mirror of
https://github.com/qdrant/landing_page.git
synced 2026-09-26 22:48:30 +02:00
Merge pull request #2147 from nleroy917/cloud_qs_ci
Update cloud quickstart to use cloud inference
This commit is contained in:
Nathan LeRoy
@@ -14,12 +14,12 @@ aliases:
|
||||
|
||||
<p align="center"><iframe width="560" height="315" src="https://www.youtube.com/embed/xvWIssi_cjQ?si=CLhFrUDpQlNog9mz&rel=0" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe></p>
|
||||
|
||||
Learn how to set up Qdrant Cloud and perform your first semantic search in just a few minutes. We'll use a sample dataset of menu items pre-embedded with the `BAAI/bge-small-en-v1.5` model.
|
||||
Learn how to set up Qdrant Cloud and perform your first semantic search in just a few minutes. We'll use a sample dataset of menu items embedded with the `sentence-transformers/all-MiniLM-L6-v2` model via [Cloud Inference](/documentation/concepts/inference/). This is one of the free embedding models available on Qdrant Cloud. For a list of the available free and paid models, refer to the Inference tab of the Cluster Detail page in the Qdrant Cloud Console.
|
||||
|
||||
## 1. Create a Cloud Cluster
|
||||
|
||||
1. Register for a [Cloud account](https://cloud.qdrant.io/signup) with your email, Google or Github credentials.
|
||||
2. Go to **Clusters** and click **Create First Cluster**.
|
||||
2. Under **Create a Free Cluster**, enter a cluster name and select your preferred cloud provider and region. Click **Create Free Cluster**.
|
||||
3. Copy your **API key** when prompted - you'll need it to connect. Store it somewhere safe as it won't be displayed again.
|
||||
|
||||
For detailed cluster setup instructions, see the [Cloud documentation](/documentation/cloud-intro/).
|
||||
@@ -29,9 +29,11 @@ For detailed cluster setup instructions, see the [Cloud documentation](/document
|
||||
Once you have a cluster, the fastest way to get started is to use our official SDKs which provide a convenient interface for working with Qdrant in your preferred programming language.
|
||||
|
||||
```bash
|
||||
pip install qdrant-client fastembed # for Python projects
|
||||
# cargo add qdrant-client fastembed # for Rust projects
|
||||
# npm install @qdrant/js-client-rest fastembed # for Node.js projects
|
||||
pip install qdrant-client # for Python projects
|
||||
# cargo add qdrant-client # for Rust projects
|
||||
# npm install @qdrant/js-client-rest # for Node.js projects
|
||||
# dotnet add package Qdrant.Client # for .NET projects
|
||||
# go get github.com/qdrant/go-client # for Go projects
|
||||
```
|
||||
|
||||
## 3. Connect to Qdrant Cloud
|
||||
@@ -40,18 +42,23 @@ Import the qdrant client and create a connection to your Qdrant Cloud cluster us
|
||||
|
||||
```python
|
||||
from qdrant_client import QdrantClient
|
||||
from qdrant_client.models import Distance, VectorParams, PointStruct, Document
|
||||
|
||||
# connect to Qdrant Cloud
|
||||
client = QdrantClient(
|
||||
url="https://xyz-example.eu-central.aws.cloud.qdrant.io",
|
||||
api_key="your-api-key",
|
||||
cloud_inference=True
|
||||
)
|
||||
```
|
||||
|
||||
```rust
|
||||
use qdrant_client::Qdrant;
|
||||
use qdrant_client::{Qdrant, Payload};
|
||||
use qdrant_client::qdrant::{CreateCollectionBuilder, Distance, VectorParamsBuilder, PointStruct, DocumentBuilder, UpsertPointsBuilder, QueryPointsBuilder, Query};
|
||||
|
||||
// Connect to Qdrant Cloud
|
||||
use serde_json::json;
|
||||
|
||||
// connect to Qdrant Cloud
|
||||
let client = Qdrant::from_url("https://xyz-example.eu-central.aws.cloud.qdrant.io:6334")
|
||||
.api_key("your-api-key")
|
||||
.build()?;
|
||||
@@ -66,6 +73,67 @@ const client = new QdrantClient({
|
||||
});
|
||||
```
|
||||
|
||||
```java
|
||||
import static io.qdrant.client.PointIdFactory.id;
