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本文主要研究一下如何使用spring-ai-alibaba本地集成ollama+deepseek
步骤
pom.xml
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>com.example</groupId>
<artifactId>ollama-deepseek-chat</artifactId>
<packaging>pom</packaging>
<version>1.0.0-SNAPSHOT</version>
<description>Spring AI Alibaba Ollama DeepSeek Chat Example</description>
<name>Spring AI Alibaba Ollama DeepSeek Chat Examples</name>
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
<java.version>17</java.version>
<maven.compiler.source>17</maven.compiler.source>
<maven.compiler.target>17</maven.compiler.target>
<!-- Spring AI -->
<spring-ai.version>1.0.0-M5</spring-ai.version>
<!-- Spring AI Alibaba -->
<spring-ai-alibaba.version>1.0.0-M5.1</spring-ai-alibaba.version>
<!-- Spring Boot -->
<spring-boot.version>3.4.0</spring-boot.version>
<!-- maven plugin -->
<maven-deploy-plugin.version>3.1.1</maven-deploy-plugin.version>
<flatten-maven-plugin.version>1.3.0</flatten-maven-plugin.version>
<maven-compiler-plugin.version>3.8.1</maven-compiler-plugin.version>
</properties>
<modules>
<module>ollama-deepseek-chat-model</module>
<module>ollama-deepseek-chat-client</module>
</modules>
<dependencyManagement>
<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-dependencies</artifactId>
<version>${spring-boot.version}</version>
<type>pom</type>
<scope>import</scope>
</dependency>
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-bom</artifactId>
<version>${spring-ai.version}</version>
<type>pom</type>
<scope>import</scope>
</dependency>
<dependency>
<groupId>com.alibaba.cloud.ai</groupId>
<artifactId>spring-ai-alibaba-starter</artifactId>
<version>${spring-ai-alibaba.version}</version>
</dependency>
</dependencies>
</dependencyManagement>
<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-ollama-spring-boot-starter</artifactId>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-maven-plugin</artifactId>
<version>${spring-boot.version}</version>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-deploy-plugin</artifactId>
<version>${maven-deploy-plugin.version}</version>
<configuration>
<skip>true</skip>
</configuration>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>${maven-compiler-plugin.version}</version>
<configuration>
<release>${java.version}</release>
<compilerArgs>
<compilerArg>-parameters</compilerArg>
</compilerArgs>
</configuration>
</plugin>
</plugins>
</build>
<repositories>
<repository>
<id>spring-milestones</id>
<name>Spring Milestones</name>
<url>https://repo.spring.io/milestone</url>
<snapshots>
<enabled>false</enabled>
</snapshots>
</repository>
<repository>
<id>aliyunmaven</id>
<name>aliyun</name>
<url>https://maven.aliyun.com/repository/public</url>
</repository>
</repositories>
<pluginRepositories>
<pluginRepository>
<id>public</id>
<name>aliyun nexus</name>
<url>https://maven.aliyun.com/repository/public</url>
<releases>
<enabled>true</enabled>
</releases>
<snapshots>
<enabled>false</enabled>
</snapshots>
</pluginRepository>
</pluginRepositories>
</project>主要是引入spring-ai-ollama-spring-boot-starter
application.yaml
server:
port: 10005
spring:
application:
name: spring-ai-alibaba-ollama-chat-model-example
ai:
ollama:
base-url: http://localhost:11434
chat:
model: deepseek-r1:8b这里配置ai.ollama.base-url、ai.ollama.chat.model
ChatModel
call
@GetMapping("/simple/chat")
public String simpleChat() {
return ollamaChatModel.call(new Prompt(DEFAULT_PROMPT)).getResult().getOutput().getContent();
}使用call是阻塞一次性输出
stream
@GetMapping("/stream/chat")
public Flux<String> streamChat(HttpServletResponse response) {
// 避免返回乱码
response.setCharacterEncoding("UTF-8");
Flux<ChatResponse> stream = ollamaChatModel.stream(new Prompt(DEFAULT_PROMPT));
return stream.map(resp -> resp.getResult().getOutput().getContent());
}使用stream配合reactor的Flux就可以实现流式输出
也可以自定义options，之后传递给Prompt
OllamaOptions customOptions = OllamaOptions.builder()
.withTopP(0.7)
.withModel("llama3")
.withTemperature(0.8)
.build();
new Prompt(DEFAULT_PROMPT, customOptions) ChatClient
初始化
ChatClient ollamaiChatClient = ChatClient.builder(chatModel)
// 实现 Chat Memory 的 Advisor
// 在使用 Chat Memory 时，需要指定对话 ID，以便 Spring AI 处理上下文。
.defaultAdvisors(
new MessageChatMemoryAdvisor(new InMemoryChatMemory())
)
// 实现 Logger 的 Advisor
.defaultAdvisors(
new SimpleLoggerAdvisor()
)
// 设置 ChatClient 中 ChatModel 的 Options 参数
.defaultOptions(
OllamaOptions.builder()
.withTopP(0.7)
.build()
)
.build();ChatClient是在ChatModel的基础上做了进一步的封装，可以设置advisor
call
@GetMapping("/simple/chat")
public String simpleChat() {
return ollamaiChatClient.prompt(DEFAULT_PROMPT).call().content();
}使用call是阻塞一次性输出
stream
@GetMapping("/stream/chat")
public Flux<String> streamChat(HttpServletResponse response) {
response.setCharacterEncoding("UTF-8");
return ollamaiChatClient.prompt(DEFAULT_PROMPT).stream().content();
}使用stream是流式输出
小结
spring-ai-alibaba提供了spring-ai-ollama-spring-boot-starter来集成ollama，通过ai.ollama.chat.model可以指定使用本地的deepseek-r1:8b。spring-ai-ollama-spring-boot-starter提供了基础的ChatModel以及高级版的ChatClient来调用ollama，ChatClient是在ChatModel的基础上做了进一步的封装，可以设置advisor，支持聊天记忆（Chat Memory）、工具/函数调用（Function Calling）、RAG。他们都提供了call及stream方式，call是阻塞一次性输出，stream是流式输出。
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