Vector Database Masterclass

Complete course on vector databases, embeddings, and RAG pipelines — from fundamentals to production.

Last Updated: April 2026 - Full course revamp with new sections on chunking strategies, hybrid search, pgvector/Supabase, and updated APIs (ChromaDB 1.5+, LangChain 1.2+, Pinecone 7.x, OpenAI latest models).

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Quick Start

1. Clone the repo

git clone https://github.com/pdichone/vector-databases-course.git
cd vector-databases-course

2. Set up environment

Option A: Using uv (Recommended - Faster)

# Install uv if you don't have it
curl -LsSf https://astral.sh/uv/install.sh | sh

# Create venv and install dependencies
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
uv pip install -r requirements.txt

Option B: Using pip (Traditional)

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

3. Set up environment variables

cp .env.example .env
# Edit .env with your API keys

4. Get your API keys

• OpenAI: https://platform.openai.com/api-keys
• Pinecone: https://app.pinecone.io/

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Course Structure

Core Files (Root)

File	Section	Description
chroma_emb.py	ChromaDB	Basic embedding functions with default embeddings
chroma_persist.py	ChromaDB	Persisting data to disk
chroma_openai_emb.py	ChromaDB	OpenAI embeddings with Chroma
app.py	ChromaDB	Quick-start demo with upsert and query
raw_emb.py	Embeddings	Raw OpenAI embeddings exploration
vector_db_llm.py	RAG	Complete RAG pipeline (no LangChain)
vector_db_llm_lang_chain.py	LangChain	RAG with LangChain + Chroma
lang_test.py	LangChain	LangChain init_chat_model and streaming demo
pinecone_intro.py	Pinecone	Pinecone basics
pinecone_db_llm_lang_chain.py	Pinecone	RAG with LangChain + Pinecone

Chunking Strategies (chunking/)

File	Description
semantic_chunking.py	Semantic chunking with LangChain experimental — splits by meaning, not character count
prod_ready.py	Production-ready chunking comparing RecursiveCharacterTextSplitter vs SemanticChunker

Hybrid Search (hybrid_search/)

File	Description
prod_hybrid_search.py	BM25 + vector search with weighted Reciprocal Rank Fusion (RRF)
final_production.py	Production-grade hybrid retrieval combining keyword and semantic search

pgvector + PostgreSQL (pg_vector_section/)

A full section on using PostgreSQL as a vector database with the pgvector extension.

Folder	Topic	Files
1-setup/	Docker setup, connections, enabling pgvector	01_docker_setup.sh, 02_basic_connection.py, 03_enable_extension.sql, 04_quick_demo.py, basic_data_types.sql
2-creating-querying/	Creating tables, inserting vectors, querying	01_create_table.sql, 02_insert_vectors.py, 03_similarity_search.py, 04_metadata_filtering.py
3-similiarity-search/	Similarity search deep dive with metadata filtering	01_create_table.sql, 02_insert_vectors.py, 03_similarity_search.py, 04_metadata_filtering.py
4-indexes/	HNSW vs IVFFlat index comparison	01_create_hnsw_index.sql, 02_create_ivfflat_index.sql, 03_index_comparison.py
5-langchain/	LangChain + pgvector integration and full RAG chain	01_setup_vectorstore.py, 02_add_documents.py, 03_similarity_search.py, 04_rag_chain.py
6-supabase/	Supabase as a managed pgvector backend	01_supabase_connection.py, 02_connection_pooling.py, 03_production_example.py

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What's New (April 2026 Update)

New Sections

• Chunking Strategies — Semantic chunking vs recursive character splitting with side-by-side comparison
• Hybrid Search — BM25 + vector search with weighted Reciprocal Rank Fusion (RRF) for better retrieval
• pgvector (PostgreSQL) — Full section: setup, CRUD, similarity search, HNSW/IVFFlat indexes, LangChain integration
• Supabase — Production-ready vector search using Supabase as a managed pgvector backend
• LangChain LCEL patterns — All LangChain files updated to use the modern init_chat_model and LCEL (LangChain Expression Language) patterns

Updates

• Updated all ChromaDB code to v1.5+ API
• Updated LangChain imports to use langchain-chroma and langchain-postgres packages
• Fixed all deprecated OpenAI model references (now using gpt-4o-mini)
• Added requirements.txt with pinned dependencies
• Added pyproject.toml and uv.lock for reproducible builds
• All code tested and working with Python 3.13

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Prerequisites

Requirement	Used In
Python 3.10+	All sections
OpenAI API key	Embeddings & LLM calls
Pinecone API key	Pinecone section only
Docker	pgvector section (for local PostgreSQL)
Supabase account	Supabase section only

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Sample Data

The data/new_articles/ directory contains sample text files for the RAG examples.

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