Great article, one bit of errata: actually ChatGPT does not expose its internal embedding, so the use of embeddings for RAG are just optional or even coincident

Great article, one bit of errata: actually ChatGPT does not expose its internal embedding, so the use of embeddings for RAG are just optional or even coincidental. You can also use ordinary search like Elasticsearch (a point that's somehow often lost). Besides, the internal embedding for ChatGPT is per-token (~word), whereas the embedding used for RAG search is per-document (retrieval document might be small like a paragraph or page, or could be as large the the whole source document), so these wouldn't be usable for this purpose anyway > One compelling feature of vector DBMSs is that they provide better integration with AI tools (e.g., Chat- GPT [16], LangChain [36]) than RDBMSs. These sys- tems natively support transforming a record’s data into an embedding upon insertion using these tools and then uses the same transformation to convert a query’s in- put arguments into an embedding to perform the ANN search; other DBMSs require the application to perform these transformations outside of the database.