3 ms·
I did the same for the FDA drug label database and 100% believe that this the future for search. Semantic search layer for context then the large language layer
by jonathan-adly 4y ago
I did the same for the FDA drug label database and 100% believe that this the future for search. Semantic search layer for context then the large language layer for human answers.
Tip - you don’t actually need GPT-3 level embedding for a decent semantic search. Sentence transformers paired with one of their models is good enough.
I like this: https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1 https://huggingface.co/sentence-transformers/multi-qa-MiniLM... - since it’s very light.
Also, perhaps I am an idiot but I just used Postgres array field to store my embeddings array to keep things simple and free.
- rohit89 4y agoDid you use the sentence-transformers model as-is or did you need to fine-tune it for medical data?
- jonathan-adly 4y agoAs is.. all it’s doing is pulling relevant context to the question. GPT-3 is doing all the heavy natural language lifting.
- rileyt 4y agoIt was less than $2 to embed all 100+ episodes with the new OpenAI embeddings and was as easy as just making a bunch of API calls. Pretty hard to beat that experience.