#embeddings
3 articles
Design a GraphRAG System (Knowledge-Graph-Augmented Retrieval)
When vanilla vector RAG fails on "summarize the entire corpus" and multi-hop questions, you build a knowledge graph first — covering entity extraction, Leiden community detection, map-reduce global search, and graph traversal for multi-hop, based on Microsoft GraphRAG and production deployments at Neo4j, LinkedIn, and Writer.
Design a Vector Database / Semantic Search Service
Index 1 billion 768-dimensional vectors and answer top-k similarity queries in under 20 ms — the ANN indexing, sharding, and filtering architecture behind Pinecone, Weaviate, and pgvector.
Design a RAG (Retrieval-Augmented Generation) Pipeline
Ground an LLM in 10 million documents (50 million chunks) with sub-2-second answers and a hallucination rate measurable by automated eval — the end-to-end ingestion, retrieval, reranking, and generation pipeline powering enterprise knowledge assistants.