Wait... What REALLY Is A Vector Database? скачать в хорошем качестве

Wait... What REALLY Is A Vector Database?

Vector databases and similarity based search/retrieval have had a massive increase in popularity with the rise of language models and retrieval augmented generation (RAG) pipelines. Many people are using VDBs like pinecone, chroma, and mongodb, but how many of us actually know how they works? In this video we cover how vector databases operate, diving into what an embedding is, how similarity is calculated, indexing and searching algorithms, and an example of a full RAG flow. Code: https://github.com/ALucek/embeddings-... Resources: Vector Embeddings for Developers: https://www.pinecone.io/learn/vector-... What are Embeddings Blog: https://platform.openai.com/docs/guid... Vector Database Blog: https://www.pinecone.io/learn/vector-... Chapters: 00:00 - Introduction 01:19 - What is an Embedding? 02:56 - What is a Vector? 05:24 - Embeddings: One Hot Encoding 07:27 - Embeddings: Co-occurence Matrix 09:43 - Embeddings: Modern Embedding Models 12:47 - Embeddings: Why + Setup 14:17 - Visualization: 1 Dimension 16:28 - Visualization: 2 Dimensions 18:31 - Visualization: 3 Dimensions 20:32 - Calculating Similarity: Why it’s Important 21:28 - Calculating Similarity: Euclidean Distance 24:02 - Calculating Similarity: Dot Product 25:56 - Calculating Similarity: Cosine Similarity 29:18 - Semantic Retrieval Overview 31:53 - Algorithms: Introduction 34:09 - Algorithms: K-Dimensional Trees 37:48 - Algorithms: Locality Sensitive Hashing 39:39 - Algorithms: Product Quantization 44:30 - Algorithms: Inverted File Index 46:19 - Algorithms: Hierarchical Navigable Small World 49:23 - RAG: Process Overview 51:33 - RAG: Chunking & Splitting 52:45 - RAG: ChromaDB Setup 54:20 - RAG: LLM Setup 55:00 - RAG: Full Pipeline 56:31 - Additional Embedding Use Cases 59:02 - Closing Thoughts #ai #programming #data