deepseek本地部署及java、python调用步骤详解
作者：Most666
这篇文章主要介绍了如何下载和使用Ollama模型,包括安装JDK 17及以上版本和Spring Boot 3.3.6,配置pom文件和application.yml,创建Controller,以及使用Python调用模型,需要的朋友可以参考下
1.下载Ollama
(需要科学上网)
https://ollama.com/
2.拉取模型
输入命令
ollama pull deepseek-v3
由于v3太大，改为r1，命令为：
ollama run deepseek-r1:1.5b
查看安装的模型
ollama ls
查看启动的模型
ollama ps
对话框输入/bye退出
3.Java调用
目前仅支持jdk17以上版本使用，本文使用的是jdk21，springboot版本为3.3.6版本过高、过低时都无法正常启动
3.1引入pom
<?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 https://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<parent>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-parent</artifactId>
<version>3.3.6</version>
<relativePath/> <!-- lookup parent from repository -->
</parent>
<groupId>com.example</groupId>
<artifactId>demo21</artifactId>
<version>0.0.1-SNAPSHOT</version>
<name>demo21</name>
<description>demo21</description>
<properties>
<java.version>21</java.version>
</properties>
<dependencies>
<dependency>
<groupId>io.springboot.ai</groupId>
<artifactId>spring-ai-ollama-spring-boot-starter</artifactId>
<version>1.0.3</version>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<optional>true</optional>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<configuration>
<annotationProcessorPaths>
<path>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
</path>
</annotationProcessorPaths>
</configuration>
</plugin>
<plugin>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-maven-plugin</artifactId>
<configuration>
<excludes>
<exclude>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
</exclude>
</excludes>
</configuration>
</plugin>
</plugins>
</build>
</project>
3.2配置application.yml
server:
port: 8088
spring:
application:
name: demo21
ai:
ollama:
base-url: http://localhost:11434
chat:
options:
model: deepseek-r1:1.5b
3.2创建Controller
import org.springframework.ai.chat.ChatResponse;
import org.springframework.ai.chat.prompt.Prompt;
import org.springframework.ai.ollama.OllamaChatClient;
import org.springframework.ai.ollama.api.OllamaOptions;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.beans.factory.annotation.Qualifier;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
@RestController
public class OllamaClientController {
@Autowired
@Qualifier("ollamaChatClient")
private OllamaChatClient ollamaChatClient;
/**
* http://localhost:8088/ollama/chat/v1?msg=java就业前景
*/
@GetMapping("/ollama/chat/v1")
public String ollamaChat(@RequestParam String msg) {
return this.ollamaChatClient.call(msg);
}
/**
* http://localhost:8088/ollama/chat/v2?msg=java就业前景
*/
@GetMapping("/ollama/chat/v2")
public Object ollamaChatV2(@RequestParam String msg) {
Prompt prompt = new Prompt(msg);
ChatResponse chatResponse = ollamaChatClient.call(prompt);
return chatResponse.getResult().getOutput().getContent();
}
/**
* http://localhost:8088/ollama/chat/v3?msg=java就业前景
*/
@GetMapping("/ollama/chat/v3")
public Object ollamaChatV3(@RequestParam String msg) {
Prompt prompt = new Prompt(
msg,
OllamaOptions.create()
.withModel("deepseek-r1:1.5b")
.withTemperature(0.4F));
ChatResponse chatResponse = ollamaChatClient.call(prompt);
return chatResponse.getResult().getOutput().getContent();
}
}
4.python调用
pip引入
pip install ollama
创建.py文件
import ollama
# 流式输出
def api_generate(text: str):
print(f'提问：{text}')
stream = ollama.generate(
stream=True,
model='deepseek-r1:1.5b',
prompt=text,
)
print('-----------------------------------------')
for chunk in stream:
if not chunk['done']:
print(chunk['response'], end='', flush=True)
else:
print('\n')
print('-----------------------------------------')
print(f'总耗时：{chunk['total_duration']}')
print('-----------------------------------------')
def api_chat(text: str):
print(f'提问：{text}')
stream = ollama.chat(
stream=True,
model='deepseek-r1:1.5b',
messages=[{"role":"user","content":text}]
)
print('-----------------------------------------')
for chunk in stream:
if not chunk['done']:
print(chunk['message'].content, end='', flush=True)
else:
print('\n')
print('-----------------------------------------')
print(f'总耗时：{chunk['total_duration']}')
print('-----------------------------------------')
if __name__ == '__main__':
# 流式输出
api_generate(text='python就业前景')
api_chat(text='python就业前景')
# 非流式输出
content = ollama.generate(model='deepseek-r1:1.5b', prompt='python就业前景')
print(content)
content = ollama.chat(model='deepseek-r1:1.5b', messages=[{"role":"user","content":'python就业前景'}])
print(content)总结
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