MistralAI

Project setup

To install langchain4j to your project, add the following dependency:

For Maven project pom.xml

<dependency>
    <groupId>dev.langchain4j</groupId>
    <artifactId>langchain4j</artifactId>
    <version>1.0.0-rc1</version>
</dependency>

<dependency>
    <groupId>dev.langchain4j</groupId>
    <artifactId>langchain4j-mistral-ai</artifactId>
    <version>1.0.0-beta4</version>
</dependency>

For Gradle project build.gradle

implementation 'dev.langchain4j:langchain4j:1.0.0-beta4'
implementation 'dev.langchain4j:langchain4j-mistral-ai:1.0.0-beta4'

API Key setup

Add your MistralAI API key to your project, you can create a class ApiKeys.java with the following code

public class ApiKeys {
    public static final String MISTRALAI_API_KEY = System.getenv("MISTRAL_AI_API_KEY");
}

Don't forget set your API key as an environment variable.

export MISTRAL_AI_API_KEY=your-api-key #For Unix OS based
SET MISTRAL_AI_API_KEY=your-api-key #For Windows OS

More details on how to get your MistralAI API key can be found here

Model Selection

You can use MistralAiChatModelName.class enum class to found appropriate model names for your use case. MistralAI updated a new selection and classification of models according to performance and cost trade-offs.

Model name	Deployment or available from	Description
open-mistral-7b	- Mistral AI La Plateforme. - Cloud platforms (Azure, AWS, GCP). - Hugging Face. - Self-hosted (On-premise, IaaS, docker, local).	OpenSource The first dense model released by Mistral AI, perfect for experimentation, customization, and quick iteration. Max tokens 32K Java Enum `MistralAiChatModelName.OPEN_MISTRAL_7B`
open-mixtral-8x7b	- Mistral AI La Plateforme. - Cloud platforms (Azure, AWS, GCP). - Hugging Face. - Self-hosted (On-premise, IaaS, docker, local).	OpenSource Ideal to handle multi-languages operations, code generationand fine-tuned. Excellent cost/performance trade-offs. Max tokens 32K Java Enum `MistralAiChatModelName.OPEN_MIXTRAL_8x7B`
open-mixtral-8x22b	- Mistral AI La Plateforme. - Cloud platforms (Azure, AWS, GCP). - Hugging Face. - Self-hosted (On-premise, IaaS, docker, local).	OpenSource It has all Mixtral-8x7B capabilities plus strong maths and coding natively capable of function calling Max tokens 64K. Java Enum `MistralAiChatModelName.OPEN_MIXTRAL_8X22B`
mistral-small-latest	- Mistral AI La Plateforme. - Cloud platforms (Azure, AWS, GCP).	Commercial Suitable for simple tasks that one can do in bulk (Classification, Customer Support, or Text Generation). Max tokens 32K Java Enum `MistralAiChatModelName.MISTRAL_SMALL_LATEST`
mistral-medium-latest	- Mistral AI La Plateforme. - Cloud platforms (Azure, AWS, GCP).	Commercial Ideal for intermediate tasks that require moderate reasoning (Data extraction, Summarizing, Writing emails, Writing descriptions. Max tokens 32K Java Enum `MistralAiChatModelName.MISTRAL_MEDIUM_LATEST`
mistral-large-latest	- Mistral AI La Plateforme. - Cloud platforms (Azure, AWS, GCP).	Commercial Ideal for complex tasks that require large reasoning capabilities or are highly specialized (Text Generation, Code Generation, RAG, or Agents). Max tokens 32K Java Enum `MistralAiChatModelName.MISTRAL_LARGE_LATEST`
mistral-embed	- Mistral AI La Plateforme. - Cloud platforms (Azure, AWS, GCP).	Commercial Converts text into numerical vectors of embeddings in 1024 dimensions. Embedding models enable retrieval and RAG applications. Max tokens 8K Java Enum `MistralAiEmbeddingModelName.MISTRAL_EMBED`

@Deprecated models:

mistral-tiny (@Deprecated)
mistral-small (@Deprecated)
mistral-medium (@Deprecated)

You can find more detail and types of use cases with their respective Mistral model here

Chat Completion

The chat models allow you to generate human-like responses with a model fined-tuned on conversational data.

