AIAdvanced#Tech#Scaling
Vector Database
V
Definition
A system that stores vector representations and retrieves nearby vectors using a stated similarity measure. It can support semantic retrieval, but it does not determine whether retrieved content is true, current, or relevant enough to use.
Source reviewed
- FAISS documentation opens in a new tab
Supports: Documents dense-vector indexes, L2 and dot-product comparison, and the performance, memory, and accuracy trade-offs across index types.
- NIST: Adversarial Machine Learning Taxonomy opens in a new tab
Supports: Documents that retrieval-augmented systems can be exposed to adversarial and data-integrity risks, so retrieved content is not a trust guarantee.
Concept Diagram
Key Takeaways
1
Vector retrieval ranks nearby numeric representations under a chosen similarity measure; it is not verification or reasoning
2
Embedding model, chunking, metadata, filters, corpus coverage, and index settings all affect the result
3
Approximate indexes trade search quality against latency, memory, build time, or cost
4
Show original records, excerpts, sources, timestamps, and access boundaries beside any generated summary
Practical Example
A research system retrieves three passages for a query. It displays each source URL, publisher, publication date, retrieval time, excerpt, and similarity score. A reviewer treats the score as a ranking signal, checks the original passages, and rejects any result that lacks a trustworthy source or does not support the question.
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Related Terms
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Compute-as-a-ServiceOn-demand access to compute infrastructure such as CPU, GPU, memory, storage, and networking through a provider. It can supply capacity for training or inference; it does not make a model accurate, an agent authorised, a workload available, or a strategy profitable.
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Sentiment EngineA workflow that collects and labels language or other public signals as a modelled sentiment measure. It can describe a selected dataset; it cannot reliably identify motive, distinguish promotion from genuine belief, forecast price, or verify a market-wide state.
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RAG (Retrieval-Augmented Generation)A system pattern that retrieves selected external records and supplies them with a query to a language model. Retrieval can improve traceability and recency when sources are cited; it does not prove that a record is true, complete, current, or safe to act on.
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InferenceThe stage where a deployed model processes an input and produces an output. An inference result can be a label, score, generated text, or structured draft; it does not verify its own input, predict a market reliably, or authorize an action.
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