LLMs streaming with AI Endpoints and LangChain4j

quarkus create app com.ovhcloud.examples.aiendpoints:quarkus-langchain4j-streaming \
    --extension='quarkus-langchain4j-mistral-ai,rest'

package com.ovhcloud.examples.aiendpoints.services;

import dev.langchain4j.service.SystemMessage;
import dev.langchain4j.service.UserMessage;
import io.quarkiverse.langchain4j.RegisterAiService;
import io.smallrye.mutiny.Multi;

@RegisterAiService
public interface ChatBotService {
  // Scope / context passed to the LLM
  @SystemMessage("You are a virtual, an AI assistant.")
  // Prompt (with detailed instructions and variable section) passed to the LLM
  @UserMessage("Answer as best possible to the following question: {question}. The answer must be in a style of a virtual assistant and add some emojis.")
  Multi<String> askAQuestion(String question);
}

### Global configurations
# Base URL for Mistral AI endpoints
quarkus.langchain4j.mistralai.base-url=${OVH_AI_ENDPOINTS_MODEL_URL}
# Activate or not the log during the request
quarkus.langchain4j.mistralai.log-requests=true
# Activate or not the log during the response
quarkus.langchain4j.mistralai.log-responses=true
# Delay before raising a timeout exception                   
quarkus.langchain4j.mistralai.timeout=60s   
# No key is needed
quarkus.langchain4j.mistralai.api-key=${OVH_AI_ENDPOINTS_ACCESS_TOKEN}
 
# Activate or not the Mistral AI embedding model                     
quarkus.langchain4j.mistralai.embedding-model.enabled=false
 
### Chat model configurations
# Activate or not the Mistral AI chat model
quarkus.langchain4j.mistralai.chat-model.enabled=true             
# Chat model name used
quarkus.langchain4j.mistralai.chat-model.model-name=${OVH_AI_ENDPOINTS_MODEL_NAME}
# Number of tokens to use
quarkus.langchain4j.mistralai.chat-model.max-tokens=1024

package com.ovhcloud.examples.aiendpoints;

import com.ovhcloud.examples.aiendpoints.services.ChatBotService;
import io.smallrye.mutiny.Multi;
import jakarta.inject.Inject;
import jakarta.ws.rs.POST;
import jakarta.ws.rs.Path;

@Path("/ovhcloud-ai")
public class AIEndpointsResource {
    // AI Service injection to use it later
    @Inject
    ChatBotService chatBotService;

    // ask resource exposition with POST method
    @Path("ask")
    @POST
    public Multi<String> ask(String question) {
        // Call the Mistral AI chat model
        return chatBotService.askAQuestion(question);
    }
}

curl http://localhost:8080/ovhcloud-ai/ask\?question\=%22Can%20you%20tell%20me%20what%20OVHcloud%20is%20and%20what%20kind%20of%20products%20it%20offers\?%22