Synchronous

Create a class and add the following code.

import dev.langchain4j.model.chat.ChatModel;
import dev.langchain4j.model.mistralai.MistralAiChatModel;

public class HelloWorld {
    public static void main(String[] args) {
        ChatModel model = MistralAiChatModel.builder()
                .apiKey(ApiKeys.MISTRALAI_API_KEY)
                .modelName(MistralAiChatModelName.MISTRAL_SMALL_LATEST)
                .build();

        String response = model.chat("Say 'Hello World'");
        System.out.println(response);
    }
}

Running the program will generate a variant of the following output

Hello World! How can I assist you today?

Streaming

Create a class and add the following code.

import dev.langchain4j.data.message.AiMessage;
import dev.langchain4j.model.chat.response.StreamingChatResponseHandler;
import dev.langchain4j.model.mistralai.MistralAiStreamingChatModel;
import dev.langchain4j.model.output.Response;

import java.util.concurrent.CompletableFuture;

public class HelloWorld {
    public static void main(String[] args) {
        MistralAiStreamingChatModel model = MistralAiStreamingChatModel.builder()
                .apiKey(ApiKeys.MISTRALAI_API_KEY)
                .modelName(MistralAiChatModelName.MISTRAL_SMALL_LATEST)
                .build();

        CompletableFuture<ChatResponse> futureResponse = new CompletableFuture<>();         
        model.chat("Tell me a joke about Java", new StreamingChatResponseHandler() {
            
            @Override
            public void onPartialResponse(String partialResponse) {
                System.out.print(partialResponse);
            }

            @Override
            public void onCompleteResponse(ChatResponse completeResponse) {
                futureResponse.complete(completeResponse);
            }

            @Override
            public void onError(Throwable error) {
                futureResponse.completeExceptionally(error);
            }    
        });

        futureResponse.join();
    }
}

You will receive each chunk of text (token) as it is generated by the LLM on the onPartialResponse method.

You can see that output below is streamed in real-time.

"Why do Java developers wear glasses? Because they can't C#"

Of course, you can combine MistralAI chat completion with other features like Set Model Parameters and Chat Memory to get more accurate responses.

In Chat Memory you will learn how to pass along your chat history, so the LLM knows what has been said before. If you don't pass the chat history, like in this simple example, the LLM will not know what has been said before, so it won't be able to correctly answer the second question ('What did I just ask?').

A lot of parameters are set behind the scenes, such as timeout, model type and model parameters. In Set Model Parameters you will learn how to set these parameters explicitly.

Function Calling

Function calling allows Mistral chat models (synchronous and streaming) to connect to external tools. For example, you can call a Tool to get the payment transaction status as shown in the Mistral AI function calling tutorial.

What are the supported mistral models?

note

Currently, function calling is available for the following models:

Mistral Small MistralAiChatModelName.MISTRAL_SMALL_LATEST
Mistral Large MistralAiChatModelName.MISTRAL_LARGE_LATEST
Mixtral 8x22B MistralAiChatModelName.OPEN_MIXTRAL_8X22B

1. Define a `Tool` class and how get the payment data

Let's assume you have a dataset of payment transaction like this. In real applications you should inject a database source or REST API client to get the data.

import java.util.*;

public class PaymentTransactionTool {

   private final Map<String, List<String>> paymentData = Map.of(
            "transaction_id", List.of("T1001", "T1002", "T1003", "T1004", "T1005"),
            "customer_id", List.of("C001", "C002", "C003", "C002", "C001"),
            "payment_amount", List.of("125.50", "89.99", "120.00", "54.30", "210.20"),
            "payment_date", List.of("2021-10-05", "2021-10-06", "2021-10-07", "2021-10-05", "2021-10-08"),
            "payment_status", List.of("Paid", "Unpaid", "Paid", "Paid", "Pending"));
   
    ...
}

Next, let's define two methods retrievePaymentStatus and retrievePaymentDate to get the payment status and payment date from the Tool class.

