Using GPT3, Supabase and Pinecone to automate a personalized marketing campaign (opens in new tab)

Yep that's true! I'd probably do something like that if I were starting again, but the ease of calling a few APIs is pretty nice. I feel like that alone will drive a lot of adoption of some of these platforms even if it can just be done locally.

I come to HN for posts like this, thank you.

most HN comment ever

I've seen really good results using BERT and other open-source models for matching symptoms / healthcare service names to CPT/HCPCS code descriptions. Even for specialized domains, some of the freely available models perform well for ANN search. While BERT may not be viewed as state-of-the-art, versions of it have relatively low memory requirements versus newer models which is nice if you're self-hosting and/or care about scalability.

I didn't use the GPT3 autocomplete API in the end (though I did play around with it) but did use the embeddings API (which I believe is still considered part of the "GPT3" model, but I could be wrong!).

I totally could! I think each use case should dictate which model you should use, in my case I was not super cost or latency sensitive since it was a small dataset and I cared more about accuracy. But I'm planning on using something like https://huggingface.co/sentence-transformers/all-MiniLM-L6-v... for my next project where latency and cost will matter more :)

I have a lot of thoughts around that last question! The Supabase article came out way after I implemented this (August of last year) so I didn't even think to do that, not sure if it was even supported back then, but I'd probably reach for that if I was re-doing the project to reduce the number of systems I needed. I think the power of having the vector search done in the same DB as the rest of the data is that sometimes you may want to have structured filtering before the semantic/vector ranking (ie. only select user N's items and rank by similarity to <query>) which is trickier to do in Pinecone. They support metadata filtering but it feels like an after thought. For the project I'm working on now (https://pinched.io) , we'd like to filter on certain parameters as well as rank by relevance, so I'm going to explore combining structured querying with semantic search (ie. pgvector or something similar on DuckDB if it adds support for this).

My understanding is pinecone is much faster, but for this small search space, I doubt pgvectors would be noticably worse.

I tested an early version of pgvector against faiss and found faiss had much better performance

https://github.com/pgvector/pgvector/issues/3

Creating the index on pinecone takes about a minute. Creating a table on postgres should take a few milliseconds!

Query (→ GPT3 completion) → vector db lookups → GPT3 synthesis

The optional step two is used when the lookups are more closely related to an answer's latent space than the original query text. This approach is called HyDE (first published here: https://arxiv.org/abs/2212.10496).

The synthesis is also optional. You can essentially summarize your lookups or refine them or do whatever you want at this stage.

If you skipped steps 2 and 4, it's just a semantic search engine. If you skip step 2, you're either doing it for latency/performance reasons, or because the user query's embeddings are more similar to the docs in the vector db.

All the embedding-enabled GPT-3 apps I've seen do the following: User query -> Retrieve closest embedding's plaintext counterpart -> feed plaintext as context to GPT-3 prompt.

To be clear, I didn't use the autocomplete GPT-3 API just the embeddings one! Pinecone has some good docs on it: https://docs.pinecone.io/docs/openai. Happy to answer any questions :)

Looks like the Classifications API is deprecated: https://platform.openai.com/docs/guides/classifications

Embeddings are superior (classifiers are deprecated).

mehh … you might be overthinking it

it’s his blog either way.

Appreciate the kind words, but I'm sure you could pick it up pretty quickly too :) The OpenAI docs are pretty helpful as a starting point: https://platform.openai.com/docs/guides/embeddings/use-cases

I played around with that and pgtrgm (https://www.postgresql.org/docs/current/pgtrgm.html) a bit but unfortunately didn't have great results. I did do a bunch of manual data cleaning though, and also had some overriding logic if certain keywords match it would avoid semantic search and default to a result (for common ones).

Pinecone is a vector database: https://www.pinecone.io/