Ollama Chat

With Ollama you can run various Large Language Models (LLMs) locally and generate text from them. Spring AI supports the Ollama text generation with OllamaChatModel.


You first need to run Ollama on your local machine. Refer to the official Ollama project README to get started running models on your local machine.

installing ollama run llama3 will download a 4.7GB model artifact.

Add Repositories and BOM

Spring AI artifacts are published in Spring Milestone and Snapshot repositories. Refer to the Repositories section to add these repositories to your build system.

To help with dependency management, Spring AI provides a BOM (bill of materials) to ensure that a consistent version of Spring AI is used throughout the entire project. Refer to the Dependency Management section to add the Spring AI BOM to your build system.


Spring AI provides Spring Boot auto-configuration for the Ollama Chat Client. To enable it add the following dependency to your project’s Maven pom.xml file:


or to your Gradle build.gradle build file.

dependencies {
    implementation 'org.springframework.ai:spring-ai-ollama-spring-boot-starter'
Refer to the Dependency Management section to add the Spring AI BOM to your build file.

Chat Properties

The prefix spring.ai.ollama is the property prefix to configure the connection to Ollama

Property Description Default


Base URL where Ollama API server is running.


The prefix spring.ai.ollama.chat.options is the property prefix that configures the Ollama chat model . It includes the Ollama request (advanced) parameters such as the model, keep-alive, and format as well as the Ollama model options properties.

Here are the advanced request parameter for the Ollama chat model:

Property Description Default


Enable Ollama chat model.



The name of the supported model to use.



The format to return a response in. Currently the only accepted value is json



Controls how long the model will stay loaded into memory following the request


The remaining options properties are based on the Ollama Valid Parameters and Values and Ollama Types. The default values are based on: Ollama type defaults.





Whether to use NUMA.



Sets the size of the context window used to generate the next token.






The number of GQA groups in the transformer layer. Required for some models, for example, it is 8 for llama2:70b.



The number of layers to send to the GPU(s). On macOS it defaults to 1 to enable metal support, 0 to disable. 1 here indicates that NumGPU should be set dynamically
























Sets the number of threads to use during computation. By default, Ollama will detect this for optimal performance. It is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores). 0 = let the runtime decide






Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt.



Maximum number of tokens to predict when generating text. (-1 = infinite generation, -2 = fill context)



Reduces the probability of generating nonsense. A higher value (e.g., 100) will give more diverse answers, while a lower value (e.g., 10) will be more conservative.



Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text.



Tail-free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting.






Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)



The temperature of the model. Increasing the temperature will make the model answer more creatively.



Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient.









Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)



Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text.



Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive.






Sets the stop sequences to use. When this pattern is encountered the LLM will stop generating text and return. Multiple stop patterns may be set by specifying multiple separate stop parameters in a modelfile.


All properties prefixed with spring.ai.ollama.chat.options can be overridden at runtime by adding a request specific Runtime Options to the Prompt call.

Runtime Options

The OllamaOptions.java provides model configurations, such as the model to use, the temperature, etc.

On start-up, the default options can be configured with the OllamaChatModel(api, options) constructor or the spring.ai.ollama.chat.options.* properties.

At run-time you can override the default options by adding new, request specific, options to the Prompt call. For example to override the default model and temperature for a specific request:

ChatResponse response = chatModel.call(
    new Prompt(
        "Generate the names of 5 famous pirates.",
In addition to the model specific OllamaOptions you can use a portable ChatOptions instance, created with the ChatOptionsBuilder#builder().


Multimodality refers to a model’s ability to simultaneously understand and process information from various sources, including text, images, audio, and other data formats.

Presently, the LLaVa and bakllava Ollama models offer multimodal support. For further details, refer to the LLaVA: Large Language and Vision Assistant.

The Ollama Message API provides an "images" parameter to incorporate a list of base64-encoded images with the message.

Spring AI’s Message interface facilitates multimodal AI models by introducing the Media type. This type encompasses data and details regarding media attachments in messages, utilizing Spring’s org.springframework.util.MimeType and a java.lang.Object for the raw media data.