// Tool to be executed to get payment status
@Tool("Get payment status of a transaction") // function description
String retrievePaymentStatus(@P("Transaction id to search payment data") String transactionId) {
    return getPaymentData(transactionId, "payment_status");
}

// Tool to be executed to get payment date
@Tool("Get payment date of a transaction") // function description
String retrievePaymentDate(@P("Transaction id to search payment data") String transactionId) {
   return getPaymentData(transactionId, "payment_date");
}

private String getPaymentData(String transactionId, String data) {
    List<String> transactionIds = paymentData.get("transaction_id");
    List<String> paymentData = paymentData.get(data);

    int index = transactionIds.indexOf(transactionId);
    if (index != -1) {
        return paymentData.get(index);
    } else {
        return "Transaction ID not found";
    }
}

It uses a @Tool annotation to define the function description and @P annotation to define the parameter description of the package dev.langchain4j.agent.tool.*. More info here

2. Define an interface as an `agent` to send chat messages.

Create an interface PaymentTransactionAgent.

import dev.langchain4j.service.SystemMessage;

interface PaymentTransactionAgent {
    @SystemMessage({
            "You are a payment transaction support agent.",
            "You MUST use the payment transaction tool to search the payment transaction data.",
            "If there a date convert it in a human readable format."
    })
    String chat(String userMessage);
}

3. Define a `main` application class to chat with the MistralAI chat model

import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.model.chat.ChatModel;
import dev.langchain4j.model.mistralai.MistralAiChatModel;
import dev.langchain4j.model.mistralai.MistralAiChatModelName;
import dev.langchain4j.service.AiServices;

public class PaymentDataAssistantApp {

    ChatModel mistralAiModel = MistralAiChatModel.builder()
            .apiKey(System.getenv("MISTRAL_AI_API_KEY")) // Please use your own Mistral AI API key
            .modelName(MistralAiChatModelName.MISTRAL_LARGE_LATEST) // Also you can use MistralAiChatModelName.OPEN_MIXTRAL_8X22B as open source model
            .logRequests(true)
            .logResponses(true)
            .build();
    
    public static void main(String[] args) {
        // STEP 1: User specify tools and query
        PaymentTransactionTool paymentTool = new PaymentTransactionTool();
        String userMessage = "What is the status and the payment date of transaction T1005?";

        // STEP 2: User asks the agent and AiServices call to the functions
        PaymentTransactionAgent agent = AiServices.builder(PaymentTransactionAgent.class)
                .chatModel(mistralAiModel)
                .tools(paymentTool)
                .chatMemory(MessageWindowChatMemory.withMaxMessages(10))
                .build();
        
        // STEP 3: User gets the final response from the agent
        String answer = agent.chat(userMessage);
        System.out.println(answer);
    }
}

and expect an answer like this:

The status of transaction T1005 is Pending. The payment date is October 8, 2021.

JSON mode

You can also use the JSON mode to get the response in JSON format. To do this, you need to set the responseFormat parameter to ResponseFormat.JSON in the MistralAiChatModel builder OR MistralAiStreamingChatModel builder.

Syncronous example:

ChatModel model = MistralAiChatModel.builder()
                .apiKey(System.getenv("MISTRAL_AI_API_KEY")) // Please use your own Mistral AI API key
                .responseFormat(ResponseFormat.JSON)
                .build();

String userMessage = "Return JSON with two fields: transactionId and status with the values T123 and paid.";
String json = model.chat(userMessage);

System.out.println(json); // {"transactionId":"T123","status":"paid"}

Streaming example:

StreamingChatModel streamingModel = MistralAiStreamingChatModel.builder()
                .apiKey(System.getenv("MISTRAL_AI_API_KEY")) // Please use your own Mistral AI API key
                .responseFormat(MistralAiResponseFormatType.JSON_OBJECT)
                .build();

String userMessage = "Return JSON with two fields: transactionId and status with the values T123 and paid.";

