Initial commit: Portfolio Website
Vollständige Next.js 15 Portfolio-Website mit: - Blog-System mit 100+ Artikeln - Supabase-Integration - Responsive Design mit Tailwind CSS - TypeScript-Konfiguration - Testing-Setup mit Vitest und Playwright Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Claude Opus 4.5
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# MongoDB Atlas Vector Search für AI-Anwendungen
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**Meta-Description:** MongoDB Atlas Vector Search für semantische Suche und RAG. Embedding API, Hybrid Queries und Integration mit LLMs.
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**Keywords:** MongoDB Atlas, Vector Search, Semantic Search, RAG, Embeddings, AI Database, LLM Integration
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---
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## Einführung
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MongoDB Atlas bietet mit **Vector Search** eine native Lösung für AI-Anwendungen. Operative Daten und Vector Embeddings in einer Datenbank – ohne Sync-Probleme zwischen separaten Systemen.
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---
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## Vector Search Architecture
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```
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┌─────────────────────────────────────────────────────────────┐
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│ MONGODB ATLAS VECTOR SEARCH │
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├─────────────────────────────────────────────────────────────┤
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│ │
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│ Single Platform: │
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│ ├── Operative Daten (Documents) │
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│ ├── Vector Embeddings (1536+ Dimensions) │
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│ ├── Metadata (für Filtering) │
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│ └── Full-Text Search (Atlas Search) │
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│ │
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│ Vector Index Types: │
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│ ├── HNSW (Hierarchical Navigable Small World) │
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│ ├── IVF (Inverted File Index) │
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│ └── Vector Quantization (Cost Reduction) │
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│ │
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│ Use Cases: │
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│ ├── Semantic Search │
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│ ├── RAG (Retrieval-Augmented Generation) │
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│ ├── Recommendation Systems │
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│ └── Image/Audio Similarity │
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│ │
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│ Performance (Dedicated Search Nodes): │
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│ └── 40-60% schnellere Query Times │
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│ │
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└─────────────────────────────────────────────────────────────┘
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```
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---
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## Setup
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```typescript
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// lib/mongodb.ts
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import { MongoClient } from 'mongodb';
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const uri = process.env.MONGODB_URI!;
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const options = {};
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let client: MongoClient;
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let clientPromise: Promise<MongoClient>;
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if (process.env.NODE_ENV === 'development') {
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const globalWithMongo = global as typeof globalThis & {
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_mongoClientPromise?: Promise<MongoClient>;
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};
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if (!globalWithMongo._mongoClientPromise) {
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client = new MongoClient(uri, options);
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globalWithMongo._mongoClientPromise = client.connect();
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}
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clientPromise = globalWithMongo._mongoClientPromise;
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} else {
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client = new MongoClient(uri, options);
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clientPromise = client.connect();
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}
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export default clientPromise;
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```
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---
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## Vector Index erstellen
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```javascript
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// Atlas Search Index Definition (JSON)
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{
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"name": "vector_index",
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"type": "vectorSearch",
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"definition": {
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"fields": [
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{
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"type": "vector",
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"path": "embedding",
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"numDimensions": 1536,
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"similarity": "cosine"
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},
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{
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"type": "filter",
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"path": "category"
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},
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{
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"type": "filter",
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"path": "createdAt"
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}
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]
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}
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}
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```
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```typescript
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// Programmatisch Index erstellen
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import { MongoClient } from 'mongodb';
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async function createVectorIndex() {
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const client = await MongoClient.connect(process.env.MONGODB_URI!);
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const db = client.db('myapp');
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await db.command({
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createSearchIndexes: 'documents',
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indexes: [
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{
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name: 'vector_index',
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type: 'vectorSearch',
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definition: {
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fields: [
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{
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type: 'vector',
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path: 'embedding',
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numDimensions: 1536,
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similarity: 'cosine'
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}
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]
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}
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}
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]
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});
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await client.close();
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}
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```
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---
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## Documents mit Embeddings speichern
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```typescript
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import OpenAI from 'openai';
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import clientPromise from '@/lib/mongodb';
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const openai = new OpenAI();
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interface Document {
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_id?: string;
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title: string;
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content: string;
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embedding: number[];
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category: string;
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createdAt: Date;
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}
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// Embedding generieren
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async function generateEmbedding(text: string): Promise<number[]> {
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const response = await openai.embeddings.create({
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model: 'text-embedding-ada-002',
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input: text
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});
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return response.data[0].embedding;
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}
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// Document speichern
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async function saveDocument(
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title: string,
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content: string,
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category: string
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): Promise<Document> {
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const client = await clientPromise;
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const db = client.db('myapp');
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// Embedding für Titel + Content
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const embedding = await generateEmbedding(`${title}\n\n${content}`);
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const document: Document = {
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title,
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content,
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embedding,
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category,
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createdAt: new Date()
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};
