3 ms·

> Search the prompt vector to find the most similar chunk vector. Does this mean that a question is changed to a similar question? Doesnt it decrease the quali

by rvba 3y ago

> Search the prompt vector to find the most similar chunk vector. Does this mean that a question is changed to a similar question? Doesnt it decrease the quality of answers significantly? If someone asks "who is the best student in California?" will the question be changed to "what is the best school in California?". This would explain the terrible drops in quality of amswers that we saw. The underlying technology is changed for easier scaling, but is much worse. It's like the current google (what has multiple problems), where it also sometimes doesnt search your keywords - even when they are in quotation marks - because it knows "better"..

_pdp_ 3y ago
Without exception, LLM frontends out there that use vector search simply do a query and hope for the best. The reality is that the answer may not be in the result. It is a hard problem to solve. We are at least 1-2 stepping stones away from having a solution that actually works!
- danielbln 3y ago
  You can do a lot with hybrid approaches, where you mix and match semantic search results with classic text based search. In addition, depending on what kind of knowledge you pull in, the LLM pretraining can often fill the gaps pretty well even if the retrieval augmentation isn't ideal. But yes, it's still pretty fuzzy, though it works more often than not.
  - kordlessagain 3y ago
    Like this: https://GitHub.com/featurebasedb/DoctorGPT https://GitHub.com/featurebasedb/DoctorGPT
charcircuit 3y ago
>Does this mean that a question is changed to a similar question? No. It is retrieving the most semantically similar document(s) to work with. For a question about students information about students will be more semantically similar than information about schools. >It's like the current google Google has used vector search for years. It's why you can just type questions in and Google will understand what you are talking about. >doesnt search your keywords - even when they are in quotation marks Quotation marks work. If there are no results it will show results without the quotes. The mobile site doesn't make this clear when it happens though.

by rvba 3y ago

_pdp_ 3y ago

Without exception, LLM frontends out there that use vector search simply do a query and hope for the best. The reality is that the answer may not be in the result. It is a hard problem to solve. We are at least 1-2 stepping stones away from having a solution that actually works!

danielbln 3y ago
You can do a lot with hybrid approaches, where you mix and match semantic search results with classic text based search. In addition, depending on what kind of knowledge you pull in, the LLM pretraining can often fill the gaps pretty well even if the retrieval augmentation isn't ideal. But yes, it's still pretty fuzzy, though it works more often than not.
- kordlessagain 3y ago
  Like this: https://GitHub.com/featurebasedb/DoctorGPT https://GitHub.com/featurebasedb/DoctorGPT

charcircuit 3y ago

>Does this mean that a question is changed to a similar question? No. It is retrieving the most semantically similar document(s) to work with. For a question about students information about students will be more semantically similar than information about schools. >It's like the current google Google has used vector search for years. It's why you can just type questions in and Google will understand what you are talking about. >doesnt search your keywords - even when they are in quotation marks Quotation marks work. If there are no results it will show results without the quotes. The mobile site doesn't make this clear when it happens though.