CompletableFuture<ChatResponse> futureResponse = new CompletableFuture<>();

streamingModel.chat(userMessage, new StreamingChatResponseHandler() {

    @Override
    public void onPartialResponse(String partialResponse) {
        System.out.print(partialResponse);
    }

    @Override
    public void onCompleteResponse(ChatResponse completeResponse) {
        futureResponse.complete(completeResponse);
    }

    @Override
    public void onError(Throwable error) {
        futureResponse.completeExceptionally(error);
    }
});

String json = futureResponse.get().content().text();

System.out.println(json); // {"transactionId":"T123","status":"paid"}

Structured Outputs

Structured Outputs ensure that a model's responses adhere to a JSON schema.

note

The documentation for using Structured Outputs in LangChain4j is available here, and in the section below you will find MistralAI-specific information.

If desired, the model may be configured with a default JSON Schema that will be used as fallback if no schema is provided in the request.

ChatModel model = MistralAiChatModel.builder()
        .apiKey(System.getenv("MISTRAL_AI_API_KEY"))
        .modelName(MISTRAL_SMALL_LATEST)
        .supportedCapabilities(Set.of(Capability.RESPONSE_FORMAT_JSON_SCHEMA)) // Enable structured outputs
        .responseFormat(ResponseFormat.builder() // Set the fallback JSON Schema (optional)
                .type(ResponseFormatType.JSON)
                .jsonSchema(JsonSchema.builder().rootElement(JsonObjectSchema.builder()
                                .addProperty("name", JsonStringSchema.builder().build())
                                .addProperty("capital", JsonStringSchema.builder().build())
                                .addProperty(
                                        "languages",
                                        JsonArraySchema.builder()
                                                .items(JsonStringSchema.builder().build())
                                                .build())
                                .required("name", "capital", "languages")
                                .build())
                        .build())
                .build())
        .build();

Guardrailing

Guardrails are a way to limit the behavior of the model to prevent it from generating harmful or unwanted content. You can set optionally safePrompt parameter in the MistralAiChatModel builder or MistralAiStreamingChatModel builder.

Syncronous example:

ChatModel model = MistralAiChatModel.builder()
                .apiKey(System.getenv("MISTRAL_AI_API_KEY"))
                .safePrompt(true)
                .build();

String userMessage = "What is the best French cheese?";
String response = model.chat(userMessage);

Streaming example:

StreamingChatModel streamingModel = MistralAiStreamingChatModel.builder()
                .apiKey(System.getenv("MISTRAL_AI_API_KEY"))
                .safePrompt(true)
                .build();

String userMessage = "What is the best French cheese?";

CompletableFuture<ChatResponse> futureResponse = new CompletableFuture<>();

streamingModel.chat(userMessage, new StreamingChatResponseHandler() {
    
    @Override
    public void onPartialResponse(String partialResponse) {
        System.out.print(partialResponse);
    }

    @Override
    public void onCompleteResponse(ChatResponse completeResponse) {
        futureResponse.complete(completeResponse);
    }

    @Override
    public void onError(Throwable error) {
        futureResponse.completeExceptionally(error);
    }
});

futureResponse.join();

Toggling the safe prompt will prepend your messages with the following @SystemMessage:

Always assist with care, respect, and truth. Respond with utmost utility yet securely. Avoid harmful, unethical, prejudiced, or negative content. Ensure replies promote fairness and positivity.

Creating `MistralAiModerationModel`

Plain Java

ModerationModel model = new MistralAiModerationModel.Builder()
    .apiKey(System.getenv("MISTRAL_AI_API_KEY"))
    .modelName("mistral-moderation-latest")
    .logRequests(true)
    .logResponses(false)
    .build();

Moderation moderation = model.moderate("I want to kill them.").content();

Examples

Mistral AI Examples

Project setup​

API Key setup​

Model Selection​

Chat Completion​

Synchronous​

Streaming​

Function Calling​

1. Define a Tool class and how get the payment data​

2. Define an interface as an agent to send chat messages.​

3. Define a main application class to chat with the MistralAI chat model​

JSON mode​

Structured Outputs​

Guardrailing​

Creating MistralAiModerationModel​

Plain Java​

Examples​