教學課程：您的第一個向量搜尋

建立具有向量索引的資料表。這會在 DescriptionVector 屬性DescriptionIndex上建立具有名為 之向量索引的Products資料表。索引使用具有 1024 個維度COSINE的距離函數，以符合 Titan Text Embeddings V2 輸出。由於未定義向量索引分割區索引鍵，您不需要SearchConditionExpression搜尋 。

aws dynamodb create-table \
    --table-name Products \
    --attribute-definitions AttributeName=ProductId,AttributeType=S \
    --key-schema AttributeName=ProductId,KeyType=HASH \
    --billing-mode PAY_PER_REQUEST \
    --vector-indexes \
        "[
            {
                \"IndexName\": \"DescriptionIndex\",
                \"VectorAttribute\": {\"AttributeName\": \"DescriptionVector\"},
                \"Projection\": {\"ProjectionType\": \"ALL\"},
                \"Dimensions\": 1024,
                \"DistanceFunction\": \"COSINE\"
            }
        ]"

此範例使用 ProjectionType ，ALL以便每個屬性可供搜尋結果使用。如果您INCLUDE改用 ，請注意已共用投影的非金鑰屬性預算：向量屬性計為一個屬性，而每個INLINE_FILTER搜尋結構描述元素計為一個。HASH搜尋結構描述元素不會計為限制。

等待索引變成作用中。在 IndexStatus為 之前執行此動作ACTIVE。

aws dynamodb describe-table \
    --table-name Products \
    --query 'Table.VectorIndexes[0].[IndexName,IndexStatus,Backfilling]'

對於作為 一部分建立的索引CreateTable，如本教學中所示， Backfilling 不會報告，且 命令會null傳回該索引。在這種情況下，請IndexStatus單獨使用 做為您的訊號。

Backfilling 會報告您新增至具有 之現有資料表的向量索引UpdateTable。在這種情況下，請等到 IndexStatus 為 ACTIVE且 Backfilling 為 false，再依賴完整的搜尋結果。

建立產品目錄。將下列 50 個產品儲存至名為 的標籤分隔檔案products.tsv。每一行都包含產品 ID、名稱和單聲道描述。目錄包含 10 個群組的 5 個相關產品，這使得最後一個步驟中的搜尋結果更容易解譯。

p01	Insulated Travel Mug	A vacuum insulated stainless steel mug that keeps hot drinks warm for up to twelve hours.
p02	Stovetop Espresso Maker	A compact aluminum pot that brews strong espresso style coffee directly on a gas or electric burner.
p03	Manual Burr Coffee Grinder	A hand cranked grinder with adjustable ceramic burrs for consistent coffee grounds.
p04	Pour Over Coffee Dripper	A ceramic cone that sits on a mug and brews a single cup of filter coffee.
p05	Electric Milk Frother	A handheld battery powered whisk that creates dense foam for lattes and cappuccinos.
p06	Lightweight Running Shoe	A breathable mesh road shoe with cushioned foam midsole for daily training runs.
p07	Trail Running Shoe	An aggressive lugged outsole shoe built for grip on loose gravel and muddy trails.
p08	Moisture Wicking Running Socks	Ankle height socks knitted from synthetic yarn that pulls sweat away from the skin.
p09	Reflective Running Vest	A lightweight vest with high visibility strips for running safely after dark.
p10	Hydration Waist Belt	An elastic belt that holds two small water flasks and a phone during long runs.
p11	Ergonomic Mesh Office Chair	An adjustable desk chair with breathable mesh back and lumbar support for long work sessions.
p12	Sit Stand Desk Converter	A height adjustable platform that raises a monitor and keyboard for standing work.
p13	Monitor Arm Mount	A clamp mounted articulating arm that lifts a display off the desk surface.
p14	Under Desk Footrest	An angled cushioned platform that supports the feet and improves seated posture.
p15	Wireless Split Keyboard	A two piece keyboard that separates for a natural shoulder width typing position.
p16	Noise Cancelling Headphones	Over ear wireless headphones that actively silence engine noise on long flights.
p17	Wireless Earbuds	Compact in ear buds with a charging case and multi hour battery for commuting.
p18	Portable Bluetooth Speaker	A water resistant rechargeable speaker sized to fit in a backpack side pocket.
p19	Studio Monitor Headphones	Wired closed back headphones with flat frequency response for audio mixing.
p20	Wired Lapel Microphone	A small clip on microphone for recording clear speech during interviews.
p21	Four Season Backpacking Tent	A double wall tent with an aluminum pole set rated for wind and heavy rain.
p22	Down Sleeping Bag	A mummy shaped bag filled with compressible down insulation for cold weather camping.
p23	Inflatable Sleeping Pad	A lightweight pad that inflates in a few breaths and packs down to bottle size.
p24	Canister Camping Stove	A screw on burner that boils water quickly using a compact fuel canister.
p25	Rechargeable Camp Lantern	A collapsible lantern with adjustable brightness and a built in battery.
p26	Cast Iron Skillet	A preseasoned heavy pan that holds heat evenly for searing and oven baking.
p27	Nonstick Frying Pan	A coated aluminum pan that releases eggs and fish without added oil.
p28	Stainless Steel Stock Pot	A tall wide pot for boiling pasta and simmering large batches of soup.
p29	Enameled Dutch Oven	A heavy lidded pot that moves from stovetop to oven for slow braising.
p30	Bamboo Cutting Board	A large reversible board with a juice groove around the edge.
p31	Padded Laptop Backpack	A water resistant pack with a suspended sleeve that protects a fifteen inch laptop.
p32	Slim Laptop Sleeve	A close fitting neoprene case that shields a notebook inside a larger bag.
p33	Rolling Carry On Suitcase	A hard shell four wheel case sized to fit most overhead cabin bins.
p34	Packing Cube Set	Zippered fabric cubes that compress clothing and organize a suitcase.
p35	Leather Messenger Bag	A single strap shoulder bag with a padded compartment and interior pockets.
p36	Daily Facial Moisturizer	A light lotion with humectants that hydrates skin without leaving residue.
p37	Mineral Sunscreen Lotion	A broad spectrum zinc based sunscreen formulated for sensitive facial skin.
p38	Gentle Foaming Cleanser	A low pH face wash that removes oil and sunscreen without stripping the skin.
p39	Vitamin C Serum	A brightening serum applied before moisturizer to even skin tone over time.
p40	Overnight Repair Cream	A rich night cream with ceramides that restores the skin barrier while sleeping.
p41	Stainless Steel Dog Bowl	A weighted nonslip bowl that resists tipping during enthusiastic feeding.
p42	Padded Dog Harness	An adjustable chest harness that distributes pull away from the neck on walks.
p43	Retractable Dog Leash	A spring loaded leash that extends and locks at several walking lengths.
p44	Interactive Cat Puzzle Feeder	A slow feed tray that makes a cat work for dry food and eat more slowly.
p45	Self Cleaning Litter Box	An enclosed box with a raking mechanism that sifts waste after each use.
p46	Bypass Pruning Shears	Sharp hardened blades that make clean cuts on green stems and small branches.
p47	Long Handled Garden Spade	A steel bladed spade with a wooden shaft for turning soil and digging beds.
p48	Adjustable Hose Spray Nozzle	A metal nozzle that shifts from a fine mist to a strong jet stream.
p49	Raised Garden Bed Kit	Interlocking cedar panels that assemble into an elevated planting box.
p50	Drip Irrigation Starter Kit	Tubing and emitters that deliver water slowly to the base of each plant.

