VIBN Frontend for Coolify deployment
This commit is contained in:
504
app/api/ai/chat/route.ts
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504
app/api/ai/chat/route.ts
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@@ -0,0 +1,504 @@
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import { NextResponse } from 'next/server';
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import { z } from 'zod';
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import { GeminiLlmClient } from '@/lib/ai/gemini-client';
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import type { LlmClient } from '@/lib/ai/llm-client';
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import { getAdminDb } from '@/lib/firebase/admin';
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import { FieldValue } from 'firebase-admin/firestore';
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import { MODE_SYSTEM_PROMPTS, ChatMode } from '@/lib/ai/chat-modes';
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import { resolveChatMode } from '@/lib/server/chat-mode-resolver';
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import {
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buildProjectContextForChat,
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determineArtifactsUsed,
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formatContextForPrompt,
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} from '@/lib/server/chat-context';
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import { logProjectEvent } from '@/lib/server/logs';
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import type { CollectorPhaseHandoff } from '@/lib/types/phase-handoff';
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// Increase timeout for Gemini 3 Pro thinking mode (can take 1-2 minutes)
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export const maxDuration = 180; // 3 minutes
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export const dynamic = 'force-dynamic';
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const ChatReplySchema = z.object({
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reply: z.string(),
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visionAnswers: z.object({
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q1: z.string().optional(), // Answer to question 1
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q2: z.string().optional(), // Answer to question 2
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q3: z.string().optional(), // Answer to question 3
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allAnswered: z.boolean().optional(), // True when all 3 are complete
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}).optional(),
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collectorHandoff: z.object({
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hasDocuments: z.boolean().optional(),
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documentCount: z.number().optional(),
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githubConnected: z.boolean().optional(),
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githubRepo: z.string().optional(),
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extensionLinked: z.boolean().optional(),
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extensionDeclined: z.boolean().optional(),
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noGithubYet: z.boolean().optional(),
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readyForExtraction: z.boolean().optional(),
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}).optional(),
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extractionReviewHandoff: z.object({
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extractionApproved: z.boolean().optional(),
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readyForVision: z.boolean().optional(),
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}).optional(),
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});
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interface ChatRequestBody {
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projectId?: string;
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message?: string;
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overrideMode?: ChatMode;
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}
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async function appendConversation(
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projectId: string,
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messages: Array<{ role: 'user' | 'assistant'; content: string }>,
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) {
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const adminDb = getAdminDb();
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const docRef = adminDb.collection('chat_conversations').doc(projectId);
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await adminDb.runTransaction(async (tx) => {
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const snapshot = await tx.get(docRef);
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const existing = (snapshot.exists ? (snapshot.data()?.messages as unknown[]) : []) ?? [];
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const now = new Date().toISOString();
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const newMessages = messages.map((m) => ({
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role: m.role,
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content: m.content,
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// Use a simple ISO string for message timestamps to avoid FieldValue
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// restrictions inside arrays.
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createdAt: now,
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}));
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tx.set(
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docRef,
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{
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projectId,
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messages: [...existing, ...newMessages],
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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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});
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}
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export async function POST(request: Request) {
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try {
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const body = (await request.json()) as ChatRequestBody;
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const projectId = body.projectId?.trim();
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const message = body.message?.trim();
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if (!projectId || !message) {
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return NextResponse.json({ error: 'projectId and message are required' }, { status: 400 });
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}
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// Verify project exists
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const adminDb = getAdminDb();
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const projectSnapshot = await adminDb.collection('projects').doc(projectId).get();
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if (!projectSnapshot.exists) {
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return NextResponse.json({ error: 'Project not found' }, { status: 404 });
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}
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const projectData = projectSnapshot.data() ?? {};
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// Resolve chat mode (uses new resolver)
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const resolvedMode = body.overrideMode ?? await resolveChatMode(projectId);
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console.log(`[AI Chat] Mode: ${resolvedMode}`);
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// Build comprehensive context with vector retrieval
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// Only include GitHub analysis for MVP generation (not needed for vision questions)
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const context = await buildProjectContextForChat(projectId, resolvedMode, message, {
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retrievalLimit: 10,
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includeVectorSearch: true,
