RAG Made Simple: ChromaDB + Python (All Local) скачать в хорошем качестве

RAG Made Simple: ChromaDB + Python (All Local)

In this video, we explore vector embeddings and ChromaDB, an open-source vector database designed for AI-native applications. You’ll learn what embeddings are, why they matter, and how ChromaDB fits into modern AI workflows such as retrieval-augmented generation (RAG), semantic search, and local AI systems. We begin with the fundamentals: how text is converted into high-dimensional numerical vectors and why this representation allows machines to understand meaning, not just keywords. From there, we introduce ChromaDB and explain how it stores, indexes, and queries embeddings efficiently, all while running locally by default with Python and JavaScript SDKs. Using a clear architectural diagram, we walk through a simple RAG pipeline: A Python script acting as the control layer A local language model (LLM) ChromaDB handling vector storage and retrieval You’ll see how a user query is embedded, how similarity search retrieves relevant context, and how that context is combined with the query before being sent to the LLM to produce grounded, context-aware responses. Next, we break down a hands-on Python example using ChromaDB. You’ll learn how to: Create a local Chroma client Work with collections Add documents with metadata Run semantic similarity searches Apply metadata filters Perform full-text search Through a simple dataset of car descriptions, you’ll see how Chroma retrieves results based on meaning, how filtering narrows results using metadata, and how distances in embedding space reflect semantic similarity. We wrap up with a short tour of the official ChromaDB documentation, showing where to find beginner guides, core concepts, and API references so you can continue learning on your own. The most important takeaway: real understanding comes from practice. Watching videos is helpful, but true learning happens when you write the code, run the queries, and experiment with your own data. Start small, build something simple, and let hands-on work turn theory into real skill.