mirror of
https://github.com/qdrant/landing_page.git
synced 2026-10-09 21:08:31 +02:00
Publish docs for version 1.17 (#2137)
* Cluster Telemetry docs (#2068) * mention cluster telemetry * small reword * Docs for read_fan_out_delay_ms * Add warning against setting threshold too low * Small docs change for inference API keys * Trigger Build * Add guide with tips for low latency search (#2136) * Update frontmatter weights * Add 'Tips for Low-Latency Search' guide * Review feedback * Temporarily bump Rust client to v1-17-upgrade branch * Docs for audit logging (#2141) * Docs for audit logging * Consistent title casing * Review feedback * Docs for optimization monitoring (#2121) * Docs for optimization monitoring * Review feedback * Optimization monitoring is cluster-wide now * Upgrade code snippet checker to 1.17 * Fix broken Python snippets * Relevance Feedback docs (#2060) * add relevance feedback in Explore page * Review * Trigger Build * Trigger Build * Create new 'Search Relevance' concept page * Tweaks * Update links to moved content * add rust snippet * TS anippets * Clarification about using point IDs * Add links * Restructure paragraphs * docs: Go snippet Signed-off-by: Anush008 <mail@anush.sh> * docs: Missed Java snippets with C# Signed-off-by: Anush008 <mail@anush.sh> * new: add python snippets * Make Java and Rust snippets testable * Remove unnecessary styling --------- Signed-off-by: Anush008 <mail@anush.sh> Co-authored-by: Evgeniya Sukhodolskaya <suxodolskaya97@gmail.com> Co-authored-by: Abdon Pijpelink <abdon.pijpelink@qdrant.com> Co-authored-by: Ivan Pleshkov <pleshkov.ivan@gmail.com> Co-authored-by: Anush008 <mail@anush.sh> Co-authored-by: George Panchuk <george.panchuk@qdrant.tech> * Docs for enable_hnsw (#2080) * Move 'Filterable HNSW Index' section under 'Vector Index' * Add docs for enable_hnsw * docs: Go snippets Signed-off-by: Anush008 <mail@anush.sh> * docs: Java snippets Signed-off-by: Anush008 <mail@anush.sh> * docs: C# snippets Signed-off-by: Anush008 <mail@anush.sh> * Add Rust snippet * TS snippets * new: add python snippets --------- Signed-off-by: Anush008 <mail@anush.sh> Co-authored-by: Anush008 <mail@anush.sh> Co-authored-by: timvisee <tim@visee.me> Co-authored-by: Ivan Pleshkov <pleshkov.ivan@gmail.com> Co-authored-by: George Panchuk <george.panchuk@qdrant.tech> * Docs for list shard keys API (#2082) * Docs for list shard keys * docs: Go snippets Signed-off-by: Anush008 <mail@anush.sh> * docs: Java snippets Signed-off-by: Anush008 <mail@anush.sh> * docs: C# snippets Signed-off-by: Anush008 <mail@anush.sh> * Add Rust snippet * docs: TS snippets * new: add python snippets --------- Signed-off-by: Anush008 <mail@anush.sh> Co-authored-by: Anush008 <mail@anush.sh> Co-authored-by: timvisee <tim@visee.me> Co-authored-by: Ivan Pleshkov <pleshkov.ivan@gmail.com> Co-authored-by: George Panchuk <george.panchuk@qdrant.tech> * Docs for update_mode (#2097) * Docs for update_mode * docs: C#, Go, Java snippets Signed-off-by: Anush008 <mail@anush.sh> * doc: Remove _ from C# snippet Signed-off-by: Anush008 <mail@anush.sh> * Add Rust snippet * Fix some snippets * Review feedback * ts snippets * new: add python snippets * fix: add generated python.md --------- Signed-off-by: Anush008 <mail@anush.sh> Co-authored-by: Anush008 <mail@anush.sh> Co-authored-by: timvisee <tim@visee.me> Co-authored-by: Ivan Pleshkov <pleshkov.ivan@gmail.com> Co-authored-by: George Panchuk <george.panchuk@qdrant.tech> * Docs for weighted RRF (#2132) * Docs for weighted RRF * docs: Go, Java, C# snippets Signed-off-by: Anush008 <mail@anush.sh> * Add Rust snippet * Apply suggestions from code review Co-authored-by: Luis Cossío <luis.cossio@qdrant.com> * Delete landing_page.sln * Review feedback * ts snippets * new: add python snippets * Trigger Build --------- Signed-off-by: Anush008 <mail@anush.sh> Co-authored-by: Anush008 <mail@anush.sh> Co-authored-by: timvisee <tim@visee.me> Co-authored-by: Luis Cossío <luis.cossio@qdrant.com> Co-authored-by: Ivan Pleshkov <pleshkov.ivan@gmail.com> Co-authored-by: George Panchuk <george.panchuk@qdrant.tech> * Clean up Go and Java snippets * Revert go snippet change; target Go client 1.17.1 * Updated Go snippet * Trigger Build * Update Rust lockfile --------- Signed-off-by: Anush008 <mail@anush.sh> Co-authored-by: Luis Cossío <luis.cossio@qdrant.com> Co-authored-by: Daniel Boros <56868953+dancixx@users.noreply.github.com> Co-authored-by: timvisee <tim@visee.me> Co-authored-by: Evgeniya Sukhodolskaya <suxodolskaya97@gmail.com> Co-authored-by: Ivan Pleshkov <pleshkov.ivan@gmail.com> Co-authored-by: Anush008 <mail@anush.sh> Co-authored-by: George Panchuk <george.panchuk@qdrant.tech>
This commit is contained in:
Abdon Pijpelink
co-authored by
Evgeniya Sukhodolskaya
Ivan Pleshkov
Anush008
George Panchuk
timvisee
Luis Cossío
Daniel Boros
Evgeniya Sukhodolskaya
Ivan Pleshkov
Anush008
George Panchuk
timvisee
Luis Cossío
Daniel Boros
parent
92ff228b79
commit
378837432e
+33
@@ -0,0 +1,33 @@
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using Qdrant.Client;
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using Qdrant.Client.Grpc;
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public class Snippet
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{
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public static async Task Run()
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{
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var client = new QdrantClient("localhost", 6334); // @hide
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await client.QueryAsync(
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collectionName: "{collection_name}",
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query: new RelevanceFeedbackInput
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{
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Target = new float[] { 0.2f, 0.1f, 0.9f, 0.7f },
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Feedback =
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{
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new FeedbackItem { Example = 111, Score = 0.68f },
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new FeedbackItem { Example = 222, Score = 0.72f },
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new FeedbackItem { Example = 33, Score = 0.61f },
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},
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Strategy =
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{
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Naive = new()
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{
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A = 0.12f,
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B = 0.43f,
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C = 0.16f,
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},
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},
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}
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);
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}
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}
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+27
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```csharp
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using Qdrant.Client;
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using Qdrant.Client.Grpc;
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await client.QueryAsync(
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collectionName: "{collection_name}",
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query: new RelevanceFeedbackInput
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{
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Target = new float[] { 0.2f, 0.1f, 0.9f, 0.7f },
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Feedback =
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{
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new FeedbackItem { Example = 111, Score = 0.68f },
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new FeedbackItem { Example = 222, Score = 0.72f },
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new FeedbackItem { Example = 33, Score = 0.61f },
