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<span class=toggle-dark><svg xmlns="http://www.w3.org/2000/svg" height="30" width="30" viewBox="0 0 48 48" fill="currentcolor"><path d="M24 42q-7.5.0-12.75-5.25T6 24t5.25-12.75T24 6q.4.0.85.025.45.025 1.15.075-1.8 1.6-2.8 3.95t-1 4.95q0 4.5 3.15 7.65Q28.5 25.8 33 25.8q2.6.0 4.95-.925T41.9 22.3q.05.6.075.975Q42 23.65 42 24q0 7.5-5.25 12.75T24 42zm0-3q5.45.0 9.5-3.375t5.05-7.925q-1.25.55-2.675.825Q34.45 28.8 33 28.8q-5.75.0-9.775-4.025T19.2 15q0-1.2.25-2.575t.9-3.125q-4.9 1.35-8.125 5.475Q9 18.9 9 24q0 6.25 4.375 10.625T24 39zm-.2-14.85z"/></svg></span><span class=toggle-light><svg xmlns="http://www.w3.org/2000/svg" height="30" width="30" viewBox="0 0 48 48" fill="currentcolor"><path d="M24 31q2.9.0 4.95-2.05T31 24t-2.05-4.95T24 17t-4.95 2.05T17 24t2.05 4.95T24 31zm0 3q-4.15.0-7.075-2.925T14 24t2.925-7.075T24 14t7.075 2.925T34 24t-2.925 7.075T24 34zM3.5 25.5q-.65.0-1.075-.425Q2 24.65 2 24t.425-1.075Q2.85 22.5 3.5 22.5h5q.65.0 1.075.425Q10 23.35 10 24t-.425 1.075T8.5 25.5zm36 0q-.65.0-1.075-.425Q38 24.65 38 24t.425-1.075T39.5 22.5h5q.65.0 1.075.425Q46 23.35 46 24t-.425 1.075-1.075.425zM24 10q-.65.0-1.075-.425Q22.5 9.15 22.5 8.5v-5q0-.65.425-1.075Q23.35 2 24 2t1.075.425T25.5 3.5v5q0 .65-.425 1.075Q24.65 10 24 10zm0 36q-.65.0-1.075-.425T22.5 44.5v-5q0-.65.425-1.075Q23.35 38 24 38t1.075.425.425 1.075v5q0 .65-.425 1.075Q24.65 46 24 46zM12 14.1l-2.85-2.8q-.45-.45-.425-1.075.025-.625.425-1.075.45-.45 1.075-.45t1.075.45L14.1 12q.4.45.4 1.05.0.6-.4 1-.4.45-1.025.45T12 14.1zm24.7 24.75L33.9 36q-.4-.45-.4-1.075t.45-1.025q.4-.45 1-.45t1.05.45l2.85 2.8q.45.45.425 1.075-.025.625-.425 1.075-.45.45-1.075.45t-1.075-.45zM33.9 14.1q-.45-.45-.45-1.05.0-.6.45-1.05l2.8-2.85q.45-.45 1.075-.425.625.025 1.075.425.45.45.45 1.075t-.45 1.075L36 14.1q-.4.4-1.025.4t-1.075-.4zM9.15 38.85q-.45-.45-.45-1.075t.45-1.075L12 33.9q.45-.45 1.05-.45.6.0 1.05.45.45.45.45 1.05.0.6-.45 1.05l-2.8 2.85q-.45.45-1.075.425-.625-.025-1.075-.425zM24 24z"/></svg></span></button></div></div></div><div class=container-fluid><div class=layout-spacing><div class="d-md-flex justify-content-between align-items-center"><nav aria-label=breadcrumb class="d-inline-block pb-2 mt-1 mt-sm-0"><ul id=breadcrumbs class="breadcrumb bg-transparent mb-0" itemscope itemtype=https://schema.org/BreadcrumbList><li class="breadcrumb-item text-capitalize active" aria-current=page itemprop=itemListElement itemscope itemtype=https://schema.org/ListItem><a itemprop=item href=/docs/><i class="material-icons size-20 align-text-bottom" itemprop=name>Home</i></a><meta itemprop=position content="1"></li><li class="breadcrumb-item text-capitalize" itemprop=itemListElement itemscope itemtype=https://schema.org/ListItem><a itemprop=item href=/docs/use-cases/><span itemprop=name>使用案例</span></a><meta itemprop=position content="2"></li><li