[eb0la]

We started with PGVector just because we already knew Postgres and it was easy to hand over to the operations people.

After some time we noticed a semi-structured field in the prompt had a 100% match with the content needed to process the prompt.

Turns out operators started puting tags both in the input and the documents that needed to match on every use case (not much, about 50 docs).

Now we look for the field first and put the corresponding file in the prompt, then we look for matches in the database using the embedding.

85% of the time we don't need the vectordb.

[alansaber]

Most vectordb is a hammer looking for a nail

[folli]

I think it can be more efficient for two-step RAG so you can reuse the natural language query directly, but for agentic RAG it might indeed be overkill.