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Python 3.13medium1 / 1 solvedmediumNo attempts yetmediumNo attempts yetmediumNo attempts yetmediumNo attempts yetmediumNo attempts yet mediumNo attempts yetmediumNo attempts yetmediumNo attempts yetmediumNo attempts yetmediumNo attempts yetmediumNo attempts yet
Retrieval-Augmented Generation (RAG)
Learn how to build production Retrieval-Augmented Generation with LangChain - semantic search over vector indexes, ingesting real documents into clean chunks, and wiring retrieval into an LLM for grounded, cited answers.
Python 3.13Module 1
6Embeddings & Vector Search
Learn how to turn text into vectors, search by meaning with cosine similarity and FAISS, fuse dense and keyword results, rerank for precision, and ship a search service that meets its latency SLOs.
Embedding Space
Index And Distance
Hybrid Fusion
Reranker Call
Latency Budget
Search Service SLO
Module 2
6Document Ingestion & Parsing
Learn how to turn messy real-world files into a clean, searchable corpus - extract ordered text from PDFs and HTML, keep tables meaningful, strip boilerplate, collapse duplicates, capture metadata, and OCR scanned pages into one trustworthy pipeline.