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},
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Strategy =
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{
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Naive = new()
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{
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A = 0.12f,
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B = 0.43f,
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C = 0.16f,
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},
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},
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}
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);
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```
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+33
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```go
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import (
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"context"
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"github.com/qdrant/go-client/qdrant"
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)
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client.Query(context.Background(), &qdrant.QueryPoints{
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CollectionName: "{collection_name}",
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Query: qdrant.NewQueryRelevanceFeedback(
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&qdrant.RelevanceFeedbackInput{
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Target: qdrant.NewVectorInput(0.01, 0.45, 0.67),
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Feedback: []*qdrant.FeedbackItem{
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{
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Example: qdrant.NewVectorInputID(qdrant.NewIDNum(111)),
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Score: 0.68,
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},
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{
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Example: qdrant.NewVectorInputID(qdrant.NewIDNum(222)),
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Score: 0.72,
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},
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{
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Example: qdrant.NewVectorInputID(qdrant.NewIDNum(333)),
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Score: 0.61,
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},
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},
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Strategy: qdrant.NewFeedbackStrategyNaive(&qdrant.NaiveFeedbackStrategy{
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A: 0.12, B: 0.43, C: 0.16,
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}),
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},
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),
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})
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```
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+48
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```java
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import static io.qdrant.client.QueryFactory.relevanceFeedback;
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import static io.qdrant.client.VectorInputFactory.vectorInput;
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import io.qdrant.client.grpc.Points.FeedbackItem;
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import io.qdrant.client.grpc.Points.FeedbackStrategy;
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import io.qdrant.client.grpc.Points.NaiveFeedbackStrategy;
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import io.qdrant.client.grpc.Points.QueryPoints;
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import io.qdrant.client.grpc.Points.RelevanceFeedbackInput;
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import java.util.List;
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client
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.queryAsync(
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QueryPoints.newBuilder()
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.setCollectionName("{collection_name}")
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.setQuery(
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relevanceFeedback(
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RelevanceFeedbackInput.newBuilder()
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.setTarget(vectorInput(0.01f, 0.45f, 0.67f))
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.addFeedback(
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FeedbackItem.newBuilder()
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.setExample(vectorInput(111))
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.setScore(0.68f)
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.build())
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.addFeedback(
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FeedbackItem.newBuilder()
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.setExample(vectorInput(222))
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.setScore(0.72f)
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.build())
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.addFeedback(
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FeedbackItem.newBuilder()
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.setExample(vectorInput(333))
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.setScore(0.61f)
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.build())
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.setStrategy(
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FeedbackStrategy.newBuilder()
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.setNaive(
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NaiveFeedbackStrategy.newBuilder()
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.setA(0.12f)
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.setB(0.43f)
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.setC(0.16f)
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.build())
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.build())
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.build()))
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.build())
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.get();
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```
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+26
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```python
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from qdrant_client import QdrantClient, models
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client = QdrantClient()
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client.query_points(
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"{collection_name}",
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query=models.RelevanceFeedbackQuery(
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relevance_feedback=models.RelevanceFeedbackInput(
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target=[0.1, 0.9, 0.23],
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feedback=[
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models.FeedbackItem(example=111, score=0.68),
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models.FeedbackItem(example=222, score=0.72),
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models.FeedbackItem(example=333, score=0.61),
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],
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strategy=models.NaiveFeedbackStrategy(
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naive=models.NaiveFeedbackStrategyParams(
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a=0.12,
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b=0.43,
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c=0.03
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)
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)
