pu_pu

4 ms·

1. 1.▲
   by pu_pu 11mo ago

   Is it open source?
2. 2.▲
   by pu_pu 11mo ago

   The biggest problem with retrieval is actually semantic relevance. I think most embedding models don't really capture sentence-level semantic content and instead act more like bag-of-words models averaging local word-level information.
3. 3.▲
   by pu_pu 1y ago

   Not at all in my opinion. Its a zero sum game against anti bot technologies also employing AI to block scrapers.
4. 4.▲
   by pu_pu 1y ago

   This really feels like a missed opportunity to build something genuinely new, something that actually plays to the strengths of LLMs, instead of just embedding a fixed set of app screens inside chat. Ideally, users should be able to descri
5. 5.▲
   by pu_pu 1y ago

   So, I have not tried it on a very complex database myself so I can't comment how well it will work in production systems I have tried this approach with a single Big Query table and it worked pretty well for my toy example. If by 3.5 y
6. 6.▲
   by pu_pu 1y ago

   Can you please add support to add descriptions of each column and enumerated types? For example, if a column contains 0 or 1 encoding the absence of presence of something, LLMs need to know what 0 and 1 stand for. Same goes of column names
7. 7.▲
   by pu_pu 1y ago

   This is not a difficult problem to solve. We can add the schema, columns and column descriptions in the system prompt. It can significantly improve performance. All it will take is a form where the user supplies details about each column an