|
||||
import static io.qdrant.client.QueryFactory.nearest;
|
||||
import static io.qdrant.client.ValueFactory.value;
|
||||
import static io.qdrant.client.VectorsFactory.vectors;
|
||||
|
||||
import io.qdrant.client.QdrantClient;
|
||||
import io.qdrant.client.QdrantGrpcClient;
|
||||
import io.qdrant.client.grpc.Points.Document;
|
||||
import io.qdrant.client.grpc.Points.PointStruct;
|
||||
import io.qdrant.client.grpc.Points.QueryPoints;
|
||||
import io.qdrant.client.grpc.Points.ScoredPoint;
|
||||
import io.qdrant.client.grpc.Points.WithPayloadSelector;
|
||||
import io.qdrant.client.grpc.Collections.Distance;
|
||||
import io.qdrant.client.grpc.Collections.VectorParams;
|
||||
import java.util.ArrayList;
|
||||
import java.util.List;
|
||||
import java.util.Map;
|
||||
|
||||
|
||||
QdrantClient client =
|
||||
new QdrantClient(
|
||||
QdrantGrpcClient.newBuilder("xyz-example.qdrant.io", 6334, true)
|
||||
.withApiKey("<your-api-key>")
|
||||
.build());
|
||||
```
|
||||
|
||||
|
||||
```csharp
|
||||
using Qdrant.Client;
|
||||
using Qdrant.Client.Grpc;
|
||||
|
||||
var client = new QdrantClient(
|
||||
host: "xyz-example.qdrant.io",
|
||||
port: 6334,
|
||||
https: true,
|
||||
apiKey: "<your-api-key>"
|
||||
);
|
||||
```
|
||||
|
||||
```go
|
||||
import (
|
||||
"context"
|
||||
"fmt"
|
||||
|
||||
"github.com/qdrant/go-client/qdrant"
|
||||
)
|
||||
|
||||
client, err := qdrant.NewClient(&qdrant.Config{
|
||||
Host: "xyz-example.qdrant.io",
|
||||
Port: 6334,
|
||||
APIKey: "<paste-your-api-key-here>",
|
||||
UseTLS: true,
|
||||
})
|
||||
|
||||
if err != nil {
|
||||
fmt.Printf("Failed to create client: %v\n", err)
|
||||
return
|
||||
}
|
||||
```
|
||||
|
||||
```bash
|
||||
# test the connection
|
||||
curl -X GET \
|
||||
@@ -79,8 +147,6 @@ We will use some sample menu items to demonstrate how to create a collection and
|
||||
|
||||
|
||||
```python
|
||||
from qdrant_client.models import Distance, VectorParams
|
||||
|
||||
# create collection
|
||||
client.create_collection(
|
||||
collection_name="items",
|
||||
@@ -89,8 +155,6 @@ client.create_collection(
|
||||
```
|
||||
|
||||
```rust
|
||||
use qdrant_client::qdrant::{CreateCollectionBuilder, Distance, VectorParamsBuilder};
|
||||
|
||||
// create collection
|
||||
client.create_collection(
|
||||
CreateCollectionBuilder::new("items")
|
||||
@@ -105,6 +169,28 @@ await client.createCollection("items", {
|
||||
});
|
||||
```
|
||||
|
||||
```java
|
||||
client.createCollectionAsync("items",
|
||||
VectorParams.newBuilder().setDistance(Distance.Cosine).setSize(384).build()).get();
|
||||
```
|
||||
|
||||
```csharp
|
||||
await client.CreateCollectionAsync(
|
||||
collectionName: "items",
|
||||
vectorsConfig: new VectorParams { Size = 384, Distance = Distance.Cosine }
|
||||
);
|
||||
```
|
||||
|
||||
```go
|
||||
client.CreateCollection(context.Background(), &qdrant.CreateCollection{
|
||||
CollectionName: "items",
|
||||
VectorsConfig: qdrant.NewVectorsConfig(&qdrant.VectorParams{
|
||||
Size: 384,
|
||||
Distance: qdrant.Distance_Cosine,
|
||||
}),
|
||||
})
|
||||
```
|
||||
|
||||
```bash
|
||||
curl -X PUT \
|
||||
'http://<your-qdrant-host>:6333/collections/items' \
|
||||
@@ -119,15 +205,9 @@ curl -X PUT \
|
||||
```
|
||||
|
||||
## 5. Populate the collection
|
||||
Next, we will populate the collection with menu items. Each item will be represented as a point in the collection, with its vector embedding and associated metadata.
|
||||
Next, we will populate the collection with menu items. Each item will be represented as a point in the collection with its associated metadata. Instead of generating embeddings locally, we pass a `Document` object with the text and model name — Qdrant Cloud Inference handles the embedding automatically.