class="breadcrumb-item text-capitalize" itemprop=itemListElement itemscope itemtype=https://schema.org/ListItem><a itemprop=item href=/docs/use-cases/app-cases/><span itemprop=name>应用搭建案例</span></a><meta itemprop=position content="3"></li><li class="breadcrumb-item text-capitalize active" itemprop=itemListElement itemscope itemtype=https://schema.org/ListItem><span itemprop=name>多轮翻译机器人</span><meta itemprop=position content="4"></li></ul></nav></div><div class="row flex-xl-nowrap"><div class="docs-toc col-xl-3 d-xl-block"><toc><div class="fw-bold text-uppercase mb-2">多轮翻译机器人</div><nav id=toc><ul><li><a href=#单文本块反思翻译>单文本块反思翻译</a><ul><li><a href=#初始翻译>初始翻译</a></li><li><a href=#反思>反思</a></li><li><a href=#提升翻译>提升翻译</a></li><li><a href=#运行效果>运行效果</a></li></ul></li><li><a href=#长文反思翻译>长文反思翻译</a><ul><li><a href=#计算-tokens>计算 tokens</a></li><li><a href=#计算单文本块大小>计算单文本块大小</a></li><li><a href=#获得切分后源文本块>获得切分后源文本块</a></li><li><a href=#多文本块翻译>多文本块翻译</a><ul><li><a href=#多文本块初始翻译>多文本块初始翻译</a></li><li><a href=#多文本块反思>多文本块反思</a></li><li><a href=#多文本块提升翻译>多文本块提升翻译</a></li></ul></li><li><a href=#批量运行>批量运行</a></li><li><a href=#运行效果-1>运行效果</a></li></ul></li><li><a href=#进一步调优>进一步调优</a><ul><li><a href=#提示词调优>提示词调优</a></li><li><a href=#其他调优>其他调优</a></li></ul></li></ul></nav></toc></div><div class="docs-toc-mobile d-print-none d-xl-none"><button id=toc-dropdown-btn class="btn-secondary dropdown-toggle" type=button data-bs-toggle=dropdown data-bs-offset=0,0 aria-expanded=false>
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||
Table of Contents</button><nav id=toc-mobile><ul class=dropdown-menu><li><a href=#单文本块反思翻译>单文本块反思翻译</a><ul><li><a href=#初始翻译>初始翻译</a></li><li><a href=#反思>反思</a></li><li><a href=#提升翻译>提升翻译</a></li><li><a href=#运行效果>运行效果</a></li></ul></li><li><a href=#长文反思翻译>长文反思翻译</a><ul><li><a href=#计算-tokens>计算 tokens</a></li><li><a href=#计算单文本块大小>计算单文本块大小</a></li><li><a href=#获得切分后源文本块>获得切分后源文本块</a></li><li><a href=#多文本块翻译>多文本块翻译</a><ul><li><a href=#多文本块初始翻译>多文本块初始翻译</a></li><li><a href=#多文本块反思>多文本块反思</a></li><li><a href=#多文本块提升翻译>多文本块提升翻译</a></li></ul></li><li><a href=#批量运行>批量运行</a></li><li><a href=#运行效果-1>运行效果</a></li></ul></li><li><a href=#进一步调优>进一步调优</a><ul><li><a href=#提示词调优>提示词调优</a></li><li><a href=#其他调优>其他调优</a></li></ul></li></ul></nav></div><div class="docs-content col-12 col-xl-9 mt-0"><div class="mb-0 d-flex"><i class="material-icons title-icon me-2">translate</i><h1 class="content-title mb-0">多轮翻译机器人</h1></div><p class="lead mb-3">如何使用 FastGPT 构建一个多轮翻译机器人，实现连续的对话翻译功能</p><div id=content class=main-content data-bs-spy=scroll data-bs-root-margin="0px 0px -65%" data-bs-target=#toc-mobile><div data-prismjs-copy data-prismjs-copy-success data-prismjs-copy-error><p>吴恩达老师提出了一种反思翻译的大语言模型(LLM)翻译工作流程——<a href=https://github.com/andrewyng/translation-agent rel=external target=_blank>GitHub - andrewyng/translation-agent<svg