quarkus:
  langchain4j:
    openai:
      api-key: #PUT_YOUR_TOKEN_HERE
      base-url: https://inference-llama33-70b-maas.apps.ai-2.kvant.cloud/v1/
      timeout: 60s
      #https://docs.langchain4j.dev/tutorials/model-parameters/
      chat-model:
        model-name: inference-llama33-70b
        temperature: 1.0
        #max-tokens: 1000
        #frequency-penalty: 2
        frequency-penalty: 0
        log-requests: true
        log-responses: true
      embedding-model:
        log-responses: true
        log-requests: true
    #embedding-model:
    #  provider: dev.langchain4j.model.embedding.onnx.bgesmallenq.BgeSmallEnQuantizedEmbeddingModel
    pgvector:
      dimension: 1024
#This configuration takes precedence over the embedding-model.provider one.
l4j:
  custom-embedding-model:
    #This is 1024 dimension model. pgvector must be configured accordingly
    model-name: inference-multilingual-e5l
    base-url: https://inference-multilingual-e5l-maas.apps.ai-2.kvant.cloud/v1
    api-key: #PUT_YOUR_TOKEN_HERE
    log-requests: false
    log-responses: false
    #Not supported by current implementation
    #dimensions: ${quarkus.langchain4j.pgvector.dimension}
rag:
  location: src/main/resources/rag