7 approaches to building AI knowledge bases, from vector RAG to continuously updated pipelines
goyalshaliniuk · x · 2026-10-14
A thread + infographic breaks down 7 ways to build AI knowledge bases beyond just hooking documents to an LLM: vector-based RAG (Qdrant/Weaviate/Milvus), knowledge graphs for multi-hop reasoning (Neo4j/GraphRAG), hybrid semantic+keyword search (Elasticsearch), structured data queries (PostgreSQL/MySQL), document-centric setups with parsing and metadata filters (LlamaIndex/Haystack), hierarchical Workspace→Topic→Chunk navigation, and continuously updated pipelines where freshness is part of knowledge quality. The approaches aren't mutually exclusive — the goal is retrieving the right info from the right source at the right time.
Related event: Seven Ways to Build AI Knowledge Bases Beyond Vector Search(5 posts)→
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