Vector Databases Explained: The Backbone of Modern AI Apps

# Vector Databases Explained

Vector databases are revolutionizing how we build AI applications. Let's explore what they are and why they matter.

## What Are Vector Databases?

Vector databases store and query high-dimensional vectors, enabling:
- Semantic search
- Recommendation systems
- Similarity matching
- RAG (Retrieval Augmented Generation)

## Popular Vector Databases

### Pinecone
- Fully managed
- Easy to use
- Excellent performance

### Weaviate
- Open source
- GraphQL API
- Hybrid search capabilities

### Qdrant
- High performance
- Rich filtering
- Easy deployment

## Use Cases

1. **Semantic Search**: Find documents by meaning, not just keywords
2. **Recommendation Engines**: Suggest similar items
3. **Question Answering**: Build RAG systems
4. **Anomaly Detection**: Identify unusual patterns

## Getting Started

```python
import pinecone

# Initialize
pinecone.init(api_key="your-api-key")
index = pinecone.Index("my-index")

# Insert vectors
index.upsert([
("id1", [0.1, 0.2, 0.3], {"text": "example"}),
])

# Query
results = index.query([0.1, 0.2, 0.3], top_k=5)
```

## Conclusion

Vector databases are essential infrastructure for modern AI applications. Choose the right one for your needs and start building!