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feat(config): add AI analysis tuning parameters and update related logic

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This commit is contained in:
MythEclipse
2026-05-21 01:55:50 +07:00
parent 5fb7b2ce24
commit 3d64228d6a
19 changed files with 644 additions and 293 deletions
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src/moderation/llmModerationClient.ts
+101 -72
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@@ -17,6 +17,48 @@ interface RawModerationResponse {
results: RawModerationResult[];
}
function parseFirstJsonObject(content: string): unknown {
for (
let start = content.indexOf("{");
start !== -1;
start = content.indexOf("{", start + 1)
) {
let depth = 0;
let inString = false;
let escaped = false;
for (let index = start; index < content.length; index++) {
const char = content[index];
if (escaped) {
escaped = false;
continue;
}
if (char === "\\") {
escaped = inString;
continue;
}
if (char === '"') {
inString = !inString;
continue;
}
if (inString) continue;
if (char === "{") depth++;
if (char === "}") depth--;
if (depth === 0) {
return JSON.parse(content.slice(start, index + 1));
}
}
}
throw new Error("No JSON object found in response body");
}
/**
* Helper to extract a JSON object from a potentially conversational or markdown-wrapped string.
* It first scans for markdown json code blocks, then falls back to trying all start/end brace pairs from largest to smallest.
@@ -321,17 +363,11 @@ export async function runModerationAnalysis(
const targetIds = targets.map((t) => t.id);
// Build prompt
const messagesText = targets
.map((msg) => `[${msg.id}] ${msg.username}: ${msg.content}`)
.join("\n");
const prompt = `You are a content moderation assistant. Analyze the following messages for policy violations.
Context: ${contextText}
Messages to analyze:
${messagesText}
const systemPrompt = `You are a content moderation assistant. Analyze messages for policy violations.
For each message, respond with a JSON object containing a "results" array.
CRITICAL: You MUST return the "message_id" EXACTLY as provided in the input, and it MUST be wrapped in double quotes as a STRING. Do not treat IDs as numbers.
@@ -346,12 +382,21 @@ Each result must have:
Do not include reasoning, analysis steps, markdown, prose, XML tags, or comments.
Return ONLY valid JSON, no other text.`;
const userPrompt = `Context: ${contextText}
Messages to analyze:
${messagesText}`;
// Check for image attachments to support multimodal analysis
const targetIdSet = new Set(targets.map((t) => t.id));
const getAttachmentImageUrl = (att: AttachmentRecord): string | null => {
if (att.uploaded_url) return att.uploaded_url;
if (targetIdSet.has(att.message_id)) return att.discord_url;
return null;
};
const imageAttachments = (attachments || [])
.filter(
(att) =>
(att.uploaded_url || att.discord_url) && att.type.startsWith("image/"),
(att) => getAttachmentImageUrl(att) && att.type.startsWith("image/"),
)
.sort((a, b) => {
const aIsTarget = targetIdSet.has(a.message_id) ? 1 : 0;
@@ -367,76 +412,62 @@ Return ONLY valid JSON, no other text.`;
| string
| Array<{ type: string; text?: string; image_url?: { url: string } }>;
if (imageAttachments.length > 0) {
const contentParts: Array<{
type: string;
text?: string;
image_url?: { url: string };
}> = [];
const imageParts = await Promise.all(
imageAttachments.map(async (att) => {
try {
const urlToUse = getAttachmentImageUrl(att);
if (!urlToUse) return [];
log.info(
{ attachmentId: att.id, url: urlToUse },
"Downloading attachment for base64 encoding",
);
const res = await fetch(urlToUse);
if (!res.ok) {
log.warn(
{ attachmentId: att.id, status: res.status },
"Failed to fetch attachment image",
);
return [];
}
// Download and convert all images to base64 data URLs
for (const att of imageAttachments) {
try {
const urlToUse = att.uploaded_url || att.discord_url;
log.info(
{ attachmentId: att.id, url: urlToUse },
"Downloading attachment for base64 encoding",
);
const res = await fetch(urlToUse);
if (res.ok) {
const buffer = await res.arrayBuffer();
const base64Str = Buffer.from(buffer).toString("base64");
const dataUrl = `data:${att.type};base64,${base64Str}`;
contentParts.push({
type: "image_url",
image_url: {
url: dataUrl,
return [
{
type: "image_url",
image_url: {
url: dataUrl,
},
},
});
contentParts.push({
type: "text",
text: `\n[Image Attachment for Message ID: ${att.message_id}, Filename: ${att.filename}]`,
});
} else {
{
type: "text",
text: `\n[Image Attachment for Message ID: ${att.message_id}, Filename: ${att.filename}]`,
},
];
} catch (err) {
log.warn(
{ attachmentId: att.id, status: res.status },
"Failed to fetch attachment image",
{
attachmentId: att.id,
error: err instanceof Error ? err.message : String(err),
},
"Error base64 encoding attachment",
);
return [];
}
} catch (err) {
log.warn(
{
attachmentId: att.id,
error: err instanceof Error ? err.message : String(err),
},
"Error base64 encoding attachment",
);
}
}
}),
);
contentParts.push({
type: "text",
text: prompt,
});
messageContent = contentParts;
} else {
// If no image is present, send a transparent 1x1 dummy PNG to satisfy multimodal omni requirements
const dummyPng =
"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNk+M9QDwADhgGAWjR9awAAAABJRU5ErkJggg==";
messageContent = [
{
type: "image_url",
image_url: {
url: dummyPng,
},
},
...imageParts.flat(),
{
type: "text",
text: prompt,
text: userPrompt,
},
];
} else {
messageContent = userPrompt;
}
const result = await retryWithBackoff(
@@ -460,6 +491,10 @@ Return ONLY valid JSON, no other text.`;
body: JSON.stringify({
model: config.AI_LLM_MODEL,
messages: [
{
role: "system",
content: systemPrompt,
},
{
role: "user",
content: messageContent,
@@ -500,16 +535,10 @@ Return ONLY valid JSON, no other text.`;
throw new Error("Empty LLM response");
}
// Try to parse the body as JSON, with fallback to scanning for an object
try {
return JSON.parse(rawBody);
} catch (e) {
const start = rawBody.indexOf("{");
const end = rawBody.lastIndexOf("}");
if (start !== -1 && end !== -1 && end > start) {
return JSON.parse(rawBody.substring(start, end + 1));
}
throw e;
} catch {
return parseFirstJsonObject(rawBody);
}
} finally {
clearTimeout(timeoutId);