三個欄位之間的分隔符號必須是文字標籤字元。如果您從瀏覽器複製目錄，請在繼續之前驗證標籤是否存活。

為一個產品產生內嵌。請先執行單一內嵌呼叫，以確認您的 Amazon Bedrock 模型存取是否有效。inputText 欄位包含要嵌入的文字、dimensions設定輸出大小 （有效值為 256、512 或 1024)，並normalize產生單位長度向量，建議用於餘弦相似性搜尋。

mkdir -p emb

aws bedrock-runtime invoke-model \
    --model-id amazon.titan-embed-text-v2:0 \
    --body '{"inputText":"A vacuum insulated stainless steel mug that keeps hot drinks warm for up to twelve hours.","dimensions":1024,"normalize":true}' \
    --cli-binary-format raw-in-base64-out \
    --content-type application/json \
    --accept application/json \
    emb/p01.json

需要 --cli-binary-format raw-in-base64-out旗標。 AWS CLI v2 預設為二進位參數的 base64 編碼，因此如果沒有此旗標，原始 JSON 內文無法正確傳送。回應會寫入 ，emb/p01.json並包含 1024 個浮點數的embedding陣列。確認維度計數。

jq '.embedding | length' emb/p01.json

輸出是 1024。

撰寫第一個產品。將內嵌轉換為 DynamoDB 項目格式並寫入。存放的向量屬性使用 DynamoDB L（清單） 類型來包裝 N類型中的每個數字。

jq '{"ProductId":{"S":"p01"},"Title":{"S":"Insulated Travel Mug"},"DescriptionVector":{"L":[.embedding[]|{"N":(.|tostring)}]}}' emb/p01.json > item-p01.json

aws dynamodb put-item --table-name Products --item file://item-p01.json

內嵌其餘產品。此迴圈會為每個剩餘的產品產生內嵌。它會略過任何已存在的檔案，因此您可以在呼叫失敗時安全地重新執行它。

while IFS=$'\t' read -r id title description; do
    [ -s "emb/$id.json" ] && continue
    body=$(jq -n --arg t "$description" '{inputText:$t,dimensions:1024,normalize:true}')
    aws bedrock-runtime invoke-model \
        --model-id amazon.titan-embed-text-v2:0 \
        --body "$body" \
        --cli-binary-format raw-in-base64-out \
        --content-type application/json \
        --accept application/json \
        "emb/$id.json" >/dev/null || echo "FAILED $id"
done < products.tsv

