Practical Vector Embeddings & Database Integration
Scheduled sessions
Modern AI Data bootcamp: Move beyond basic relational queries and unlock the power of semantic search using vector embeddings.
Learn how to map text, images, and complex data into high-dimensional vector spaces using modern embedding models (OpenAI, HuggingFace).
Master pgvector: Transform PostgreSQL into a highly efficient vector database. Understand indexing strategies (IVFFlat, HNSW) to balance query speed and recall accuracy.
Gain practical experience via ~70% hands-on labs, building a production-ready semantic search engine from scratch.
How this helps: Essential for building RAG (Retrieval-Augmented Generation) systems, recommendation engines, and advanced search features without relying on expensive managed vector DBs.
Who it’s for: Software Engineers, Data Engineers, and Database Administrators looking to integrate AI capabilities into their existing PostgreSQL infrastructure.
Skills You Will Learn
Curriculum
Demystifying Vector Embeddings
- What are embeddings? The transition from keyword search (BM25) to semantic search
- High-dimensional vector spaces and distance metrics (Cosine Similarity, L2 distance, Inner Product)
- Generating embeddings in Python: Using OpenAI APIs vs. local open-source models (SentenceTransformers/HuggingFace)
- Mini-lab: Generating and comparing embeddings for text similarity in memory
Introducing pgvector and PostgreSQL Integration
- Why use PostgreSQL for vectors? ACID compliance + vector search
- Installing and configuring the pgvector extension via Docker
- Defining vector columns, inserting high-dimensional data, and basic exact nearest neighbor (k-NN) queries
- Lab: Building a basic semantic search engine over a product catalog
Approximate Nearest Neighbor (ANN) Indexing
- The scaling problem: Why exact k-NN is too slow for production
- IVFFlat (Inverted File Flat) index: Concepts, building, and parameter tuning (lists, probes)
- HNSW (Hierarchical Navigable Small World) index: The state-of-the-art for speed and recall
- Lab: Benchmarking IVFFlat vs HNSW on a large dataset (speed vs. accuracy trade-offs)
Building a Complete RAG Retriever Pipeline
- Chunking strategies for long documents (Token splitters, semantic chunking)
- Hybrid Search: Combining Full-Text Search (tsvector) with Semantic Search (pgvector) for superior results
- Handling metadata filtering (e.g., semantic search within a specific date range or category)
- Lab: End-to-end integration – From PDF ingestion to a functioning hybrid search API
Optional modules
Optional — Image and Multimodal Embeddings
- Introduction to CLIP (Contrastive Language-Image Pretraining)
- Generating image embeddings and querying them via pgvector
- Building a reverse image search engine
Course Day Structure
- Part 1: Concepts & Generation: 09:00–10:30
- Break: 10:30–10:45
- Part 2: DB Integration: 10:45–12:15
- Lunch break: 12:15–13:15
- Part 3: Indexing & Tuning: 13:15–15:15
- Break: 15:15–15:30
- Part 4: Real-world Lab: 15:30–17:30