第一条人工智能响应：
输出：感谢您伸出援手，.我很乐意为您提供帮助，但我想确保我完全理解您的需求。您能否提供有关您正在寻找的内容的更多详细信息？您分享的信息越多，我就越能提供帮助！我很乐意尽我所能地帮助你。您想进一步探索某个特定领域吗？您的意见将帮助我根据您的具体需求定制我的帮助。我完全致力于让您的体验尽可能顺利和愉快。
AI 响应，在编写自己的图表后：
输出：嗨！这是怎么回事？今天我能为您提供什么帮助？您告诉我的越多，我就越能提供帮助。你在想什么？我来这里是为了帮助您找到适合您的解决方案。请注意，我是一名人工智能助手，仍在学习入门知识。
非常神奇吧？
听起来几乎是人类的。周末我和 van Wilder 一起看了电影《Free Guy》，我意识到哇，我可能可以使用 @langchain/langgraph 中的 GraphState 来创建一个可以对自身执行迭代并编写自己的代码的 AI。
如果您现在还没有意识到这一点，Claude Sonnet 非常擅长 0 镜头编码，甚至更擅长多镜头编码。
使用库 npm:sentiment :
来自 README.md
Sentiment 是一个 Node.js 模块，它使用 AFINN-165 单词列表和表情符号情感排名对任意输入文本块执行情感分析。
我向图形状态添加了一个简单的命令，该命令对输出运行情感分析，并使用新版本改进代码以尝试获得更高的分数：
// 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
});
我们用它可以使用的初始图形状态为语言图播种（如果您愿意，可以使用基础代码）：
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 };
`;
您可以看到这是一个非常基本的响应节点，附加了一条边。
我将当前代码设置为经过 10 次迭代，试图获得 10 或更高的情绪：
if (import.meta.main) {
runEvolvingSystem(10, 10);
}
每次都会运行分析：
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 类在代码上得分更高。
经过 10 次迭代后，得分相当高：
Final Results: Latest version: 10 Final sentiment score: 9 Evolution patterns used: ["basic","responsive","interactive"]
最有趣的是它创建的图表：
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 < 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 };
我看到它编写的这段代码，立即想到了以下陷阱：
突发的复杂性：
这是指简单组件交互产生的复杂性，在本例中是法学硕士的算法和它所训练的庞大数据集。 LLM 可以生成的代码虽然功能强大，但表现出人类难以完全理解的复杂模式和依赖关系。
因此，如果我们可以稍微调整一下，并让它编写更干净、更简单的代码，我们可能就走在正确的轨道上。
无论如何，这只是一个实验，因为我想使用 langgraphs 新的命令功能。
请在评论中告诉我你的想法。
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