From 0645b274daac3120fd6f279ffa68a5ccba9feacd Mon Sep 17 00:00:00 2001 From: Carson Yang Date: Tue, 5 Nov 2024 10:11:59 +0800 Subject: [PATCH] Docs: add docs for loop node (#3069) --- .../assets/imgs/fastgpt-loop-node-config.png | Bin 0 -> 377839 bytes .../imgs/fastgpt-loop-node-example-1.png | Bin 0 -> 69201 bytes .../imgs/fastgpt-loop-node-example-2.png | Bin 0 -> 254912 bytes .../imgs/fastgpt-loop-node-example-3.png | Bin 0 -> 63741 bytes .../imgs/fastgpt-loop-node-example-4.png | Bin 0 -> 148060 bytes .../imgs/fastgpt-loop-node-example-5.png | Bin 0 -> 294946 bytes docSite/assets/imgs/fastgpt-loop-node.png | Bin 0 -> 165424 bytes .../zh-cn/docs/workflow/modules/loop.md | 297 ++++++++++++++++++ 8 files changed, 297 insertions(+) create mode 100644 docSite/assets/imgs/fastgpt-loop-node-config.png create mode 100644 docSite/assets/imgs/fastgpt-loop-node-example-1.png create mode 100644 docSite/assets/imgs/fastgpt-loop-node-example-2.png create mode 100644 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支持与判断器节点配合 + +## 应用场景 + +【**循环运行**】节点的主要作用是通过自动化的方式扩展工作流的处理能力,使 FastGPT 能够更好地处理批量任务和复杂的数据处理流程。特别是在处理大规模数据或需要多轮迭代的场景下,循环运行节点能显著提升工作流的效率和自动化程度。 + +【**循环运行**】节点特别适合以下场景: + +1. **批量数据处理** + - 批量翻译文本 + - 批量总结文档 + - 批量生成内容 + +2. **数据流水线处理** + - 对搜索结果逐条分析 + - 对知识库检索结果逐条处理 + - 对 HTTP 请求返回的数组数据逐项处理 + +3. **递归或迭代任务** + - 长文本分段处理 + - 多轮优化内容 + - 链式数据处理 + +## 使用方法 + +### 输入参数设置 + +【**循环运行**】节点需要配置两个核心输入参数: + +1. **数组 (必填)**:接收一个数组类型的输入,可以是: + - 字符串数组 (`Array`) + - 数字数组 (`Array`) + - 布尔数组 (`Array`) + - 对象数组 (`Array`) + +2. **循环体 (必填)**:定义每次循环需要执行的节点流程,包含: + - 循环体开始:标记循环开始的位置。 + - 循环体结束:标记循环结束的位置,并可选择输出结果变量。 + +### 循环体配置 + +![循环体配置](/imgs/fastgpt-loop-node-config.png) + +1. 在循环体内部,可以添加任意类型的节点,如: + - AI 对话节点 + - HTTP 请求节点 + - 内容提取节点 + - 文本加工节点等 + +2. 循环体结束节点配置: + - 通过下拉菜单选择要输出的变量 + - 该变量将作为当前循环的结果被收集 + - 所有循环的结果将组成一个新的数组作为最终输出 + +## 场景示例 + +### 批量处理数组 + +假设我们有一个包含多个文本的数组,需要对每个文本进行 AI 处理。这是循环运行节点最基础也最常见的应用场景。 + +#### 实现步骤 + +1. 准备输入数组 + + ![准备输入数组](/imgs/fastgpt-loop-node-example-1.png) + + 使用【代码运行】节点创建测试数组: + + ```javascript + const texts = [ + "这是第一段文本", + "这是第二段文本", + "这是第三段文本" + ]; + return { textArray: texts }; + ``` + +2. 