Author
John Rupert Firth
1 reading card from 1 book · 1957.
1 card
A synopsis of linguistic theory 1930–1955 · 1957
The embedding model's version is part of the schema: change the model, recompute the column.
Firth's idea, formalised: a model reads a text and turns it into a vector of a few hundred or thousand numbers, so that texts with close meaning end up as close vectors. Closeness is measured — usually cosine — not checked by equality. Hence a new structure in the warehouse: the vector index (HNSW, IVF), which finds approximate nearest neighbours without comparing against every row. On top of it sits RAG: retrieve the relevant fragments by meaning, then let a generative model answer using them. What changes for the data engineer: the embedding model's version is part of the schema — change the model, recompute the column, otherwise you compare vectors from different spaces. Stored next to the vector: the text, a hash of it, the model, the dimension. Chunking is a modelling decision, not a detail. Metadata filters (language, date, customer) are applied before or together with the vector search, not after. And vectors do not replace keys and joins: they are one more column in a table that stays relational.
“You shall know a word by the company it keeps.”