Module 425 minutes
Vector Databases and Embeddings
Work with vector databases for semantic search. Choose and implement the right solution.
vector-databasesembeddingssemantic-search
Learning Objectives
- ✓Understand vector embeddings
- ✓Choose vector database
- ✓Implement semantic search
- ✓Optimize performance
Semantic Search with Vectors
Vector databases enable AI to find similar content by meaning, not just keywords.
Vector Database Options
Pinecone: Managed, scalable, easy
Weaviate: Open source, GraphQL
Chroma: Lightweight, embeddable
Qdrant: Fast, open source
Creating Embeddings
```python
OpenAI embeddings
embedding = client.embeddings.create(
model="text-embedding-3-small",
input="your text here"
).data[0].embedding
```
Storing in Vector DB
```python
import pinecone
index = pinecone.Index("your-index")
index.upsert(vectors=[
("id1", embedding, {"text": "content"})
])
```
Querying
```python
results = index.query(
vector=query_embedding,
top_k=5,
include_metadata=True
)
```
Key Takeaways
- →Embeddings convert text to vectors for semantic similarity
- →Choose vector DB based on scale and hosting needs
- →Use metadata for filtering results
- →Batch operations for efficiency
- →Monitor index size and costs
Practice Exercises
Apply what you've learned with these practical exercises:
- 1.Create embeddings for sample documents
- 2.Set up vector database
- 3.Implement semantic search
- 4.Compare different embedding models