Diagram ingestion, embeddings, metadata filters, and hybrid dense + sparse search
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Build semantic search with embeddings, hybrid retrieval, rerankers, and feedback analytics
Plan chunking, batching, model selection, and storage workflows for embeddings
Map ingestion, embeddings, vector stores, and LLM orchestration for grounded generation
Common questions about ai vector database architecture generator
Use HNSW/IVF for high recall, PQ/OPQ for cost, and disk-based indexes for large corpora. Show build and maintenance flows.
Store metadata alongside vectors; use prefilters or postfilters for facets like tenant, region, or permissions.
Capture CDC or change events to re-embed and upsert. Keep vector and source IDs aligned for deletes.
Shard by hash or tenant, add replicas for reads, and use autoscaling for embedding workers. Add cache for hot vectors.
Use TLS, auth tokens, row-level filters, and isolation per tenant. Log queries for audits.
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