This may be an issue with ChatGPT, but I’ve never experienced this using their web or desktop app. I asked a question in English, which included AI-generated text, and I received an answer in Chinese. There are no Chinese text or characters in my codebase.
My prompt:
When I get responses back from the AI model, there’s quite a bit of formatting embedded in the text that isn’t covered by my app. Can you address this? I’m not sure what sort of markup this is, but surely it’s some sort of standard? I want to be sure my app comprehensively adheres to all formatting. Here’s an example of a response which has only partially-formatted text:
Certainly! Variable elimination is an algorithm used for exact inference in Bayesian networks. It works by systematically eliminating variables to simplify the computation of the posterior probabilities. Here’s a step-by-step summary of how to use variable elimination:
Steps Involved in Variable Elimination
Identify Query and Evidence Variables:
- Let (X) be the query variable.
- Let (e) be the set of evidence variables with their observed values.
- Let (Y) be the set of hidden variables (neither query nor evidence variables).
Initialization:
- List out factors for the conditional probability tables (CPTs) corresponding to the query, evidence, and hidden variables.
Factor Reduction with Evidence:
- Incorporate the evidence (e) into the factors by setting the values of evidence variables and reducing the dimensions of the factors accordingly.
Variable Ordering:
- Choose an ordering for the sequence in which you will eliminate the hidden variables (Y). This ordering can greatly affect the efficiency of the algorithm.
Eliminate Hidden Variables:
- For each hidden variable in the chosen ordering:
- Multiply all factors that include the current hidden variable to form a joint factor.
- Sum out (marginalize) the hidden variable from this joint factor to produce a new factor that no longer depends on it.
- Replace the original factors involving the hidden variable with this new factor.
Multiplication of Remaining Factors:
- After all hidden variables have been eliminated, multiply the remaining factors. These should now only depend on the query variable (X) and possibly evidence variables (E) (though the evidence variables should be fixed at this point).
Normalization:
- Normalize the resulting distribution so that it sums to 1. If the query involves multiple query variables, the resulting factor provides the joint distribution of these variables.
Detailed Example
Let’s work through a small example to clarify these steps:
Given Bayesian Network
Assume a Bayesian network with three variables (A), (B), and (C), where:
(P(A)) is the prior probability of (A).(P(B|A)) is the conditional probability of (B) given (A).
(P(C|A)) is the conditional probability of (C) given (A).
We want to compute the posterior (P(A | b, c)), given (B = b) and (C = c).
Step-by-Step Execution:
Identify Query, Evidence, and Hidden Variables:
- Query variable (X = A)
- Evidence variables (E = {B = b, C = c})
- There are no hidden variables in this specific simple network, but we’ll proceed with the elimination steps as if there were potential hidden variables.
Initialization:
- Factors: (f_1(A) = P(A)), (f_2(B|A)), and (f_3(C|A)).
Incorporate Evidence:
- Set (B = b) and (C = c): This reduces (f_2) and (f_3) to specific values based on the observed evidence.
Multiplication of Relevant Factors:
- Combine these factors into one joint factor. Since both (B) and (C) depend on (A):
[
f(A) = P(A) \cdot P(b|A) \cdot P(c|A).
]Summing Out Hidden Variables:
- There are no additional hidden variables to sum out in this simple network.
Normalization:
- The resulting factor has only the query variable (A). Normalize this factor to get the final posterior distribution:
[
P(A | b, c) = \alpha P(A) \cdot P(b|A) \cdot P(c|A),
]
where (\alpha) is a normalizing constant to ensure the probabilities sum to 1.General Algorithm (Pseudocode)
Here is the pseudocode for the variable elimination algorithm as provided:
`python
function ELIMINATION-ASK(X, e, bn):
returns a distribution over X
inputs:
X, the query variable
e, observed values for variables E
bn, a Bayesian network with variables vars
factors ←
for each V in ORDER(vars) do:
factors ← [MAKE-FACTOR(V, e)] + factors
if V is a hidden variable then:
factors ← SUM-OUT(V, factors)
return NORMALIZE(POINTWISE-PRODUCT(factors))
`Explanation of Key Functions:
MAKE-FACTOR(V, e): Generates a factor for variable (V) incorporating evidence (e).
