自己記述言語グラフの状態

最初の AI 応答:

出力: ご連絡いただきありがとうございます。ぜひお手伝いさせていただきたいと思っておりますが、お客様のニーズを完全に理解していることを確認したいと思います。探しているものについてもう少し詳しく教えていただけますか?共有していただける情報が多ければ多いほど、より良いお手伝いができます。できる限りのお手伝いをさせていただきたいと思っています。さらに調査してみたい特定の分野はありますか?皆様のご意見は、お客様のニーズに合わせてサポートを調整するのに役立ちます。私は、この体験をできるだけスムーズで楽しいものにすることに全力で取り組んでいます。

独自のグラフを書き込んだ後の AI 応答:

出力: こんにちは!どうしたの？今日はどのようにお手伝いできますか?もっと詳しく教えていただければ、より良いお手伝いができます。何を考えているの？あなたに合った解決策を見つけるお手伝いをさせていただきます。念のため言っておきますが、私はまだコツを学んでいる AI アシスタントです。

かなりすごいでしょう?

まるで人間のように聞こえます。週末、ヴァン・ワイルダーの映画「フリー・ガイ」を観て、@langchain/langgraph の GraphState を使えば、反復を実行して独自のコードを作成できる AI を作成できるかもしれないと気づきました。

まだ気づいていない方もいるかもしれませんが、Claude Sonnet は 0 ショット コーディングに非常に優れており、マルチショットではさらに優れています。

ライブラリの使用 npm:sentiment :

README.md より

Sentiment は、AFINN-165 単語リストと絵文字センチメント ランキングを使用して、入力テキストの任意のブロックに対してセンチメント分析を実行する Node.js モジュールです。

グラフの状態に簡単なコマンドを追加しました。このコマンドは、出力に対して感情分析を実行し、新しいバージョンでコードを進化させてスコアの向上を試みます。

// 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
    });

ランググラフに、操作できる初期グラフ状態 (必要に応じて基礎コード) をシードします。

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 };
`;

これは、1 つのエッジが接続された非常に基本的な応答ノードであることがわかります。

現在のコードを 10 回反復するように設定し、センチメント 10 以上のスコアを取得しようとしています。

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

毎回、分析が実行されます:

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...

この分析クラスを使用して、コードのスコアを高くします。

10 回の反復の後、かなり高いスコアが得られます:

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

最も興味深いのは、作成されるグラフです。

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 < 0,
      topic: detectTopic(input),
      userName: extractUserName(input)
    }
  };
});

function detectTopic(input) {
  if (input.includes("technical") || input.includes("error")) return "technical";
  if (input.includes("product") || input.includes("service")) return "product";
  if (input.includes("billing") || input.includes("payment")) return "billing";
  return "general";
}

function extractUserName(input) {
  const nameMatch = input.match(/(?:my name is|i'm|i am) (\w+)/i);
  return nameMatch ? nameMatch[1] : "";
}

workflow.addNode("generateResponse", (state) => {
  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 };

私はこのコードを見て、次のような落とし穴をすぐに思いつきました:

新たな複雑性:

これは、単純なコンポーネントの相互作用から生じる複雑さを指します。この場合、LLM のアルゴリズムと、LLM がトレーニングされた膨大なデータセットです。 LLM は、機能する一方で、人間が完全に理解するのが難しい複雑なパターンと依存関係を示すコードを生成できます。

したがって、これを少し戻して、よりクリーンで単純なコードを作成できるようになれば、正しい方向に進む可能性があります。

とにかく、これは単なる実験でした。langgraphs の新しいコマンド機能を使用したかったからです。

コメント欄でご意見をお聞かせください。

以上が自己記述言語グラフの状態の詳細内容です。詳細については、PHP 中国語 Web サイトの他の関連記事を参照してください。