Java library for the Qdrant vector search engine.
Java client library with handy utility methods and overloads for interfacing with Qdrant.
Important
Requires Java 8 or above.
To install the library, add the following lines to your build config file.
<dependency> <groupId>io.qdrant</groupId> <artifactId>client</artifactId> <version>1.20.0</version> </dependency>
libraryDependencies += "io.qdrant" % "client" % "1.20.0"
implementation 'io.qdrant:client:1.20.0'Note
Please make sure to include all necessary dependencies listed here in your project.
- Usage examples are available throughout the Qdrant documentation and API Reference.
- JavaDoc Reference
A client can be instantiated with
QdrantClient client = new QdrantClient(QdrantGrpcClient.newBuilder("localhost").build());
which creates a client that will connect to Qdrant on https://localhost:6334.
Internally, the high-level client uses a low-level gRPC client to interact with Qdrant. Additional constructor overloads provide more control over how the gRPC client is configured. The following example configures a client to use TLS, validating the certificate using the root CA to verify the server's identity instead of the system's default, and also configures API key authentication:
ManagedChannel channel = Grpc.newChannelBuilder( "localhost:6334", TlsChannelCredentials.newBuilder() .trustManager(new File("ssl/ca.crt")) .build()) .build(); QdrantClient client = new QdrantClient( QdrantGrpcClient.newBuilder(channel) .withApiKey("<apikey>") .build());
The client implements AutoCloseable,
though a client will typically be created once and used for the lifetime of the
application. When a client is constructed by passing a ManagedChannel, the
client does not shut down the channel on close by default. The client can be
configured to shut down the channel on closing with
ManagedChannel channel = Grpc.newChannelBuilder( "localhost:6334", TlsChannelCredentials.create()) .build(); QdrantClient client = new QdrantClient( QdrantGrpcClient.newBuilder(channel, true) .withApiKey("<apikey>") .build());
All client methods return ListenableFuture<T>.
Once a client has been created, create a new collection
client.createCollectionAsync("{collection_name}", VectorParams.newBuilder() .setDistance(Distance.Cosine) .setSize(4) .build()) .get();
Insert vectors into a collection
// import static convenience methods import static io.qdrant.client.PointIdFactory.id; import static io.qdrant.client.ValueFactory.value; import static io.qdrant.client.VectorsFactory.vectors; List<PointStruct> points = List.of( PointStruct.newBuilder() .setId(id(1)) .setVectors(vectors(0.32f, 0.52f, 0.21f, 0.52f)) .putAllPayload( Map.of( "color", value("red"), "rand_number", value(32))) .build(), PointStruct.newBuilder() .setId(id(2)) .setVectors(vectors(0.42f, 0.52f, 0.67f, 0.632f)) .putAllPayload( Map.of( "color", value("black"), "rand_number", value(53), "extra_field", value(true))) .build()); UpdateResult updateResult = client.upsertAsync("{collection_name}", points).get();
Search for similar vectors
List<ScoredPoint> points = client .searchAsync( SearchPoints.newBuilder() .setCollectionName("{collection_name}") .addAllVector(List.of(0.6235f, 0.123f, 0.532f, 0.123f)) .setLimit(5) .build()) .get();
Search for similar vectors with filtering condition
// import static convenience methods import static io.qdrant.client.ConditionFactory.range; List<ScoredPoint> points = client.searchAsync(SearchPoints.newBuilder() .setCollectionName("{collection_name}") .addAllVector(List.of(0.6235f, 0.123f, 0.532f, 0.123f)) .setFilter(Filter.newBuilder() .addMust(range("rand_number", Range.newBuilder().setGte(3).build())) .build()) .setLimit(5) .build() ).get();