ghostfaceschiller t1_jdekpke wrote on March 23, 2023 at 8:51 PM

Reply to comment by psdwizzard in [N] ChatGPT plugins by Singularian2501

Trivially easy to build using the embeddings api, already a bunch of 3rd party tools that give you this. I’d be surprised if it doesn’t exist as one of the default tools within a week of the initial rollout.

EDIT: OK yeah it does already exist a part of the initial rollout - https://github.com/openai/chatgpt-retrieval-plugin#memory-feature

willer t1_jdgps4b wrote on March 24, 2023 at 7:37 AM

I read through the docs, and in this release, ChatGPT only calls the /query API. So you can't implement long term memory of your chats yourself, as it won't send your messages and the responses to this service. Your retrieval API acts in effect as a readonly memory store of external memories, like a document library.

ghostfaceschiller t1_jdgrba9 wrote on March 24, 2023 at 7:59 AM

Fr??? Wow what an insane oversight

Or I guess maybe they don’t wanna rack up all the extra embeddings calls, bc I assume like 100% if users would turn that feature on

BigDoooer t1_jdele0p wrote on March 23, 2023 at 8:55 PM

I’m not familiar with these. Can you give the name/location if one to check out?

ghostfaceschiller t1_jdenoo2 wrote on March 23, 2023 at 9:10 PM

Here's a standalone product which is a chatbot with a memory. But look at LangChain for several ways to implement the same thing.

The basic idea is: periodically feed your conversation history to the embeddings API and save the embeddings to a local vectorstore, which is the "long-term memory". Then, any time you send a message or question to the bot, first send that message to embeddings API (super cheap and fast), run a local comparison, and prepend any relevant contextual info ("memories") to your prompt as it gets sent to the bot.

xt-89 t1_jdessgl wrote on March 23, 2023 at 9:43 PM

This also opens the door to a lot of complex algorithms for retrieving the correct memories