配置循环运行节点 + + ![配置循环运行节点](/imgs/fastgpt-loop-node-example-2.png) + + - 数组输入:选择上一步代码运行节点的输出变量 `textArray`。 + - 循环体内添加一个【AI 对话】节点,用于处理每个文本。这里我们输入的 prompt 为:`请将这段文本翻译成英文`。 + - 再添加一个【指定回复】节点,用于输出翻译后的文本。 + - 循环体结束节点选择输出变量为 AI 回复内容。 + +#### 运行流程 + +![运行流程](/imgs/fastgpt-loop-node-example-3.png) + +1. 【代码运行】节点执行,生成测试数组 +2. 【循环运行】节点接收数组,开始遍历 +3. 对每个数组元素: + - 【AI 对话】节点处理当前元素 + - 【指定回复】节点输出翻译后的文本 + - 【循环体结束】节点收集处理结果 +4. 完成所有元素处理后,输出结果数组 + +### 长文本翻译 + +在处理长文本翻译时,我们经常会遇到以下挑战: + +- 文本长度超出 LLM 的 token 限制 +- 需要保持翻译风格的一致性 +- 需要维护上下文的连贯性 +- 翻译质量需要多轮优化 + +【**循环运行**】节点可以很好地解决这些问题。 + +#### 实现步骤 + +1. 文本预处理与分段 + + ![文本预处理与分段](/imgs/fastgpt-loop-node-example-4.png) + + 使用【代码运行】节点进行文本分段,代码如下: + + ```javascript + const MAX_HEADING_LENGTH = 7; // 最大标题长度 + const MAX_HEADING_CONTENT_LENGTH = 200; // 最大标题内容长度 + const MAX_HEADING_UNDERLINE_LENGTH = 200; // 最大标题下划线长度 + const MAX_HTML_HEADING_ATTRIBUTES_LENGTH = 100; // 最大HTML标题属性长度 + const MAX_LIST_ITEM_LENGTH = 200; // 最大列表项长度 + const MAX_NESTED_LIST_ITEMS = 6; // 最大嵌套列表项数 + const MAX_LIST_INDENT_SPACES = 7; // 最大列表缩进空格数 + const MAX_BLOCKQUOTE_LINE_LENGTH = 200; // 最大块引用行长度 + const MAX_BLOCKQUOTE_LINES = 15; // 最大块引用行数 + const MAX_CODE_BLOCK_LENGTH = 1500; // 最大代码块长度 + const MAX_CODE_LANGUAGE_LENGTH = 20; // 最大代码语言长度 + const MAX_INDENTED_CODE_LINES = 20; // 最大缩进代码行数 + const MAX_TABLE_CELL_LENGTH = 200; // 最大表格单元格长度 + const MAX_TABLE_ROWS = 20; // 最大表格行数 + const MAX_HTML_TABLE_LENGTH = 2000; // 最大HTML表格长度 + const MIN_HORIZONTAL_RULE_LENGTH = 3; // 最小水平分隔线长度 + const MAX_SENTENCE_LENGTH = 400; // 最大句子长度 + const MAX_QUOTED_TEXT_LENGTH = 300; // 最大引用文本长度 + const MAX_PARENTHETICAL_CONTENT_LENGTH = 200; // 最大括号内容长度 + const MAX_NESTED_PARENTHESES = 5; // 最大嵌套括号数 + const MAX_MATH_INLINE_LENGTH = 100; // 最大行内数学公式长度 + const MAX_MATH_BLOCK_LENGTH = 500; // 最大数学公式块长度 + const MAX_PARAGRAPH_LENGTH = 1000; // 最大段落长度 + const MAX_STANDALONE_LINE_LENGTH = 800; // 最大独立行长度 + const MAX_HTML_TAG_ATTRIBUTES_LENGTH = 100; // 最大HTML标签属性长度 + const MAX_HTML_TAG_CONTENT_LENGTH = 1000; // 最大HTML标签内容长度 + const LOOKAHEAD_RANGE = 100; // 向前查找句子边界的字符数 + + const AVOID_AT_START = `[\\s\\]})>,']`; // 避免在开头匹配的字符 + const PUNCTUATION = `[.!?