mirror of
https://github.com/qdrant/landing_page.git
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Add code snippets for the text search guide (#2056)
* Python snippets * TS snippets * Rust snippets * Java snippets * C# snippets * Go snippets * Generate .md files * Hide client connection in Rust snippets * Switch to builder pattern for Rust snippets * Use Document::new instead of DocumentBuilder::new * docs: Updated Go snippets Signed-off-by: Anush008 <mail@anush.sh> * docs: Updated Java snippets Signed-off-by: Anush008 <anushshetty90@gmail.com> * docs: Updated C# snippets Signed-off-by: Anush008 <anushshetty90@gmail.com> * docs: Updated BM25 avg_len Java snippet Signed-off-by: Anush008 <anushshetty90@gmail.com> * chore: review and format rust snippets * chore: trigger pr update * chore: remove qdrant_storage folder * fix: remove tokio::main * chore: trigger pr update * Regenerate .md files * George's feedback: set bm25 params at ingest-time, change Python client init to use cloud inference explicitly, and mark bm25 options unavailable in FastEmbed --------- Signed-off-by: Anush008 <mail@anush.sh> Signed-off-by: Anush008 <anushshetty90@gmail.com> Co-authored-by: Anush008 <mail@anush.sh> Co-authored-by: Anush008 <anushshetty90@gmail.com> Co-authored-by: Daniel Boros <dancixx@gmail.com>
This commit is contained in:
Abdon Pijpelink
co-authored by
Anush008
Anush008
Daniel Boros
Anush008
Anush008
Daniel Boros
parent
b5e0908946
commit
2497c368ed
+28
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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: "books",
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query: new Document
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{
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Text = "Mieville",
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Model = "qdrant/bm25",
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Options =
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{
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["language"] = "none",
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["tokenizer"] = "multilingual",
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["ascii_folding"] = true,
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},
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},
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usingVector: "author-bm25",
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payloadSelector: true,
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limit: 10
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);
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}
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}
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+22
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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: "books",
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query: new Document
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{
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Text = "Mieville",
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Model = "qdrant/bm25",
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Options =
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{
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["language"] = "none",
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["tokenizer"] = "multilingual",
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["ascii_folding"] = true,
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},
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},
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usingVector: "author-bm25",
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payloadSelector: true,
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limit: 10
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);
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```
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+15
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```go
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client.Query(context.Background(), &qdrant.QueryPoints{
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CollectionName: "books",
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Query: qdrant.NewQueryNearest(
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qdrant.NewVectorInputDocument(&qdrant.Document{
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Model: "qdrant/bm25",
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Text: "Mieville",
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Options: qdrant.NewValueMap(map[string]any{"language": "none", "tokenizer": "multilingual", "ascii_folding": true}),
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}),
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),
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Using: qdrant.PtrOf("author-bm25"),
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WithPayload: qdrant.NewWithPayload(true),
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Limit: qdrant.PtrOf(uint64(10)),
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})
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```
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+23
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```java
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import static io.qdrant.client.QueryFactory.nearest;
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import static io.qdrant.client.WithPayloadSelectorFactory.enable;
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import io.qdrant.client.QdrantClient;
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import io.qdrant.client.QdrantGrpcClient;
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import io.qdrant.client.grpc.Points.*;
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QdrantClient client =
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client
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.queryAsync(
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QueryPoints.newBuilder()
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.setCollectionName("books")
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.setQuery(
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nearest(
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Document.newBuilder().setText("Mieville").setModel("qdrant/bm25").build()))
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.setUsing("author-bm25")
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.setLimit(10)
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.setWithPayload(enable(true))
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.build())
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.get();
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```
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+22
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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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url="https://xyz-example.qdrant.io:6333",
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api_key="<your-api-key>",
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cloud_inference=True,
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)
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# Note: these BM25 options are not supported by FastEmbed
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client.query_points(
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collection_name="books",
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query=models.Document(
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text="Mieville",
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model="qdrant/bm25",
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options={"language": "none", "tokenizer": "multilingual", "ascii_folding": True},
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),
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using="author-bm25",
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limit=10,
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with_payload=True,
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)
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```
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+26
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```rust
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use std::collections::HashMap;
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use qdrant_client::Qdrant;
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use qdrant_client::qdrant::{DocumentBuilder, Query, QueryPointsBuilder, Value};
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let mut options = HashMap::new();
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options.insert("language".to_string(), Value::from("none"));
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options.insert("tokenizer".to_string(), Value::from("multilingual"));
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options.insert("ascii_folding".to_string(), Value::from(true));
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client
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.query(
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QueryPointsBuilder::new("books")
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.query(Query::new_nearest(
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DocumentBuilder::new("Mieville", "qdrant/bm25")
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.options(options)