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includeGitHubAnalysis: resolvedMode === 'mvp_mode', // Only load repo analysis when generating MVP
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});
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console.log(`[AI Chat] Context built: ${context.retrievedChunks.length} vector chunks retrieved`);
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// Get mode-specific system prompt
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const systemPrompt = MODE_SYSTEM_PROMPTS[resolvedMode];
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// Format context for LLM
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const contextSummary = formatContextForPrompt(context);
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// Prepare enhanced system prompt with context
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const enhancedSystemPrompt = `${systemPrompt}
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## Current Project Context
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${contextSummary}
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---
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You have access to:
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- Project artifacts (product model, MVP plan, marketing plan)
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- Knowledge items (${context.knowledgeSummary.totalCount} total)
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- Extraction signals (${context.extractionSummary.totalCount} analyzed)
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${context.retrievedChunks.length > 0 ? `- ${context.retrievedChunks.length} relevant chunks from vector search (most similar to user's query)` : ''}
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${context.repositoryAnalysis ? `- GitHub repository analysis (${context.repositoryAnalysis.totalFiles} files)` : ''}
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${context.sessionHistory.totalSessions > 0 ? `- Complete Cursor session history (${context.sessionHistory.totalSessions} sessions, ${context.sessionHistory.messages.length} messages in chronological order)` : ''}
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Use this context to provide specific, grounded responses. The session history shows your complete conversation history with the user - use it to understand what has been built and discussed.`;
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// Load existing conversation history
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const conversationDoc = await adminDb.collection('chat_conversations').doc(projectId).get();
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const conversationData = conversationDoc.exists ? conversationDoc.data() : null;
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const conversationHistory = Array.isArray(conversationData?.messages)
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? conversationData.messages
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: [];
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// Build full message context (history + current message)
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const messages = [
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...conversationHistory.map((msg: any) => ({
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role: msg.role as 'user' | 'assistant',
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content: msg.content as string,
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})),
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{
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role: 'user' as const,
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content: message,
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},
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];
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console.log(`[AI Chat] Sending ${messages.length} messages to LLM (${conversationHistory.length} from history + 1 new)`);
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console.log(`[AI Chat] Mode: ${resolvedMode}, Phase: ${projectData.currentPhase}, Has extraction: ${!!context.phaseHandoffs?.extraction}`);
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// Log system prompt length
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console.log(`[AI Chat] System prompt length: ${enhancedSystemPrompt.length} chars (~${Math.ceil(enhancedSystemPrompt.length / 4)} tokens)`);
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// Log each message length
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messages.forEach((msg, i) => {
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console.log(`[AI Chat] Message ${i + 1} (${msg.role}): ${msg.content.length} chars (~${Math.ceil(msg.content.length / 4)} tokens)`);
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});
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const totalInputChars = enhancedSystemPrompt.length + messages.reduce((sum, msg) => sum + msg.content.length, 0);
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console.log(`[AI Chat] Total input: ${totalInputChars} chars (~${Math.ceil(totalInputChars / 4)} tokens)`);
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// Log system prompt preview (first 500 chars)
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console.log(`[AI Chat] System prompt preview: ${enhancedSystemPrompt.substring(0, 500)}...`);
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// Log last user message
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const lastUserMsg = messages[messages.length - 1];
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console.log(`[AI Chat] User message: ${lastUserMsg.content}`);
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// Safety check: extraction_review_mode requires extraction results
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if (resolvedMode === 'extraction_review_mode' && !context.phaseHandoffs?.extraction) {
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console.warn(`[AI Chat] WARNING: extraction_review_mode active but no extraction results found for project ${projectId}`);
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}
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const llm: LlmClient = new GeminiLlmClient();
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// Configure thinking mode based on task complexity
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// Simple modes (collector, extraction_review) don't need deep thinking
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// Complex modes (mvp, vision) benefit from extended reasoning
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const needsThinking = resolvedMode === 'mvp_mode' || resolvedMode === 'vision_mode';
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const reply = await llm.structuredCall<{
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reply: string;
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visionAnswers?: {
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q1?: string;
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q2?: string;
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q3?: string;
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allAnswered?: boolean;
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};
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collectorHandoff?: {
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hasDocuments?: boolean;
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documentCount?: number;
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githubConnected?: boolean;
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githubRepo?: string;
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extensionLinked?: boolean;
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extensionDeclined?: boolean;
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noGithubYet?: boolean;
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readyForExtraction?: boolean;
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};
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extractionReviewHandoff?: {
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extractionApproved?: boolean;
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readyForVision?: boolean;
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};
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}>({
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model: 'gemini',
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systemPrompt: enhancedSystemPrompt,
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messages: messages, // Full conversation history!