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const result = await db.collection<Document>('documents').insertOne(document);
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return { ...document, _id: result.insertedId.toString() };
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}
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// Bulk Import
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async function bulkImportDocuments(docs: Array<{ title: string; content: string; category: string }>) {
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const client = await clientPromise;
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const db = client.db('myapp');
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const documentsWithEmbeddings = await Promise.all(
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docs.map(async (doc) => ({
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...doc,
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embedding: await generateEmbedding(`${doc.title}\n\n${doc.content}`),
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createdAt: new Date()
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}))
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);
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await db.collection('documents').insertMany(documentsWithEmbeddings);
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}
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```
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---
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## Vector Search Queries
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```typescript
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// Basic Vector Search
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async function searchDocuments(query: string, limit: number = 5) {
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const client = await clientPromise;
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const db = client.db('myapp');
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const queryEmbedding = await generateEmbedding(query);
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const results = await db.collection('documents').aggregate([
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{
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$vectorSearch: {
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index: 'vector_index',
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path: 'embedding',
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queryVector: queryEmbedding,
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numCandidates: 100,
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limit: limit
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}
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},
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{
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$project: {
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_id: 1,
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title: 1,
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content: 1,
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category: 1,
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score: { $meta: 'vectorSearchScore' }
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}
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}
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]).toArray();
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return results;
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}
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// Vector Search mit Filter
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async function searchByCategory(
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query: string,
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category: string,
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limit: number = 5
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) {
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const client = await clientPromise;
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const db = client.db('myapp');
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const queryEmbedding = await generateEmbedding(query);
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const results = await db.collection('documents').aggregate([
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{
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$vectorSearch: {
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index: 'vector_index',
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path: 'embedding',
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queryVector: queryEmbedding,
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filter: {
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category: category
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},
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numCandidates: 100,
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limit: limit
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}
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},
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{
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$project: {
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title: 1,
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content: 1,
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score: { $meta: 'vectorSearchScore' }
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}
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}
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]).toArray();
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return results;
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}
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// Hybrid Search (Vector + Full-Text)
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async function hybridSearch(query: string, limit: number = 10) {
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const client = await clientPromise;
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const db = client.db('myapp');
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const queryEmbedding = await generateEmbedding(query);
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const results = await db.collection('documents').aggregate([
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{
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$vectorSearch: {
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index: 'vector_index',
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path: 'embedding',
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queryVector: queryEmbedding,
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numCandidates: 150,
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limit: 50
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}
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},
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{
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$addFields: {
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vectorScore: { $meta: 'vectorSearchScore' }
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}
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},
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{
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$unionWith: {
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coll: 'documents',
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pipeline: [
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{
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$search: {
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index: 'text_index',
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text: {
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query: query,
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path: ['title', 'content']
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}
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}
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},
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{
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$addFields: {
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textScore: { $meta: 'searchScore' }
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}
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},
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{ $limit: 50 }
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]
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}
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},
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{
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$group: {
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_id: '$_id',
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title: { $first: '$title' },
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content: { $first: '$content' },
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vectorScore: { $max: '$vectorScore' },
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textScore: { $max: '$textScore' }
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}
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},
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{
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$addFields: {
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combinedScore: {
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$add: [
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{ $ifNull: ['$vectorScore', 0] },
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{ $multiply: [{ $ifNull: ['$textScore', 0] }, 0.5] }
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]
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}
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}
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},
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{ $sort: { combinedScore: -1 } },
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{ $limit: limit }
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]).toArray();
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return results;
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}
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```
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---
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## RAG Implementation
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```typescript
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import OpenAI from 'openai';
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const openai = new OpenAI();
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async function ragQuery(userQuestion: string) {
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// 1. Relevante Dokumente finden
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const relevantDocs = await searchDocuments(userQuestion, 5);
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// 2. Context aufbauen
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const context = relevantDocs
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.map(doc => `Title: ${doc.title}\nContent: ${doc.content}`)
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.join('\n\n---\n\n');
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// 3. LLM mit Context aufrufen
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const response = await openai.chat.completions.create({
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model: 'gpt-4-turbo-preview',
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messages: [
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{
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role: 'system',
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content: `Du bist ein hilfreicher Assistent. Beantworte Fragen basierend auf dem folgenden Kontext. Wenn der Kontext die Antwort nicht enthält, sage das ehrlich.