|
||||
|
||||
```python
|
||||
from qdrant_client.models import PointStruct
|
||||
from fastembed import TextEmbedding
|
||||
|
||||
# load the embedding model
|
||||
model = TextEmbedding('BAAI/bge-small-en-v1.5')
|
||||
|
||||
menu_items = [
|
||||
("Pad Thai with Tofu", "Stir-fried rice noodles with tofu bean sprouts scallions and crushed peanuts in traditional tamarind sauce", "$13.95", "Noodles"),
|
||||
("Grilled Salmon Fillet", "Wild-caught Atlantic salmon grilled with lemon butter and fresh herbs served with seasonal vegetables", "$24.50", "Seafood Entrees"),
|
||||
@@ -161,19 +241,20 @@ menu_items = [
|
||||
("Coconut Shrimp", "Jumbo shrimp breaded in shredded coconut served with sweet chili sauce", "$14.25", "Seafood Appetizers")
|
||||
]
|
||||
|
||||
# embedding generator
|
||||
# points generator
|
||||
points = []
|
||||
embeddings = model.embed([f"{item[0]} {item[1]}" for item in menu_items])
|
||||
for i, embedding in enumerate(embeddings):
|
||||
vector = embedding.tolist()
|
||||
for i, menu_item in enumerate(menu_items):
|
||||
point = PointStruct(
|
||||
id=i,
|
||||
vector=vector,
|
||||
vector=Document(
|
||||
text=f"{menu_item[0]} {menu_item[1]}",
|
||||
model="sentence-transformers/all-MiniLM-L6-v2"
|
||||
),
|
||||
payload={
|
||||
"item_name": menu_items[i][0],
|
||||
"description": menu_items[i][1],
|
||||
"price": menu_items[i][2],
|
||||
"category": menu_items[i][3],
|
||||
"item_name": menu_item[0],
|
||||
"description": menu_item[1],
|
||||
"price": menu_item[2],
|
||||
"category": menu_item[3],
|
||||
}
|
||||
)
|
||||
points.append(point)
|
||||
@@ -186,17 +267,6 @@ client.upsert(
|
||||
```
|
||||
|
||||
```rust
|
||||
use fastembed::{EmbeddingModel, InitOptions, TextEmbedding};
|
||||
use qdrant_client::qdrant::{PointStruct, UpsertPointsBuilder};
|
||||
use qdrant_client::{Qdrant, Payload};
|
||||
use serde_json::json;
|
||||
|
||||
// load the embedding model
|
||||
let mut model = TextEmbedding::try_new(
|
||||
InitOptions::new(EmbeddingModel::BGESmallENV15).with_show_download_progress(true),
|
||||
)
|
||||
.expect("Failed to load embedding model");
|
||||
|
||||
// generate embeddings and prepare points
|
||||
let menu_items = vec![
|
||||
(
|
||||
@@ -381,28 +451,22 @@ let menu_items = vec![
|
||||
),
|
||||
];
|
||||
|
||||
let embeddings = model
|
||||
.embed(
|
||||
menu_items
|
||||
.iter()
|
||||
.map(|item| format!("{} {}", item.0, item.1))
|
||||
.collect::<Vec<_>>(),
|
||||
None,
|
||||
)
|
||||
.expect("Failed to generate embeddings");
|
||||
|
||||
let points = embeddings
|
||||
let points = menu_items
|
||||
.into_iter()
|
||||
.enumerate()
|
||||
.map(|(idx, embedding)| {
|
||||
.map(|(idx, menu_item)| {
|
||||
PointStruct::new(
|
||||
idx as u64,
|
||||
embedding,
|
||||
DocumentBuilder::new(
|
||||
format!("{} {}", menu_item.0, menu_item.1),
|
||||
"sentence-transformers/all-MiniLM-L6-v2"
|
||||
)
|
||||
.build(),
|
||||
Payload::try_from(json!({
|
||||
"item_name": menu_items[idx].0,
|
||||
"description": menu_items[idx].1,
|
||||
"price": menu_items[idx].2,
|
||||
"category": menu_items[idx].3,
|
||||
"item_name": menu_item.0,
|
||||
"description": menu_item.1,
|
||||
"price": menu_item.2,
|
||||
"category": menu_item.3,
|
||||
}))
|
||||
.unwrap(),
|
||||
)
|
||||
@@ -415,13 +479,6 @@ let _ = client
|
||||
```
|
||||
|
||||
```typescript
|
||||
import { TextEmbedding, EmbeddingModel } from 'fastembed';
|
||||
|
||||
// load the embedding model
|
||||
const model = await FlagEmbedding.init({
|
||||
model: EmbeddingModel.BGESmallENV15,
|
||||
});
|
||||
|
||||
let menuItems = [
|
||||
[
|
||||
"Pad Thai with Tofu",
|
||||
@@ -609,16 +666,18 @@ let menuItems = [
|
||||
const points: any[] = [];
|
||||
let idx = 0;
|
||||
|
||||
const embeddings = model.embed(menuItems.map(item => `${item[0]} ${item[1]}`));
|
||||
for await (const embedding of embeddings) {
|
||||
for (const menuItem of menuItems) {
|
||||
points.push({
|
||||
id: idx,
|
||||
vector: Array.from(embedding[0]),
|
||||
vector: {
|
||||
text: `${menuItem[0]} ${menuItem[1]}`,
|
||||
model: "sentence-transformers/all-MiniLM-L6-v2",
|
||||
},
|
||||
payload: {
|
||||
item_name: menuItems[idx][0],
|
||||
description: menuItems[idx][1],
|
||||
price: menuItems[idx][2],
|
||||