Below is a straightforward code example excerpted from OllamaChatModelMultimodalIT.java, illustrating the fusion of user text with an image.

byte[] imageData = new ClassPathResource("/multimodal.test.png").getContentAsByteArray();

var userMessage = new UserMessage("Explain what do you see on this picture?",
        List.of(new Media(MimeTypeUtils.IMAGE_PNG, imageData)));

ChatResponse response = chatModel.call(
    new Prompt(List.of(userMessage), OllamaOptions.create().withModel("llava")));


It takes as an input the multimodal.test.png image:

Multimodal Test Image

along with the text message "Explain what do you see on this picture?", and generates a response like this:

The image shows a small metal basket filled with ripe bananas and red apples. The basket is placed on a surface,
which appears to be a table or countertop, as there's a hint of what seems like a kitchen cabinet or drawer in
the background. There's also a gold-colored ring visible behind the basket, which could indicate that this
photo was taken in an area with metallic decorations or fixtures. The overall setting suggests a home environment
where fruits are being displayed, possibly for convenience or aesthetic purposes.

Sample Controller

Create a new Spring Boot project and add the spring-ai-ollama-spring-boot-starter to your pom (or gradle) dependencies.

Add a application.properties file, under the src/main/resources directory, to enable and configure the Ollama chat model:

replace the base-url with your Ollama server URL.

This will create a OllamaChatModel implementation that you can inject into your class. Here is an example of a simple @Controller class that uses the chat model for text generations.

public class ChatController {

    private final OllamaChatModel chatModel;

    public ChatController(OllamaChatModel chatModel) {
        this.chatModel = chatModel;

    public Map generate(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        return Map.of("generation", chatModel.call(message));

	public Flux<ChatResponse> generateStream(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        Prompt prompt = new Prompt(new UserMessage(message));
        return chatModel.stream(prompt);


Manual Configuration

If you don’t want to use the Spring Boot auto-configuration, you can manually configure the OllamaChatModel in your application. The OllamaChatModel implements the ChatModel and StreamingChatModel and uses the Low-level OllamaApi Client to connect to the Ollama service.

To use it add the spring-ai-ollama dependency to your project’s Maven pom.xml file:


or to your Gradle build.gradle build file.

dependencies {
    implementation 'org.springframework.ai:spring-ai-ollama'
Refer to the Dependency Management section to add the Spring AI BOM to your build file.
The spring-ai-ollama dependency provides access also to the OllamaEmbeddingModel. For more information about the OllamaEmbeddingModel refer to the Ollama Embedding Model section.

Next, create an OllamaChatModel instance and use it to text generations requests:

var ollamaApi = new OllamaApi();

var chatModel = new OllamaChatModel(ollamaApi,

ChatResponse response = chatModel.call(
    new Prompt("Generate the names of 5 famous pirates."));

// Or with streaming responses
Flux<ChatResponse> response = chatModel.stream(
    new Prompt("Generate the names of 5 famous pirates."));

The OllamaOptions provides the configuration information for all chat requests.

Low-level OllamaApi Client

The OllamaApi provides a lightweight Java client for the Ollama Chat Completion API Ollama Chat Completion API.

The following class diagram illustrates the OllamaApi chat interfaces and building blocks:

OllamaApi Chat Completion API Diagram

Here is a simple snippet showing how to use the API programmatically:

The OllamaApi is low level api and is not recommended for direct use. Use the OllamaChatModel instead.
OllamaApi ollamaApi =
    new OllamaApi("YOUR_HOST:YOUR_PORT");

// Sync request
var request = ChatRequest.builder("orca-mini")
    .withStream(false) // not streaming
                .withContent("You are a geography teacher. You are talking to a student.")
                .withContent("What is the capital of Bulgaria and what is the size? "
                        + "What is the national anthem?")

ChatResponse response = ollamaApi.chat(request);

// Streaming request
var request2 = ChatRequest.builder("orca-mini")
    .withStream(true) // streaming
        .withContent("What is the capital of Bulgaria and what is the size? " + "What is the national anthem?")

Flux<ChatResponse> streamingResponse = ollamaApi.streamingChat(request2);