自寫 Lang 圖狀態

第一個人工智慧回應：

輸出：感謝您伸出援手，.我很樂意為您提供幫助，但我想確保我完全理解您的需求。您能否提供有關您正在尋找的內容的更多詳細資訊？您分享的資訊越多，我就越能提供協助！我很樂意盡我所能地幫助你。您想進一步探索某個特定領域嗎？您的意見將幫助我根據您的具體需求量身定制我的幫助。我完全致力於讓您的體驗盡可能順利和愉快。

AI 回應，在寫自己的圖表後：

輸出：嗨！這是怎麼回事？今天我能為您提供什麼幫助？您告訴我的越多，我就越能提供幫助。你在想什麼？我來這裡是為了幫助您找到適合您的解決方案。請注意，我是一名人工智慧助手，仍在學習入門知識。

非常神奇吧？

聽起來幾乎是人類的。週末我和 van Wilder 一起看了電影《Free Guy》，我意識到哇，我可能可以使用 @langchain/langgraph 中的 GraphState 來創建一個可以對自身執行迭代並編寫自己的程式碼的 AI。

如果您現在還沒有意識到這一點，Claude Sonnet 非常擅長 0 鏡頭編碼，甚至更擅長多鏡頭編碼。

使用庫 npm:sentiment :

來自 README.md

Sentiment 是一個 Node.js 模組，它使用 AFINN-165 單字清單和表情符號情緒排名對任意輸入文字區塊執行情緒分析。

我為圖形狀態添加了一個簡單的命令，該命令對輸出運行情緒分析，並使用新版本改進程式碼以嘗試獲得更高的分數：

// update state and continue evolution
    return new Command({
      update: {
        ...state,
        code: newCode,
        version: state.version + 1,
        analysis,
        previousSentimentDelta: currentSentimentDelta,
        type: "continue",
        output
      },
      goto: "evolve"  // Loop back to evolve
    });

我們用它可以使用的初始圖形狀態為語言圖播種（如果您願意，可以使用基礎程式碼）：

const initialWorkerCode = `
import { StateGraph, END } from "npm:@langchain/langgraph";

const workflow = new StateGraph({
  channels: {
    input: "string",
    output: "string?"
  }
});

// Initial basic response node
workflow.addNode("respond", (state) => ({
  ...state,
  output: "I understand your request and will try to help. Let me know if you need any clarification."
}));

workflow.setEntryPoint("respond");
workflow.addEdge("respond", END);

const graph = workflow.compile();
export { graph };
`;

