Re-embedding When You Change Models
When you change embedding models, the vectors created by the old model usually cannot be treated as interchangeable…
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Operations
Scaling, monitoring, backups, and production deployments.
Keeping Your Index Fresh
Keeping an AI database index fresh means making sure search results reflect the current state of the source…
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Building Data Ingestion Pipelines for AI Databases
A data ingestion pipeline for an AI database turns source content into searchable, reliable, and up-to-date records. The…
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Backup and Disaster Recovery for a Vector Store
A vector store disaster recovery plan should protect more than the embedding vectors themselves. It should preserve the…
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Index Warm-Up and Cold Starts in AI Databases
First queries after a restart are often slow because the database process is running before the important search…
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Monitoring and Observability for AI Databases
Monitoring an AI database means watching both system health and retrieval quality. Traditional database metrics such as latency,…
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Scaling to Billions of Vectors
Scaling to billions of vectors requires more than choosing a fast nearest neighbor index. At that size, the…
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Reducing Memory Costs with Quantization
Quantization reduces the memory cost of AI database search by storing vectors in a lower-precision form instead of…
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Cost Optimization for Vector Search
Cost optimization for vector search starts with understanding what the system is really paying for: memory to keep…
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Recall vs QPS: Reading Benchmarks Correctly
Recall and QPS only become useful when they are read together. In vector search benchmarks, higher recall usually…
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