Run this notebook: Open in Colab Open in Kaggle

# Basic requirements
# !pip install numpy sentence-transformers matplotlib scikit-learn

# Vector databases (install as needed)
# !pip install chromadb qdrant-client weaviate-client pymilvus

2. Test your setup:

Run the cell below to verify that NumPy and the sentence-transformers library are installed correctly. The SentenceTransformer model (all-MiniLM-L6-v2) converts any text string into a 384-dimensional dense vector that captures its semantic meaning. This embedding step is the bridge between raw text and the numerical world of vector databases – every document you store and every query you issue will first pass through an encoder like this one.

import numpy as np
from sentence_transformers import SentenceTransformer

# Load embedding model
model = SentenceTransformer('all-MiniLM-L6-v2')

# Test embedding
text = "Vector databases are awesome!"
embedding = model.encode(text)

print(f"✅ Setup successful!")
print(f"Text: {text}")
print(f"Embedding shape: {embedding.shape}")
print(f"First 5 dimensions: {embedding[:5]}")

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📖 Learning Path

Beginner Track (3.5 hours)

1. Start with 01_vector_db_basics.ipynb to understand concepts

2. Try 02_chroma_guide.ipynb for hands-on practice

3. Build a simple semantic search app

Intermediate Track (7 hours)

1. Complete Beginner Track

2. Learn 03_qdrant_guide.ipynb for production patterns

3. Explore 04_weaviate_guide.ipynb for hybrid search

4. Study 05_milvus_guide.ipynb for scale

Production Track (Full course)

• Complete all notebooks

• Review README.md for all 11 database options

• Build a RAG (Retrieval-Augmented Generation) system

• Deploy to production

🎯 Prerequisites

Before starting, you should understand:

• ✅ Phase 1 - Tokenization: How to convert text to tokens

• ✅ Phase 2 - Embeddings: How to convert text to vectors

• Basic Python and numpy

• API concepts (REST, requests)

🗄️ Database Comparison

Database	Best For	Difficulty	Cost
Chroma	Learning, prototyping	Easy	Free
Qdrant	Production apps	Medium	Free/Paid
Weaviate	Enterprise, GraphQL	Medium	Free/Paid
Milvus	Massive scale (billions)	Hard	Free/Paid
FAISS	Research, benchmarks	Easy	Free
Pinecone	Managed cloud	Easy	Paid
pgvector	Existing PostgreSQL	Medium	Free

See README.md for all 11+ options with detailed examples!

💡 Common Use Cases

1. Semantic Search: Find similar documents based on meaning

2. RAG Systems: Retrieve relevant context for LLMs

3. Recommendations: Find similar products/content

4. Question Answering: Match questions to answers

5. Duplicate Detection: Find near-duplicate content

6. Image Search: Find similar images by visual features

🔗 Additional Resources

• README.md: Complete guide with 11 databases

• Phase 1: Tokenization notebooks (7 notebooks)

• Phase 2: Embeddings notebooks (10 notebooks)

• LangChain Docs

• Pinecone Learning Center

🚦 Ready to Start?

Open 01_vector_db_basics.ipynb and begin your journey!

Each notebook is designed to be run step-by-step. Execute cells in order and experiment with the code.

Happy learning! 🎉

🗺️ Your Complete Learning Journey

Phase 1: Tokenization (7 notebooks)
   ↓
Phase 2: Embeddings (10 notebooks)  
   ↓
Phase 3: Vector Databases (5 notebooks) ← You are here!
   ↓
Phase 4: LLM Applications (Coming soon)

Total progress: 22 notebooks completed across 3 phases! 🎯