category: menuItems[idx][3],
|
||||
item_name: menuItem[0],
|
||||
description: menuItem[1],
|
||||
price: menuItem[2],
|
||||
category: menuItem[3],
|
||||
},
|
||||
});
|
||||
idx++;
|
||||
@@ -628,18 +687,193 @@ for await (const embedding of embeddings) {
|
||||
await client.upsert("items", { points });
|
||||
```
|
||||
|
||||
```java
|
||||
String[][] menuItems = {
|
||||
{"Pad Thai with Tofu", "Stir-fried rice noodles with tofu bean sprouts scallions and crushed peanuts in traditional tamarind sauce", "$13.95", "Noodles"},
|
||||
{"Grilled Salmon Fillet", "Wild-caught Atlantic salmon grilled with lemon butter and fresh herbs served with seasonal vegetables", "$24.50", "Seafood Entrees"},
|
||||
{"Mushroom Risotto", "Creamy arborio rice with mixed mushrooms parmesan truffle oil and fresh thyme", "$16.75", "Vegetarian"},
|
||||
{"Bibimbap Bowl", "Korean rice bowl with seasoned vegetables fried egg gochujang sauce and choice of protein", "$14.50", "Korean Bowls"},
|
||||
{"Falafel Wrap", "Crispy chickpea fritters with hummus tahini cucumber tomato and pickled vegetables in warm pita", "$11.25", "Mediterranean"},
|
||||
{"Shrimp Tacos", "Three soft tacos with grilled shrimp cabbage slaw chipotle aioli and fresh lime", "$13.00", "Tacos"},
|
||||
{"Vegetable Curry", "Mixed vegetables in aromatic coconut curry sauce with jasmine rice and naan bread", "$12.95", "Indian Curries"},
|
||||
{"Tuna Poke Bowl", "Fresh ahi tuna with avocado edamame cucumber seaweed salad over sushi rice with spicy mayo", "$16.50", "Poke Bowls"},
|
||||
{"Margherita Pizza", "Fresh mozzarella san marzano tomatoes basil and extra virgin olive oil on wood-fired crust", "$14.00", "Pizza"},
|
||||
{"Chicken Tikka Masala", "Tandoori chicken in creamy tomato sauce with aromatic spices served with basmati rice", "$15.95", "Indian Entrees"},
|
||||
{"Greek Salad", "Romaine lettuce tomatoes cucumbers kalamata olives feta cheese red onion with lemon oregano dressing", "$10.50", "Salads"},
|
||||
{"Lobster Roll", "Fresh Maine lobster meat with light mayo on toasted buttery roll served with chips", "$22.00", "Seafood Sandwiches"},
|
||||
{"Quinoa Buddha Bowl", "Organic quinoa with roasted chickpeas kale sweet potato tahini dressing and hemp seeds", "$13.50", "Healthy Bowls"},
|
||||
{"Beef Pho", "Traditional Vietnamese beef noodle soup with rice noodles fresh herbs bean sprouts and lime", "$12.75", "Noodle Soups"},
|
||||
{"Eggplant Parmesan", "Breaded eggplant layered with marinara mozzarella and parmesan served with pasta", "$15.25", "Italian Entrees"},
|
||||
{"Crab Cakes", "Maryland-style lump crab cakes with remoulade sauce and mixed greens", "$18.50", "Seafood Appetizers"},
|
||||
{"Tofu Stir Fry", "Crispy tofu with broccoli bell peppers snap peas in garlic ginger sauce over steamed rice", "$12.50", "Vegetarian Entrees"},
|
||||
{"Salmon Sushi Platter", "12 pieces of fresh salmon nigiri and sashimi with wasabi pickled ginger and soy sauce", "$19.95", "Sushi"},
|
||||
{"Caprese Sandwich", "Fresh mozzarella tomatoes basil pesto balsamic glaze on ciabatta bread", "$11.75", "Sandwiches"},
|
||||
{"Tom Yum Soup", "Spicy and sour Thai soup with shrimp lemongrass galangal mushrooms and kaffir lime leaves", "$11.50", "Soups"},
|
||||
{"Lentil Dal", "Red lentils simmered with turmeric cumin coriander served with rice and naan", "$11.95", "Vegan Entrees"},
|
||||
{"Fish and Chips", "Beer-battered cod with crispy fries malt vinegar and tartar sauce", "$16.00", "British Classics"},
|
||||
{"Veggie Burger", "House-made black bean and quinoa patty with avocado sprouts tomato on brioche bun", "$13.25", "Burgers"},
|
||||
{"Miso Ramen", "Rich miso broth with ramen noodles soft-boiled egg bamboo shoots nori and scallions", "$14.50", "Ramen"},
|
||||
{"Stuffed Bell Peppers", "Roasted bell peppers filled with rice vegetables herbs and melted cheese", "$13.75", "Vegetarian Entrees"},
|
||||
{"Scallop Risotto", "Pan-seared sea scallops over creamy parmesan risotto with white wine and lemon", "$26.50", "Seafood Specials"},
|
||||
{"Spring Rolls", "Fresh rice paper rolls with vegetables tofu rice noodles herbs and peanut dipping sauce", "$8.95", "Appetizers"},
|
||||
{"Oyster Po Boy", "Fried oysters with lettuce tomato pickles and remoulade on french bread", "$15.50", "Sandwiches"},
