Designing a Retrieval Pipeline
A retrieval pipeline is the set of stages that turns a user query into a small, useful set…
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Architecture
Storage engines, index structures, and system design.
Consistency Models in Vector Databases
Consistency in a vector database describes what an application can expect to see after data is written, updated,…
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Data Tiering and Tenant Lifecycle in AI Databases
Data tiering helps AI database teams control infrastructure cost by placing each tenant’s data on the storage tier…
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Edge and Embedded Vector Databases
Edge and embedded vector databases let applications run similarity search close to the user, device, or application process…
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Serverless Vector Database Architecture
Serverless vector database architecture separates the work of storing vector data from the work of searching it, then…
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Memory Management in Vector Databases
Memory management in vector databases is the practice of deciding which parts of a vector search system must…
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How Upserts and Updates Work in Vector Databases
Upserts and updates in vector databases are the operations that keep searchable embeddings aligned with changing source data.…
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Vector Compression and Quantization: A Storage View
Vector compression and quantization reduce the storage and memory footprint of AI database indexes by storing lower-precision versions…
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Real-Time vs Batch Indexing in AI Databases
Real-time indexing makes new or changed data searchable as quickly as possible, while batch indexing processes data in…
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Namespaces, Collections, and Partitions in AI Databases
Namespaces, collections, and partitions are all ways to organize data in AI databases, but they operate at different…
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