width="16" height="16" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path fill="currentcolor" d="M14 5c-.552.0-1-.448-1-1s.448-1 1-1h6c.552.0 1 .448 1 1v6c0 .552-.448 1-1 1s-1-.448-1-1V6.414l-7.293 7.293c-.391.39-1.024.39-1.414.0-.391-.391-.391-1.024.0-1.414L17.586 5H14zM5 7c-.552.0-1 .448-1 1v11c0 .552.448 1 1 1h11c.552.0 1-.448 1-1v-4.563c0-.552.448-1 1-1s1 .448 1 1V19c0 1.657-1.343 3-3 3H5c-1.657.0-3-1.343-3-3V8c0-1.657 1.343-3 3-3h4.563c.552.0 1 .448 1 1s-.448 1-1 1H5z"/></svg></a>，具体工作流程如下：</p><ol><li>提示一个 LLM 将文本从 <code>source_language</code> 翻译到 <code>target_language</code>；</li><li>让 LLM 反思翻译结果并提出建设性的改进建议；</li><li>使用这些建议来改进翻译。</li></ol><p>这个翻译流程应该是目前比较新的一种翻译方式，利用 LLM 对自己的翻译结果进行改进来获得较好的翻译效果</p><p>项目中展示了可以利用对长文本进行分片，然后分别进行反思翻译处理，以突破 LLM 对 tokens 数量的限制，真正实现长文本一键高效率高质量翻译。</p><p>项目还通过给大模型限定国家地区，已实现更精确的翻译，如美式英语、英式英语之分；同时提出一些可能能带来更好效果的优化，如对于一些 LLM 未曾训练到的术语（或有多种翻译方式的术语）建立术语表，进一步提升翻译的精确度等等</p><p>而这一切都能通过 Fastgpt 工作流轻松实现，本文将手把手教你如何复刻吴恩达老师的 translation-agent</p><h1 id=单文本块反思翻译>单文本块反思翻译 <a href=#%e5%8d%95%e6%96%87%e6%9c%ac%e5%9d%97%e5%8f%8d%e6%80%9d%e7%bf%bb%e8%af%91 class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h1><p>先从简单的开始，即不超出 LLM tokens 数量限制的单文本块翻译</p><h2 id=初始翻译>初始翻译 <a href=#%e5%88%9d%e5%a7%8b%e7%bf%bb%e8%af%91 class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h2><p>第一步先让 LLM 对源文本块进行初始翻译（翻译的提示词在源项目中都有）</p><p><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/translate1.png alt loading=lazy class=medium-zoom-image></p><p>通过<code>文本拼接</code>模块引用 源语言、目标语言、源文本这三个参数，生成提示词，传给 LLM，让它给出第一版的翻译</p><h2 id=反思>反思 <a href=#%e5%8f%8d%e6%80%9d class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h2><p>然后让 LLM 对第一步生成的初始翻译给出修改建议，称之为 反思</p><p><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/translate2.png alt loading=lazy class=medium-zoom-image></p><p>这时的提示词接收 5 个参数，源文本、初始翻译、源语言、目标语言 以及限定词地区国家，这样 LLM 会对前面生成的翻译提出相当多的修改建议，为后续的提升翻译作准备</p><h2 id=提升翻译>提升翻译 <a href=#%e6%8f%90%e5%8d%87%e7%bf%bb%e8%af%91 class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h2><p><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/translate3.png alt loading=lazy class=medium-zoom-image></p><p>在前文生成了初始翻译以及相应的反思后，将这二者输入给第三次 LLM 翻译，这样我们就能获得一个比较高质量的翻译结果</p><p>完整的工作流如下</p><p><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/translate4.png alt loading=lazy class=medium-zoom-image></p><h2 id=运行效果>运行效果 <a href=#%e8%bf%90%e8%a1%8c%e6%95%88%e6%9e%9c class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h2><p>由于考虑之后对这个反思翻译的复用，所以创建了一个插件，那么在下面我直接调用这个插件就能使用反思翻译，效果如下</p><p>随机挑选了一段哈利波特的文段</p><p><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/translate5.png alt loading=lazy class=medium-zoom-image></p><p><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/translate6.png alt loading=lazy class=medium-zoom-image></p><p>可以看到反思翻译后的效果还是好上不少的，其中反思的输出如下</p><p><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/translate7.png