…]|\\.{3}|[\\u2026\\u2047-\\u2049]|[\\p{Emoji_Presentation}\\p{Extended_Pictographic}]`; // 标点符号 + const QUOTE_END = `(?:'(?=\`)|''(?=\`\`))`; // 引号结束 + const SENTENCE_END = `(?:${PUNCTUATION}(?] {0,${MAX_HTML_HEADING_ATTRIBUTES_LENGTH}}>)[^\\r\\n]{1,${MAX_HEADING_CONTENT_LENGTH}}(?:)?(?:\\r?\\n|$))` + + "|" + + // New pattern for citations + `(?:\\[[0-9]+\\][^\\r\\n]{1,${MAX_STANDALONE_LINE_LENGTH}})` + + "|" + + // 2. List items (bulleted, numbered, lettered, or task lists, including nested, up to three levels, with length constraints) + `(?:(?:^|\\r?\\n)[ \\t]{0,3}(?:[-*+•]|\\d{1,3}\\.\\w\\.|\\[[ xX]\\])[ \\t]+${SENTENCE_PATTERN.replace(/{MAX_LENGTH}/g, String (MAX_LIST_ITEM_LENGTH))}` + + `(?:(?:\\r?\\n[ \\t]{2,5}(?:[-*+•]|\\d{1,3}\\.\\w\\.|\\[[ xX]\\])[ \\t]+${SENTENCE_PATTERN.replace(/{MAX_LENGTH}/g, String (MAX_LIST_ITEM_LENGTH))}){0,${MAX_NESTED_LIST_ITEMS}}` + + `(?:\\r?\\n[ \\t]{4,${MAX_LIST_INDENT_SPACES}}(?:[-*+•]|\\d{1,3}\\.\\w\\.|\\[[ xX]\\])[ \\t]+${SENTENCE_PATTERN.replace(/{MAX_LENGTH}/g, String (MAX_LIST_ITEM_LENGTH))}){0,${MAX_NESTED_LIST_ITEMS}})?)` + + "|" + + // 3. Block quotes (including nested quotes and citations, up to three levels, with length constraints) + `(?:(?:^>(?:>|\\s{2,}){0,2}${SENTENCE_PATTERN.replace(/{MAX_LENGTH}/g, String(MAX_BLOCKQUOTE_LINE_LENGTH))}\\r?\\n?){1,$ {MAX_BLOCKQUOTE_LINES}})` + + "|" + + // 4. Code blocks (fenced, indented, or HTML pre/code tags, with length constraints) + `(?:(?:^|\\r?\\n)(?:\`\`\`|~~~)(?:\\w{0,${MAX_CODE_LANGUAGE_LENGTH}})?\\r?\\n[\\s\\S]{0,${MAX_CODE_BLOCK_LENGTH}}?(?:\`\`\`|~~~)\\r?\\n?` + + `|(?:(?:^|\\r?\\n)(?: {4}|\\t)[^\\r\\n]{0,${MAX_LIST_ITEM_LENGTH}}(?:\\r?\\n(?: {4}|\\t)[^\\r\\n]{0,${MAX_LIST_ITEM_LENGTH}}){0,$ {MAX_INDENTED_CODE_LINES}}\\r?\\n?)` + + `|(?:
(?:)?[\\s\\S]{0,${MAX_CODE_BLOCK_LENGTH}}?(?:)?
))` + + "|" + + // 5. Tables (Markdown, grid tables, and HTML tables, with length constraints) + `(?:(?:^|\\r?\\n)(?:\\|[^\\r\\n]{0,${MAX_TABLE_CELL_LENGTH}}\\|(?:\\r?\\n\\|[-:]{1,${MAX_TABLE_CELL_LENGTH}}\\|){0,1}(?:\\r?\\n\\|[^\\r\\n]{0,$ {MAX_TABLE_CELL_LENGTH}}\\|){0,${MAX_TABLE_ROWS}}` + + `|[\\s\\S]{0,${MAX_HTML_TABLE_LENGTH}}?