ls emb/*.json | wc -l

計數必須是 50，才能繼續。如果任何呼叫已列印 FAILED，請再次執行迴圈；它只會重試缺少的檔案。

載入剩餘的產品。建置每個 25 個項目的請求承載，這是 BatchWriteItem 接受的最大值。

batch=0
count=0
echo -n '{"Products":[' > batch-0.json
while IFS=$'\t' read -r id title description; do
    if [ "$count" -eq 25 ]; then
        echo ']}' >> "batch-$batch.json"
        batch=$((batch+1)); count=0
        echo -n '{"Products":[' > "batch-$batch.json"
    fi
    [ "$count" -gt 0 ] && echo -n ',' >> "batch-$batch.json"
    jq -c --arg id "$id" --arg title "$title" \
        '{PutRequest:{Item:{ProductId:{S:$id},Title:{S:$title},
          DescriptionVector:{L:[.embedding[]|{"N":(.|tostring)}]}}}}' \
        "emb/$id.json" >> "batch-$batch.json"
    count=$((count+1))
done < <(tail -n +2 products.tsv)
echo ']}' >> "batch-$batch.json"

迴圈從重新導向而非管道讀取，因為管道while迴圈在某些 shell 中以子殼執行，這會捨棄 batch和 count值，並產生格式不正確的批次檔案。

提交每個批次。 BatchWriteItem可以部分成功，因此請重新提交它在 中傳回的任何內容UnprocessedItems。

for f in batch-*.json; do
    cp "$f" pending.json
    for attempt in 1 2 3 4 5; do
        aws dynamodb batch-write-item \
            --request-items file://pending.json \
            --output json > resp.json
        left=$(jq '(.UnprocessedItems.Products // []) | length' resp.json)
        echo "$f attempt $attempt: unprocessed=$left"
        [ "$left" -eq 0 ] && break
        jq '.UnprocessedItems' resp.json > pending.json
        sleep 2
    done
done

確認所有 50 個項目都存在。

aws dynamodb scan --table-name Products --select COUNT --query 'Count'

產生查詢內嵌和搜尋。使用您用於存放項目的相同模型和維度計數嵌入搜尋片語。

aws bedrock-runtime invoke-model \
    --model-id amazon.titan-embed-text-v2:0 \
    --body '{"inputText":"How can I make my desk more comfortable to work at","dimensions":1024,"normalize":true}' \
    --cli-binary-format raw-in-base64-out \
    --content-type application/json \
    --accept application/json \
    embedding-query.json

jq '[.embedding[]|{"N":(.|tostring)}]' embedding-query.json > query-vector.json

aws dynamodb search-vectors \
    --table-name Products \
    --index-name DescriptionIndex \
    --search-vector file://query-vector.json \
    --top-k 5 \
    --projection-expression "ProductId, Title" \
    --return-consumed-capacity TOTAL

查詢向量和預存向量必須來自相同的內嵌模型，且必須具有相同數量的維度。使用不同的模型或維度計數會產生無意義的結果或驗證錯誤。

讀取結果。DynamoDB 會傳回依相似性排序的結果，最相似的項目優先。每個結果都包含投影屬性Item和 Score。

{
    "SearchResults": [
        {
            "Item": {
                "ProductId": { "S": "p11" },
                "Title": { "S": "Ergonomic Mesh Office Chair" }
            },
            "Score": 0.6130197048187256
        },
        {
            "Item": {
                "ProductId": { "S": "p12" },
                "Title": { "S": "Sit Stand Desk Converter" }
            },
            "Score": 0.781868577003479
        },
        {
            "Item": {
                "ProductId": { "S": "p14" },
                "Title": { "S": "Under Desk Footrest" }
            },
            "Score": 0.816369354724884
        },
        {
            "Item": {
                "ProductId": { "S": "p13" },
                "Title": { "S": "Monitor Arm Mount" }
            },
            "Score": 0.8283305168151855
        },
        {
            "Item": {
                "ProductId": { "S": "p15" },
                "Title": { "S": "Wireless Split Keyboard" }
            },
            "Score": 0.8469693064689636
        }
    ],
    "ConsumedCapacity": {
        "VectorSearchRequestBytes": 31449.0
    }
}

分數取決於內嵌模型和確切的輸入文字，因此您的值會略有不同。重要的是已選取哪些項目，以及依何種順序選取。查詢不包含單字組、監視器或鍵盤，但搜尋會在目錄中的其他 45 個項目前面傳回所有五個桌面和辦公室產品。不會顯示來自咖啡、營地或寵物群組的任何內容。

嘗試其他查詢，以查看不同群組的相同行為。使用 重複上一個步驟，"Something to brew fresh coffee at home"而最上方的結果是濃縮咖啡製作器、傾倒在滴水器上，以及鮮奶。使用 重複此動作"Keeping my dog safe on walks"，而繫結和固定帶會先出現，接著是僅鬆散相關的項目，因為目錄只包含兩個緊密相符的產品。最後一個案例值得注意：搜尋一律會傳回您請求的項目數量，即使目錄不包含這麼多的良好相符項目。使用 Score值，而不是結果計數來判斷相符品質。

如何讀取分數取決於索引使用的距離函數：

此索引使用 COSINE，因此第一個結果的分數最低。使用與預存描述相同的文字搜尋，會先傳回分數為零或接近零的項目。