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schema: ChatReplySchema,
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temperature: 0.4,
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thinking_config: needsThinking ? {
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thinking_level: 'high',
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include_thoughts: false,
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} : undefined,
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});
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// Store all vision answers when provided
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if (reply.visionAnswers) {
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const updates: any = {};
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if (reply.visionAnswers.q1) {
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updates['visionAnswers.q1'] = reply.visionAnswers.q1;
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console.log('[AI Chat] Storing vision answer Q1');
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}
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if (reply.visionAnswers.q2) {
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updates['visionAnswers.q2'] = reply.visionAnswers.q2;
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console.log('[AI Chat] Storing vision answer Q2');
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}
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if (reply.visionAnswers.q3) {
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updates['visionAnswers.q3'] = reply.visionAnswers.q3;
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console.log('[AI Chat] Storing vision answer Q3');
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}
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// If all answers are complete, trigger MVP generation
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if (reply.visionAnswers.allAnswered) {
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updates['visionAnswers.allAnswered'] = true;
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updates['readyForMVP'] = true;
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console.log('[AI Chat] ✅ All 3 vision answers complete - ready for MVP generation');
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}
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if (Object.keys(updates).length > 0) {
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updates['visionAnswers.updatedAt'] = new Date().toISOString();
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await adminDb.collection('projects').doc(projectId).set(updates, { merge: true })
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.catch((error) => {
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console.error('[ai/chat] Failed to store vision answers', error);
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});
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}
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}
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// Best-effort: append this turn to the persisted conversation history
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appendConversation(projectId, [
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{ role: 'user', content: message },
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{ role: 'assistant', content: reply.reply },
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]).catch((error) => {
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console.error('[ai/chat] Failed to append conversation history', error);
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});
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// If in collector mode, always update handoff state based on actual project context
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// This ensures the checklist updates even if AI doesn't return collectorHandoff
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if (resolvedMode === 'collector_mode') {
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// Derive handoff state from actual project context
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const hasDocuments = (context.knowledgeSummary.bySourceType['imported_document'] ?? 0) > 0;
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const documentCount = context.knowledgeSummary.bySourceType['imported_document'] ?? 0;
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const githubConnected = !!context.project.githubRepo;
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const extensionLinked = context.project.extensionLinked ?? false;
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// Check if AI indicated readiness (from reply if provided, otherwise check reply text)
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let readyForExtraction = reply.collectorHandoff?.readyForExtraction ?? false;
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// Fallback: If AI says certain phrases, assume user confirmed readiness
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// IMPORTANT: These phrases must be SPECIFIC to avoid false positives
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if (!readyForExtraction && reply.reply) {
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const replyLower = reply.reply.toLowerCase();
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// Check for explicit analysis/digging phrases (not just "perfect!")
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const analysisKeywords = ['analyze', 'analyzing', 'digging', 'extraction', 'processing'];
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const hasAnalysisKeyword = analysisKeywords.some(keyword => replyLower.includes(keyword));
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// Only trigger if AI mentions BOTH readiness AND analysis action
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if (hasAnalysisKeyword) {
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const confirmPhrases = [
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'let me analyze what you',
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'i\'ll start digging into',
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'i\'m starting the analysis',
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'running the extraction',
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'processing what you\'ve shared',
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];
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readyForExtraction = confirmPhrases.some(phrase => replyLower.includes(phrase));
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if (readyForExtraction) {
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console.log(`[AI Chat] Detected readiness from AI reply text: "${reply.reply.substring(0, 100)}"`);
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}
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}
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}
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const handoff: CollectorPhaseHandoff = {
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phase: 'collector',
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readyForNextPhase: readyForExtraction,
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confidence: readyForExtraction ? 0.9 : 0.5,
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confirmed: {
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hasDocuments,
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documentCount,
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githubConnected,
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githubRepo: context.project.githubRepo ?? undefined,
