106 lines
3.6 KiB
TypeScript
106 lines
3.6 KiB
TypeScript
import { NextResponse } from 'next/server';
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import { getAdminAuth, getAdminDb } from '@/lib/firebase/admin';
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import { getAlloyDbClient } from '@/lib/db/alloydb';
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import { GeminiLlmClient } from '@/lib/ai/gemini-client';
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import { z } from 'zod';
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const ThemeGroupingSchema = z.object({
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themes: z.array(z.object({
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theme: z.string().describe('A short, descriptive theme name (2-4 words)'),
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description: z.string().describe('A brief description of what this theme represents'),
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insightIds: z.array(z.string()).describe('Array of insight IDs that belong to this theme'),
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})),
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});
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export async function POST(
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request: Request,
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{ params }: { params: Promise<{ projectId: string }> }
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) {
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try {
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const { projectId } = await params;
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// Authentication (skip in development if no auth header)
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const authHeader = request.headers.get('Authorization');
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const isDevelopment = process.env.NODE_ENV === 'development';
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if (!isDevelopment || authHeader?.startsWith('Bearer ')) {
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if (!authHeader?.startsWith('Bearer ')) {
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return NextResponse.json({ error: 'Unauthorized' }, { status: 401 });
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}
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const token = authHeader.substring(7);
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const auth = getAdminAuth();
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const decoded = await auth.verifyIdToken(token);
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if (!decoded?.uid) {
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return NextResponse.json({ error: 'Invalid token' }, { status: 401 });
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}
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}
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// Get insights from request body
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const { insights } = await request.json();
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if (!insights || insights.length === 0) {
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return NextResponse.json({
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success: true,
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themes: [],
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});
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}
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console.log('[API /knowledge/themes] Grouping', insights.length, 'insights into themes');
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// Prepare insights for AI analysis
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const insightsContext = insights.map((insight: any, index: number) =>
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`[${insight.id}] ${insight.content?.substring(0, 200) || insight.title}`
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).join('\n\n');
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// Use AI to group insights into themes
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const llm = new GeminiLlmClient();
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const systemPrompt = `You are an expert at analyzing and categorizing information. Given a list of insights/knowledge chunks, group them into meaningful themes. Each theme should represent a coherent topic or concept. Aim for 3-7 themes depending on the diversity of content.`;
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const userPrompt = `Analyze these insights and group them into themes:
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${insightsContext}
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Group these insights into themes. Each insight ID is in brackets at the start of each line. Return the themes with their associated insight IDs.`;
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try {
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const result = await llm.structuredCall({
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model: 'gemini',
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systemPrompt,
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messages: [{ role: 'user', content: userPrompt }],
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schema: ThemeGroupingSchema,
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temperature: 0.3,
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});
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console.log('[API /knowledge/themes] Generated', result.themes.length, 'themes');
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return NextResponse.json({
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success: true,
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themes: result.themes,
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});
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} catch (aiError) {
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console.error('[API /knowledge/themes] AI grouping failed:', aiError);
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// Fallback: create a single "Ungrouped" theme with all insights
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return NextResponse.json({
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success: true,
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themes: [{
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theme: 'All Insights',
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description: 'Ungrouped insights',
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insightIds: insights.map((i: any) => i.id),
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}],
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});
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}
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} catch (error) {
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console.error('[API /knowledge/themes] Error:', error);
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return NextResponse.json(
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{
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error: 'Failed to group insights into themes',
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details: error instanceof Error ? error.message : String(error)
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},
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{ status: 500 }
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);
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}
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}
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