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.build(),
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))
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.using("author-bm25")
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.limit(10)
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.with_payload(true)
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.build(),
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)
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.await?;
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```
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+12
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```typescript
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client.query("books", {
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query: {
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text: "Mieville",
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model: "qdrant/bm25",
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options: { language: "none", tokenizer: "multilingual", ascii_folding: true },
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},
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using: "author-bm25",
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limit: 10,
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with_payload: true,
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});
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```
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+37
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package snippet
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// @hide-start
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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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// @hide-end
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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: "books",
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Query: qdrant.NewQueryNearest(
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qdrant.NewVectorInputDocument(&qdrant.Document{
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Model: "qdrant/bm25",
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Text: "Mieville",
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Options: qdrant.NewValueMap(map[string]any{"language": "none", "tokenizer": "multilingual", "ascii_folding": true}),
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}),
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),
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Using: qdrant.PtrOf("author-bm25"),
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WithPayload: qdrant.NewWithPayload(true),
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Limit: qdrant.PtrOf(uint64(10)),
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})
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}
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+28
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package com.example.snippets_amalgamation;
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import static io.qdrant.client.QueryFactory.nearest;
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import static io.qdrant.client.WithPayloadSelectorFactory.enable;
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import io.qdrant.client.QdrantClient;
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import io.qdrant.client.QdrantGrpcClient;
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import io.qdrant.client.grpc.Points.*;
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public class Snippet {
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public static void run() throws Exception {
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QdrantClient client =
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new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build()); // @hide
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client
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.queryAsync(
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QueryPoints.newBuilder()
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.setCollectionName("books")
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.setQuery(
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nearest(
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Document.newBuilder().setText("Mieville").setModel("qdrant/bm25").build()))
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.setUsing("author-bm25")
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.setLimit(10)
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.setWithPayload(enable(true))
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.build())
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.get();
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}
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}
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+20
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from qdrant_client import QdrantClient, models
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client = QdrantClient(
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url="https://xyz-example.qdrant.io:6333",
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api_key="<your-api-key>",
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cloud_inference=True,
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)
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# Note: these BM25 options are not supported by FastEmbed
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client.query_points(
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collection_name="books",
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query=models.Document(
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text="Mieville",
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model="qdrant/bm25",
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options={"language": "none", "tokenizer": "multilingual", "ascii_folding": True},
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),
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using="author-bm25",
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limit=10,
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with_payload=True,
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)
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+30
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use std::collections::HashMap;
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use qdrant_client::Qdrant;
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use qdrant_client::qdrant::{DocumentBuilder, Query, QueryPointsBuilder, Value};
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pub async fn main() -> anyhow::Result<()> {
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let client = Qdrant::from_url("http://localhost:6334").build()?; // @hide
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let mut options = HashMap::new();
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options.insert("language".to_string(), Value::from("none"));
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options.insert("tokenizer".to_string(), Value::from("multilingual"));
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options.insert("ascii_folding".to_string(), Value::from(true));
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client
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.query(
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QueryPointsBuilder::new("books")
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.query(Query::new_nearest(
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DocumentBuilder::new("Mieville", "qdrant/bm25")
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.options(options)
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.build(),
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))
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.using("author-bm25")
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.limit(10)
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.with_payload(true)
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.build(),
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)
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.await?;
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Ok(())
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}
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+14
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import { QdrantClient } from "@qdrant/js-client-rest"; // @hide
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const client = new QdrantClient({ host: "localhost", port: 6333 }); // @hide
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client.query("books", {
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query: {
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text: "Mieville",
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model: "qdrant/bm25",
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options: { language: "none", tokenizer: "multilingual", ascii_folding: true },
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},
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using: "author-bm25",
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limit: 10,
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with_payload: true,
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});
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