Gen AI — Vector Databases

What are vector databases?

Databases optimized for storing and searching high-dimensional vectors.

Popular options

Database	Type	Best For
Pinecone	Managed	Production
ChromaDB	Local	Development
FAISS	Library	Research
Weaviate	Self-hosted	Flexibility

ChromaDB

import chromadb

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

# Add documents
collection.add(
    documents=["Hello world", "How are you?"],
    ids=["doc1", "doc2"]
)

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

Pinecone

from pinecone import Pinecone

pc = Pinecone(api_key="your-key")
index = pc.Index("my-index")

# Upsert vectors
index.upsert(vectors=[
    {"id": "doc1", "values": embedding1, "metadata": {"text": "Hello"}},
    {"id": "doc2", "values": embedding2, "metadata": {"text": "World"}}
])

# Query
results = index.query(
    vector=query_embedding,
    top_k=5,
    include_metadata=True
)

FAISS

import faiss
import numpy as np

dimension = 1536
index = faiss.IndexFlatL2(dimension)

# Add vectors
vectors = np.array([...]).astype('float32')
index.add(vectors)

# Search
distances, indices = index.search(query_vector, k=5)

Mini Practice

1. Set up ChromaDB locally
2. Store and query embeddings
3. Compare vector databases
4. Build a semantic search app

Up Next

Continue with RAG - Retrieval-Augmented Generation.