alt loading=lazy class=medium-zoom-image></p><h1 id=长文反思翻译>长文反思翻译 <a href=#%e9%95%bf%e6%96%87%e5%8f%8d%e6%80%9d%e7%bf%bb%e8%af%91 class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h1><p>在掌握了对短文本块的反思翻译后，我们能轻松的通过分片和循环，实现对长文本也即多文本块的反思翻译</p><p>整体的逻辑是，首先对传入文本的 tokens数量做判断，如果不超过设置的 tokens 限制，那么直接调用单文本块反思翻译，如果超过设置的 tokens限制，那么切割为合理的大小，再分别进行对应的反思翻译处理</p><h2 id=计算-tokens>计算 tokens <a href=#%e8%ae%a1%e7%ae%97-tokens class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h2><p><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/translate8.png alt loading=lazy class=medium-zoom-image></p><p>首先，我使用了 Laf函数 模块来实现对输入文本的 tokens 的计算</p><p>laf函数的使用相当简单，即开即用，只需要在 laf 创建个应用，然后安装 tiktoken 依赖，导入如下代码即可</p><div class=prism-codeblock><pre id=d6d644e class=language-TypeScript>
|
||
<code>const { Tiktoken } = require("tiktoken/lite");
|
||
const cl100k_base = require("tiktoken/encoders/cl100k_base.json");
|
||
|
||
interface IRequestBody {
|
||
str: string
|
||
}
|
||
|
||
interface RequestProps extends IRequestBody {
|
||
systemParams: {
|
||
appId: string,
|
||
variables: string,
|
||
histories: string,
|
||
cTime: string,
|
||
chatId: string,
|
||
responseChatItemId: string
|
||
}
|
||
}
|
||
|
||
interface IResponse {
|
||
message: string;
|
||
tokens: number;
|
||
}
|
||
|
||
export default async function (ctx: FunctionContext): Promise<IResponse> {
|
||
const { str = "" }: RequestProps = ctx.body
|
||
|
||
const encoding = new Tiktoken(
|
||
cl100k_base.bpe_ranks,
|
||
cl100k_base.special_tokens,
|
||
cl100k_base.pat_str
|
||
);
|
||
const tokens = encoding.encode(str);
|
||
encoding.free();
|
||
|
||
return {
|
||
message: 'ok',
|
||
tokens: tokens.length
|
||
};
|
||
}</code>
|
||
</pre></div><p>再回到 Fastgpt，点击“同步参数”，再连线将源文本传入，即可计算 tokens 数量</p><h2 id=计算单文本块大小>计算单文本块大小 <a href=#%e8%ae%a1%e7%ae%97%e5%8d%95%e6%96%87%e6%9c%ac%e5%9d%97%e5%a4%a7%e5%b0%8f class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h2><p><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/translate9.png alt loading=lazy class=medium-zoom-image></p><p>由于不涉及第三方包，只是一些数据处理，所以直接使用 代码运行 模块处理即可</p><div class=prism-codeblock><pre id=9cbf339 class=language-TypeScript>
|
||
<code>function main({tokenCount, tokenLimit}){
|
||
const numChunks = Math.ceil(tokenCount / tokenLimit);
|
||
let chunkSize = Math.floor(tokenCount / numChunks);
|
||
|
||
const remainingTokens = tokenCount % tokenLimit;
|
||
if (remainingTokens > 0) {
|
||
chunkSize += Math.floor(remainingTokens / numChunks);
|
||
}
|
||
|
||
return {chunkSize};
|
||
}</code>
|
||
</pre></div><p>通过上面的代码，我们就能算出不超过 token限制的合理单文本块大小是多少了</p><h2 id=获得切分后源文本块>获得切分后源文本块 <a href=#%e8%8e%b7%e5%be%97%e5%88%87%e5%88%86%e5%90%8e%e6%ba%90%e6%96%87%e6%9c%ac%e5%9d%97 class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h2><p><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/translate10.png alt loading=lazy class=medium-zoom-image></p><p>通过单文本块大小和源文本，我们再编写一个函数调用 langchain 的 textsplitters 包来实现文本分片，具体代码如下</p><div class=prism-codeblock><pre id=cea75f3 class=language-TypeScript>
