📄️ Lesson 1: Embeddings
Intorducing the concept of Vectors and Embeddings.
📄️ Lesson 2: Introducing Ollama
Running embedding models locally with Ollama.
📄️ Lesson 3: Vector DBs Basics
Storing and searching embeddings efficiently with a vector database.
📄️ Lesson 4: Adding Traditional Storage
Understand what role traditional storage plays in the context of Vector Databases.
📄️ Lesson 5: Chunking & LangChain
Breaking documents into chunks and storing them in a vector database with LangChain and Qdrant.
📄️ Lesson 6: Building a RAG
Storing and searching embeddings efficiently with a vector database.
📄️ Lesson 7: Multi-Dimensional Data Visualization
Learn how to visualize high-dimensional data using UMAP and other techniques in Python.