switch to embedding real data hard-coded into the scripts

qdrant-landing/content/documentation/cloud-quickstart.md

@@ -14,7 +14,7 @@ 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 1,000 IMDB movies pre-embedded with the `jinaai/jina-embeddings-v2-base-en` model to get you started quickly.
+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 1,000 IMDB movies pre-embedded with the `BAAI/bge-small-en-v1.5` model.
 
 ## 1. Create a Cloud Cluster
 
@@ -73,104 +73,345 @@ curl -X GET \
   --header 'api-key: <api-key-value>'
 ```
 
-## 4. Load the Sample Dataset
+## 4. Create our collection
+
+We will use some sample menu items to demonstrate how to create a collection and add data to it. Each menu item has a name, description, price, and category. First, we need to create a collection in Qdrant to store our menu items.
 
-We'll load a pre-embedded dataset of 1,000 H&M products using a [Qdrant snapshot](https://qdrant.tech/documentation/concepts/snapshots/). This snapshot contains vectors created with the `BAAI/bge-small-en-v1.5` model (384 dimensions) and will automatically create the collection for you.
 
 ```python
-client.recover_snapshot("products", "https://snapshots.qdrant.io/hm_ecommerce_bge_small_v1.5_en.snapshot")
+from qdrant_client.models import Distance, VectorParams
+
+# create collection
+client.create_collection(
+    collection_name="items",
+    vectors_config=VectorParams(size=384, distance=Distance.COSINE),
+)
 ```
 
 ```rust
-// recovering from snapshot is not yet supported in Rust client
+use qdrant_client::qdrant::{CreateCollectionBuilder, Distance, VectorParamsBuilder};
-// please use bash/cURL to restore from the snapshot manually
+
+// create collection
+client.create_collection(
+  CreateCollectionBuilder::new("items")
+    .vectors_config(VectorParamsBuilder::new(384, Distance::Cosine)),
+)
+.await?;
 ```
 
 ```typescript
-await client.recoverSnapshot("products", {
+await client.createCollection("items", {
-  location: "https://snapshots.qdrant.io/hm_ecommerce_bge_small_v1.5_en.snapshot",
+  vectors: { size: 384, distance: "Cosine" },
 });
 ```
 
 ```curl
 curl -X PUT \
-  'http://<your-qdrant-host>:6333/collections/products/snapshots/recover' \
+  'http://<your-qdrant-host>:6333/collections/items' \
   --header 'api-key: <api-key-value>' \
   --header 'Content-Type: application/json' \
   --data-raw '{
-    "location": "https://snapshots.qdrant.io/hm_ecommerce_bge_small_v1.5_en.snapshot"
+  "vectors": {
+    "size": 384,
+    "distance": "Cosine"
+  }
 }'
 ```
 
-## 5. Search the Products
+## 5. Populate the collection
-Now we can search the product dataset! We'll use the same `BAAI/bge-small-en-v1.5` model to embed our query text, then find similar products in 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.
 
 ```python
+from qdrant_client.models import PointStruct
 from fastembed import TextEmbedding
 
 # load the embedding model
 model = TextEmbedding('BAAI/bge-small-en-v1.5')
 
-# generate query embedding
+menu_items = [
-query_text = "womens graphic tee shirt"
+    ("Pad Thai with Tofu", "Stir-fried rice noodles with tofu bean sprouts scallions and crushed peanuts in traditional tamarind sauce", "$13.95", "Noodles"),
-query_vector = next(iter(model.embed(query_text)))
+    ("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")
+]
 
-# search for similar products
+# embedding generator
-results = client.query_points(
+points = []
-    collection_name="products",
+embeddings = model.embed([f"{item[0]} {item[1]}" for item in menu_items])
-    using="dense",
+for i, embedding in enumerate(embeddings):
-    query=query_vector,
+    vector = embedding.tolist()
-    with_payload=True,
+    point = PointStruct(
-    limit=5
+        id=i,
+        vector=vector,
+        payload={
+            "item_name": item[0],
+            "description": item[1],
+            "price": item[2],
+            "category": item[3],
+        }
+    )
+    points.append(point)
+
+# upsert points to collection
+client.upsert(
+  collection_name="items",
+  points=points,
 )
-
-# print results
-for result in results.points:
-    print(f"Product: {result.payload.get('prod_name', 'N/A')}")
-    print(f"Score: {result.score}")
-    print(f"Description: {result.payload['detail_desc'][:50]}...")
-    print("---")
 ```
 
 ```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)
+    InitOptions::new(EmbeddingModel::BGESmallENV15).with_show_download_progress(true),
-        .with_show_download_progress(true),
 )
 .expect("Failed to load embedding model");
 
-// generate query embedding
+// generate embeddings and prepare points
-let query_text = "womens graphic t shirt";
+let menu_items = vec![
-let query_embeddings = model
+    (
-    .embed(vec![query_text], None)
+        "Pad Thai with Tofu",
-    .expect("Failed to generate embeddings");
+        "Stir-fried rice noodles with tofu bean sprouts scallions and crushed peanuts in traditional tamarind sauce",
-let query_vector = query_embeddings[0].clone();
+        "$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",
+    ),
+];
 