|
||
<code>import cloud from '@lafjs/cloud'
|
||
import { TokenTextSplitter } from "@langchain/textsplitters";
|
||
|
||
interface IRequestBody {
|
||
text: string
|
||
chunkSize: number
|
||
}
|
||
|
||
interface RequestProps extends IRequestBody {
|
||
systemParams: {
|
||
appId: string,
|
||
variables: string,
|
||
histories: string,
|
||
cTime: string,
|
||
chatId: string,
|
||
responseChatItemId: string
|
||
}
|
||
}
|
||
|
||
interface IResponse {
|
||
output: string[];
|
||
}
|
||
|
||
export default async function (ctx: FunctionContext): Promise<IResponse>{
|
||
const { text = '', chunkSize=1000 }: RequestProps = ctx.body;
|
||
|
||
const splitter = new TokenTextSplitter({
|
||
encodingName:"gpt2",
|
||
chunkSize: Number(chunkSize),
|
||
chunkOverlap: 0,
|
||
});
|
||
|
||
const output = await splitter.splitText(text);
|
||
|
||
return {
|
||
output
|
||
}
|
||
}</code>
|
||
</pre></div><p>这样我们就获得了切分好的文本，接下去的操作就类似单文本块反思翻译</p><h2 id=多文本块翻译>多文本块翻译 <a href=#%e5%a4%9a%e6%96%87%e6%9c%ac%e5%9d%97%e7%bf%bb%e8%af%91 class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h2><p>这里应该还是不能直接调用前面的单文本块反思翻译，因为提示词中会涉及一些上下文的处理（或者可以修改下前面写好的插件，多传点参数进去）</p><p>详细的和前面类似，就是提示词进行一些替换，以及需要做一些很简单的数据处理，整体效果如下</p><h3 id=多文本块初始翻译>多文本块初始翻译 <a href=#%e5%a4%9a%e6%96%87%e6%9c%ac%e5%9d%97%e5%88%9d%e5%a7%8b%e7%bf%bb%e8%af%91 class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h3><p><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/translate11.png alt loading=lazy class=medium-zoom-image></p><h3 id=多文本块反思>多文本块反思 <a href=#%e5%a4%9a%e6%96%87%e6%9c%ac%e5%9d%97%e5%8f%8d%e6%80%9d class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h3><p><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/translate12.png alt loading=lazy class=medium-zoom-image></p><h3 id=多文本块提升翻译>多文本块提升翻译 <a href=#%e5%a4%9a%e6%96%87%e6%9c%ac%e5%9d%97%e6%8f%90%e5%8d%87%e7%bf%bb%e8%af%91 class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h3><p><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/translate13.png alt loading=lazy class=medium-zoom-image></p><h2 id=批量运行>批量运行 <a href=#%e6%89%b9%e9%87%8f%e8%bf%90%e8%a1%8c class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h2><p>长文反思翻译比较关键的一个部分，就是对多个文本块进行循环反思翻译</p><p>Fastgpt 提供了工作流线路可以返回去执行的功能，所以我们可以写一个很简单的判断函数，来判断结束或是接着执行</p><p><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/translate14.png alt loading=lazy class=medium-zoom-image></p><p>也就是通过判断当前处理的这个文本块，是否是最后一个文本块，从而判断是否需要继续执行，就这样，我们实现了长文反思翻译的效果</p><p>完整工作流如下</p><p><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/translate15.png alt loading=lazy class=medium-zoom-image></p><h2 id=运行效果-1>运行效果 <a href=#%e8%bf%90%e8%a1%8c%e6%95%88%e6%9e%9c-1 