AI — Vector Databases

What are Vector Databases?

Databases optimized for storing and querying vector embeddings.

Popular Vector Databases

Database	Type
Pinecone	Managed
Weaviate	Open-source
Chroma	Lightweight
Milvus	Open-source
FAISS	Library

Using Chroma

import chromadb

client = chromadb.Client()
collection = client.create_collection("my_docs")

# Add documents
collection.add(
    documents=["Hello world", "Hi there"],
    ids=["doc1", "doc2"]
)

# Query
results = collection.query(query_texts=["greeting"], n_results=2)

Using Pinecone

import pinecone

pinecone.init(api_key="your-key", environment="us-west1-gcp")
index = pinecone.Index("my-index")

# Upsert vectors
index.upsert(vectors=[("vec1", [0.1, 0.2, 0.3])])

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

Mini Practice

1. Set up vector database
2. Store embeddings
3. Query similar vectors
4. Build search application

Up Next

Continue with RAG — retrieval augmented generation.