mirror of
https://github.com/qdrant/landing_page.git
synced 2026-10-01 00:48:32 +02:00
docs: Added Rust client usage (#401)
* docs: Rust quick-start * docs: rust auth.md * docs: Rust /collections usage * docs: filtering Rust * docs: filtering Rust * docs: Rust /indexing * docs: Rust /payload * docs: Rust /points * docs: Rust /storage * docs: Rust /snapshots * docs: Rust /search * New line after code blocks * Add Rust imports * Functions end with semicolon, values end without it * Reformat using rustfmt * Use TokenizerType ID from type * Define score as Rust let if * Add example of update collection call, implemented in Rust client 1.7.0 * chore: hide update_collection() --------- Co-authored-by: timvisee <tim@visee.me>
This commit is contained in:
Anush
@@ -21,7 +21,7 @@ docker pull qdrant/qdrant
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Then, run the service:
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```bash
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docker run -p 6333:6333 \
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docker run -p 6333:6333 -p 6334:6334 \
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-v $(pwd)/qdrant_storage:/qdrant/storage:z \
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qdrant/qdrant
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```
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@@ -29,8 +29,9 @@ docker run -p 6333:6333 \
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Under the default configuration all data will be stored in the `./qdrant_storage` directory. This will also be the only directory that both the Container and the host machine can both see.
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Qdrant is now accessible:
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- API: [localhost:6333](http://localhost:6333)
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- REST API: [localhost:6333](http://localhost:6333)
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- Web UI: [localhost:6333/dashboard](http://localhost:6333/dashboard)
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- GRPC API: [localhost:6334](http://localhost:6334)
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## Initialize the client
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@@ -46,6 +47,13 @@ import { QdrantClient } from "@qdrant/js-client-rest";
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const client = new QdrantClient({ host: "localhost", port: 6333 });
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```
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```rust
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use qdrant_client::client::QdrantClient;
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// The Rust client uses Qdrant's GRPC interface
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let client = QdrantClient::from_url("http://localhost:6334").build()?;
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```
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<aside role="status">By default, Qdrant starts with no encryption or authentication . This means anyone with network access to your machine can access your Qdrant container instance. Please read <a href="https://qdrant.tech/documentation/security/">Security</a> carefully for details on how to secure your instance.</aside>
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## Create a collection
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@@ -67,7 +75,24 @@ await client.createCollection("test_collection", {
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});
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```
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<aside role="status">TypeScript examples use async/await syntax, so should be called in an async function.</aside>
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```rust
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use qdrant_client::qdrant::{vectors_config::Config, VectorParams, VectorsConfig};
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client
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.create_collection(&CreateCollection {
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collection_name: "test_collection".to_string(),
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vectors_config: Some(VectorsConfig {
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config: Some(Config::Params(VectorParams {
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size: 4,
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distance: Distance::Dot.into(),
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..Default::default()
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})),
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}),
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..Default::default()
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})
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.await?;
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```
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<aside role="status">TypeScript, Rust examples use async/await syntax, so should be used in an async block.</aside>
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## Add vectors
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@@ -108,6 +133,37 @@ const operationInfo = await client.upsert("test_collection", {
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console.debug(operationInfo);
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```
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```rust
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use qdrant_client::qdrant::PointStruct;
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use serde_json::json;
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let points = vec![
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PointStruct::new(
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1,
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vec![0.05, 0.61, 0.76, 0.74],
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json!(
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{"city": "Berlin"}
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)
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.try_into()
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.unwrap(),
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),
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PointStruct::new(
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2,
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vec![0.19, 0.81, 0.75, 0.11],
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json!(
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{"city": "London"}
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)
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.try_into()
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.unwrap(),
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),
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// ..truncated
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];
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let operation_info = client
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.upsert_points_blocking("test_collection".to_string(), points, None)
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.await?;
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dbg!(operation_info);
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```
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**Response:**
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```python
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@@ -118,6 +174,16 @@ operation_id=0 status=<UpdateStatus.COMPLETED: 'completed'>
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{ operation_id: 0, status: 'completed' }
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```
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```rust
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PointsOperationResponse {
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result: Some(UpdateResult {
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operation_id: 0,
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status: Completed,
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}),
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time: 0.006347708,
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}
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```
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## Run a query
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Let's ask a basic question - Which of our stored vectors are most similar to the query vector `[0.2, 0.1, 0.9, 0.7]`?