class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h2><p>首先输入全局设置</p><p><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/translate16.png alt loading=lazy class=medium-zoom-image></p><p>然后输入需要翻译的文本，这里我选择了一章哈利波特的英文原文来做翻译，其文本长度通过 openai 对 tokens 数量的判断如下</p><p><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/translate17.png alt loading=lazy class=medium-zoom-image></p><p>实际运行效果如下</p><p><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/translate18.png alt loading=lazy class=medium-zoom-image></p><p>可以看到还是能满足阅读需求的</p><h1 id=进一步调优>进一步调优 <a href=#%e8%bf%9b%e4%b8%80%e6%ad%a5%e8%b0%83%e4%bc%98 class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h1><h2 id=提示词调优>提示词调优 <a href=#%e6%8f%90%e7%a4%ba%e8%af%8d%e8%b0%83%e4%bc%98 class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h2><p>在源项目中，给 AI 的系统提示词还是比较的简略的，我们可以通过比较完善的提示词，来督促 LLM 返回更合适的翻译，进一步提升翻译的质量</p><p>比如初始翻译中，</p><div class=prism-codeblock><pre id=0d7e47c class=language-TypeScript>
|
||
<code># Role: 资深翻译专家
|
||
|
||
## Background:
|
||
你是一位经验丰富的翻译专家,精通{{source_lang}}和{{target_lang}}互译,尤其擅长将{{source_lang}}文章译成流畅易懂的{{target_lang}}。你曾多次带领团队完成大型翻译项目,译文广受好评。
|
||
|
||
## Attention:
|
||
- 翻译过程中要始终坚持"信、达、雅"的原则,但"达"尤为重要
|
||
- 译文要符合{{target_lang}}的表达习惯,通俗易懂,连贯流畅
|
||
- 避免使用过于文绉绉的表达和晦涩难懂的典故引用
|
||
|
||
## Constraints:
|
||
- 必须严格遵循四轮翻译流程:直译、意译、校审、定稿
|
||
- 译文要忠实原文,准确无误,不能遗漏或曲解原意
|
||
|
||
## Goals:
|
||
- 通过四轮翻译流程,将{{source_lang}}原文译成高质量的{{target_lang}}译文
|
||
- 译文要准确传达原文意思,语言表达力求浅显易懂,朗朗上口
|
||
- 适度使用一些熟语俗语、流行网络用语等,增强译文的亲和力
|
||
- 在直译的基础上,提供至少2个不同风格的意译版本供选择
|
||
|
||
## Skills:
|
||
- 精通{{source_lang}} {{target_lang}}两种语言,具有扎实的语言功底和丰富的翻译经验
|
||
- 擅长将{{source_lang}}表达习惯转换为地道自然的{{target_lang}}
|
||
- 对当代{{target_lang}}语言的发展变化有敏锐洞察,善于把握语言流行趋势
|
||
|
||
## Workflow:
|
||
1. 第一轮直译:逐字逐句忠实原文,不遗漏任何信息
|
||
2. 第二轮意译:在直译的基础上用通俗流畅的{{target_lang}}意译原文,至少提供2个不同风格的版本
|
||
3. 第三轮校审:仔细审视译文,消除偏差和欠缺,使译文更加地道易懂
|
||
4. 第四轮定稿:择优选取,反复修改润色,最终定稿出一个简洁畅达、符合大众阅读习惯的译文
|
||
|
||
## OutputFormat:
|
||
- 只需要输出第四轮定稿的回答
|
||
|
||
## Suggestions:
|
||
- 直译时力求忠实原文,但不要过于拘泥逐字逐句
|
||
- 意译时在准确表达原意的基础上,用最朴实无华的{{target_lang}}来表达
|
||
- 校审环节重点关注译文是否符合{{target_lang}}表达习惯,是否通俗易懂
|
||
- 定稿时适度采用一些熟语谚语、网络流行语等,使译文更接地气- 善于利用{{target_lang}}的灵活性,用不同的表述方式展现同一内容,提高译文的可读性</code>
|
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</pre></div><p>从而返回更准确更高质量的初始翻译，后续的反思和提升翻译也可以修改更准确的提示词，如下</p><p><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/translate19.png alt loading=lazy class=medium-zoom-image></p><p>然后再让我们来看看运行效果</p><p><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/translate20.png alt loading=lazy class=medium-zoom-image></p><p>给了和之前相同的一段文本进行测试，测试效果还是比较显著的，就比如红框部分，之前的翻译如下</p><p><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/translate21.png alt loading=lazy class=medium-zoom-image></p><p>从“让你的猫头鹰给我写信”这样有失偏颇的翻译，变成“给我写信，你的猫头鹰会知道怎么找到我”这样较为准确的翻译</p><h2 id=其他调优>其他调优 <a