Ollama Embeddings

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

An embedding is a vector (list) of floating point numbers. The distance between two vectors measures their relatedness. Small distances suggest high relatedness and large distances suggest low relatedness.

Prerequisites

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.

Note, installing ollama run llama2 will download a 4GB docker image.

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.

Auto-configuration

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

<dependency>
   <groupId>org.springframework.ai</groupId>
   <artifactId>spring-ai-ollama-spring-boot-starter</artifactId>
</dependency>

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.

The spring.ai.ollama.embedding.options.* properties are used to configure the default options used for all embedding requests. (It is used as OllamaEmbeddingClient#withDefaultOptions() instance).

Embedding Properties

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

Property Description Default

spring.ai.ollama.base-url

Base URL where Ollama API server is running.

localhost:11434

The prefix spring.ai.ollama.embedding.options is the property prefix that configures the EmbeddingClient implementation for Ollama.

Property Description Default

spring.ai.ollama.embedding.enabled

Enable Ollama embedding client.

true

spring.ai.ollama.embedding.model (DEPRECATED)

The name of the model to use. Deprecated use the spring.ai.ollama.embedding.options.model instead

mistral

spring.ai.ollama.embedding.options.model

The name of the supported models to use.

mistral

spring.ai.ollama.embedding.options.numa

Whether to use NUMA.

false

spring.ai.ollama.embedding.options.num-ctx

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

2048

spring.ai.ollama.embedding.options.num-batch

???

-

spring.ai.ollama.embedding.options.num-gqa

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

-

spring.ai.ollama.embedding.options.num-gpu

The number of layers to send to the GPU(s). On macOS it defaults to 1 to enable metal support, 0 to disable.

-

spring.ai.ollama.embedding.options.main-gpu

???

-

spring.ai.ollama.embedding.options.low-vram

???

-

spring.ai.ollama.embedding.options.f16-kv

???

-

spring.ai.ollama.embedding.options.logits-all

???

-

spring.ai.ollama.embedding.options.vocab-only

???

-

spring.ai.ollama.embedding.options.use-mmap

???

-

spring.ai.ollama.embedding.options.use-mlock

???

-

spring.ai.ollama.embedding.options.embedding-only

???

-

spring.ai.ollama.embedding.options.rope-frequency-base

???

-

spring.ai.ollama.embedding.options.rope-frequency-scale

???

-

spring.ai.ollama.embedding.options.num-thread

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).

-

spring.ai.ollama.embedding.options.num-keep

???

-

spring.ai.ollama.embedding.options.seed

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.

0

spring.ai.ollama.embedding.options.num-predict

Maximum number of tokens to predict when generating text. (Default: 128, -1 = infinite generation, -2 = fill context)

128

spring.ai.ollama.embedding.options.top-k

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.

40

spring.ai.ollama.embedding.options.top-p

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.

0.9

spring.ai.ollama.embedding.options.tfs-z

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.

1

spring.ai.ollama.embedding.options.typical-p

???

-

spring.ai.ollama.embedding.options.repeat-last-n

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

64

spring.ai.ollama.embedding.options.temperature

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

0.8

spring.ai.ollama.embedding.options.repeat-penalty

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.

1.1

spring.ai.ollama.embedding.options.presence-penalty

???

-

spring.ai.ollama.embedding.options.frequency-penalty

???

-

spring.ai.ollama.embedding.options.mirostat

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

0

spring.ai.ollama.embedding.options.mirostat-tau

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.

0.1

spring.ai.ollama.embedding.options.mirostat-eta

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

5.0

spring.ai.ollama.embedding.options.penalize-newline

???

-

spring.ai.ollama.embedding.options.stop

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.

-
The spring.ai.ollama.embedding.options.* properties are based on the Ollama Valid Parameters and Values and Ollama Types
All properties prefixed with spring.ai.ollama.embedding.options can be overridden at runtime by adding a request specific Embedding Options to the EmbeddingRequest call.

Embedding Options

The OllamaOptions.java provides the Ollama configurations, such as the model to use, the low level GPU and CPU tunning, etc.

The default options can be configured using the spring.ai.ollama.embedding.options properties as well.

At start-time use the OllamaEmbeddingClient#withDefaultOptions() to configure the default options used for all embedding requests. At run-time you can override the default options, using a OllamaOptions instance as part of your EmbeddingRequest.

For example to override the default model name for a specific request:

EmbeddingResponse embeddingResponse = embeddingClient.call(
    new EmbeddingRequest(List.of("Hello World", "World is big and salvation is near"),
        OllamaOptions.create()
            .withModel("Different-Embedding-Model-Deployment-Name"));

Sample Controller (Auto-configuration)

This will create a EmbeddingClient implementation that you can inject into your class. Here is an example of a simple @Controller class that uses the EmbeddingClient implementation.

@RestController
public class EmbeddingController {

    private final EmbeddingClient embeddingClient;

    @Autowired
    public EmbeddingController(EmbeddingClient embeddingClient) {
        this.embeddingClient = embeddingClient;
    }

    @GetMapping("/ai/embedding")
    public Map embed(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        EmbeddingResponse embeddingResponse = this.embeddingClient.embedForResponse(List.of(message));
        return Map.of("embedding", embeddingResponse);
    }
}

Manual Configuration

If you are not using Spring Boot, you can manually configure the OllamaEmbeddingClient. For this add the spring-ai-ollama dependency to your project’s Maven pom.xml file:

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-ollama</artifactId>
</dependency>

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 OllamaChatClient. For more information about the OllamaChatClient refer to the Ollama Chat Client section.

Next, create an OllamaEmbeddingClient instance and use it to compute the similarity between two input texts:

var ollamaApi = new OllamaApi();

var embeddingClient = new OllamaEmbeddingClient(ollamaApi)
    .withDefaultOptions(OllamaOptions.create()
			.withModel(OllamaOptions.DEFAULT_MODEL)
            .toMap());

EmbeddingResponse embeddingResponse = embeddingClient
	.embedForResponse(List.of("Hello World", "World is big and salvation is near"));

The OllamaOptions provides the configuration information for all embedding requests.