Embeddings & Vector Databases
Understand how text becomes numbers, how vector databases store and search meaning, and build a semantic search pipeline from scratch.
Curriculum
Lesson 1: What Are Embeddings? Text as Vectors
Understand how embedding models map words, sentences, and documents to points in high-dimensional space — and why proximity in that space means semantic similarity.
Lesson 2: Generating Embeddings with APIs
Call the OpenAI and Anthropic embedding APIs, inspect the raw vectors, and compute cosine similarity between sentences by hand.
Lesson 3: Choosing the Right Embedding Model
Compare embedding model families by dimension count, context length, cost, and benchmark performance. Know when to use a hosted API vs. a local model like nomic-embed.
Attention in transformers, visually explained
Deep dive into the attention mechanism and how transformers create meaningful embeddings from text.
Lesson 4: Vector Database Fundamentals
Learn what a vector database is, how it differs from a relational database, and get hands-on with ChromaDB — the most learner-friendly vector store.
Lesson 5: Similarity Search: ANN & Distance Metrics
Explore cosine similarity, dot product, and Euclidean distance. Understand why Approximate Nearest Neighbor (ANN) search is essential at scale.
Lesson 6: HNSW Indexing & Performance Trade-offs
Understand the Hierarchical Navigable Small World (HNSW) algorithm that makes billion-scale vector search feasible. Learn the recall vs. speed trade-off.
Vector Databases simply explained!
What vector databases are, how they store embeddings, and why they're essential for AI search.
Lesson 7: Building a Semantic Search Pipeline
Wire together an embedding model and a vector database into a complete semantic search system: ingest documents, embed them, and retrieve by meaning — not keywords.
Lesson 8: Capstone — Document Intelligence System
Build an end-to-end document intelligence system that ingests a PDF, chunks it, embeds every chunk, stores them in ChromaDB, and answers natural language questions over the content.
Semantic Search with Vector Databases
Build a semantic search system from scratch using embeddings and vector similarity.
Course Quiz
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