how to use multiple mode for RAG

[noday]

app supply a model list, user can select a chat model to chat. how to select model with model name in langchain4j.

[dosubot[bot]]

To use multiple models in a RAG (Retrieval-Augmented Generation) application with LangChain4j and allow the user to select a chat model by its name, you can create a factory or a registry that maps model names to their respective instances. Here is an example of how you can achieve this:

1. Define your models and register them with a name.
2. Create a method to select and use the model based on the user's choice.

Here's a sample implementation:

import java.util.HashMap;
import java.util.List;
import java.util.Map;
import dev.langchain4j.data.message.AiMessage;
import dev.langchain4j.memory.ChatMemory;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.model.chat.ChatLanguageModel;
import dev.langchain4j.chain.ConversationalRetrievalChain;
import dev.langchain4j.rag.content.retriever.ContentRetriever;

public class ChatModelRegistry {

    private final Map<String, ChatLanguageModel> models = new HashMap<>();

    public void registerModel(String name, ChatLanguageModel model) {
        models.put(name, model);
    }

    public ChatLanguageModel getModel(String name) {
        return models.get(name);
    }

    public String generateResponse(String modelName, String userMessage, ContentRetriever contentRetriever) {
        ChatLanguageModel model = getModel(modelName);
        if (model == null) {
            throw new IllegalArgumentException("Model not found: " + modelName);
        }
        ConversationalRetrievalChain chain = ConversationalRetrievalChain.builder()
                .chatLanguageModel(model)
                .chatMemory(MessageWindowChatMemory.withMaxMessages(10))
                .contentRetriever(contentRetriever)
                .build();
        return chain.execute(userMessage);
    }
}

// Example usage
public class Main {
    public static void main(String[] args) {
        ChatModelRegistry registry = new ChatModelRegistry();

        // Register models
        registry.registerModel("model1", new Model1());
        registry.registerModel("model2", new Model2());

        // User selects a model and sends a message
        String selectedModel = "model1";
        String userMessage = "Hello, how are you?";

        // Create a ContentRetriever instance (replace with actual implementation)
        ContentRetriever contentRetriever = new MyContentRetriever();

        // Generate response
        String response = registry.generateResponse(selectedModel, userMessage, contentRetriever);
        System.out.println("Response: " + response);
    }
}

// Example model implementations
class Model1 implements ChatLanguageModel {
    @Override
    public AiMessage generate(List<ChatMessage> messages) {
        // Implement model-specific logic here
        return new AiMessage("Response from Model1");
    }
}

class Model2 implements ChatLanguageModel {
    @Override
    public AiMessage generate(List<ChatMessage> messages) {
        // Implement model-specific logic here
        return new AiMessage("Response from Model2");
    }
}

// Example ContentRetriever implementation
class MyContentRetriever implements ContentRetriever {
    @Override
    public List<TextSegment> retrieve(String query) {
        // Implement content retrieval logic here
        return List.of(new TextSegment("Retrieved content based on query: " + query));
    }
}

In this example, ChatModelRegistry is used to register and retrieve models by name. The generateResponse method uses the ConversationalRetrievalChain class to maintain a memory of the conversation and generate responses using the selected model. The Main class demonstrates how to register models, create a ContentRetriever instance, and generate a response based on the user's selection ^[1][2][3][4].

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[langchain4j]

@noday please clarify what do you mean?

[noday]

I want to dynamically specify the corresponding streamingChatLanguageModel based on the modelName selected by the user during the conversation

[langchain4j]

This is not possible yet, so you have to create a new StreamingChatLanguageModel instance every time user changes the model.
It is planned to be supported here: #837