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extensionLinked,
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},
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uncertain: {
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extensionDeclined: reply.collectorHandoff?.extensionDeclined ?? false,
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noGithubYet: reply.collectorHandoff?.noGithubYet ?? false,
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},
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missing: [],
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questionsForUser: [],
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sourceEvidence: [],
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version: '1.0',
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timestamp: new Date().toISOString(),
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};
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// Persist to project phaseData
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await adminDb.collection('projects').doc(projectId).set(
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{
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'phaseData.phaseHandoffs.collector': handoff,
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},
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{ merge: true }
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).catch((error) => {
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console.error('[ai/chat] Failed to persist collector handoff', error);
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});
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console.log(`[AI Chat] Collector handoff persisted:`, {
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hasDocuments: handoff.confirmed.hasDocuments,
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githubConnected: handoff.confirmed.githubConnected,
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extensionLinked: handoff.confirmed.extensionLinked,
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readyForExtraction: handoff.readyForNextPhase,
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});
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// Auto-transition to extraction phase if ready
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if (handoff.readyForNextPhase) {
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console.log(`[AI Chat] Collector complete - triggering backend extraction`);
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// Mark collector as complete
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await adminDb.collection('projects').doc(projectId).update({
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'phaseData.collectorCompletedAt': new Date().toISOString(),
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}).catch((error) => {
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console.error('[ai/chat] Failed to mark collector complete', error);
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});
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// Trigger backend extraction (async - don't await)
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import('@/lib/server/backend-extractor').then(({ runBackendExtractionForProject }) => {
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runBackendExtractionForProject(projectId).catch((error) => {
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console.error(`[AI Chat] Backend extraction failed for project ${projectId}:`, error);
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});
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});
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}
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}
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// Handle extraction review → vision phase transition
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if (resolvedMode === 'extraction_review_mode') {
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// Check if AI indicated extraction is approved and ready for vision
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let readyForVision = reply.extractionReviewHandoff?.readyForVision ?? false;
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// Fallback: Check reply text for approval phrases
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if (!readyForVision && reply.reply) {
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const replyLower = reply.reply.toLowerCase();
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// Check for vision transition phrases
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const visionKeywords = ['vision', 'mvp', 'roadmap', 'plan'];
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const hasVisionKeyword = visionKeywords.some(keyword => replyLower.includes(keyword));
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if (hasVisionKeyword) {
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const confirmPhrases = [
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'ready to move to',
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'ready for vision',
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'let\'s move to vision',
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'moving to vision',
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'great! let\'s define',
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'perfect! now let\'s',
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];
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readyForVision = confirmPhrases.some(phrase => replyLower.includes(phrase));
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if (readyForVision) {
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console.log(`[AI Chat] Detected vision readiness from AI reply text: "${reply.reply.substring(0, 100)}"`);
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}
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}
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}
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if (readyForVision) {
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console.log(`[AI Chat] Extraction review complete - transitioning to vision phase`);
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// Mark extraction review as complete and transition to vision
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await adminDb.collection('projects').doc(projectId).update({
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currentPhase: 'vision',
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phaseStatus: 'in_progress',
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'phaseData.extractionReviewCompletedAt': new Date().toISOString(),
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}).catch((error) => {
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console.error('[ai/chat] Failed to transition to vision phase', error);
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});
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}
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}
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// Save conversation history
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const newConversationHistory = [
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...conversationHistory,
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{
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role: 'user' as const,
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content: message,
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createdAt: new Date().toISOString(),
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},