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@@ -138,6 +204,22 @@ let searchResult = await client.search("test_collection", {
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console.debug(searchResult);
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```
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```rust
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use qdrant_client::qdrant::SearchPoints;
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let search_result = client
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.search_points(&SearchPoints {
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collection_name: "test_collection".to_string(),
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vector: vec![0.2, 0.1, 0.9, 0.7],
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limit: 3,
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with_payload: Some(true.into()),
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..Default::default()
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})
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.await?;
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dbg!(search_result);
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```
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**Response:**
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```python
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@@ -152,26 +234,61 @@ ScoredPoint(id=3, version=0, score=1.208, payload={"city": "Moscow"}, vector=Non
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id: 4,
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version: 0,
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score: 1.362,
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payload: { city: "New York" },
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payload: null,
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vector: null,
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},
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{
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id: 1,
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version: 0,
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score: 1.273,
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payload: { city: "Berlin" },
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payload: null,
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vector: null,
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},
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{
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id: 3,
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version: 0,
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score: 1.208,
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payload: { city: "Moscow" },
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payload: null,
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vector: null,
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},
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];
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```
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```rust
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SearchResponse {
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result: [
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ScoredPoint {
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id: Some(PointId {
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point_id_options: Some(Num(4)),
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}),
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payload: {},
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score: 1.362,
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version: 0,
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vectors: None,
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},
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ScoredPoint {
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id: Some(PointId {
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point_id_options: Some(Num(1)),
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}),
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payload: {},
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score: 1.273,
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version: 0,
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vectors: None,
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},
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ScoredPoint {
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id: Some(PointId {
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point_id_options: Some(Num(3)),
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}),
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payload: {},
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score: 1.208,
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version: 0,
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vectors: None,
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},
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],
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time: 0.003635125,
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}
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```
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The results are returned in decreasing similarity order. Note that payload and vector data is missing in these results by default.
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See [payload and vector in the result](../concepts/search#payload-and-vector-in-the-result) on how to enable it.
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@@ -188,6 +305,7 @@ search_result = client.search(
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query_filter=Filter(
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must=[FieldCondition(key="city", match=MatchValue(value="London"))]
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),
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with_payload=True,
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limit=3,
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)
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@@ -200,12 +318,32 @@ searchResult = await client.search("test_collection", {
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filter: {
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must: [{ key: "city", match: { value: "London" } }],
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},
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with_payload: true,
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limit: 3,
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});
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console.debug(searchResult);
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```
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```rust
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use qdrant_client::qdrant::{Condition, Filter, SearchPoints};
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let search_result = client
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.search_points(&SearchPoints {
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collection_name: "test_collection".to_string(),
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vector: vec![0.2, 0.1, 0.9, 0.7],
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filter: Some(Filter::all([Condition::matches(
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"city",
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"London".to_string(),
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)])),
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limit: 2,
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..Default::default()
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})
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.await?;
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dbg!(search_result);
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```
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**Response:**
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```python
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@@ -224,6 +362,36 @@ ScoredPoint(id=2, version=0, score=0.871, payload={"city": "London"}, vector=Non
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];
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```
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```rust
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SearchResponse {
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result: [
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ScoredPoint {
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id: Some(
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PointId {
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point_id_options: Some(
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Num(
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2,
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),
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),
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},
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),
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payload: {
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"city": Value {
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kind: Some(
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StringValue(
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"London",
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),
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),
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},
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},
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score: 0.871,
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version: 0,
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vectors: None,
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},
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],
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time: 0.004001083,
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}
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```
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You have just conducted vector search. You loaded vectors into a database and queried the database with a vector of your own. Qdrant found the closest results and presented you with a similarity score.
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## Next steps
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