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What's the best current way to make a bunch of documents searchable via LLMs like this? I've tried the using OpenAI w embeddings (iirc), but this was slow, got
by jamestimmins 3y ago
What's the best current way to make a bunch of documents searchable via LLMs like this?
I've tried the using OpenAI w embeddings (iirc), but this was slow, got expensive quickly, and it struggled to answer questions about the text accurately. Curious if there's better standard approaches now.
- linguistbreaker 3y agoI just came across this project which seems to be aiming at streamlining exactly that : https://github.com/Mintplex-Labs/anything-llm https://github.com/Mintplex-Labs/anything-llm
- Jianghong94 3y agoA couple of things come to mind: 1. embedding methods: there're a couple of ways to do that, the most used one is OpenAi's text-davinci-002, although in my use case(short sentence description of API) it didn't work pretty well; 2. how you truncate documents into pieces: for this langchain has some implementation and helpful pointers. I think you have to do lots of experiment on this till you find your best information retrieval strategy
- JimmyRuska 3y agoYou could copy the steps listed in alpaca-lora with your own data. Try thebloke's superhot ggml models. Train it in lambda labs or replicate