Creating a complete Spring Boot AI application with a trained model, a vector database, and a Vaadin front-end is beyond the scope of this platform. However, I can provide you with a high-level outline of the necessary steps and some code snippets to help you get started.

1. Set up a Spring Boot project with necessary dependencies:

• Spring Data MongoDB
• Spring Web
• Vaadin
• Your preferred machine learning library (e.g., TensorFlow, PyTorch, or Deeplearning4j)

Add the following dependencies to your pom.xml:

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<dependencies>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-data-mongodb</artifactId>
    </dependency>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-web</artifactId>
    </dependency>
    <dependency>
        <groupId>com.vaadin</groupId>
        <artifactId>vaadin-spring-boot-starter</artifactId>
    </dependency>
    <!-- Add your machine learning library here -->
</dependencies>

2. Create your MongoDB repository and service:

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import org.springframework.data.mongodb.repository.MongoRepository;
import your.package.Client;

public interface ClientRepository extends MongoRepository<Client, String> {
}

import java.util.List;
import your.package.Client;

@Service
public class ClientService {
    @Autowired
    private ClientRepository clientRepository;

    public List<Client> getAllClients() {
        return clientRepository.findAll();
    }

    // Add other methods to fetch data from the database
}

3. Set up your machine learning model and vector database:

You'll need to create a separate class or component to handle your machine learning model and vector database. This will depend on the specific library you're using.

4. Create a Vaadin UI:

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import com.vaadin.flow.component.button.Button;
import com.vaadin.flow.component.grid.Grid;
import com.vaadin.flow.component.orderedlayout.VerticalLayout;
import com.vaadin.flow.router.Route;
import your.package.Client;
import your.package.ClientService;

@Route("")
public class MainView extends VerticalLayout {
    private ClientService clientService;
    private Grid<Client> grid;

    public MainView(ClientService clientService) {
        this.clientService = clientService;
        buildLayout();
    }

    private void buildLayout() {
        grid = new Grid<>(Client.class);
        grid.setColumns("name", "email");
        grid.setItems(clientService.getAllClients());

        add(grid);

        Button refreshButton = new Button("Refresh", event -> {
            grid.setItems(clientService.getAllClients());
        });
        add(refreshButton);
    }
}

5. Create a Spring Boot application:

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import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import your.package.ClientService;

@SpringBootApplication
public class Application {
    public static void main(String[] args) {
        SpringApplication.run(Application.class, args);
    }

    @Bean
    public ClientService clientService() {
        // Initialize your machine learning model and vector database here
        return new ClientService();
    }
}

This is a very basic outline, and you'll need to customize it based on your specific requirements. You'll also need to handle the conversion of your MongoDB data into a vector database and integrate it with your machine learning model.

Mistral AI provides several models, but for converting MongoDB data into a vector database, you might want to consider using the "Sentence Transformers" or "Universal Sentence Encoder" models. These models can convert text data into dense vectors, which can then be stored in a vector database.

For the vector database, you can use libraries like Milvus, Faiss, or Annoy. In this example, I'll use Milvus, which is an open-source vector database built for AI applications.

Here's a basic outline of how you can create a Spring Java service to map and ingest data from MongoDB to a Milvus vector database:

1. Add Milvus dependency to your pom.xml:

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<dependency>
    <groupId>io.milvus</groupId>
    <artifactId>milvus-sdk-java</artifactId>
    <version>2.0.0-beta.1</version>
</dependency>

2. Create a Milvus service:

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import io.milvus.param.R;
import io.milvus.param.collection.AddCollectionParam;
import io.milvus.param.collection.CreateIndexParam;
import io.milvus.param.entity.*;
import io.milvus.param.metric.DistanceMetric;
import io.milvus.param.metric.L2Distance;
import io.milvus.param.partition.AutoID;
import io.milvus.param.partition.PartitionTag;
import io.milvus.response.collection.CreateCollectionResponse;
import io.milvus.response.collection.CreateIndexResponse;
import io.milvus.response.entity.InsertEntityResponse;
import org.springframework.stereotype.Service;

@Service
public class MilvusService {
    // Initialize Milvus client here

    public void createCollection(String collectionName, int dim) {
        AddCollectionParam param = AddCollectionParam.newBuilder()
                .withCollectionName(collectionName)
                .withDim(dim)
                .withDescription("MongoDB data collection")
                .withMetricType(DistanceMetric.L2)
                .withIndexType(R.IndexType.IVF_FLAT)
                .withIndexParam(new CreateIndexParam.IndexIVFFlatParam().withNlist(1024))
                .build();
        CreateCollectionResponse response = milvusClient.createCollection(param);
    }

    public void insertEntities(String collectionName, List<Entity> entities) {
        for (Entity entity : entities) {
            InsertEntityResponse response = milvusClient.insert(collectionName, entity);
        }
    }
}

3. Update your ClientService to convert MongoDB data into vectors and ingest them into Milvus:

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import io.milvus.param.entity.Entity;
import your.package.Client;

@Service
public class ClientService {
    // ...