SUM-OUT(V, factors): Sums out the variable (V) from the list of factors.
POINTWISE-PRODUCT(factors): Computes the product of a list of factors.
NORMALIZE(factors): Normalizes the final factor to ensure it is a valid probability distribution.
Conclusion
By following these steps, variable elimination systematically reduces the complexity of the Bayesian network inference problem, making it manageable even for networks with a considerable number of variables. The main computational challenges are related to the size of the intermediate factors, which depends on the variable elimination ordering and the network structure.
The response:
你好！
看起来你的AI模型回复使用了Markdown格式，这是一种广泛使用的标记语言，用于格式化文本。为了确保你的应用程序能够全面支持和渲染所有这些格式，我建议你集成一些专门的库来解析和渲染Markdown及数学公式。
以下是具体的解决方案：
1. 使用Markdown解析库
当前，你的parseFormatting函数只处理了一些基本的Markdown语法，如加粗、斜体和内联代码，但无法全面处理标题、代码块、链接、图片等复杂格式。集成一个成熟的Markdown解析库会更有效。
推荐库：
marked.js: 一个快速、可扩展的Markdown解析器。
Showdown: 一个流行的JavaScript Markdown到HTML转换库。
集成示例（使用marked.js）：
首先，在你的index.html中引入marked.js：
<!-- 在<head>部分添加 -->
<script src="https://cdn.jsdelivr.net/npm/marked/marked.min.js"></script>
然后，修改你的sendToChatGPT或相关函数，使用marked来解析Markdown：
// 假设你有一个元素用于显示AI的回复，例如：
const explanationTextElement = document.getElementById('explanation-text');
// 修改显示AI回复的部分
async function sendToChatGPT(prompt) {
// ... 你的API调用逻辑 ...
// 假设 `responseText` 是从API获取的Markdown文本
const responseText = data.choices[0].message.content;
// 使用marked解析Markdown
explanationTextElement.innerHTML = marked.parse(responseText);
// 如果有需要，添加到历史记录等
addToHistory('explain', text, null, responseText);
}
2. 渲染数学公式
在你的示例中，AI回复包含了LaTeX数学公式（如(…)和[…]）。为了在网页中正确显示这些公式，你需要集成一个数学公式渲染库。
推荐库：
KaTeX: 一个快速的LaTeX数学公式渲染库。
MathJax: 一个功能强大的JavaScript数学显示引擎。
集成示例（使用KaTeX）：
首先，在你的index.html中引入KaTeX的CSS和JS：
<!-- 在<head>部分添加 -->
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.15.3/dist/katex.min.css">
<script defer src="https://cdn.jsdelivr.net/npm/katex@0.15.3/dist/katex.min.js"></script>
<script defer src="https://cdn.jsdelivr.net/npm/katex@0.15.3/dist/contrib/auto-render.min.js"
onload="renderMathInElement(document.body);"></script>
然后，确保在将Markdown转换为HTML后，KaTeX能够自动渲染数学公式。
修改前面的sendToChatGPT函数：
async function sendToChatGPT(prompt) {
// ... 你的API调用逻辑 ...
