Vector Databases: Embeddings, Semantic Search, and Hybrid Retrieval - Alexey Grigorev

In this workshop, Alexey Grigorev, founder of DataTalks.Club, dives deep into the technical shift from lexical to semantic retrieval in modern AI applications. We explore the evolution of RAG pipelines, specifically moving from basic text search to advanced vector search architectures and production-ready deployments. Links: LLM Zoomcamp: https://github.com/DataTalksClub/llm-... Materials: https://github.com/DataTalksClub/llm-... Guardrails workshop: https://aishippinglabs.com/workshops/... You’ll learn about: Why meaning-based search beats keyword matching How to shift from lexical to semantic retrieval in RAG pipelines How to implement vector search with PGVector and SQLite How to use ONNX to make AI models 33x lighter and faster How to manage large datasets with Approximate Nearest Neighbors (ANN) How to automatically filter irrelevant or out-of-scope queries A clear guide to Embeddings, Cosine Similarity, and Scoring TIMECODES: 0:00 Evolution from Text Search to Vector Search 7:33 RAG Architecture and Prompt Engineering with Context 11:26 Environment Setup for Sentence Transformers and PyTorch 16:45 Fundamentals of Word and Sentence Embeddings 20:09 Utilizing Sentence-BERT for Semantic Similarity 25:16 Mathematics of Vector Similarity and Cosine Similarity 28:17 FAQ Dataset Embedding and Batch Processing Workflows 33:53 High-Performance Matrix Multiplication for Similarity Scoring 38:18 Implementing Top-K Search and Argmax using NumPy 43:23 MinSearch Integration and Metadata Filtering Strategies 48:54 Hybrid Search Advantages and Lexical-Semantic Integration 52:30 Refactoring RAG Pipelines for Vector Search Swapping 57:59 Benchmarking and Justification for Vector Search Deployment 1:02:47 Approximate Nearest Neighbors vs Exact Search Performance 1:07:01 Decoupling Data Ingestion from Production Deployment 1:12:44 Vector Index Implementation with SQLite Search 1:18:48 Production Vector Search using PGVector and Postgres 1:23:44 LLM Guardrails and Filtering Irrelevant User Queries 1:35:13 ONNX Model Optimization for Lightweight Production AI Connect with DataTalks.Club: Join the community - https://datatalks.club/slack.html Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/... Check other upcoming events - https://lu.ma/dtc-events GitHub: https://github.com/DataTalksClub LinkedIn -   / datatalks-club   Twitter -   / datatalksclub   Website - https://datatalks.club/ Connect with Alexey Twitter -   / al_grigor   Linkedin -   / agrigorev   Check our free online courses: ML Engineering course - http://mlzoomcamp.com Data Engineering course - https://github.com/DataTalksClub/data... MLOps course - https://github.com/DataTalksClub/mlop... LLM course - https://github.com/DataTalksClub/llm-... Open-source LLM course: https://github.com/DataTalksClub/open... AI Dev Tools course: https://github.com/DataTalksClub/ai-d... 👉🏼 Read about all our courses in one place - https://datatalks.club/blog/guide-to-... 👋🏼 Support/inquiries If you want to support our community, use this link - https://github.com/sponsors/alexeygri... If you’re a company, reach us at [email protected] ag #vectorsearch #semanticsearch #llmzoomcamp #aiengineering #nlp #embeddings #pgvector #postgres #sqlite #onnx #aioptimization #machinelearning #generativeai #datatalksclub #python #searchinfrastructure #cosinesimilarity #hybridsearch #semanticretrieval