    @Autowired
    private MilvusService milvusService;

    // Assuming you have a method to convert Client to vector
    public float[] convertClientToVector(Client client) {
        // Your conversion logic here
    }

    public void ingestClientsToMilvus(List<Client> clients) {
        String collectionName = "mongo_data";
        int dim = 128; // Dimension of your vector

        milvusService.createCollection(collectionName, dim);

        List<Entity> entities = new ArrayList<>();
        for (Client client : clients) {
            float[] vector = convertClientToVector(client);
            Entity entity = new Entity(1L, new DataVector(vector));
            entities.add(entity);
        }

        milvusService.insertEntities(collectionName, entities);
    }
}

Remember to replace the convertClientToVector method with your actual conversion logic. You might need to use a natural language processing library to convert text data into vectors.

This is a very basic outline, and you'll need to customize it based on your specific requirements. You'll also need to handle the initialization and cleanup of the Milvus client.

To run the Mistral model locally, you'll need to install the necessary dependencies and run the model using the provided scripts or code. The exact process will depend on the specific model you're using.

The Universal Sentence Encoder (USE) is a TensorFlow model that encodes text into high-dimensional vectors. You can use it in your Spring Boot application to convert text data from MongoDB into vectors.

Here's how you can download, install, and use the Universal Sentence Encoder in your Spring Boot application:

1. Download the TensorFlow Java library:

Add the following dependency to your pom.xml:

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<dependency>
    <groupId>org.tensorflow</groupId>
    <artifactId>tensorflow</artifactId>
    <version>2.7.0</version>
</dependency>

2. Download the Universal Sentence Encoder model:

You can download the model from the TensorFlow Hub. The model is available in two versions: 4 and 5. Version 5 is larger and more accurate, but it also takes more resources to run.

Here's how you can download the model using the TensorFlow Java API:

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import org.tensorflow.framework.Graph;
import org.tensorflow.framework.Tensor;
import org.tensorflow.framework.TensorInfo;
import org.tensorflow.op.Ops;
import org.tensorflow.op.core.ConstantOp;
import org.tensorflow.op.core.Placeholder;
import org.tensorflow.op.core.Session;
import org.tensorflow.op.core.SessionRunner;
import org.tensorflow.op.core.TensorShape;
import org.tensorflow.op.core.TensorType;
import org.tensorflow.op.lang.Const;
import org.tensorflow.op.lang.Placeholder;
import org.tensorflow.op.strings.EncodeString;
import org.tensorflow.op.strings.StringSplit;
import org.tensorflow.op.strings.StringToNumber;
import org.tensorflow.proto.framework.GraphDef;
import org.tensorflow.proto.framework.NodeDef;
import org.tensorflow.proto.framework.TensorProto;
import org.tensorflow.types.TString;

import java.io.FileInputStream;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.List;

public class USEService {
    private Session session;

    public void loadModel(String modelPath) throws IOException {
        Path path = Paths.get(modelPath);
        byte[] modelBytes = Files.readAllBytes(path);
        GraphDef graphDef = GraphDef.parseFrom(modelBytes);
        Graph graph = new Graph();
        graph.importGraphDef(graphDef);
        session = new Session(graph);
    }
}

Replace modelPath with the path to the downloaded model file.

3. Use the model to encode text:

Here's how you can use the model to encode text into vectors:

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public List<float[]> encodeText(String[] texts) {
    Ops ops = Ops.create(session.graph());
    Placeholder inputPlaceholder = Placeholder.create(ops, "input_text", TensorType.string());
    Tensor inputTensor = inputPlaceholder.feed(ops, Const.create(ops, texts));
    EncodeString encodeOp = EncodeString.create(ops, inputTensor);
    Tensor encodedTensor = encodeOp.output(0);
    StringSplit splitOp = StringSplit.create(ops, encodedTensor, Const.create(ops, new TString(" ")));
    Tensor splitTensor = splitOp.output(0);
    StringToNumber stringToNumberOp = StringToNumber.create(ops, splitTensor, Const.create(ops, 0));
    Tensor floatTensor = stringToNumberOp.output(0);
    float[][] floats = floatTensor.floatValue();
    List<float[]> vectors = new ArrayList<>();
    for (float[] vector : floats) {
        vectors.add(vector);
    }
    return vectors;
}

This method takes an array of strings and returns a list of vectors.

4. Use the model in your ClientService:

You can now use the USEService in your ClientService to convert MongoDB data into vectors:

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@Service
public class ClientService {
    // ...

    @Autowired
    private USEService useService;

    public void ingestClientsToMilvus(List<Client> clients) {
        // ...

        String[] texts = clients.stream().map(Client::getText).toArray(String[]::new);
        List<float[]> vectors = useService.encodeText(texts);

        // ...
    }
}

This is a very basic outline, and you'll need to customize it based on your specific requirements. You'll also need to handle the initialization and cleanup of the TensorFlow session.

Remember to replace Client::getText with the actual method to get the text data from your Client objects.

Also, note that the Universal Sentence Encoder model is quite large, and it might take a while to load and use it. You might want to consider loading it once and reusing the same session for all requests.

Finally, make sure you have the necessary TensorFlow and Java runtime dependencies installed on your machine. You can find more information about this in the TensorFlow Java documentation.