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Knowledge Graphs3 articles

Knowledge Graphs

Articles

  • GraphRAG Frameworks in Production: Comparing Microsoft GraphRAG, LightRAG, Fast-GraphRAG, and Neo4j Hybrid Architectures

    Standard dense vector retrieval fails on two specific query topologies: global corpus sense-making and multi-hop associative entity traversal. Standard vector search relies on flat chunk embeddings (cosine similarity over top-k chunks), which isolates information into disconnected fragments. If a query requires connecting entity A to entity C through intermediate entity B across documents, or synthesizing thematic patterns across an entire million-token repository, vector databases return disjoi

    1 min

  • GraphRAG Frameworks and Architectures in Production: Comparing Microsoft GraphRAG, LightRAG, Neo4j GenAI, and Kùzu

    Standard dense retrieval-augmented generation (RAG) relies on vector embeddings to retrieve top-k chunks based on cosine similarity. While effective for point-lookup queries against localized text segments, dense vector search breaks down under two common production workloads: multi-hop relational reasoning across disconnected documents and global corpus-wide summarization. When answering questions that require traversing relationship paths across disparate data points, or synthesizing broad th

    1 min

  • GraphRAG vs. Vector RAG in Production: Architecture, Community Summaries, and Cost-Latency Trade-Offs

    Retrieval-Augmented Generation (RAG) has become the standard architecture for grounding Large Language Models in external knowledge bases. However, production implementations frequently encounter structural limits when relying entirely on naive vector search. Standard Vector RAG fragments documents into arbitrary chunks and retrieves top-k passages via cosine similarity in embedding space. While effective for localized fact retrieval, this approach struggles with global, corpus-wide synthesis an

    1 min