))` + + "|" + + // 6. Horizontal rules (Markdown and HTML hr tag) + `(?:^(?:[-*_]){${MIN_HORIZONTAL_RULE_LENGTH},}\\s*$|)` + + "|" + + // 10. Standalone lines or phrases (including single-line blocks and HTML elements, with length constraints) + `(?!${AVOID_AT_START})(?:^(?:<[a-zA-Z][^>]{0,${MAX_HTML_TAG_ATTRIBUTES_LENGTH}}>)?${SENTENCE_PATTERN.replace(/{MAX_LENGTH}/g, String (MAX_STANDALONE_LINE_LENGTH))}(?:)?(?:\\r?\\n|$))` + + "|" + + // 7. Sentences or phrases ending with punctuation (including ellipsis and Unicode punctuation) + `(?!${AVOID_AT_START})${SENTENCE_PATTERN.replace(/{MAX_LENGTH}/g, String(MAX_SENTENCE_LENGTH))}` + + "|" + + // 8. Quoted text, parenthetical phrases, or bracketed content (with length constraints) + "(?:" + + `(?)?${SENTENCE_PATTERN.replace(/{MAX_LENGTH}/g, String(MAX_PARAGRAPH_LENGTH))}(?:

)?(?=\\r? \\n\\r?\\n|$))` + + "|" + + // 11. HTML-like tags and their content (including self-closing tags and attributes, with length constraints) + `(?:<[a-zA-Z][^>]{0,${MAX_HTML_TAG_ATTRIBUTES_LENGTH}}(?:>[\\s\\S]{0,${MAX_HTML_TAG_CONTENT_LENGTH}}?|\\s*/>))` + + "|" + + // 12. LaTeX-style math expressions (inline and block, with length constraints) + `(?:(?:\\$\\$[\\s\\S]{0,${MAX_MATH_BLOCK_LENGTH}}?\\$\\$)|(?:\\$[^\\$\\r\\n]{0,${MAX_MATH_INLINE_LENGTH}}\\$))` + + "|" + + // 14. Fallback for any remaining content (with length constraints) + `(?!${AVOID_AT_START})${SENTENCE_PATTERN.replace(/{MAX_LENGTH}/g, String(MAX_STANDALONE_LINE_LENGTH))}` + + ")", + "gmu" + ); + + function main({text}){ + const chunks = []; + let currentChunk = ''; + const tokens = countToken(text) + + const matches = text.match(regex); + if (matches) { + matches.forEach((match) => { + if (currentChunk.length + match.length <= 1000) { + currentChunk += match; + } else { + if (currentChunk) { + chunks.push(currentChunk); + } + currentChunk = match; + } + }); + if (currentChunk) { + chunks.push(currentChunk); + } + } + + return {chunks, tokens}; + } + ``` + + 这里我们用到了 [Jina AI 开源的一个强大的正则表达式](https://x.com/JinaAI_/status/1823756993108304135),它能利用所有可能的边界线索和启发式方法来精确切分文本。 + +2. 配置循环运行节点 + + ![配置循环运行节点](/imgs/fastgpt-loop-node-example-5.png) + + - 数组输入:选择上一步代码运行节点的输出变量 `chunks`。 + - 循环体内添加一个【代码运行】节点,对源文本进行格式化。 + - 添加一个【搜索词库】节点,将专有名词的词库作为知识库,在翻译前进行搜索。 + - 添加一个【AI 对话】节点,使用 CoT 思维链,让 LLM 显式地、系统地生成推理链条,展示翻译的完整思考过程。 + - 添加一个【代码运行】节点,将【AI 对话】节点最后一轮的翻译结果提取出来。 + - 添加一个【指定回复】节点,输出翻译后的文本。 + - 循环体结束节点选择输出变量为【取出翻译文本】的输出变量 `result`。 + + +