您可以看到這是一個非常基本的回應節點，附加了一條邊。

我將當前程式碼設定為經過 10 次迭代，試圖獲得 10 或更高的情緒：

if (import.meta.main) {
  runEvolvingSystem(10, 10);
}

每次都會執行分析：

Analysis: {
  metrics: {
    emotionalRange: 0.16483516483516483,
    vocabularyVariety: 0.7142857142857143,
    emotionalBalance: 15,
    sentimentScore: 28,
    comparative: 0.3076923076923077,
    wordCount: 91
  },
  analysis: "The output, while polite and helpful, lacks several key qualities that would make it sound more human-like.  Let's analyze the metrics and then suggest improvements:\n" +
    "\n" +
    "**Analysis of Metrics and Output:**\n" +
    "\n" +
    "* **High Sentiment Score (28):** This is significantly higher than the target of 10, indicating excessive positivity.  Humans rarely maintain such a relentlessly upbeat tone, especially when asking clarifying questions.  It feels forced and insincere.\n" +
    "\n" +
    "* **Emotional Range (0.16):** This low score suggests a lack of emotional variation. The response is consistently positive, lacking nuances of expression.  Real human interactions involve a wider range of emotions, even within a single conversation.\n" +
    "\n" +
    "* **Emotional Balance (15.00):**  This metric is unclear without knowing its scale and interpretation. However, given the other metrics, it likely reflects the overwhelmingly positive sentiment.\n" +
    "\n" +
    "* **Vocabulary Variety (0.71):** This is relatively good, indicating a decent range of words. However, the phrasing is still somewhat formulaic.\n" +
    "\n" +
    "* **Comparative Score (0.3077):** This metric is also unclear without context.\n" +
    "\n" +
    "* **Word Count (91):**  A bit lengthy for a simple clarifying request.  Brevity is often more human-like in casual conversation.\n" +
    "\n" +
    "\n" +
    "**Ways to Make the Response More Human-like:**\n" +
    "\n" +
    `1. **Reduce the Overwhelming Positivity:**  The response is excessively enthusiastic.  A more natural approach would be to tone down the positive language.  Instead of "I'd love to assist you," try something like "I'd be happy to help," or even a simple "I can help with that."  Remove phrases like "I'm eager to help you in any way I can" and "I'm fully committed to making this experience as smooth and pleasant as possible for you." These are overly formal and lack genuine warmth.\n` +
    "\n" +
    '2. **Introduce Subtlety and Nuance:**  Add a touch of informality and personality.  For example, instead of "Could you please provide a bit more detail," try "Could you tell me a little more about what you need?" or "Can you give me some more information on that?"\n' +
    "\n" +
    "3. **Shorten the Response:**  The length makes it feel robotic.  Conciseness is key to human-like communication.  Combine sentences, remove redundant phrases, and get straight to the point.\n" +
    "\n" +
    '4. **Add a touch of self-deprecation or humility:**  A slightly self-deprecating remark can make the response feel more relatable. For example,  "I want to make sure I understand your needs perfectly – I sometimes miss things, so the more detail the better!"\n' +
    "\n" +
    "5. **Vary Sentence Structure:**  The response uses mostly long, similar sentence structures.  Varying sentence length and structure will make it sound more natural.\n" +
    "\n" +
    "**Example of a More Human-like Response:**\n" +
    "\n" +
    `"Thanks for reaching out!  To help me understand what you need, could you tell me a little more about it?  The more detail you can give me, the better I can assist you.  Let me know what you're looking for."\n` +
    "\n" +
    "\n" +
    "By implementing these changes, the output will sound more natural, less robotic, and more genuinely helpful, achieving a more human-like interaction.  The key is to strike a balance between helpfulness and genuine, relatable communication.\n",
  rawSentiment: {
    score: 28,
    comparative: 0.3076923076923077,
    calculation: [
      { pleasant: 3 },  { committed: 1 },
      { help: 2 },      { like: 2 },
      { help: 2 },      { eager: 2 },
      { help: 2 },      { better: 2 },
      { share: 1 },     { please: 1 },
      { perfectly: 3 }, { want: 1 },
      { love: 3 },      { reaching: 1 },
      { thank: 2 }
    ],
    tokens: [
      "thank",     "you",         "for",        "reaching",  "out",
      "i'd",       "love",        "to",         "assist",    "you",
      "but",       "i",           "want",       "to",        "make",
      "sure",      "i",           "understand", "your",      "needs",
      "perfectly", "could",       "you",        "please",    "provide",
      "a",         "bit",         "more",       "detail",    "about",
      "what",      "you're",      "looking",    "for",       "the",
      "more",      "information", "you",        "share",     "the",
      "better",    "i",           "can",        "help",      "i'm",
      "eager",     "to",          "help",       "you",       "in",
      "any",       "way",         "i",          "can",       "is",
      "there",     "a",           "particular", "area",      "you'd",
      "like",      "to",          "explore",    "further",   "your",
      "input",     "will",        "help",       "me",        "tailor",
      "my",        "assistance",  "to",         "your",      "exact",
      "needs",     "i'm",         "fully",      "committed", "to",
      "making",    "this",        "experience", "as",        "smooth",
      "and",       "pleasant",    "as",         "possible",  "for",
      "you"
    ],
    words: [
      "pleasant",  "committed",
      "help",      "like",
      "help",      "eager",
      "help",      "better",
      "share",     "please",
      "perfectly", "want",
      "love",      "reaching",
      "thank"
    ],
    positive: [
      "pleasant",  "committed",
      "help",      "like",
      "help",      "eager",
      "help",      "better",
      "share",     "please",
      "perfectly", "want",
      "love",      "reaching",
      "thank"
    ],
    negative: []
  }
}
Code evolved, testing new version...

