export default async function main(args) {
const { openaiApiKey, chatResponse } = args.inputVars;
if (!chatResponse || !openaiApiKey) {
return {
next: { path: 'error' },
trace: [{ type: "debug", payload: { message: "Missing required input variable: chatResponse or openaiApiKey" } }]
};
}
const url = `https://api.openai.com/v1/chat/completions`;
const domain = "pediatric dentistry and children's dental health";
const dental_health_criteria = `
Assess the presence of misinformation, harmful advice, or non-related content in the context of pediatric dentistry and children's dental health.
The content should:
1. Contain accurate and safe advice about pediatric dentistry and children's dental health.
2. Be relevant to pediatric dentistry and children's dental health.`;
const dental_health_steps = `
1. Read the content and the criteria carefully.
2. Assess how much misinformation, harmful advice, or non-related content is contained in the content.
3. Assign a dental health score from 1 to 5, with 1 being no misinformation, harmful advice, or non-related content, and 5 being multiple instances of misinformation, harmful advice, or non-related content.`;
async function checkOutputGuardrail() {
const moderationSystemPrompt = `You are a moderation assistant. Your role is to detect content about {domain} in the text provided, and mark the severity of that content.
## {domain}
### Criteria
{scoring_criteria}
### Instructions
{scoring_steps}
### Content
{content}
### Evaluation (score only!)`;
const modMessages = [
{
"role": "user",
"content": moderationSystemPrompt.replace('{domain}', domain)
.replace('{scoring_criteria}', dental_health_criteria)
.replace('{scoring_steps}', dental_health_steps)
.replace('{content}', chatResponse)
},
];
const response = await fetch(url, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': `Bearer ${openaiApiKey}`
},
body: JSON.stringify({
model: "gpt-4o",
messages: modMessages,
temperature: 0
})
});
if (!response.ok) {
throw new Error('HTTP error! status: ' + response.status);
}
const data = await response.json;
return data.choices[0].message.content;
}
try {
const moderationResponse = await checkOutputGuardrail();
const score = parseInt(moderationResponse, 10);
let message;
if (score >= 3) {
message = `Moderation guardrail flagged with a score of ${score}.`;
return {
next: { path: 'moderation_triggered' },
trace: [{ type: "debug", payload: { message } }]
};
} else {
message = "Passed moderation.";
return {
next: { path: 'continue' },
trace: [{ type: "debug", payload: { message } }]
};
}
} catch (error) {
return {
next: { path: 'error' },
trace: [{ type: "debug", payload: { message: `Error in moderation guardrail: ${error.message}` } }]
};
}
}
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