Beginner LevelAI, LLMs, Embeddings & MCP
What is a Vector Database (Pinecone, ChromaDB, Milvus, Qdrant)?
❓ Interview Prompt: Explain vector database storage, high-dimensional vector indexing, metadata filtering, and k-NN query execution.
A Vector Database is a specialized storage engine optimized for indexing, storing, and querying high-dimensional vector embeddings efficiently.
Key Differences from Relational DBs:
•
Relational DBs: Execute exact matches (
WHERE id = 5).•
Vector DBs: Execute Approximate Nearest Neighbor (ANN) search to find vectors closest in semantic space in sub-linear time ($O(\log N)$).
Key Features:
•
Hybrid Indexing: Combines vector similarity scores with metadata filtering (
WHERE category = 'finance').•
Popular Engines: Pinecone, Qdrant, ChromaDB, Milvus, Weaviate, pgvector (PostgreSQL extension).
Code Implementation Example
typescript
import { QdrantClient } from '@qdrant/js-client-rest';
const client = new QdrantClient({ url: 'http://localhost:6333' });
// Query vector DB with metadata filter
const searchResult = await client.search('documents', {
vector: [0.02, -0.15, 0.43], // 768-dim query embedding sample
filter: {
must: [{ key: 'author', match: { value: 'Alice' } }]
},
limit: 5
});💡Vector database query execution combining embedding similarity with metadata filtering.
🎯 Interview Key Takeaway
Vector databases execute sub-linear Approximate Nearest Neighbor (ANN) search across high-dimensional embeddings.
#Vector Database#ANN#Qdrant#Pinecone#Beginner
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