Vector data and embeddings for AI
Embeddings power modern AI search and retrieval, and they are only as good as the data behind them. This guide explains vector data and embeddings and how external data feeds them.
What embeddings are
Embeddings turn text, images or other content into numerical vectors that capture meaning, enabling semantic search, recommendation and retrieval. They are foundational to modern AI applications.
Why source data matters
Embeddings inherit the coverage, quality and bias of the content they are built from. Sourcing the right corpus, comprehensive, current, rights-cleared, is what makes embeddings useful and lawful.
Storage and retrieval
Vectors are stored in vector databases or indexes that support similarity search. Choices around dimensionality, indexing and refresh affect performance and cost.
Common use cases
Semantic search, retrieval-augmented generation, recommendation, deduplication and clustering.
Licensing and privacy
Building embeddings from content is a use that licences may or may not permit, and the right to use content for AI must be confirmed. Where source content contains personal data, the GDPR applies, and embeddings can retain information about individuals.
In a managed model
A managed partner can source rights-cleared corpora suited to embedding, with documented provenance and privacy handling.
From source corpus to embeddings
Embeddings inherit everything about the corpus they are built from, its coverage, recency, quality and bias. Sourcing the right corpus, comprehensive for the domain, current, deduplicated and rights-cleared, therefore matters more than the choice of embedding model for many applications. Garbage or gaps in equals garbage or gaps out, expressed as confidently wrong similarity results.
Rights and privacy in vector pipelines
Two issues are easy to overlook. First, building embeddings from third-party content is a use that the licence may or may not permit, confirm the right to use the content for AI. Second, embeddings can retain information about the underlying data, including personal data, so where the source contains personal data the GDPR still applies to the vectors and the index. Treat the corpus, embeddings and index as one governed asset.
- Embeddings capture meaning and power semantic search and retrieval.
- They inherit the coverage, quality and bias of their source data.
- Confirm the right to use content for embeddings and AI.
- Embeddings can retain personal information; the GDPR applies.
Sources & further reading
- Industry references on embeddings and vector databases.
- EUR-Lex: Directive (EU) 2019/790 (text and data mining).
- EUR-Lex: Regulation (EU) 2016/679 (GDPR).
- EUR-Lex: Regulation (EU) 2024/1689 (AI Act).
We source rights-cleared corpora suited to embeddings, with documented provenance. Get a no-obligation quote.