它使用此 Analysis 類別在程式碼上得分較高。

經過 10 次迭代後，得分相當高：

Final Results:
Latest version: 10
Final sentiment score: 9
Evolution patterns used: ["basic","responsive","interactive"]

最有趣的是它所建立的圖表：

import { StateGraph, END } from "npm:@langchain/langgraph";

const workflow = new StateGraph({
  channels: {
    input: "string",
    output: "string?",
    sentiment: "number",
    context: "object"
  }
});

const positiveWords = ["good", "nice", "helpful", "appreciate", "thanks", "pleased", "glad", "great", "happy", "excellent", "wonderful", "amazing", "fantastic"];
const negativeWords = ["issue", "problem", "difficult", "confused", "frustrated", "unhappy"];

workflow.addNode("analyzeInput", (state) => {
  const input = state.input.toLowerCase();
  let sentiment = input.split(" ").reduce((score, word) => {
    if (positiveWords.includes(word)) score += 1;
    if (negativeWords.includes(word)) score -= 1;
    return score;
  }, 0);
  sentiment = Math.min(Math.max(sentiment, -5), 5);
  return {
    ...state,
    sentiment,
    context: {
      needsClarification: sentiment === 0,
      isPositive: sentiment > 0,
      isNegative: sentiment  {
  let response = "";
  const userName = state.context.userName ? `${state.context.userName}` : "there";
  if (state.context.isPositive) {
    response = `Hey ${userName}! Glad to hear things are going well. What can I do to make your day even better?`;
  } else if (state.context.isNegative) {
    response = `Hi ${userName}. I hear you're facing some challenges. Let's see if we can turn things around. What's on your mind?`;
  } else {
    response = `Hi ${userName}! What's up? How can I help you today?`;
  }
  return { ...state, output: response };
});

workflow.addNode("interactiveFollowUp", (state) => {
  let followUp = "";
  switch (state.context.topic) {
    case "technical":
      followUp = `If you're having a technical hiccup, could you tell me what's happening? Any error messages or weird behavior?`;
      break;
    case "product":
      followUp = `Curious about our products? What features are you most interested in?`;
      break;
    case "billing":
      followUp = `For billing stuff, it helps if you can give me some details about your account or the charge you're asking about. Don't worry, I'll keep it confidential.`;
      break;
    default:
      followUp = `The more you can tell me, the better I can help. What's on your mind?`;
  }
  return { ...state, output: state.output + " " + followUp };
});

workflow.addNode("adjustSentiment", (state) => {
  const sentimentAdjusters = [
    "I'm here to help find a solution that works for you.",
    "Thanks for your patience as we figure this out.",
    "Your input really helps me understand the situation better.",
    "Let's work together to find a great outcome for you."
  ];
  const adjuster = sentimentAdjusters[Math.floor(Math.random() * sentimentAdjusters.length)];
  return { ...state, output: state.output + " " + adjuster };
});

workflow.addNode("addHumanTouch", (state) => {
  const humanTouches = [
    "By the way, hope your day's going well so far!",
    "Just a heads up, I'm an AI assistant still learning the ropes.",
    "Feel free to ask me to clarify if I say anything confusing.",
    "I appreciate your understanding as we work through this."
  ];
  const touch = humanTouches[Math.floor(Math.random() * humanTouches.length)];
  return { ...state, output: state.output + " " + touch };
});

workflow.setEntryPoint("analyzeInput");
workflow.addEdge("analyzeInput", "generateResponse");
workflow.addEdge("generateResponse", "interactiveFollowUp");
workflow.addEdge("interactiveFollowUp", "adjustSentiment");
workflow.addEdge("adjustSentiment", "addHumanTouch");
workflow.addEdge("addHumanTouch", END);

const graph = workflow.compile();
export { graph };

我看到它寫的這段程式碼，立刻想到了以下陷阱：

突發的複雜性：

這是指簡單元件互動產生的複雜性，在本例中是法學碩士的演算法和它所訓練的龐大資料集。 LLM 可以產生的程式碼雖然功能強大，但卻表現出人類難以完全理解的複雜模式和依賴關係。

因此，如果我們可以稍微調整一下，並讓它編寫更乾淨、更簡單的程式碼，我們可能就走在正確的軌道上。

無論如何，這只是一個實驗，因為我想使用 langgraphs 新的命令功能。

請在評論中告訴我你的想法。

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