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docs: document how to programatically configure vertex embeddings to use a service account #1741

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Nov 15, 2024
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docs: document how to programatically configure vertex embeddings to use a service account #1741

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25 changes: 23 additions & 2 deletions ...src/main/antora/modules/ROOT/pages/api/embeddings/vertexai-embeddings-text.adoc
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Original file line number Diff line number Diff line change
Expand Up @@ -144,8 +144,8 @@ Next, create a `VertexAiTextEmbeddingModel` and use it for text generations:

[source,java]
----
VertexAiEmbeddigConnectionDetails connectionDetails =
VertexAiEmbeddigConnectionDetails.builder()
VertexAiEmbeddingConnectionDetails connectionDetails =
VertexAiEmbeddingConnectionDetails.builder()
.withProjectId(System.getenv(<VERTEX_AI_GEMINI_PROJECT_ID>))
.withLocation(System.getenv(<VERTEX_AI_GEMINI_LOCATION>))
.build();
Expand All @@ -160,3 +160,24 @@ EmbeddingResponse embeddingResponse = this.embeddingModel
.embedForResponse(List.of("Hello World", "World is big and salvation is near"));
----

=== Load credentials from a Google Service Account

To programmatically load the GoogleCredentials from a Service Account json file, you can use the following:

[source,java]
----
GoogleCredentials credentials = GoogleCredentials.fromStream(<INPUT_STREAM_TO_CREDENTIALS_JSON>)
.createScoped("https://www.googleapis.com/auth/cloud-platform");
credentials.refreshIfExpired();

VertexAiEmbeddingConnectionDetails connectionDetails =
VertexAiEmbeddingConnectionDetails.builder()
.withProjectId(System.getenv(<VERTEX_AI_GEMINI_PROJECT_ID>))
.withLocation(System.getenv(<VERTEX_AI_GEMINI_LOCATION>))
.withApiEndpoint(endpoint)
.withPredictionServiceSettings(
PredictionServiceSettings.newBuilder()
.setEndpoint(endpoint)
.setCredentialsProvider(FixedCredentialsProvider.create(credentials))
.build());
----