=> 1.0版本链接

项目简介

百度 “文心千帆 WENXINWORKSHOP” 大模型的spring-boot-starter，可以帮助您快速接入百度的AI能力。
完整对接文心千帆的官方API文档。
支持文生图，内置对话记忆，支持对话的流式返回。
支持单个模型的QPS控制，支持排队机制。
即将增加插件支持。

快速开始

使用demo (1.x版，2.x请阅读文档)

【基于Springboot 3.0开发，所以要求JDK版本为17及以上】

1、添加依赖

Maven

<dependency>
  <groupId>io.github.gemingjia</groupId>
  <artifactId>wenxin-starter</artifactId>
  <version>2.0.0-beta4</version>
</dependency>

Gradle

dependencies {
  implementation 'io.github.gemingjia:wenxin-starter:2.0.0-beta4' 
}

2、添加access-token

application.yml & application.yaml

gear:
  wenxin:
    access-token: xx.xxxxxxxxxx.xxxxxx.xxxxxxx.xxxxx-xxxx
-------------或-----------------
# 推荐
gear:
  wenxin:
    api-key: xxxxxxxxxxxxxxxxxxx
    secret-key: xxxxxxxxxxxxxxxxxxxxxxxxxxxxx

application.properties

gear.wenxin.access-token=xx.xxxxxxxxxx.xxxxxx.xxxxxxx.xxxxx-xxxx

模型qps设置

gear:
  wenxin:
    model-qps:
      # 模型名 QPS数量
      - Ernie 10
      - Lamma 10
      - ChatGLM 10

3、调用示例


@Configuration
public class ClientConfig {

    @Bean
    @Qualifier("Ernie")
    public ChatModel ernieClient() {
        
        ModelConfig modelConfig = new ModelConfig();
        // 模型名称，需跟设置的QPS数值的名称一致 (建议与官网名称一致)
        modelConfig.setModelName("Ernie");
        // 模型url
        modelConfig.setModelUrl("https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions");
        // 单独设置某个模型的access-token, 优先级高于全局access-token, 统一使用全局的话可以不设置
        modelConfig.setAccessToken("xx.xx.xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx");
  
        ModelHeader modelHeader = new ModelHeader();
        // 一分钟内允许的最大请求次数
        modelHeader.set_X_Ratelimit_Limit_Requests(100);
        // 一分钟内允许的最大tokens消耗，包含输入tokens和输出tokens
        modelHeader.set_X_Ratelimit_Limit_Tokens(2000);
        // 达到RPM速率限制前，剩余可发送的请求数配额，如果配额用完，将会在0-60s后刷新
        modelHeader.set_X_Ratelimit_Remaining_Requests(1000);
        // 达到TPM速率限制前，剩余可消耗的tokens数配额，如果配额用完，将会在0-60s后刷新
        modelHeader.set_X_Ratelimit_Remaining_Tokens(5000);
  
        modelConfig.setModelHeader(modelHeader);
  
        return new ChatClient(modelConfig);
    }

}

@RestController
public class ChatController {

    // 要调用的模型的客户端（示例为文心）
    @Resource
    @Qualifier("Ernie")
    private ChatModel chatClient;
  
    /**
     * chatClient.chatStream(msg) 单轮流式对话
     * chatClient.chatStream(new ChatErnieRequest()) 单轮流式对话, 参数可调
     * chatClient.chatsStream(msg, msgId) 连续对话
     * chatClient.chatsStream(new ChatErnieRequest(), msgId) 连续对话, 参数可调
     */
  
    /**
     * 以下两种方式均可
     */
    // 连续对话，流式
    @GetMapping(value = "/stream/chats", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
    public Flux<String> chatSingleStream(@RequestParam String msg, @RequestParam String uid) {
        // 单次对话 chatClient.chatStream(msg)
        Flux<ChatResponse> responseFlux = chatClient.chatsStream(msg, uid);
        return responseFlux.map(ChatResponse::getResult);
    }
  
    // 连续对话，流式
    @GetMapping(value = "/stream/chats1", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
    public SseEmitter chats(@RequestParam String msg, @RequestParam String uid) {
        SseEmitter emitter = new SseEmitter();
        // 支持参数设置 ChatErnieRequest（Ernie系列模型）、ChatBaseRequest（其他模型）
        // 单次对话 chatClient.chatsStream(msg)
        chatClient.chatsStream(msg, uid).subscribe(response -> {
            try {
                emitter.send(SseEmitter.event().data(response.getResult()));
            } catch (IOException e) {
                throw new RuntimeException(e);
            }
        });
        return emitter;
    }

}

    /**
     * Prompt模板被百度改的有点迷，等稳定一下再做适配...
     */

Star History

更新日志

v2.0.0-alpha1 // 始终上传失败...建议自己拉仓库install

JDK 8专版

v2.0.0 - bata4

修复修复定时任务导致的序列化问题

v2.0.0 - bata3

修复修复并发场景下导致的丢对话任务的问题
修复网络异常情况下导致的消息错乱问题
新增导入导出消息的api
新增消息存储与获取的api
新增 Prompt与ImageClient
优化整体性能
其余改动请查看commit.

v2.0.0 - bata

！ 2.x 版本与 1.x 版本不兼容

重构 SDK架构，大幅提升性能
重构客户端生成方式，支持自定义多模型，不再需要适配
完善普通chat接口现已可用

使用文档

开源协议

MIT License

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