Quarkus LangChain4j Vertex Gemini AI Embedding does not have text-embedding-004:batchEmbedContents or gemini-embedding-001:batchEmbedContents

[semekita]

I am using the following dependency:

        <dependency>
            <groupId>io.quarkiverse.langchain4j</groupId>
            <artifactId>quarkus-langchain4j-vertex-ai-gemini</artifactId>
        </dependency> 

Here is some portion of my config:

quarkus.langchain4j.vertexai.gemini.embedding-model.model-id=gemini-embedding-001
quarkus.langchain4j.vertexai.gemini.embedding-model.log-requests=true
quarkus.langchain4j.vertexai.gemini.embedding-model.log-responses=true
quarkus.langchain4j.vertexai.gemini.embedding-model.timeout=300s
quarkus.langchain4j.vertexai.gemini.embedding-model.task-type=QUESTION_ANSWERING
quarkus.langchain4j.vertexai.gemini.base-url=https://us-central1-aiplatform.googleapis.com
quarkus.langchain4j.vertexai.gemini.location=us-central1
quarkus.langchain4j.vertexai.gemini.chat-model.model-id=gemini-2.5-pro
quarkus.langchain4j.vertexai.gemini.project-id=${GOOGLE_PROJECT_ID:myproject}

when start my code for embedding, I am getting following error below:

• method: POST
• url: https://us-central1-aiplatform.googleapis.com/v1/projects/myproject/locations/us-central1/publishers/google/models/text-embedding-004:batchEmbedContents

• The requested URL /v1/projects/myproject/locations/us-central1/publishers/google/models/text-embedding-004:batchEmbedContents was not found on this server. That?s all we know.

It does not matter what model I use, I am getting 404. Checking google documentation gemini-embedding-001 or text-embedding-004 don't have batchEmbedContents for embdding.

Please advise

[cescoffier]

\CC @geoand

[geoand]

Perhaps you can try quarkus-langchain4j-ai-gemini instead

[semekita]

I can't use quarkus-langchain4j-ai-gemini since that is not for enterprise. Did anyone can test or use quarkus-langchain4j-vertex-ai-gemini? There is quarkus-langchain4j-vertex-ai in quarkus it has right api calls but it does not have embedding model.

[geoand]

Can you show me what the code you are using looks like?

[sberyozkin]

Might be a restriction related to the

quarkus.langchain4j.vertexai.gemini.location=us-central1
quarkus.langchain4j.vertexai.gemini.chat-model.model-id=gemini-2.5-pro

combination, the location or model

[semekita]

Here is the class in quarkus being used to make call to google:
https://github.com/quarkiverse/quarkus-langchain4j/blob/main/model-providers/google/vertex-ai-gemini/runtime/src/main/java/io/quarkiverse/langchain4j/vertexai/runtime/gemini/VertxAiGeminiRestApi.java

[semekita]

Here is the code.

@ApplicationScoped
public class DocumentIngestionService {

   
    public void ingest(@Observes StartupEvent ev,
                       EmbeddingStore store, EmbeddingModel embeddingModel,
                       @ConfigProperty(name = "rag.location") Path documents) {

        System.out.print("Model >>>>>>>>>>>>>>>>> " + embeddingModel.toString());
        store.removeAll(); // cleanup the store to start fresh (just for demo purposes)
        List<Document> list = FileSystemDocumentLoader.loadDocumentsRecursively(documents);

        ingestDocument(list, store, embeddingModel);
       
        Log.info("Documents ingested successfully");
    }
    private void ingestDocument(List<Document> list, EmbeddingStore store, EmbeddingModel embeddingModel) {
         EmbeddingStoreIngestor ingestor = EmbeddingStoreIngestor.builder()
                .embeddingStore(store)
                .embeddingModel(embeddingModel)
                .documentSplitter(recursive(100, 25))
                .build();
        ingestor.ingest(list);
    }
}

Here is the link to quarkus code being used by the library https://github.com/quarkiverse/quarkus-langchain4j/blob/main/model-providers/google/vertex-ai-gemini/runtime/src/main/java/io/quarkiverse/langchain4j/vertexai/runtime/gemini/VertxAiGeminiRestApi.java

[geoand]

@sberyozkin @jmartisk I guess we shouldn't embedAll?

[semekita]

That was my reaction too it should not be embedAll.

for vertex gemini ai doc is here:
https://docs.cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings#googlegenaisdk_embeddings_docretrieval_with_txt-drest

for Gemini ai:
https://ai.google.dev/api/embeddings#v1beta.models.batchEmbedContents

[semekita]

@cescoffier @sberyozkin @geoand @jmartisk

Do you know the proper way to use vertex gemini ai since I can’t use gemini ai it is not for enterprise.
I would really appreciate if someone can help me.
I tried different properties but it didn’t work.
Location and project id are required.

[gastaldi]

Looks like discussion transfers only work inside the same org, maybe ir's easier to start a new one there

[geoand]

maybe ir's easier to start a new one there

Very likely so

[maestrus]

@geoand did you have some updates for this? Vertex ai acording documentation accepts this kind of endpoint
"https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/gemini-embedding-001:predict" and not https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/gemini-embedding-001:embedContent
Could you fix this please?
And this is the response
{
"predictions": [
{
"embeddings": {
"statistics": {
"truncated": false,
"token_count": 6
},
"values": [ ... ]
}
}
]
}
Thanks

[sberyozkin]

@maestrus Can you please create a bug issue in the Quarkus langchain4j ? So it is only predict vs embedContent, right ? Can you also consider a minor PR to update the path and throw an exception for the embedAll ?

Thanks

[sberyozkin]

Thanks @maestrus for opening quarkiverse/quarkus-langchain4j#2376, let me close this discussion