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{
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role: 'assistant' as const,
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content: reply.reply,
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createdAt: new Date().toISOString(),
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},
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];
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await adminDb.collection('chat_conversations').doc(projectId).set(
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{
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projectId,
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userId: projectData.userId,
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messages: newConversationHistory,
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updatedAt: new Date().toISOString(),
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},
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{ merge: true }
|
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).catch((error) => {
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console.error('[ai/chat] Failed to save conversation history', error);
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||||
});
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console.log(`[AI Chat] Conversation history saved (${newConversationHistory.length} total messages)`);
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// Determine which artifacts were used
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const artifactsUsed = determineArtifactsUsed(context);
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// Log successful interaction
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logProjectEvent({
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projectId,
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userId: projectData.userId ?? null,
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eventType: 'chat_interaction',
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mode: resolvedMode,
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phase: projectData.currentPhase ?? null,
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artifactsUsed,
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usedVectorSearch: context.retrievedChunks.length > 0,
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vectorChunkCount: context.retrievedChunks.length,
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promptVersion: '2.0', // Updated with vector search
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modelUsed: process.env.VERTEX_AI_MODEL || 'gemini-3-pro-preview',
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success: true,
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errorMessage: null,
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metadata: {
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||||
knowledgeCount: context.knowledgeSummary.totalCount,
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||||
extractionCount: context.extractionSummary.totalCount,
|
||||
hasGithubRepo: !!context.repositoryAnalysis,
|
||||
},
|
||||
}).catch((err) => console.error('[ai/chat] Failed to log event:', err));
|
||||
|
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return NextResponse.json({
|
||||
reply: reply.reply,
|
||||
mode: resolvedMode,
|
||||
projectPhase: projectData.currentPhase ?? null,
|
||||
artifactsUsed,
|
||||
usedVectorSearch: context.retrievedChunks.length > 0,
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('[ai/chat] Error handling chat request', error);
|
||||
|
||||
// Log error (best-effort) - extract projectId from request body if available
|
||||
const errorProjectId = typeof (error as { projectId?: string })?.projectId === 'string'
|
||||
? (error as { projectId: string }).projectId
|
||||
: null;
|
||||
|
||||
if (errorProjectId) {
|
||||
logProjectEvent({
|
||||
projectId: errorProjectId,
|
||||
userId: null,
|
||||
eventType: 'error',
|
||||
mode: null,
|
||||
phase: null,
|
||||
artifactsUsed: [],
|
||||
usedVectorSearch: false,
|
||||
promptVersion: '2.0',
|
||||
modelUsed: process.env.VERTEX_AI_MODEL || 'gemini-3-pro-preview',
|
||||
success: false,
|
||||
errorMessage: error instanceof Error ? error.message : String(error),
|
||||
}).catch((err) => console.error('[ai/chat] Failed to log error:', err));
|
||||
}
|
||||
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: 'Failed to process chat message',
|
||||
details: error instanceof Error ? error.message : String(error),
|
||||
},
|
||||
{ status: 500 },
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
37
app/api/ai/conversation/reset/route.ts
Normal file
37
app/api/ai/conversation/reset/route.ts
Normal file
@@ -0,0 +1,37 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getAdminDb } from '@/lib/firebase/admin';
|
||||
|
||||
export async function POST(request: Request) {
|
||||
try {
|
||||
const url = new URL(request.url);
|
||||
const body = await request
|
||||
.json()
|
||||
.catch(() => ({ projectId: url.searchParams.get('projectId') }));
|
||||
|
||||
const projectId = (body?.projectId ?? url.searchParams.get('projectId') ?? '').trim();
|
||||
|
||||
if (!projectId) {
|
||||
return NextResponse.json(
|
||||
{ error: 'projectId is required' },
|
||||
{ status: 400 },
|
||||
);
|
||||
}
|
||||
|
||||
const adminDb = getAdminDb();
|
||||
const docRef = adminDb.collection('chat_conversations').doc(projectId);
|
||||
await docRef.delete();
|
||||
|
||||
return NextResponse.json({ success: true });
|
||||
} catch (error) {
|
||||
console.error('[ai/conversation/reset] Failed to reset conversation', error);
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: 'Failed to reset conversation',
|
||||
details: error instanceof Error ? error.message : String(error),
|
||||
},
|
||||
{ status: 500 },
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
54
app/api/ai/conversation/route.ts
Normal file
54
app/api/ai/conversation/route.ts
Normal file
@@ -0,0 +1,54 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getAdminDb } from '@/lib/firebase/admin';
|
||||
|
||||
type StoredMessageRole = 'user' | 'assistant';
|
||||
|
||||
type ConversationMessage = {
|
||||
role: StoredMessageRole;
|
||||
content: string;
|
||||
createdAt?: { _seconds: number; _nanoseconds: number };
|
||||
};
|
||||
|
||||
type ConversationResponse = {
|
||||
messages: ConversationMessage[];
|
||||
};
|
||||
|
||||
export async function GET(request: Request) {
|
||||
try {
|
||||
const url = new URL(request.url);
|
||||
const projectId = (url.searchParams.get('projectId') ?? '').trim();
|
||||
|
||||
if (!projectId) {
|
||||
return NextResponse.json(
|
||||
{ error: 'projectId is required' },
|
||||
{ status: 400 },
|
||||
);
|
||||
}
|
||||
|
||||
const adminDb = getAdminDb();
|
||||
const docRef = adminDb.collection('chat_conversations').doc(projectId);
|
||||
const snapshot = await docRef.get();
|
||||
|
||||
if (!snapshot.exists) {
|
||||
const empty: ConversationResponse = { messages: [] };
|
||||
return NextResponse.json(empty);
|
||||
}
|
||||
|
||||
const data = snapshot.data() as { messages?: ConversationMessage[] };
|
||||
const messages = Array.isArray(data.messages) ? data.messages : [];
|
||||
|
||||
const response: ConversationResponse = { messages };
|
||||
return NextResponse.json(response);
|
||||
} catch (error) {
|
||||
console.error('[ai/conversation] Failed to load conversation', error);
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: 'Failed to load conversation',
|
||||
details: error instanceof Error ? error.message : String(error),
|
||||
},
|
||||
{ status: 500 },
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
Reference in New Issue
Block a user