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)
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)
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)
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```
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+19
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```rust
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use qdrant_client::qdrant::{
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FeedbackItemBuilder, FeedbackStrategyBuilder, PointId, Query, QueryPointsBuilder,
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RelevanceFeedbackInputBuilder, VectorInput,
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};
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use qdrant_client::Qdrant;
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let _points = client.query(
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QueryPointsBuilder::new("{collection_name}")
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.query(Query::new_relevance_feedback(
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RelevanceFeedbackInputBuilder::new(vec![0.01, 0.45, 0.67])
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.add_feedback(FeedbackItemBuilder::new(VectorInput::new_id(PointId::from(111)), 0.68))
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.add_feedback(FeedbackItemBuilder::new(VectorInput::new_id(PointId::from(222)), 0.72))
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.add_feedback(FeedbackItemBuilder::new(VectorInput::new_id(PointId::from(333)), 0.61))
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.strategy(FeedbackStrategyBuilder::naive(0.12, 0.43, 0.16))
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))
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.limit(10u64)
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).await?;
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```
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+25
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```typescript
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import { QdrantClient } from "@qdrant/js-client-rest";
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const client = new QdrantClient({ host: "localhost", port: 6333 });
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client.query("{collection_name}", {
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query: {
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relevance_feedback: {
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target: [0.1, 0.9, 0.23],
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feedback: [
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{ example: 111, score: 0.68 },
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{ example: 222, score: 0.72 },
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{ example: 333, score: 0.61 },
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],
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strategy: {
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naive: {
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a: 0.12,
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b: 0.43,
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c: 0.03,
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},
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},
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},
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},
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});
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```
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+45
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package snippet
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import (
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"context"
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"github.com/qdrant/go-client/qdrant"
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)
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func Main() {
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// @hide-start
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client, err := qdrant.NewClient(&qdrant.Config{
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Host: "localhost",
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Port: 6334,
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})
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if err != nil {
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panic(err)
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}
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// @hide-end
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client.Query(context.Background(), &qdrant.QueryPoints{
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CollectionName: "{collection_name}",
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Query: qdrant.NewQueryRelevanceFeedback(
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&qdrant.RelevanceFeedbackInput{
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Target: qdrant.NewVectorInput(0.01, 0.45, 0.67),
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Feedback: []*qdrant.FeedbackItem{
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{
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Example: qdrant.NewVectorInputID(qdrant.NewIDNum(111)),
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Score: 0.68,
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},
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{
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Example: qdrant.NewVectorInputID(qdrant.NewIDNum(222)),
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Score: 0.72,
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},
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{
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Example: qdrant.NewVectorInputID(qdrant.NewIDNum(333)),
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Score: 0.61,
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},
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},
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Strategy: qdrant.NewFeedbackStrategyNaive(&qdrant.NaiveFeedbackStrategy{
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A: 0.12, B: 0.43, C: 0.16,
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}),
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},
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),
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})
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}
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+22
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```http
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POST /collections/{collection_name}/points/query
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{
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"query": {
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"relevance_feedback: {
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"target": [0.1, 0.9, 0.23, ...],
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"feedback": [
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{ "example": 111, "score": 0.68 },
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{ "example": 222, "score": 0.72 },
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{ "example": 333, "score": 0.61 }
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],
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"strategy": {
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"naive": {
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"a": 0.12,
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"b": 0.43,
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"c": 0.03
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}
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}
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]
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}
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}
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```
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+57