|
||||
{"Portobello Mushroom Steak", "Grilled portobello cap marinated in balsamic with roasted vegetables and quinoa", "$14.95", "Vegan Entrees"},
|
||||
{"Coconut Shrimp", "Jumbo shrimp breaded in shredded coconut served with sweet chili sauce", "$14.25", "Seafood Appetizers"},
|
||||
};
|
||||
|
||||
List<PointStruct> points = new ArrayList<>();
|
||||
for (int i = 0; i < menuItems.length; i++) {
|
||||
points.add(
|
||||
PointStruct.newBuilder()
|
||||
.setId(id(i))
|
||||
.setVectors(vectors(
|
||||
Document.newBuilder()
|
||||
.setText(menuItems[i][0] + " " + menuItems[i][1])
|
||||
.setModel("sentence-transformers/all-MiniLM-L6-v2")
|
||||
.build()))
|
||||
.putAllPayload(Map.of(
|
||||
"item_name", value(menuItems[i][0]),
|
||||
"description", value(menuItems[i][1]),
|
||||
"price", value(menuItems[i][2]),
|
||||
"category", value(menuItems[i][3])))
|
||||
.build()
|
||||
);
|
||||
}
|
||||
|
||||
client.upsertAsync("items", points).get();
|
||||
```
|
||||
|
||||
```csharp
|
||||
var menuItems = new[] {
|
||||
("Pad Thai with Tofu", "Stir-fried rice noodles with tofu bean sprouts scallions and crushed peanuts in traditional tamarind sauce", "$13.95", "Noodles"),
|
||||
("Grilled Salmon Fillet", "Wild-caught Atlantic salmon grilled with lemon butter and fresh herbs served with seasonal vegetables", "$24.50", "Seafood Entrees"),
|
||||
("Mushroom Risotto", "Creamy arborio rice with mixed mushrooms parmesan truffle oil and fresh thyme", "$16.75", "Vegetarian"),
|
||||
("Bibimbap Bowl", "Korean rice bowl with seasoned vegetables fried egg gochujang sauce and choice of protein", "$14.50", "Korean Bowls"),
|
||||
("Falafel Wrap", "Crispy chickpea fritters with hummus tahini cucumber tomato and pickled vegetables in warm pita", "$11.25", "Mediterranean"),
|
||||
("Shrimp Tacos", "Three soft tacos with grilled shrimp cabbage slaw chipotle aioli and fresh lime", "$13.00", "Tacos"),
|
||||
("Vegetable Curry", "Mixed vegetables in aromatic coconut curry sauce with jasmine rice and naan bread", "$12.95", "Indian Curries"),
|
||||
("Tuna Poke Bowl", "Fresh ahi tuna with avocado edamame cucumber seaweed salad over sushi rice with spicy mayo", "$16.50", "Poke Bowls"),
|
||||
("Margherita Pizza", "Fresh mozzarella san marzano tomatoes basil and extra virgin olive oil on wood-fired crust", "$14.00", "Pizza"),
|
||||
("Chicken Tikka Masala", "Tandoori chicken in creamy tomato sauce with aromatic spices served with basmati rice", "$15.95", "Indian Entrees"),
|
||||
("Greek Salad", "Romaine lettuce tomatoes cucumbers kalamata olives feta cheese red onion with lemon oregano dressing", "$10.50", "Salads"),
|
||||
("Lobster Roll", "Fresh Maine lobster meat with light mayo on toasted buttery roll served with chips", "$22.00", "Seafood Sandwiches"),
|
||||
("Quinoa Buddha Bowl", "Organic quinoa with roasted chickpeas kale sweet potato tahini dressing and hemp seeds", "$13.50", "Healthy Bowls"),
|
||||
("Beef Pho", "Traditional Vietnamese beef noodle soup with rice noodles fresh herbs bean sprouts and lime", "$12.75", "Noodle Soups"),
|
||||
("Eggplant Parmesan", "Breaded eggplant layered with marinara mozzarella and parmesan served with pasta", "$15.25", "Italian Entrees"),
|
||||
("Crab Cakes", "Maryland-style lump crab cakes with remoulade sauce and mixed greens", "$18.50", "Seafood Appetizers"),
|
||||
("Tofu Stir Fry", "Crispy tofu with broccoli bell peppers snap peas in garlic ginger sauce over steamed rice", "$12.50", "Vegetarian Entrees"),
|
||||
("Salmon Sushi Platter", "12 pieces of fresh salmon nigiri and sashimi with wasabi pickled ginger and soy sauce", "$19.95", "Sushi"),
|
||||
("Caprese Sandwich", "Fresh mozzarella tomatoes basil pesto balsamic glaze on ciabatta bread", "$11.75", "Sandwiches"),
|
||||
("Tom Yum Soup", "Spicy and sour Thai soup with shrimp lemongrass galangal mushrooms and kaffir lime leaves", "$11.50", "Soups"),
|
||||
("Lentil Dal", "Red lentils simmered with turmeric cumin coriander served with rice and naan", "$11.95", "Vegan Entrees"),
|
||||
("Fish and Chips", "Beer-battered cod with crispy fries malt vinegar and tartar sauce", "$16.00", "British Classics"),
|
||||
