Pgvector",
     platforms: [
-        .macOS(.v10_15),
+        .macOS(.v13),
     ],
     products: [
         .library(

README.md

@@ -18,13 +18,13 @@ Follow the instructions for your database library:
 Enable the extension
 
 ```swift
-try await connection.query("CREATE EXTENSION IF NOT EXISTS vector", logger: logger)
+try await client.query("CREATE EXTENSION IF NOT EXISTS vector")
 ```
 
 Create a table
 
 ```swift
-try await connection.query("CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3))", logger: logger)
+try await client.query("CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3))")
 ```
 
 Insert vectors
@@ -33,14 +33,14 @@ Insert vectors
 let embedding1 = "[1,1,1]"
 let embedding2 = "[2,2,2]"
 let embedding3 = "[1,1,2]"
-try await connection.query("INSERT INTO items (embedding) VALUES (\(embedding1)::vector), (\(embedding2)::vector), (\(embedding3)::vector)", logger: logger)
+try await client.query("INSERT INTO items (embedding) VALUES (\(embedding1)::vector), (\(embedding2)::vector), (\(embedding3)::vector)")
 ```
 
 Get the nearest neighbors
 
 ```swift
 let embedding = "[1,1,1]"
-let rows = try await connection.query("SELECT id, embedding::text FROM items ORDER BY embedding <-> \(embedding)::vector LIMIT 5", logger: logger)
+let rows = try await client.query("SELECT id, embedding::text FROM items ORDER BY embedding <-> \(embedding)::vector LIMIT 5")
 for try await row in rows {
     print(row)
 }
@@ -49,9 +49,9 @@ for try await row in rows {
 Add an approximate index
 
 ```swift
-try await connection.query("CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)", logger: logger)
+try await client.query("CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)")
 // or
-try await connection.query("CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100)", logger: logger)
+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

Tests/PgvectorTests/PostgresNIOTests.swift

@@ -5,39 +5,40 @@ import Testing
 
 final class PostgresNIOTests {
     @Test func example() async throws {
-        let config = PostgresConnection.Configuration(
+        let config = PostgresClient.Configuration(
             host: "localhost",
             port: 5432,
             username: ProcessInfo.processInfo.environment["USER"]!,
             password: nil,
             database: "pgvector_swift_test",
             tls: .disable
         )
-        let logger = Logger(label: "postgres-logger")
 
-        let connection = try await PostgresConnection.connect(
-            configuration: config,
-            id: 1,
-            logger: logger
-        )
+        let client = PostgresClient(configuration: config)
 
-        try await connection.query("CREATE EXTENSION IF NOT EXISTS vector", logger: logger)
-        try await connection.query("DROP TABLE IF EXISTS nio_items", logger: logger)
-        try await connection.query("CREATE TABLE nio_items (id bigserial PRIMARY KEY, embedding vector(3))", logger: logger)
+        try await withThrowingTaskGroup(of: Void.self) { taskGroup in
+            taskGroup.addTask {
+                await client.run()
+            }
 
-        let embedding1 = "[1,1,1]"
-        let embedding2 = "[2,2,2]"
-        let embedding3 = "[1,1,2]"
-        try await connection.query("INSERT INTO nio_items (embedding) VALUES (\(embedding1)::vector), (\(embedding2)::vector), (\(embedding3)::vector)", logger: logger)
+            try await client.query("CREATE EXTENSION IF NOT EXISTS vector")
+            try await client.query("DROP TABLE IF EXISTS nio_items")
+            try await client.query("CREATE TABLE nio_items (id bigserial PRIMARY KEY, embedding vector(3))")
 
-        let embedding = "[1,1,1]"
-        let rows = try await connection.query("SELECT id, embedding::text FROM nio_items ORDER BY embedding <-> \(embedding)::vector LIMIT 5", logger: logger)
-        for try await row in rows {
-            print(row)
-        }
+            let embedding1 = "[1,1,1]"
+            let embedding2 = "[2,2,2]"
+            let embedding3 = "[1,1,2]"
+            try await client.query("INSERT INTO nio_items (embedding) VALUES (\(embedding1)::vector), (\(embedding2)::vector), (\(embedding3)::vector)")
 
-        try await connection.query("CREATE INDEX ON nio_items USING ivfflat (embedding vector_l2_ops) WITH (lists = 1)", logger: logger)
+            let embedding = "[1,1,1]"
+            let rows = try await client.query("SELECT id, embedding::text FROM nio_items ORDER BY embedding <-> \(embedding)::vector LIMIT 5")
+            for try await row in rows {
+                print(row)
+            }
 
-        try await connection.close()
+            try await client.query("CREATE INDEX ON nio_items USING ivfflat (embedding vector_l2_ops) WITH (lists = 1)")
+
+            taskGroup.cancelAll()
+        }
     }
 }