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Aug 20, 2025
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Put examples with OpenAI tokens under testing #1362

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14 changes: 7 additions & 7 deletions modules/ROOT/pages/genai-integrations.adoc
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Original file line number Diff line number Diff line change
Expand Up @@ -92,12 +92,12 @@ The embeddings are stored as properties on nodes or relationships with the type
====

.Create an embedding property for the Godfather
[source,cypher,role=test-skip]
[source,cypher]
----
MATCH (m:Movie {title:'Godfather, The'})
WHERE m.plot IS NOT NULL AND m.title IS NOT NULL
WITH m, m.title || ' ' || m.plot AS titleAndPlot // <1>
WITH m, genai.vector.encode(titleAndPlot, 'OpenAI', { token: $token }) AS propertyVector // <2>
WITH m, genai.vector.encode(titleAndPlot, 'OpenAI', { token: $openaiToken }) AS propertyVector // <2>
CALL db.create.setNodeVectorProperty(m, 'embedding', propertyVector) // <3>
RETURN m.embedding AS embedding
----
Expand Down Expand Up @@ -155,14 +155,14 @@ Each returned row contains the following columns:
.Create embeddings from a limited number of properties and store them
====

[source, cypher, role=test-skip]
[source, cypher]
----
MATCH (m:Movie WHERE m.plot IS NOT NULL)
WITH m
LIMIT 20
WITH collect(m) AS moviesList // <1>
WITH moviesList, [movie IN moviesList | movie.title || ': ' || movie.plot] AS batch // <2>
CALL genai.vector.encodeBatch(batch, 'OpenAI', { token: $token }) YIELD index, vector
CALL genai.vector.encodeBatch(batch, 'OpenAI', { token: $openaiToken }) YIELD index, vector
WITH moviesList, index, vector
CALL db.create.setNodeVectorProperty(moviesList[index], 'embedding', vector) // <3>
----
Expand All @@ -174,7 +174,7 @@ CALL db.create.setNodeVectorProperty(moviesList[index], 'embedding', vector) //

.Create embeddings from a large number of properties and store them
====
[source, cypher, role=test-skip]
[source, cypher]
----
MATCH (m:Movie WHERE m.plot IS NOT NULL)
WITH collect(m) AS moviesList, // <1>
Expand All @@ -183,9 +183,9 @@ WITH collect(m) AS moviesList, // <1>
UNWIND range(0, total-1, batchSize) AS batchStart // <3>
CALL (moviesList, batchStart, batchSize) { // <4>
WITH [movie IN moviesList[batchStart .. batchStart + batchSize] | movie.title || ': ' || movie.plot] AS batch // <5>
CALL genai.vector.encodeBatch(batch, 'OpenAI', { token: $token }) YIELD index, vector
CALL genai.vector.encodeBatch(batch, 'OpenAI', { token: $openaiToken }) YIELD index, vector
CALL db.create.setNodeVectorProperty(moviesList[batchStart + index], 'embedding', vector) // <6>
} IN CONCURRENT TRANSACTIONS OF 1 ROW <7>
} IN CONCURRENT TRANSACTIONS OF 1 ROW // <7>
----

<1> xref:functions/aggregating.adoc#functions-collect[Collect] all returned `Movie` nodes into a `LIST<NODE>`.
Expand Down