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package com.example.snippets_amalgamation;
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import static io.qdrant.client.QueryFactory.relevanceFeedback;
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import static io.qdrant.client.VectorInputFactory.vectorInput;
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import io.qdrant.client.grpc.Points.FeedbackItem;
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import io.qdrant.client.grpc.Points.FeedbackStrategy;
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import io.qdrant.client.grpc.Points.NaiveFeedbackStrategy;
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import io.qdrant.client.grpc.Points.QueryPoints;
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import io.qdrant.client.grpc.Points.RelevanceFeedbackInput;
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import java.util.List;
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public class Snippet {
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public static void run() throws Exception {
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// @hide-start
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io.qdrant.client.QdrantClient client =
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new io.qdrant.client.QdrantClient(io.qdrant.client.QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
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// @hide-end
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client
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.queryAsync(
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QueryPoints.newBuilder()
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.setCollectionName("{collection_name}")
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.setQuery(
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relevanceFeedback(
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RelevanceFeedbackInput.newBuilder()
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.setTarget(vectorInput(0.01f, 0.45f, 0.67f))
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.addFeedback(
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FeedbackItem.newBuilder()
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.setExample(vectorInput(111))
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.setScore(0.68f)
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.build())
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.addFeedback(
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FeedbackItem.newBuilder()
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.setExample(vectorInput(222))
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.setScore(0.72f)
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.build())
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.addFeedback(
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FeedbackItem.newBuilder()
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.setExample(vectorInput(333))
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.setScore(0.61f)
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.build())
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.setStrategy(
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FeedbackStrategy.newBuilder()
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.setNaive(
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NaiveFeedbackStrategy.newBuilder()
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.setA(0.12f)
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.setB(0.43f)
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.setC(0.16f)
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.build())
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.build())
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.build()))
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.build())
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.get();
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}
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}
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+24
@@ -0,0 +1,24 @@
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from qdrant_client import QdrantClient, models
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client = QdrantClient()
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client.query_points(
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"{collection_name}",
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query=models.RelevanceFeedbackQuery(
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relevance_feedback=models.RelevanceFeedbackInput(
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target=[0.1, 0.9, 0.23],
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feedback=[
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models.FeedbackItem(example=111, score=0.68),
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models.FeedbackItem(example=222, score=0.72),
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models.FeedbackItem(example=333, score=0.61),
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],
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strategy=models.NaiveFeedbackStrategy(
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naive=models.NaiveFeedbackStrategyParams(
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a=0.12,
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b=0.43,
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c=0.03
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)
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)
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)
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)
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)
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+23
@@ -0,0 +1,23 @@
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use qdrant_client::qdrant::{
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FeedbackItemBuilder, FeedbackStrategyBuilder, PointId, Query, QueryPointsBuilder,
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RelevanceFeedbackInputBuilder, VectorInput,
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};
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use qdrant_client::Qdrant;
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pub async fn main() -> anyhow::Result<()> {
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let client = qdrant_client::Qdrant::from_url("http://localhost:6334").build()?; // @hide
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let _points = client.query(
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QueryPointsBuilder::new("{collection_name}")
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.query(Query::new_relevance_feedback(
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RelevanceFeedbackInputBuilder::new(vec![0.01, 0.45, 0.67])
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.add_feedback(FeedbackItemBuilder::new(VectorInput::new_id(PointId::from(111)), 0.68))
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.add_feedback(FeedbackItemBuilder::new(VectorInput::new_id(PointId::from(222)), 0.72))
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.add_feedback(FeedbackItemBuilder::new(VectorInput::new_id(PointId::from(333)), 0.61))
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.strategy(FeedbackStrategyBuilder::naive(0.12, 0.43, 0.16))
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))
|
||||
.limit(10u64)
|
||||
).await?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
+23
@@ -0,0 +1,23 @@
|
||||
import { QdrantClient } from "@qdrant/js-client-rest";
|
||||
|
||||
const client = new QdrantClient({ host: "localhost", port: 6333 });
|
||||
|
||||
client.query("{collection_name}", {
|
||||
query: {
|
||||
relevance_feedback: {
|
||||
target: [0.1, 0.9, 0.23],
|
||||
feedback: [
|
||||
{ example: 111, score: 0.68 },
|
||||
{ example: 222, score: 0.72 },
|
||||
{ example: 333, score: 0.61 },
|
||||
],
|
||||
strategy: {
|
||||
naive: {
|
||||
a: 0.12,
|
||||
b: 0.43,
|
||||
c: 0.03,
|
||||
},
|
||||
},
|
||||
},
|
||||
},
|
||||
});
|
||||
Reference in New Issue
Block a user