feat: add AI-powered finance and project risk analysis features with pgvector support for RAG embeddings

This commit is contained in:
poyrazavsever
2026-06-06 21:47:50 +03:00
parent 21af4b7a62
commit 83379a5edc
17 changed files with 1104 additions and 241 deletions
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import { createClient } from "@/lib/supabase/server";
import { ClientDetailClient, type ClientDetailData, type ClientActivity } from "./client-detail-client";
import { notFound } from "next/navigation";
export default async function ClientDetailPage({ params }: { params: Promise<{ id: string }> }) {
const { id } = await params;
const supabase = await createClient();
const { data: { user } } = await supabase.auth.getUser();
if (!user) return null;
const { data: clientData, error } = await supabase
.from("clients")
.select("id, name, company_name, email, phone, website, pipeline_stage, status, notes")
.eq("id", id)
.eq("user_id", user.id)
.single();
if (error || !clientData) {
notFound();
}
const { data: activitiesData } = await supabase
.from("client_activities")
.select("id, type, title, content, activity_date, created_at")
.eq("client_id", id)
.eq("user_id", user.id)
.order("activity_date", { ascending: false });
const client: ClientDetailData = clientData as ClientDetailData;
const activities: ClientActivity[] = (activitiesData || []) as ClientActivity[];
return <ClientDetailClient client={client} activities={activities} />;
}