-let results = client
+let embeddings = model
-    .query(
+    .embed(
-        QueryPointsBuilder::new("products")
+        menu_items
-            .query(query_vector)
+            .iter()
-            .using("dense")
+            .map(|item| format!("{} {}", item.0, item.1))
-            .with_payload(true)
+            .collect::<Vec<_>>(),
-            .limit(5),
+        None,
     )
-    .await
+    .expect("Failed to generate embeddings");
-    .expect("Query failed");
 
-let na_str = "N/A".to_string();
+let points = embeddings
+    .into_iter()
+    .enumerate()
+    .map(|(idx, embedding)| {
+        PointStruct::new(
+            idx as u64,
+            embedding,
+            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,
+            }))
+            .unwrap(),
+        )
+    })
+    .collect::<Vec<_>>();
 
-for result in results.result {
+let _ = client
-    let payload = result.payload;
+    .upsert_points(UpsertPointsBuilder::new("items", points).wait(true))
-    println!("Product: {}", payload.get("prod_name")
+    .await;
-        .and_then(|v| v.as_str()).unwrap_or(&na_str));
-    println!("Score: {}", result.score);
-    println!("Description: {}", payload.get("detail_desc")
-        .and_then(|v| v.as_str()).unwrap_or(&na_str));
-    println!("---");
-}
 ```
 
 ```typescript
@@ -181,30 +422,295 @@ const model = await FlagEmbedding.init({
   model: EmbeddingModel.BGESmallENV15,
 });
 
-// // generate query embedding
+let menuItems = [
-const queryText = "womens graphic t shirt";
+    [
+        "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",
+        "$14.25",
+        "Seafood Appetizers",
+    ],
+] as const;
+
+// generate embeddings and prepare points
+const points: any[] = [];
+let idx = 0;
+
+const embeddings = model.embed(menuItems.map(item => `${item[0]} ${item[1]}`));
+for await (const embedding of embeddings) {
+  points.push({
+    id: idx,
+    vector: Array.from(embedding[0]),
+    payload: {
+      prod_name: menuItems[idx][0],
+      detail_desc: menuItems[idx][1],
+      price: menuItems[idx][2],
+      category: menuItems[idx][3],
+    },
+  });
+  idx++;
+}
+
+// upsert points to collection
+await client.upsert("items", { points });
+```
+
+## 6. Search the Products
+Now we can search the product 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.
+
+```python
+# generate query embedding
+query_text = "vegetarian dishes"
+query_vector = next(iter(model.embed(query_text)))
+
+# search for similar products
+results = client.query_points(
+    collection_name="items",
+    query=query_vector,
+    with_payload=True,
+    limit=5
+)
+
+# print results
+for result in results.points:
+    print(f"Item: {result.payload.get('item_name', 'N/A')}")
+    print(f"Score: {result.score}")
+    print(f"Description: {result.payload['description'][:150]}...")
+    print(f"Price: {result.payload.get('price', 'N/A')}")
+    print("---")
+```
+
+```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)
+            .with_payload(true)
+            .limit(5),
+    )
+    .await
+    .expect("Query failed");
+
+let na_str = "N/A".to_string();
+
+for result in results.result {
+    let payload = result.payload;
+    println!("Item: {}", payload.get("item_name")
+        .and_then(|v| v.as_str()).unwrap_or(&na_str));
+    println!("Score: {}", result.score);
+    println!("Description: {}", payload.get("description")
+        .and_then(|v| v.as_str()).unwrap_or(&na_str));
+    println!("Price: {}", payload.get("price")
+        .and_then(|v| v.as_str()).unwrap_or(&na_str));
+    println!("---");
+}
+```
+
+```typescript
+// generate query embedding
+const queryText = "vegetarian dishes";
 const queryEmbedding = (await model.embed([queryText]).next()).value!
 
-// // search for similar movies
+// search for similar items
-const results = await client.query("products", {
+const results = await client.query("items", {
   query: Array.from(queryEmbedding[0]),
-  using: "dense",
   with_payload: true,
   limit: 5,
 });
 
-// // print results
+// print results
 for (const result of results.points) {
-  console.log(`Product: ${result.payload?.prod_name || 'N/A'}`);
+  console.log(`Item: ${result.payload?.item_name || 'N/A'}`);
   console.log(`Score: ${result.score}`);
-  console.log(`Description: ${result.payload?.detail_desc || 'N/A'}`);
+  console.log(`Description: ${result.payload?.description || 'N/A'}`);
+  console.log(`Price: ${result.payload?.price || 'N/A'}`);
   console.log('---');
 }
 ```
 
 ## That's Vector Search!
 
-You've just performed semantic search on real product data. The query "womens graphic tee shirt" returned similar products based on meaning, not just keyword matching.
+You've just performed semantic search on real product data. The query "vegetarian dishes" returned similar products based on meaning, not just keyword matching.
 
 ## What's Next?