IntermediateИнженерия
RAG & Vector Databases
Complete guide to Retrieval-Augmented Generation: from embeddings and chunking to production RAG pipelines with Pinecone, Qdrant, pgvector. Covers semantic search, hybrid search (BM25 + dense), reranking, evaluation (RAGAS), and patterns for documents, code, and multimodal data.
4modules
12lessons
360 mintotal time
Developers building search and Q&A systems on LLMsaudience
Module 1
Embeddings & Vector Search
What embeddings are, how similarity is computed, which models to choose, and where to store vectors.
Module 2
Chunking & Preprocessing
How to split documents, load different formats, and clean data before indexing.
Module 3
Advanced Retrieval
Hybrid search, reranking, and query expansion for complex questions.
Module 4
RAG Pipeline & Evaluation
An end-to-end pipeline, automated evaluation via RAGAS, and production patterns.