spring-ai/models/spring-ai-ollama

1. Ollama Chat and Embedding

1.1 OllamaApi

OllamaApi provides is lightweight Java client for Ollama models.

The OllamaApi provides the Chat completion as well as Embedding endpoints.

Following class diagram illustrates the OllamaApi interface and building blocks for chat completion:

The OllamaApi can supports all Ollama Models providing synchronous chat completion, streaming chat completion and embedding:

ChatResponse chat(ChatRequest chatRequest)

Flux<ChatResponse> streamingChat(ChatRequest chatRequest)

EmbeddingResponse embeddings(EmbeddingRequest embeddingRequest)

NOTE: OllamaApi expose also the Ollama generation endpoint but later if inferior compared to the Ollama chat endpoint.

The OllamaApiOptions is helper class used as type-safe option builder. It provides toMap to convert the content into Map<String, Object>.

Here is a simple snippet how to use the OllamaApi programmatically:

var request = ChatRequest.builder("orca-mini")
	.withStream(false)
	.withMessages(List.of(Message.builder(Role.user)
		.withContent("What is the capital of Bulgaria and what is the size? " + "What it the national anthem?")
		.build()))
	.withOptions(Options.builder().withTemperature(0.9f).build())
	.build();

ChatResponse response = ollamaApi.chat(request);

var request = ChatRequest.builder("orca-mini")
	.withStream(true)
	.withMessages(List.of(Message.builder(Role.user)
		.withContent("What is the capital of Bulgaria and what is the size? " + "What it the national anthem?")
		.build()))
	.withOptions(Options.builder().withTemperature(0.9f).build().toMap())
	.build();

Flux<ChatResponse> response = ollamaApi.streamingChat(request);

List<ChatResponse> responses = response.collectList().block();

EmbeddingRequest request = new EmbeddingRequest("orca-mini", "I like to eat apples");

EmbeddingResponse response = ollamaApi.embeddings(request);

1.2 OllamaChatClient and OllamaEmbeddingClient

The OllamaChatClient implements the Spring-Ai ChatClient and StreamingChatClient interfaces.

The OllamaEmbeddingClient implements the Spring AI EmbeddingClient interface.

Both the OllamaChatClient and the OllamaEmbeddingClient leverage the OllamaApi.

You can configure the clients like this:

@Bean
public OllamaApi ollamaApi() {
	return new OllamaApi(baseUrl);
}

@Bean
public OllamaChatClient ollamaChat(OllamaApi ollamaApi) {
	return new OllamaChatClient(ollamaApi).withModel(MODEL)
		.withOptions(OllamaApiOptions.Options.builder().withTemperature(0.9f).build());
}

@Bean
public OllamaEmbeddingClient ollamaEmbedding(OllamaApi ollamaApi) {
	return new OllamaEmbeddingClient(ollamaApi).withModel("orca-mini");
}

or you can leverage the spring-ai-ollama-spring-boot-starter Spring Boot starter. For this add the following dependency:

<dependency>
	<artifactId>spring-ai-ollama-spring-boot-starter</artifactId>
	<groupId>org.springframework.ai</groupId>
    <version>0.8.0-SNAPSHOT</version>
</dependency>

Use the OllamaChatProperties to configure the Ollama Chat client:

Property	Description	Default
spring.ai.ollama.chat.model	Model to use.	llama2
spring.ai.ollama.chat.base-url	The base url of the Ollama server.	http://localhost:11434
spring.ai.ollama.chat.enabled	Allows you to disable the Ollama Chat autoconfiguration.	true
spring.ai.ollama.chat.temperature	Controls the randomness of the output. Values can range over [0.0,1.0]	0.8
spring.ai.ollama.chat.topP	The maximum cumulative probability of tokens to consider when sampling.	-
spring.ai.ollama.chat.topK	Max number or responses to generate.	-
spring.ai.options.chat.options (WIP)	A Map<String,Object> used to configure the Chat client.	-

and OllamaEmbeddingProperties to configure the Ollama Embedding client:

Property	Description	Default
spring.ai.ollama.embedding.model	Model to use.	llama2
spring.ai.ollama.embedding.base-url	The base url of the Ollama server.	http://localhost:11434
spring.ai.ollama.embedding.enabled	Allows you to disable the Ollama embedding autoconfiguration.	true
spring.ai.options.embedding.options (WIP)	A Map<String,Object> used to configure the embedding client.	-