Rust client for Qdrant vector search engine.
cargo add qdrant-clientPackage is available in crates.io
The client uses gRPC via the Tonic library.
To change anything in the protocol buffer definitions, you need the protoc Protocol Buffers compiler, along with Protocol Buffers resource files.
Refer to the Tonic installation guide for more details.
Run Qdrant with enabled gRPC interface:
# With env variable
docker run -p 6333:6333 -p 6334:6334 \
-e QDRANT__SERVICE__GRPC_PORT="6334" \
qdrant/qdrantOr by updating the configuration file:
service:
grpc_port: 6334More info about gRPC in documentation.
Add necessary dependencies:
cargo add qdrant-client anyhow tonic tokio serde-json --features tokio/rt-multi-threadAdd search example from examples/search.rs to your src/main.rs:
use anyhow::Result;
use qdrant_client::prelude::*;
use qdrant_client::qdrant::vectors_config::Config;
use qdrant_client::qdrant::{
Condition, CreateCollection, Filter, SearchPoints, VectorParams, VectorsConfig,
};
use serde_json::json;
#[tokio::main]
async fn main() -> Result<()> {
// Example of top level client
// You may also use tonic-generated client from `src/qdrant.rs`
let client = QdrantClient::from_url("http://localhost:6334").build()?;
let collections_list = client.list_collections().await?;
dbg!(collections_list);
// collections_list = ListCollectionsResponse {
// collections: [
// CollectionDescription {
// name: "test",
// },
// ],
// time: 1.78e-6,
// }
let collection_name = "test";
client.delete_collection(collection_name).await?;
client
.create_collection(&CreateCollection {
collection_name: collection_name.into(),
vectors_config: Some(VectorsConfig {
config: Some(Config::Params(VectorParams {
size: 10,
distance: Distance::Cosine.into(),
..Default::default()
})),
}),
..Default::default()
})
.await?;
let collection_info = client.collection_info(collection_name).await?;
dbg!(collection_info);
let payload: Payload = json!(
{
"foo": "Bar",
"bar": 12,
"baz": {
"qux": "quux"
}
}
)
.try_into()
.unwrap();
let points = vec![PointStruct::new(0, vec![12.; 10], payload)];
client
.upsert_points_blocking(collection_name, points, None)
.await?;
let search_result = client
.search_points(&SearchPoints {
collection_name: collection_name.into(),
vector: vec![11.; 10],
filter: Some(Filter::all([Condition::matches("bar", 12)])),
limit: 10,
with_payload: Some(true.into()),
..Default::default()
})
.await?;
dbg!(&search_result);
// search_result = SearchResponse {
// result: [
// ScoredPoint {
// id: Some(
// PointId {
// point_id_options: Some(
// Num(
// 0,
// ),
// ),
// },
// ),
// payload: {
// "bar": Value {
// kind: Some(
// IntegerValue(
// 12,
// ),
// ),
// },
// "foo": Value {
// kind: Some(
// StringValue(
// "Bar",
// ),
// ),
// },
// },
// score: 1.0000001,
// version: 0,
// vectors: None,
// },
// ],
// time: 9.5394e-5,
// }
let found_point = search_result.result.into_iter().next().unwrap();
let mut payload = found_point.payload;
let baz_payload = payload.remove("baz").unwrap().into_json();
println!("baz: {}", baz_payload);
// baz: {"qux":"quux"}
Ok(())
}Or run the example from this project directly:
cargo run --example searchQdrant Cloud is a managed service for Qdrant.
The client needs to be configured properly to access the service.
- make sure to use the correct port (6334)
- make sure to pass your API KEY
async fn make_client() -> Result<QdrantClient> {
let client = QdrantClient::from_url("http://xxxxxxxxxx.eu-central.aws.cloud.qdrant.io:6334")
// using an env variable for the API KEY for example
.with_api_key(std::env::var("QDRANT_API_KEY"))
.build()?;
Ok(client)
}
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