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> - vectorDB startups -> don't need embeddings anymore they don't provide embedings, but storage and query engines for embeddings, so still very relevant > -

> - vectorDB startups -> don't need embeddings anymore they don't provide embedings, but storage and query engines for embeddings, so still very relevant > - file processing startups -> don't need to process files anymore curious what is that exactly?.. > - vertical ai agent startups -> GPT marketplace sure, those startups will be selling their agents on marketplace

make3 3y ago
they definitely do provide embeddings, https://openai.com/blog/new-models-and-developer-products-announced-at-devday https://openai.com/blog/new-models-and-developer-products-an... ctrl+f retrieval, "... won't need to ... compute or store embeddings"
- riku_iki 3y ago
  I mean embeddingsDB startups don't provide embeddings. They provide databases which allows to store and query computed embeddings (e.g. computed by ChatGPT), so they are complimentary services.
  - taf2 3y ago
    Yeah I still see a chat bot being able to look for related information in a database as useful. But I see it as just one of many tools a good chat experience will require. 128k context means for me there other applications to explore and larger tasks to accomplish with fewer api requests. Better chat history and context not getting lost
visarga 3y ago
It's easy to host your query engine somewhere else and integrate it as a search function in chatGPT. Quite easy to switch providers of search.
- obmelvin 3y ago
  As in, use an existing search and call it via 'function calling' as part of the assistants routine - rather than uploading documents to the assistant API?
dragonwriter 3y ago
> they don't provide embedings, but storage and query engines for embeddings, so still very relevant But you don't need any of the chain of: extract data, calculate embeddings, store data indexed by embeddings, detect need to retrieve data by embeddings and stuff it into LLM context along with your prompt if you use OpenAI's Assistants API, which, in addition to letting you store your own prompts and manage associated threads, also lets you upload data for it to extract, store, and use for RAG on the level of either a defined Assistant or a particular conversation (Thread.)

make3 3y ago

they definitely do provide embeddings, https://openai.com/blog/new-models-and-developer-products-announced-at-devday https://openai.com/blog/new-models-and-developer-products-an... ctrl+f retrieval, "... won't need to ... compute or store embeddings"

riku_iki 3y ago
I mean embeddingsDB startups don't provide embeddings. They provide databases which allows to store and query computed embeddings (e.g. computed by ChatGPT), so they are complimentary services.
- taf2 3y ago
  Yeah I still see a chat bot being able to look for related information in a database as useful. But I see it as just one of many tools a good chat experience will require. 128k context means for me there other applications to explore and larger tasks to accomplish with fewer api requests. Better chat history and context not getting lost

visarga 3y ago

It's easy to host your query engine somewhere else and integrate it as a search function in chatGPT. Quite easy to switch providers of search.

obmelvin 3y ago
As in, use an existing search and call it via 'function calling' as part of the assistants routine - rather than uploading documents to the assistant API?

dragonwriter 3y ago

> they don't provide embedings, but storage and query engines for embeddings, so still very relevant But you don't need any of the chain of: extract data, calculate embeddings, store data indexed by embeddings, detect need to retrieve data by embeddings and stuff it into LLM context along with your prompt if you use OpenAI's Assistants API, which, in addition to letting you store your own prompts and manage associated threads, also lets you upload data for it to extract, store, and use for RAG on the level of either a defined Assistant or a particular conversation (Thread.)