pgvector support for Java and Scala
Supports JDBC, Spring JDBC, and Slick
For Java, add to pom.xml under <dependencies>:
<dependency>
<groupId>com.pgvector</groupId>
<artifactId>pgvector</artifactId>
<version>0.1.2</version>
</dependency>For Scala, add to build.sbt:
libraryDependencies += "com.pgvector" % "pgvector" % "0.1.2"And follow the instructions for your database library:
Import the PGvector class
import com.pgvector.PGvector;Register the vector type with your connection
PGvector.addVectorType(conn);Create a table
Statement createStmt = conn.createStatement();
createStmt.executeUpdate("CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3))");Insert a vector
PreparedStatement insertStmt = conn.prepareStatement("INSERT INTO items (embedding) VALUES (?)");
insertStmt.setObject(1, new PGvector(new float[] {1, 1, 1}));
insertStmt.executeUpdate();Get the nearest neighbors
PreparedStatement neighborStmt = conn.prepareStatement("SELECT * FROM items ORDER BY embedding <-> ? LIMIT 5");
neighborStmt.setObject(1, new PGvector(new float[] {1, 1, 1}));
ResultSet rs = neighborStmt.executeQuery();
while (rs.next()) {
System.out.println((PGvector) rs.getObject(2));
}Add an approximate index
Statement indexStmt = conn.createStatement();
indexStmt.executeUpdate("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
Import the PGvector class
import com.pgvector.PGvector;Create a table
jdbcTemplate.execute("CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3))");Insert a vector
Object[] insertParams = new Object[] { new PGvector(new float[] {1, 1, 1}) };
jdbcTemplate.update("INSERT INTO items (embedding) VALUES (?)", insertParams);Get the nearest neighbors
Object[] neighborParams = new Object[] { new PGvector(new float[] {1, 1, 1}) };
List<Map<String, Object>> rows = jdbcTemplate.queryForList("SELECT * FROM items ORDER BY embedding <-> ? LIMIT 5", neighborParams);
for (Map row : rows) {
System.out.println(row.get("embedding"));
}Add an approximate index
jdbcTemplate.execute("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
Import the PGvector class
import com.pgvector.PGvectorRegister the vector type with your connection
PGvector.addVectorType(conn)Create a table
val createStmt = conn.createStatement()
createStmt.executeUpdate("CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3))")Insert a vector
val insertStmt = conn.prepareStatement("INSERT INTO items (embedding) VALUES (?)")
insertStmt.setObject(1, new PGvector(Array[Float](1, 1, 1)))
insertStmt.executeUpdate()Get the nearest neighbors
val neighborStmt = conn.prepareStatement("SELECT * FROM items ORDER BY embedding <-> ? LIMIT 5")
neighborStmt.setObject(1, new PGvector(Array[Float](1, 1, 1)))
val rs = neighborStmt.executeQuery()
while (rs.next()) {
println(rs.getObject("embedding").asInstanceOf[PGvector])
}Add an approximate index
val indexStmt = conn.createStatement()
indexStmt.executeUpdate("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
Import the PGvector class
import com.pgvector.PGvectorAdd a vector column
class Items(tag: Tag) extends Table[(String)](tag, "items") {
def embedding = column[String]("embedding", O.SqlType("vector(3)"))
def * = (embedding)
}Insert a vector
val embedding = new PGvector(Array[Float](1, 1, 1)).toString
db.run(sqlu"INSERT INTO items (embedding) VALUES ($embedding::vector)")Get the nearest neighbors
val embedding = new PGvector(Array[Float](1, 1, 1)).toString
db.run(sql"SELECT * FROM items ORDER BY embedding <-> $embedding::vector LIMIT 5".as[(String)])Add an approximate index
db.run(sqlu"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
View 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-java.git
cd pgvector-java
createdb pgvector_java_test
sbt test
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent