mirror of
https://github.com/qdrant/landing_page.git
synced 2026-09-28 23:48:31 +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
@@ -131,6 +131,33 @@ client.search("{collection_name}", {
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});
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```
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```rust
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use qdrant_client::{
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client::QdrantClient,
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qdrant::{Condition, Filter, SearchParams, SearchPoints},
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};
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let client = QdrantClient::from_url("http://localhost:6334").build()?;
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client
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.search_points(&SearchPoints {
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collection_name: "{collection_name}".to_string(),
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filter: Some(Filter::must([Condition::matches(
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"city",
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"London".to_string(),
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)])),
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params: Some(SearchParams {
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hnsw_ef: Some(128),
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exact: Some(false),
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..Default::default()
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}),
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vector: vec![0.2, 0.1, 0.9, 0.7],
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limit: 3,
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..Default::default()
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})
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.await?;
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```
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In this example, we are looking for vectors similar to vector `[0.2, 0.1, 0.9, 0.7]`.
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Parameter `limit` (or its alias - `top`) specifies the amount of most similar results we would like to retrieve.
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@@ -207,6 +234,22 @@ client.search("{collection_name}", {
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});
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```
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```rust
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use qdrant_client::{client::QdrantClient, qdrant::SearchPoints};
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let client = QdrantClient::from_url("http://localhost:6334").build()?;
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client
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.search_points(&SearchPoints {
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collection_name: "{collection_name}".to_string(),
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vector: vec![0.2, 0.1, 0.9, 0.7],
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vector_name: Some("image".to_string()),
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limit: 3,
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..Default::default()
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})
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.await?;
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```
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Search is processing only among vectors with the same name.
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### Filtering results by score
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@@ -253,6 +296,23 @@ client.search("{collection_name}", {
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});
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```
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```rust
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use qdrant_client::{client::QdrantClient, qdrant::SearchPoints};
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let client = QdrantClient::from_url("http://localhost:6334").build()?;
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client
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.search_points(&SearchPoints {
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collection_name: "{collection_name}".to_string(),
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vector: vec![0.2, 0.1, 0.9, 0.7],
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with_payload: Some(true.into()),
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with_vectors: Some(true.into()),
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limit: 3,
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..Default::default()
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})
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.await?;
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```
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You can use `with_payload` to scope to or filter a specific payload subset.
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You can even specify an array of items to include, such as `city`,
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`village`, and `town`:
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@@ -290,6 +350,22 @@ client.search("{collection_name}", {
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});
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```
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```rust
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use qdrant_client::{client::QdrantClient, qdrant::SearchPoints};
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let client = QdrantClient::from_url("http://localhost:6334").build()?;
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client
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.search_points(&SearchPoints {
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collection_name: "{collection_name}".to_string(),
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vector: vec![0.2, 0.1, 0.9, 0.7],
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with_payload: Some(vec!["city", "village", "town"].into()),
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limit: 3,
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..Default::default()
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})
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.await?;
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```
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Or use `include` or `exclude` explicitly. For example, to exclude `city`:
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```http
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@@ -331,6 +407,32 @@ client.search("{collection_name}", {
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});
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```
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```rust
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use qdrant_client::{
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client::QdrantClient,
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qdrant::{
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with_payload_selector::SelectorOptions, PayloadIncludeSelector, SearchPoints,
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WithPayloadSelector,
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},
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};
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let client = QdrantClient::from_url("http://localhost:6334").build()?;
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client
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.search_points(&SearchPoints {
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collection_name: "{collection_name}".to_string(),
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vector: vec![0.2, 0.1, 0.9, 0.7],
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with_payload: Some(WithPayloadSelector {
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selector_options: Some(SelectorOptions::Include(PayloadIncludeSelector {
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fields: vec!["city".to_string()],
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})),
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}),
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limit: 3,
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..Default::default()
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})
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.await?;
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```
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It is possible to target nested fields using a dot notation:
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- `payload.nested_field` - for a nested field
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- `payload.nested_array[].sub_field` - for projecting nested fields within an array
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@@ -449,6 +551,42 @@ client.searchBatch("{collection_name}", {
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});
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```
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```rust
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use qdrant_client::{
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client::QdrantClient,
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qdrant::{Condition, Filter, SearchBatchPoints, SearchPoints},
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};
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let client = QdrantClient::from_url("http://localhost:6334").build()?;
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let filter = Filter::must([Condition::matches("city", "London".to_string())]);
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let searches = vec![
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SearchPoints {
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collection_name: "{collection_name}".to_string(),
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vector: vec![0.2, 0.1, 0.9, 0.7],
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filter: Some(filter.clone()),
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limit: 3,
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..Default::default()
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},
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SearchPoints {
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collection_name: "{collection_name}".to_string(),
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vector: vec![0.5, 0.3, 0.2, 0.3],
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filter: Some(filter),
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limit: 3,
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..Default::default()
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},
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];
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client
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.search_batch_points(&SearchBatchPoints {
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collection_name: "{collection_name}".to_string(),
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search_points: searches,
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read_consistency: None,
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})
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.await?;
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```
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The result of this API contains one array per search requests.
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```json
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@@ -544,6 +682,32 @@ client.recommend("{collection_name}", {
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});
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```
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```rust
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use qdrant_client::{
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client::QdrantClient,
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qdrant::{Condition, Filter, RecommendPoints, RecommendStrategy},
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};
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let client = QdrantClient::from_url("http://localhost:6334").build()?;
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client
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.recommend(&RecommendPoints {
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collection_name: "{collection_name}".to_string(),
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positive: vec![100.into(), 200.into()],
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positive_vectors: vec![vec![100.0, 231.0].into()],
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negative: vec![718.into()],
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negative_vectors: vec![vec![0.2, 0.3, 0.4, 0.5].into()],
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strategy: Some(RecommendStrategy::AverageVector.into()),
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filter: Some(Filter::must([Condition::matches(
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"city",
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"London".to_string(),
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)])),
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limit: 3,
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..Default::default()
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})
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.await?;
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```
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Example result of this API would be
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```json
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@@ -582,11 +746,11 @@ A new strategy introduced in v1.6, is called `best_score`. It is based on the id
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The way it works is that each candidate is measured against every example, then we select the best positive and best negative scores. The final score is chosen with this step formula:
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```rust
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if best_positive_score > best_negative_score {
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score = best_positive_score
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let score = if best_positive_score > best_negative_score {
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best_positive_score;
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} else {
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score = -(best_negative_score * best_negative_score)
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}
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-(best_negative_score * best_negative_score);
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};
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```
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<aside role="alert">The performance of `best_score` strategy will be linearly impacted by the amount of examples.</aside>
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@@ -637,6 +801,21 @@ client.recommend("{collection_name}", {
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});
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```
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```rust
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use qdrant_client::qdrant::RecommendPoints;
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client
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.recommend(&RecommendPoints {
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collection_name: "{collection_name}".to_string(),
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positive: vec![100.into(), 231.into()],
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negative: vec![718.into()],
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using: Some("image".to_string()),
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limit: 10,
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..Default::default()
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})
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.await?;
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```
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Parameter `using` specifies which stored vectors to use for the recommendation.
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## Batch recommendation API
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@@ -746,6 +925,44 @@ client.recommend_batch("{collection_name}", {
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});
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```
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```rust
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use qdrant_client::{
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client::QdrantClient,
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qdrant::{Condition, Filter, RecommendBatchPoints, RecommendPoints},
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};
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let client = QdrantClient::from_url("http://localhost:6334").build()?;
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let filter = Filter::must([Condition::matches("city", "London".to_string())]);
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let recommend_queries = vec![
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RecommendPoints {
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collection_name: "{collection_name}".to_string(),
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positive: vec![100.into(), 231.into()],
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negative: vec![718.into()],
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filter: Some(filter.clone()),
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limit: 3,
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..Default::default()
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},
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RecommendPoints {
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collection_name: "{collection_name}".to_string(),
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positive: vec![200.into(), 67.into()],
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negative: vec![300.into()],
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filter: Some(filter),
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limit: 3,
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..Default::default()
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},
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];
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client
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.recommend_batch(&RecommendBatchPoints {
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collection_name: "{collection_name}".to_string(),
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recommend_points: recommend_queries,
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..Default::default()
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})
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.await?;
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```
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The result of this API contains one array per recommendation requests.
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```json
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@@ -816,6 +1033,24 @@ client.search("{collection_name}", {
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});
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```
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```rust
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use qdrant_client::{client::QdrantClient, qdrant::SearchPoints};
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let client = QdrantClient::from_url("http://localhost:6334").build()?;
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client
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.search_points(&SearchPoints {
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collection_name: "{collection_name}".to_string(),
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vector: vec![0.2, 0.1, 0.9, 0.7],
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with_vectors: Some(true.into()),
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with_payload: Some(true.into()),
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limit: 10,
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offset: Some(100),
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..Default::default()
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})
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.await?;
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```
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Is equivalent to retrieving the 11th page with 10 records per page.
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<aside role="alert">Large offset values may cause performance issues</aside>
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@@ -914,7 +1149,7 @@ POST /collections/{collection_name}/points/search/groups
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client.search_groups(
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collection_name="{collection_name}",
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# Same as in the regular search() API
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query_vector=[1.1],
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query_vector=g,
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# Grouping parameters
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group_by="document_id", # Path of the field to group by
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limit=4, # Max amount of groups
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@@ -931,6 +1166,21 @@ client.searchPointGroups("{collection_name}", {
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});
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```
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```rust
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use qdrant_client::qdrant::SearchPointGroups;
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client
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.search_groups(&SearchPointGroups {
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collection_name: "{collection_name}".to_string(),
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vector: vec![1.1],
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group_by: "document_id".to_string(),
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limit: 4,
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group_size: 2,
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..Default::default()
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})
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.await?;
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```
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### Recommend groups
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REST API ([Schema](https://qdrant.github.io/qdrant/redoc/index.html#tag/points/operation/recommend_point_groups)):
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@@ -973,6 +1223,22 @@ client.recommendPointGroups("{collection_name}", {
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});
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```
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```rust
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use qdrant_client::qdrant::RecommendPointGroups;
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client
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.recommend_groups(&RecommendPointGroups {
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collection_name: "{collection_name}".to_string(),
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positive: vec![1.into()],
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negative: vec![2.into(), 5.into()],
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group_by: "document_id".to_string(),
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limit: 4,
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group_size: 10,
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..Default::default()
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})
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.await?;
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```
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In either case (search or recommend), the output would look like this:
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```json
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@@ -983,13 +1249,13 @@ In either case (search or recommend), the output would look like this:
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"id": "a",
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"hits": [
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{ "id": 0, "score": 0.91 },
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{ "id": 1, "score": 0.85 },
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{ "id": 1, "score": 0.85 }
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]
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},
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{
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"id": "b",
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"hits": [
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{ "id": 1, "score": 0.85 },
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{ "id": 1, "score": 0.85 }
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]
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},
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{
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@@ -1097,13 +1363,33 @@ client.searchPointGroups("{collection_name}", {
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limit: 2,
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group_size: 2,
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with_lookup: {
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collection: "documents",
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collection: w,
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with_payload: ["title", "text"],
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with_vectors: false,
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},
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});
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```
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```rust
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use qdrant_client::qdrant::{SearchPointGroups, WithLookup};
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client
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.search_groups(&SearchPointGroups {
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collection_name: "{collection_name}".to_string(),
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vector: vec![1.1],
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group_by: "document_id".to_string(),
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limit: 2,
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group_size: 2,
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with_lookup: Some(WithLookup {
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collection: "documents".to_string(),
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with_payload: Some(vec!["title", "text"].into()),
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with_vectors: Some(false.into()),
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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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For the `with_lookup` parameter, you can also use the shorthand `with_lookup="documents"` to bring the whole payload and vector(s) without explicitly specifying it.
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The looked up result will show up under `lookup` in each group.
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Reference in New Issue
Block a user