("Veggie Burger", "House-made black bean and quinoa patty with avocado sprouts tomato on brioche bun", "$13.25", "Burgers"),
|
||||
("Miso Ramen", "Rich miso broth with ramen noodles soft-boiled egg bamboo shoots nori and scallions", "$14.50", "Ramen"),
|
||||
("Stuffed Bell Peppers", "Roasted bell peppers filled with rice vegetables herbs and melted cheese", "$13.75", "Vegetarian Entrees"),
|
||||
("Scallop Risotto", "Pan-seared sea scallops over creamy parmesan risotto with white wine and lemon", "$26.50", "Seafood Specials"),
|
||||
("Spring Rolls", "Fresh rice paper rolls with vegetables tofu rice noodles herbs and peanut dipping sauce", "$8.95", "Appetizers"),
|
||||
("Oyster Po Boy", "Fried oysters with lettuce tomato pickles and remoulade on french bread", "$15.50", "Sandwiches"),
|
||||
("Portobello Mushroom Steak", "Grilled portobello cap marinated in balsamic with roasted vegetables and quinoa", "$14.95", "Vegan Entrees"),
|
||||
("Coconut Shrimp", "Jumbo shrimp breaded in shredded coconut served with sweet chili sauce", "$14.25", "Seafood Appetizers"),
|
||||
};
|
||||
|
||||
var points = new List<PointStruct>();
|
||||
for (int i = 0; i < menuItems.Length; i++)
|
||||
{
|
||||
var item = menuItems[i];
|
||||
points.Add(new PointStruct
|
||||
{
|
||||
Id = (ulong)i,
|
||||
Vectors = new Document
|
||||
{
|
||||
Text = $"{item.Item1} {item.Item2}",
|
||||
Model = "sentence-transformers/all-MiniLM-L6-v2",
|
||||
},
|
||||
Payload =
|
||||
{
|
||||
["item_name"] = item.Item1,
|
||||
["description"] = item.Item2,
|
||||
["price"] = item.Item3,
|
||||
["category"] = item.Item4,
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
await client.UpsertAsync("items", points);
|
||||
```
|
||||
|
||||
```go
|
||||
type MenuItem struct {
|
||||
Name, Description, Price, Category string
|
||||
}
|
||||
|
||||
menuItems := []MenuItem{
|
||||
{"Pad Thai with Tofu", "Stir-fried rice noodles with tofu bean sprouts scallions and crushed peanuts in traditional tamarind sauce", "$13.95", "Noodles"},
|
||||
{"Grilled Salmon Fillet", "Wild-caught Atlantic salmon grilled with lemon butter and fresh herbs served with seasonal vegetables", "$24.50", "Seafood Entrees"},
|
||||
{"Mushroom Risotto", "Creamy arborio rice with mixed mushrooms parmesan truffle oil and fresh thyme", "$16.75", "Vegetarian"},
|
||||
{"Bibimbap Bowl", "Korean rice bowl with seasoned vegetables fried egg gochujang sauce and choice of protein", "$14.50", "Korean Bowls"},
|
||||
{"Falafel Wrap", "Crispy chickpea fritters with hummus tahini cucumber tomato and pickled vegetables in warm pita", "$11.25", "Mediterranean"},
|
||||
{"Shrimp Tacos", "Three soft tacos with grilled shrimp cabbage slaw chipotle aioli and fresh lime", "$13.00", "Tacos"},
|
||||
{"Vegetable Curry", "Mixed vegetables in aromatic coconut curry sauce with jasmine rice and naan bread", "$12.95", "Indian Curries"},
|
||||
{"Tuna Poke Bowl", "Fresh ahi tuna with avocado edamame cucumber seaweed salad over sushi rice with spicy mayo", "$16.50", "Poke Bowls"},
|
||||
{"Margherita Pizza", "Fresh mozzarella san marzano tomatoes basil and extra virgin olive oil on wood-fired crust", "$14.00", "Pizza"},
|
||||
{"Chicken Tikka Masala", "Tandoori chicken in creamy tomato sauce with aromatic spices served with basmati rice", "$15.95", "Indian Entrees"},
|
||||
{"Greek Salad", "Romaine lettuce tomatoes cucumbers kalamata olives feta cheese red onion with lemon oregano dressing", "$10.50", "Salads"},
|
||||
{"Lobster Roll", "Fresh Maine lobster meat with light mayo on toasted buttery roll served with chips", "$22.00", "Seafood Sandwiches"},
|
||||
{"Quinoa Buddha Bowl", "Organic quinoa with roasted chickpeas kale sweet potato tahini dressing and hemp seeds", "$13.50", "Healthy Bowls"},
|
||||
{"Beef Pho", "Traditional Vietnamese beef noodle soup with rice noodles fresh herbs bean sprouts and lime", "$12.75", "Noodle Soups"},
|
||||
{"Eggplant Parmesan", "Breaded eggplant layered with marinara mozzarella and parmesan served with pasta", "$15.25", "Italian Entrees"},
|
||||
{"Crab Cakes", "Maryland-style lump crab cakes with remoulade sauce and mixed greens", "$18.50", "Seafood Appetizers"},
|
||||
{"Tofu Stir Fry", "Crispy tofu with broccoli bell peppers snap peas in garlic ginger sauce over steamed rice", "$12.50", "Vegetarian Entrees"},
