99 lines
3.2 KiB
TypeScript
99 lines
3.2 KiB
TypeScript
import { NextResponse } from 'next/server';
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import { FieldValue } from 'firebase-admin/firestore';
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import { GeminiLlmClient } from '@/lib/ai/gemini-client';
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import { runChatExtraction } from '@/lib/ai/chat-extractor';
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import { getKnowledgeItem } from '@/lib/server/knowledge';
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import { createChatExtraction } from '@/lib/server/chat-extraction';
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import { getAdminDb } from '@/lib/firebase/admin';
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import type { ProjectPhaseScores } from '@/lib/types/project-artifacts';
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interface ExtractFromChatRequest {
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knowledgeItemId?: string;
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}
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// Increase Vercel/Next timeout for large transcripts
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export const maxDuration = 60;
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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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if (!projectId) {
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return NextResponse.json({ error: 'Missing projectId' }, { status: 400 });
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}
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const body = (await request.json()) as ExtractFromChatRequest;
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const knowledgeItemId = body.knowledgeItemId?.trim();
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if (!knowledgeItemId) {
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return NextResponse.json({ error: 'knowledgeItemId is required' }, { status: 400 });
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}
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const knowledgeItem = await getKnowledgeItem(projectId, knowledgeItemId);
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if (!knowledgeItem) {
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return NextResponse.json({ error: 'Knowledge item not found' }, { status: 404 });
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}
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console.log(`[extract-from-chat] Starting extraction for knowledgeItemId=${knowledgeItemId}, content length=${knowledgeItem.content.length}`);
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const llm = new GeminiLlmClient();
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const extractionData = await runChatExtraction(knowledgeItem, llm);
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console.log(`[extract-from-chat] Extraction complete for knowledgeItemId=${knowledgeItemId}`);
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const overallCompletion = extractionData.summary_scores.overall_completion ?? 0;
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const overallConfidence = extractionData.summary_scores.overall_confidence ?? 0;
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const extraction = await createChatExtraction({
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projectId,
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knowledgeItemId,
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data: extractionData,
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overallCompletion,
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overallConfidence,
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});
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const adminDb = getAdminDb();
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const projectRef = adminDb.collection('projects').doc(projectId);
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const snapshot = await projectRef.get();
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const docData = snapshot.data() ?? {};
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const existingScores = (docData.phaseScores ?? {}) as ProjectPhaseScores;
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const phaseHistory = Array.isArray(docData.phaseHistory) ? [...docData.phaseHistory] : [];
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phaseHistory.push({
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phase: 'extractor',
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status: 'completed',
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knowledgeItemId,
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timestamp: new Date().toISOString(),
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});
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existingScores.extractor = {
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knowledgeItemId,
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overallCompletion,
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overallConfidence,
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updatedAt: new Date().toISOString(),
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};
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await projectRef.set(
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{
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currentPhase: 'analyzed',
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phaseScores: existingScores,
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phaseStatus: 'in_progress',
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phaseHistory,
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updatedAt: FieldValue.serverTimestamp(),
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},
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{ merge: true },
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);
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return NextResponse.json({ extraction });
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} catch (error) {
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console.error('[extract-from-chat] Extraction failed', error);
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return NextResponse.json(
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{
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error: 'Failed to extract product signals',
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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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