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大模型工具Ollama
官网:https://ollama.com/
Ollama是一个用于部署和运行各种开源大模型的工具;
它能够帮助用户快速在本地运行各种大模型，极大地简化了大模型在本地运行的过程。用户通过执行几条命令就能在本地运行开源大模型，如Lama 2等;
综上，Ollama是一个大模型部署运行工具，在该工具里面可以部署运行各种大模型，方便开发者在本地搭建一套大模型运行环境;
下载:https://ollama.com/download
下载Ollama
说明:Ollama的运行会受到所使用模型大小的影响;
1、例如，运行一个7B(70亿参数)的模型至少需要8GB的可用内存(RAM)，而运行一个13B(130亿参数)的模型需要16GB的内存，33B(330亿参数)的型需要32GB的内存;
2、需要考虑有足够的磁盘空间，大模型的文件大小可能比较大，建议至少为Ollama和其模型预留50GB的磁盘空间3、性能较高的CPU可以提供更好的运算速度和效率，多核处理器能够更好地处理并行任务，选择具有足够核心数的CPU:
4、显卡(GPU):Ollama支持纯CPU运行，但如果电脑配备了NVIDIA GPU，可以利用GPU进行加速，提高模型的运行速度和性能;
命令行使用ollama 打开终端，输入ollama -h,查看到所有的命令
service ollama start启动allama
输入ollama -v查看当前版本，能输出版本则安装成功
运行模型单行对话
拉取并运行llama2模型
ollama run llama2
直接输入该命令会检查目录下是否有该模型，没有会自动下载，下载好后自动运行该模型
其他模型见library (ollama.com)
# 查看 Ollama 版本
ollama -v# 查看已安装的模型
ollama list# 删除指定模型
ollama rm [modelname]# 模型存储路径
# C:Users<username>.ollamamodels
ollama run qwen:0.5b
默认Ollama api会监听11434端口，可以使用命令进行查看netstat -ano |findstr 114341
//加依赖
<dependency>
<groupld>org.springframework,ai</groupld>
<artifactld>spring-ai-ollama-spring-boot-starter</artifactld>
</dependency>
//写代码
注入OllamaChatClient
@Resource
private OllamaChatClient ollamaChatClient,
//调用call方法
ollamaChatClient.call(msg);
完整pom文件
<?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>3.3.0</version><relativePath/> <!-- lookup parent from repository --></parent><groupId>com.zzq</groupId><artifactId>spring-ai-ollama</artifactId><version>0.0.1-SNAPSHOT</version><name>spring-ai-ollama</name><description>spring-ai-ollama</description><properties><java.version>17</java.version><!-- 快照版本--><spring-ai.version>1.0.0-SNAPSHOT</spring-ai.version></properties><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><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-devtools</artifactId><scope>runtime</scope><optional>true</optional></dependency><dependency><groupId>org.projectlombok</groupId><artifactId>lombok</artifactId><optional>true</optional></dependency><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-test</artifactId><scope>test</scope></dependency></dependencies><dependencyManagement><dependencies><dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-bom</artifactId><version>${spring-ai.version}</version><type>pom</type><scope>import</scope></dependency></dependencies></dependencyManagement><build><plugins><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><!-- 快照版本--><repositories><repository><id>spring-snapshot</id><name>Spring Snapshots</name><url>https://repo.spring.io/snapshot</url><releases><enabled>false</enabled></releases></repository></repositories>
</project>
application文件内容
spring:application:name:spring-ai-05-ollamaai:ollama:base-url: http://localhost:11434chat:options:model: qwen:0.5b
controller
package com.zzq.controller;import jakarta.annotation.Resource;
import org.springframework.ai.ollama.OllamaChatModel;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;@RestController
public class OllamaController {@Resourceprivate OllamaChatModel ollamaChatModel;@RequestMapping(value = "/ai/ollama")public Object ollama(@RequestParam(value = "msg")String msg){String called=ollamaChatModel.call(msg);System.out.println(called);return called;}
}
package com.zzq.controller;import jakarta.annotation.Resource;
import org.springframework.ai.chat.model.ChatResponse;
import org.springframework.ai.chat.prompt.Prompt;
import org.springframework.ai.ollama.OllamaChatModel;
import org.springframework.ai.ollama.api.OllamaOptions;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;@RestController
public class OllamaController {@Resourceprivate OllamaChatModel ollamaChatModel;@RequestMapping(value = "/ai/ollama")public Object ollama(@RequestParam(value = "msg")String msg){String called=ollamaChatModel.call(msg);System.out.println(called);return called;}@RequestMapping(value = "/ai/ollama2")public Object ollama2(@RequestParam(value = "msg")String msg){ChatResponse chatResponse=ollamaChatModel.call(new Prompt(msg, OllamaOptions.create().withModel("qwen:0.5b")//使用哪个大模型.withTemperature(0.4F)));//温度，温度值越高，准确率下降，温度值越低，准确率上升System.out.println(chatResponse.getResult().getOutput().getContent());return chatResponse.getResult().getOutput().getContent();}
}