|
||||
{"Salmon Sushi Platter", "12 pieces of fresh salmon nigiri and sashimi with wasabi pickled ginger and soy sauce", "$19.95", "Sushi"},
|
||||
{"Caprese Sandwich", "Fresh mozzarella tomatoes basil pesto balsamic glaze on ciabatta bread", "$11.75", "Sandwiches"},
|
||||
{"Tom Yum Soup", "Spicy and sour Thai soup with shrimp lemongrass galangal mushrooms and kaffir lime leaves", "$11.50", "Soups"},
|
||||
{"Lentil Dal", "Red lentils simmered with turmeric cumin coriander served with rice and naan", "$11.95", "Vegan Entrees"},
|
||||
{"Fish and Chips", "Beer-battered cod with crispy fries malt vinegar and tartar sauce", "$16.00", "British Classics"},
|
||||
{"Veggie Burger", "House-made black bean and quinoa patty with avocado sprouts tomato on brioche bun", "$13.25", "Burgers"},
|
||||
{"Miso Ramen", "Rich miso broth with ramen noodles soft-boiled egg bamboo shoots nori and scallions", "$14.50", "Ramen"},
|
||||
{"Stuffed Bell Peppers", "Roasted bell peppers filled with rice vegetables herbs and melted cheese", "$13.75", "Vegetarian Entrees"},
|
||||
{"Scallop Risotto", "Pan-seared sea scallops over creamy parmesan risotto with white wine and lemon", "$26.50", "Seafood Specials"},
|
||||
{"Spring Rolls", "Fresh rice paper rolls with vegetables tofu rice noodles herbs and peanut dipping sauce", "$8.95", "Appetizers"},
|
||||
{"Oyster Po Boy", "Fried oysters with lettuce tomato pickles and remoulade on french bread", "$15.50", "Sandwiches"},
|
||||
{"Portobello Mushroom Steak", "Grilled portobello cap marinated in balsamic with roasted vegetables and quinoa", "$14.95", "Vegan Entrees"},
|
||||
{"Coconut Shrimp", "Jumbo shrimp breaded in shredded coconut served with sweet chili sauce", "$14.25", "Seafood Appetizers"},
|
||||
}
|
||||
|
||||
points := make([]*qdrant.PointStruct, len(menuItems))
|
||||
for i, item := range menuItems {
|
||||
points[i] = &qdrant.PointStruct{
|
||||
Id: qdrant.NewIDNum(uint64(i)),
|
||||
Vectors: qdrant.NewVectorsDocument(&qdrant.Document{
|
||||
Text: item.Name + " " + item.Description,
|
||||
Model: "sentence-transformers/all-MiniLM-L6-v2",
|
||||
}),
|
||||
Payload: qdrant.NewValueMap(map[string]any{
|
||||
"item_name": item.Name,
|
||||
"description": item.Description,
|
||||
"price": item.Price,
|
||||
"category": item.Category,
|
||||
}),
|
||||
}
|
||||
}
|
||||
|
||||
client.Upsert(context.Background(), &qdrant.UpsertPoints{
|
||||
CollectionName: "items",
|
||||
Points: points,
|
||||
})
|
||||
```
|
||||
|
||||
## 6. Search the Menu Items
|
||||
Now we can search the menu item dataset! We'll use the same `BAAI/bge-small-en-v1.5` model to embed our query text, then find the best dishes matching that embedding.
|
||||
Now we can search the menu item dataset! We'll use the same `sentence-transformers/all-MiniLM-L6-v2` model in Cloud Inference to embed our query text, then find the best dishes matching that embedding.
|
||||
|
||||
```python
|
||||
# generate query embedding
|
||||
query_text = "vegetarian dishes"
|
||||
query_vector = next(iter(model.embed(query_text)))
|
||||
|
||||
# search for similar menu items
|
||||
results = client.query_points(
|
||||
collection_name="items",
|
||||
query=query_vector,
|
||||
query=Document(text=query_text, model="sentence-transformers/all-MiniLM-L6-v2"),
|
||||
with_payload=True,
|
||||
limit=5
|
||||
)
|
||||
@@ -656,15 +890,17 @@ for result in results.points:
|
||||
```rust
|
||||
// generate query embedding
|
||||
let query_text = "vegetarian dishes";
|
||||
let query_embeddings = model
|
||||
.embed(vec![query_text], None)
|
||||
.expect("Failed to generate embeddings");
|
||||
let query_vector = query_embeddings[0].clone();
|
||||
|
||||
let results = client
|
||||
.query(
|
||||
QueryPointsBuilder::new("items")
|
||||
.query(query_vector)
|
||||
.query(Query::new_nearest(
|
||||
DocumentBuilder::new(
|
||||
query_text,
|
||||
"sentence-transformers/all-MiniLM-L6-v2"
|
||||
)
|
||||
.build(),
|
||||
))
|
||||
.with_payload(true)
|
||||
.limit(5),
|
||||
)
|
||||
@@ -689,11 +925,13 @@ for result in results.result {
|
||||
```typescript
|
||||
// generate query embedding
|
||||
const queryText = "vegetarian dishes";
|
||||
const queryEmbedding = (await model.embed([queryText]).next()).value!