const responseText = data.choices[0].message.content;
// 使用marked解析Markdown
explanationTextElement.innerHTML = marked.parse(responseText);
// 渲染数学公式
if (typeof renderMathInElement === 'function') {
renderMathInElement(explanationTextElement, {
// 配置选项（根据需要调整）
delimiters: [
{left: "\\(", right: "\\)", display: false},
{left: "\\[", right: "\\]", display: true}
]
});
}
addToHistory('explain', text, null, responseText);
}
3. 安全性考虑
当你将Markdown转换为HTML时，有潜在的安全风险，如跨站脚本攻击（XSS）。为了避免这些风险，建议在渲染前对HTML进行消毒。
推荐库：
- DOMPurify: 一个快速、健壮的客户端XSS过滤库。
集成示例：
首先，引入DOMPurify：
<!-- 在<head>部分添加 -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/dompurify/2.3.10/purify.min.js"></script>
然后，修改你的渲染逻辑以使用DOMPurify：
async function sendToChatGPT(prompt) {
// ... 你的API调用逻辑 ...
const responseText = data.choices[0].message.content;
// 使用marked解析Markdown
let rawHtml = marked.parse(responseText);
// 使用DOMPurify清理HTML
let cleanHtml = DOMPurify.sanitize(rawHtml);
explanationTextElement.innerHTML = cleanHtml;
// 渲染数学公式
if (typeof renderMathInElement === 'function') {
renderMathInElement(explanationTextElement, {
delimiters: [
{left: "\\(", right: "\\)", display: false},
{left: "\\[", right: "\\]", display: true}
]
});
}
addToHistory('explain', text, null, responseText);
}
4. 处理代码块
AI回复中可能包含多行代码块，这些需要特殊处理以保持格式和语法高亮。
推荐库：
- highlight.js: 一个可自动检测语言的语法高亮库。
集成示例：
首先，引入highlight.js的CSS和JS：
<!-- 在<head>部分添加 -->
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/styles/default.min.css">
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/highlight.min.js"></script>
<script>hljs.highlightAll();</script>
然后，确保在Markdown转换之后，对代码块进行高亮：
async function sendToChatGPT(prompt) {
// ... 你的API调用逻辑 ...
const responseText = data.choices[0].message.content;
// 使用marked解析Markdown
let rawHtml = marked.parse(responseText);
// 使用DOMPurify清理HTML
let cleanHtml = DOMPurify.sanitize(rawHtml);
explanationTextElement.innerHTML = cleanHtml;
// 渲染数学公式
if (typeof renderMathInElement === 'function') {
renderMathInElement(explanationTextElement, {
delimiters: [
{left: "\\(", right: "\\)", display: false},
{left: "\\[", right: "\\]", display: true}
]
});
}
// 高亮代码块
hljs.highlightAll();
addToHistory('explain', text, null, responseText);
}
5. 总结
通过集成上述库，你的应用将能够全面支持和渲染AI模型返回的各种Markdown格式，包括标题、列表、代码块、数学公式等。同时，使用DOMPurify确保了渲染内容的安全性，防止潜在的XSS攻击。
完整的集成步骤：
引入必要的库：marked.js、KaTeX或MathJax、highlight.js、DOMPurify。
修改渲染逻辑：使用marked解析Markdown，使用DOMPurify消毒HTML，使用KaTeX或MathJax渲染数学公式，使用highlight.js高亮代码块。
测试不同格式：确保各种Markdown格式在应用中正确显示和渲染。
示例index.html中的引入部分：
<head>
<!-- 其他内容 -->
<script src="https://cdn.jsdelivr.net/npm/marked/marked.min.js"></script>
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.15.3/dist/katex.min.css">
<script defer src="https://cdn.jsdelivr.net/npm/katex@0.15.3/dist/katex.min.js"></script>
<script defer src="https://cdn.jsdelivr.net/npm/katex@0.15.3/dist/contrib/auto-render.min.js"
onload="renderMathInElement(document.body);"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/highlight.min.js"></script>
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/styles/default.min.css">
<script>hljs.highlightAll();</script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/dompurify/2.3.10/purify.min.js"></script>
</head>
通过这些修改，你的应用程序将能够全面支持和渲染AI模型返回的所有格式化文本，提供更好的用户体验。
如果你有任何进一步的问题，欢迎随时提问！