Vector Database
A vector database is an industry name for a data store that saves vectors and searches for similar ones. It often filters other details and uses fast approximate indexes, but there is no single universal standard.
Another example
A shared search system stores document vectors, customer IDs, source times, and an HNSW index. Each query first filters to the allowed customer, then returns scored sources with measured recall and delay. The database alone does not ground answers, write them, or design access rules.
Main parts
- 01vector records
- 02similarity index
- 03metadata filter
Use it when
Use this term when a system must save and search vectors with clear filter, index, durability, and operating needs.
Do not use it when
Test a simpler store you already have before adding a separate vector database. Do not call approximate search exact.
Name used in code
vector + metadata → filtered nearest neighbors Before you ship
Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback.
Outcome: Use or evaluate Vector Database to make AI behavior measurable and tied to evidence. User context: A shared search system stores document vectors, customer IDs, source times, and an HNSW index. Each query first filters to the allowed customer, then returns scored sources with measured recall and delay. The database alone does not ground answers, write them, or design access rules. AI method or concept: Vector Database. Why it fits: Use this term when a system must save and search vectors with clear filter, index, durability, and operating needs. Do not use it when: Test a simpler store you already have before adding a separate vector database. Do not call approximate search exact. AI requirements: Define the input and source evidence. Set model and tool permissions. Use versioned evaluation data and measures. Define failure, decline-to-answer, privacy, speed, and cost limits. Operational safeguards: Keep a clear trace and hide sensitive log data. Show users a safe fallback. Mark steps that need human review. Define how to roll back the model or prompt. Acceptance criteria: Record baseline and target measures on a versioned evaluation set. Test edge cases and hostile inputs. Verify fallback, monitoring, permissions, and rollback. Evidence and limits (evidence boundary): No single official standard controls the whole term. Its meaning may change across teams, platforms, or frameworks. Unknowns to confirm: Target task, model and version, evaluation owner, source data, risk limit, tool permissions, and production fallback.
Check this request
Review the current use of Vector Database. Definition: A vector database is an industry name for a data store that saves vectors and searches for similar ones. It often filters other details and uses fast approximate indexes, but there is no single universal standard. Release checks: Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback. Before changing code, report the evidence you found, gaps, severity, and the smallest safe fix.