|
||||
|
||||
// search for similar items
|
||||
const results = await client.query("items", {
|
||||
query: Array.from(queryEmbedding[0]),
|
||||
query: {
|
||||
text: queryText,
|
||||
model: "sentence-transformers/all-MiniLM-L6-v2",
|
||||
},
|
||||
with_payload: true,
|
||||
limit: 5,
|
||||
});
|
||||
@@ -708,6 +946,90 @@ for (const result of results.points) {
|
||||
}
|
||||
```
|
||||
|
||||
```java
|
||||
// generate query embedding
|
||||
String queryText = "vegetarian dishes";
|
||||
|
||||
// search for similar menu items
|
||||
List<ScoredPoint> results = client
|
||||
.queryAsync(
|
||||
QueryPoints.newBuilder()
|
||||
.setCollectionName("items")
|
||||
.setQuery(nearest(
|
||||
Document.newBuilder()
|
||||
.setText(queryText)
|
||||
.setModel("sentence-transformers/all-MiniLM-L6-v2")
|
||||
.build()))
|
||||
.setWithPayload(
|
||||
WithPayloadSelector.newBuilder().setEnable(true).build())
|
||||
.setLimit(5)
|
||||
.build())
|
||||
.get();
|
||||
|
||||
// print results
|
||||
for (ScoredPoint result : results) {
|
||||
System.out.println("Item: " + result.getPayloadMap().get("item_name").getStringValue());
|
||||
System.out.println("Score: " + result.getScore());
|
||||
System.out.println("Description: " + result.getPayloadMap().get("description").getStringValue());
|
||||
System.out.println("Price: " + result.getPayloadMap().get("price").getStringValue());
|
||||
System.out.println("---");
|
||||
}
|
||||
```
|
||||
|
||||
```csharp
|
||||
// generate query embedding
|
||||
var queryText = "vegetarian dishes";
|
||||
|
||||
// search for similar menu items
|
||||
var results = await client.QueryAsync(
|
||||
collectionName: "items",
|
||||
query: new Document
|
||||
{
|
||||
Text = queryText,
|
||||
Model = "sentence-transformers/all-MiniLM-L6-v2",
|
||||
},
|
||||
payloadSelector: true,
|
||||
limit: 5
|
||||
);
|
||||
|
||||
// print results
|
||||
foreach (var result in results)
|
||||
{
|
||||
Console.WriteLine($"Item: {result.Payload["item_name"].StringValue}");
|
||||
Console.WriteLine($"Score: {result.Score}");
|
||||
Console.WriteLine($"Description: {result.Payload["description"].StringValue}");
|
||||
Console.WriteLine($"Price: {result.Payload["price"].StringValue}");
|
||||
Console.WriteLine("---");
|
||||
}
|
||||
```
|
||||
|
||||
```go
|
||||
// generate query embedding
|
||||
queryText := "vegetarian dishes"
|
||||
|
||||
// search for similar menu items
|
||||
results, err := client.Query(context.Background(), &qdrant.QueryPoints{
|
||||
CollectionName: "items",
|
||||
Query: qdrant.NewQueryNearest(
|
||||
qdrant.NewVectorInputDocument(&qdrant.Document{
|
||||
Text: queryText,
|
||||
Model: "sentence-transformers/all-MiniLM-L6-v2",
|
||||
}),
|
||||
),
|
||||
WithPayload: qdrant.NewWithPayload(true),
|
||||
Limit: qdrant.PtrOf(uint64(5)),
|
||||
})
|
||||
|
||||
// print results
|
||||
for _, result := range results {
|
||||
fmt.Printf("Item: %s\n", result.Payload["item_name"].GetStringValue())
|
||||
fmt.Printf("Score: %f\n", result.Score)
|
||||
fmt.Printf("Description: %s\n", result.Payload["description"].GetStringValue())
|
||||
fmt.Printf("Price: %s\n", result.Payload["price"].GetStringValue())
|
||||
fmt.Println("---")
|
||||
}
|
||||
```
|
||||
|
||||
## That's Vector Search!
|
||||
|
||||
You've just performed semantic search on real menu item data. The query "vegetarian dishes" returned similar menu items based on meaning, not just keyword matching.
|
||||
|
||||
Reference in New Issue
Block a user