GraphRAG — Combining Knowledge Graphs With Vector Search
Build GraphRAG systems using knowledge graph traversal and vector search together to handle complex multi-hop questions and relationship-aware context retrieval.
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Build GraphRAG systems using knowledge graph traversal and vector search together to handle complex multi-hop questions and relationship-aware context retrieval.
Choose between long-context LLMs and RAG by understanding the lost-in-the-middle problem, cost dynamics, and latency tradeoffs.
Master metadata filtering in RAG systems: design schemas, implement self-querying, combine filters with vector similarity, and isolate tenants securely.
Build RAG systems that handle PDFs, tables, images, and charts by combining text extraction, table embeddings, and vision encoders for unified multimodal search.
Build production-ready RAG systems with semantic chunking, embedding optimization, reranking, citation tracking, and hallucination detection.