href=#%e5%85%b6%e4%bb%96%e8%b0%83%e4%bc%98 class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h2><p>比如限定词调优，源项目中已经做了示范，就是加上国家地区这个限定词，实测确实会有不少提升</p><p>出于 LLM 的卓越能力，我们能够通过设置不同的prompt来获取不同的翻译结果，也就是可以很轻松地通过设置特殊的限定词，来实现特定的，更精确的翻译</p><p>而对于一些超出 LLM 理解的术语等，也可以利用 Fastgpt 的知识库功能进行相应扩展，进一步完善翻译机器人的功能</p></div><div class="gitinfo d-flex flex-wrap justify-content-between align-items-center opacity-85 pt-3"><div id=edit-this-page class=mt-1><a href=https://github.com/labring/FastGPT/blob/main/docSite/content/zh-cn/docs/use-cases/app-cases/multi_turn_translation_bot.md alt=多轮翻译机器人 rel="noopener noreferrer" target=_blank><span class="me-1 align-text-bottom"><svg width="20" height="20" viewBox="0 0 32 32" xmlns="http://www.w3.org/2000/svg" fill="currentcolor"><path d="M16 .396c-8.839.0-16 7.167-16 16 0 7.073 4.584 13.068 10.937 15.183.803.151 1.093-.344 1.093-.772.0-.38-.009-1.385-.015-2.719-4.453.964-5.391-2.151-5.391-2.151-.729-1.844-1.781-2.339-1.781-2.339-1.448-.989.115-.968.115-.968 1.604.109 2.448 1.645 2.448 1.645 1.427 2.448 3.744 1.74 4.661 1.328.14-1.031.557-1.74 1.011-2.135-3.552-.401-7.287-1.776-7.287-7.907.0-1.751.62-3.177 1.645-4.297-.177-.401-.719-2.031.141-4.235.0.0 1.339-.427 4.4 1.641 1.281-.355 2.641-.532 4-.541 1.36.009 2.719.187 4 .541 3.043-2.068 4.381-1.641 4.381-1.641.859 2.204.317 3.833.161 4.235 1.015 1.12 1.635 2.547 1.635 4.297.0 6.145-3.74 7.5-7.296 7.891.556.479 1.077 1.464 1.077 2.959.0 2.14-.02 3.864-.02 4.385.0.416.28.916 1.104.755 6.4-2.093 10.979-8.093 10.979-15.156.0-8.833-7.161-16-16-16z"/></svg></span>编辑此页面</a></div></div></div><div><hr class=doc-hr><div id=doc-nav class=d-print-none><div class="row flex-xl-nowrap"><div class="col-sm-6 pt-2 doc-next"><a href=/docs/use-cases/app-cases/translate-subtitle-using-gpt/><div class="card h-100 my-1"><div class="card-body py-2"><p class="card-title fs-5 fw-semibold lh-base mb-0"><i class="material-icons align-middle">navigate_before</i> 长字幕翻译</p><p class="card-text ms-2">利用 AI 自我反思提升翻译质量，同时循环迭代执行 AI 工 …</p></div></div></a></div><div class="col-sm-6 pt-2 doc-prev"><a class=ms-auto href=/docs/use-cases/app-cases/english_essay_correction_bot/><div class="card h-100 my-1 text-end"><div class="card-body py-2"><p class="card-title fs-5 fw-semibold lh-base mb-0">英语作文纠错机器人 <i class="material-icons align-middle">navigate_next</i></p><p class="card-text me-2">使用 FastGPT 创建一个用于英语作文纠错的机器人，帮助 …</p></div></div></a></div></div></div></div></div></div></div></div><footer class="shadow py-3 d-print-none"><div class="row align-items-center" style=height:90px><div class=col><div class="text-sm-start text-center mx-md-2"><p class=mb-0>© 2025 the FastGPT Authors.</p><p class=github-badge><span class=badge-subject>云操作系统</span><span class="badge-value bg-blue"><a style=color:#fff href=https://sealos.io/ target=_blank>Sealos</a></span>
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