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Kontext:
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${context}`
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},
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{
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role: 'user',
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content: userQuestion
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}
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],
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temperature: 0.7,
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max_tokens: 1000
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});
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return {
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answer: response.choices[0].message.content,
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sources: relevantDocs.map(d => ({
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title: d.title,
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score: d.score
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}))
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};
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}
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```
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---
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## Automated Embedding mit Voyage AI
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```typescript
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// MongoDB Atlas Embedding API (Preview)
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// Automatisches Embedding ohne eigene OpenAI-Calls
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// Index Definition mit Auto-Embedding
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{
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"name": "auto_vector_index",
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"type": "vectorSearch",
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"definition": {
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"fields": [
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{
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"type": "vector",
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"path": "embedding",
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"numDimensions": 1024,
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"similarity": "cosine"
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}
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],
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"embeddingModel": {
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"name": "voyage-3",
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"inputType": "document",
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"fieldMappings": [
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{
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"source": "content",
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"target": "embedding"
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}
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]
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}
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}
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}
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// Documents ohne manuelles Embedding speichern
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async function saveDocumentAutoEmbed(title: string, content: string) {
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const client = await clientPromise;
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const db = client.db('myapp');
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// Embedding wird automatisch generiert!
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await db.collection('documents').insertOne({
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title,
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content,
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createdAt: new Date()
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});
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}
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```
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---
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## Vector Quantization (Kostenoptimierung)
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```javascript
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// Index mit Quantization für große Datasets
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{
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"name": "quantized_vector_index",
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"type": "vectorSearch",
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"definition": {
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"fields": [
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{
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"type": "vector",
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"path": "embedding",
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"numDimensions": 1536,
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"similarity": "cosine",
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"quantization": {
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"type": "scalar" // Reduziert Speicher um ~75%
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}
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}
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]
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}
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}
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```
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---
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## Fazit
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MongoDB Atlas Vector Search bietet:
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1. **Unified Platform**: Operative Daten + Vectors in einer DB
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2. **Hybrid Search**: Kombination von Vector + Full-Text
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3. **Auto-Embedding**: Voyage AI Integration für automatische Embeddings
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4. **Skalierbarkeit**: Milliarden Vectors mit Quantization
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Ideal für AI-Features in bestehenden MongoDB-Anwendungen.
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---
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## Bildprompts
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1. "Vector embeddings flowing into search results, semantic similarity visualization"
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2. "MongoDB document with vector arrows connecting similar documents"
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3. "RAG pipeline showing document retrieval and LLM generation, AI workflow"
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---
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## Quellen
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- [MongoDB Atlas Vector Search](https://www.mongodb.com/products/platform/atlas-vector-search)
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- [MongoDB Vector Search Overview](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/)
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- [MongoDB Embedding API](https://www.mongodb.com/company/blog/product-release-announcements/introducing-the-embedding-and-reranking-api-on-mongodb-atlas)
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- [Vector Databases for LLM 2026](https://www.secondtalent.com/resources/top-vector-databases-for-llm-applications/)
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Reference in New Issue
Block a user