쿠키 기본 설정 선택

당사는 사이트와 서비스를 제공하는 데 필요한 필수 쿠키 및 유사한 도구를 사용합니다. 고객이 사이트를 어떻게 사용하는지 파악하고 개선할 수 있도록 성능 쿠키를 사용해 익명의 통계를 수집합니다. 필수 쿠키는 비활성화할 수 없지만 '사용자 지정' 또는 ‘거부’를 클릭하여 성능 쿠키를 거부할 수 있습니다.

사용자가 동의하는 경우 AWS와 승인된 제3자도 쿠키를 사용하여 유용한 사이트 기능을 제공하고, 사용자의 기본 설정을 기억하고, 관련 광고를 비롯한 관련 콘텐츠를 표시합니다. 필수가 아닌 모든 쿠키를 수락하거나 거부하려면 ‘수락’ 또는 ‘거부’를 클릭하세요. 더 자세한 내용을 선택하려면 ‘사용자 정의’를 클릭하세요.

.vectors.upsert algorithm

포커스 모드
.vectors.upsert algorithm - Neptune Analytics
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The .vectors.upsert algorithm is used to add a new embedding or update an existing one for a node.

.vectors.upsert  syntax

CALL neptune.algo.vectors.upsert( "a target node (required)", [the embedding to upsert for the target node (required)] ) YIELD node, embedding, success RETURN node, embedding, success

.vectors.upsert  input

  • a target node   (required)   –   type: Node or NodeId.

    The node for which you want to upsert an embedding.

  • an embedding   (required)   –   type: a list of floating-point values.

    The embedding that you want to upsert for the target node.

    If the node has an existing embedding, this must match the dimension of the existing one or an exception is thrown.

.vectors.upsert  outputs

If the target node already has an existing embedding then .vectors.upsert replaces it with the one supplied. Otherwise .vectors.upsert adds the supplied embedding for the target node.

  • node   –   The target node.

  • embedding   –   The embedding that was supplied to be upserted.

  • success   –   A Boolean value: true indicates that the upsert succeded, and false that it failed.

.vectors.upsert  query examples

CALL neptune.algo.vectors.upsert( "person933", [0.1, 0.2, 0.3, ..] ) YIELD node, embedding, success RETURN node, embedding, success
UNWIND [ {id: "933", embedding: [1,2,3,4]}, {id: "934", embedding: [-1,-2,-3,-4]} ] as entry MATCH (n:person) WHERE id(n)=entry.id WITH n, entry.embedding as embedding CALL neptune.algo.vectors.upsert(n, embedding) YIELD success RETURN n, embedding, success

Sample  .vectors.upsert  output

Here is an example of the output returned by .vectors.upsert when run against the sample Wikipedia dataset using the following query:

aws neptune-graph execute-query \ --graph-identifier ${graphIdentifier} \ --query-string "MATCH (n{\`~id\`:\"0\"}) CALL neptune.algo.vectors.get(n) YIELD embedding AS vector MATCH (m{`~id`: '1'}) CALL neptune.algo.vectors.upsert(m, vector) YIELD node, embedding, success RETURN node, embedding, success" --language open_cypher \ /tmp/out.txt { "results": [ { "node": { "~id": "1", "~entityType": "node", "~labels": [], "~properties": { "title": "24-hour clock", "views": 2450.62548828125, "wiki_id": 9985, "paragraph_id": 1, "url": "https://simple.wikipedia.org/wiki?curid=9985", "langs": 30, "text": "A time in the 24-hour clock is written in the form hours:minutes (for example\\, 01:23)\\, or hours:minutes:seconds (01:23:45). Numbers under 10 have a zero in front (called a leading zero); e.g. 09:07. Under the 24-hour clock system\\, the day begins at midnight\\, 00:00\\, and the last minute of the day begins at 23:59 and ends at 24:00\\, which is identical to 00:00 of the following day. 12:00 can only be mid-day. Midnight is called 24:00 and is used to mean the end of the day and 00:00 is used to mean the beginning of the day. For example\\, you would say \"Tuesday at 24:00\" and \"Wednesday at 00:00\" to mean exactly the same time." } }, "embedding": [ 0.07711287587881088, 0.3197174072265625, -0.2051590085029602, 0.6302579045295715, 0.032093219459056857, 0.200703963637352, 0.16665680706501008, -0.31295087933540347, 0.17575109004974366, 0.5308129191398621, -0.37528499960899355, 0.3338659405708313, -0.046272162348032, 0.07841536402702332, -0.3490406274795532, 0.27182886004447939, 0.3073517680168152, -0.08306130766868592, 0.5035958886146545, 0.254621684551239, -0.40407684445381167, 0.28878292441368105, -0.22588828206062318, -0.13185778260231019, -0.21559733152389527, 0.4900434613227844, 0.03866531699895859, 0.507415771484375, -0.3067346513271332, 0.10740984976291657, 0.08998646587133408, -0.2652775049209595, -0.28492602705955508, 0.33600345253944399, -0.27227747440338137, 0.3691731095314026, -0.2815995514392853, 0.0856710895895958, -0.13187488913536073, 0.4753035008907318, -0.2241700142621994, 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