simonwillisonblog VS pgvector

Simon Willison has pointed out several examples of prompt injection attacks and why it may never be a solved problem:

I want more people to have link blogs.

I have one in the sidebar of https://simonwillison.net/ which I've been running since November 2003. You can search through all 6,836 links here: https://simonwillison.net/search/?type=blogmark

I can post things to it with a bookmarklet. It has an Atom feed.

It's such a low-friction way of publishing. A lot of https://daringfireball.net works like this too. I also like https://waxy.org/ and https://kottke.org/ for this.

I'd love to see more of these.

My approach is to publish to my own blog at https://simonwillison.net and then copy and paste content from that into a Substack newsletter at https://simonw.substack.com a few times a month.

It's been working really well.

Substack don't have an API, but they do support copy and paste - so I built myself a tool that assembles my blog content into rich text I can copy and paste straight into the Substack editor.

I wrote about how that works here: https://simonwillison.net/2023/Apr/4/substack-observable/

Hah, yeah securing something like WordPress can be a challenge, especially if you're running a bunch of plugins.

My blog is a pretty straight-forward Django setup without many other dependencies, so it's a lot less of an attack surface: https://github.com/simonw/simonwillisonblog

That specific prompt is just an example and it's pretty bad, it was the shortest and simplest prompt I could come up with that would be easily understood.

You can set response content-types (text, html, json, etc...). If you use json it will get pretty good results because I have some is some logic to attempt to pick out json or json5 objects from the text output. I dont yet have logic to support json arrays, but I'm hoping to add that soon.

But still client side validation is needed for applications with untrusted input. I dont attempt to solve prompt injection. I saw a lot of interesting posts on this topic from this blog https://simonwillison.net/. I need to find sometime to read more about it.

Try this one instead, it should be better

I've started to consider "commit to writing about it" as the price I have to pay for giving into the lure of another project. It's one of the main reasons I publish so much content on https://simonwillison.net/ and https://til.simonwillison.net

A project with a published write-up unlocks so much more value than one which you complete without giving others a chance of understanding what you built.

I've maintained internal blogs (sometimes just a Slack channel or Confluence area) at previous employers for this purpose too.

I think you are asking specifically about practical LLM engineering and not the underlying science.

Honestly this is all moving so fast you can do well by reading the news, following a few reddits/substacks, and skimming the prompt engineering papers as they come out every week (!).

https://www.latent.space/p/ai-engineer provides an early manifesto for this nascent layer of the stack.

Zvi writes a good roundup (though he is concerned mostly with alignment so skip if you don’t like that angle): https://thezvi.substack.com/p/ai-18-the-great-debate-debates

Simon W has some good writeups too: https://simonwillison.net/

I strongly recommend playing with the OpenAI APIs and working with langchain in a Colab notebook to get a feel for how these all fit together. Also, the tools here are incredibly simple and easy to understand (very new) so looking at, say, https://github.com/minimaxir/simpleaichat/tree/main/simpleai... or https://github.com/smol-ai/developer and digging in to the prompts, what goes in system vs assistant roles, how you gourde the LLM, etc.

Simon Willison's Weblog

# Install Postgres and pgvector !apt-get update && apt install postgresql postgresql-server-dev-14 !git clone --branch v0.6.2 https://github.com/pgvector/pgvector.git !cd pgvector && make && make install # Start database !service postgresql start !sudo -u postgres psql -U postgres -c "ALTER USER postgres PASSWORD 'pass';"

When talking about Vector Databases, in the market we can find the specialized ones and multi-model, most of the major database providers like Oracle, PostgreSQL or MongoDB, for mention some of them, have integrated a specific solution to retrieve vector data.

For this example, I wanted to focus on how pgvector  – an open-source vector similarity search for Postgres – can be used to identify data similarities that exist in enterprise data.

pgvector supports dense vector search well, but it does not have plan to support sparse vector.

There's an issue in the pgvector repo about someone having several ~10-20million row tables and getting acceptable performance with the right hardware and some performance tuning: https://github.com/pgvector/pgvector/issues/455

I'm in the early stages of evaluating pgvector myself. but having used pinecone I currently am liking pgvector better because of it being open source. The indexing algorithm is clear, one can understand and modify the parameters. Furthermore the database is postgresql, not a proprietary document store. When the other data in the problem is stored relationally, it is very convenient to have the vectors stored like this as well. And postgresql has good observability and metrics. I think when it comes to flexibility for specialized applications, pgvector seems like the clear winner. But I can definitely see pinecone's appeal if vector search is not a core component of the problem/business, as it is very easy to use and scales very easily

The Spring AI project aims to streamline the development of applications that incorporate artificial intelligence functionality without unnecessary complexity. On this example we use features like: Embedding, Prompts, ETL and save all embedding on PGvector(Postgres Vector database)

Official GitHub repository of the pgvector extension

pgvector 0.6.0 was just released and will be available on Supabase projects soon. Again, a special shout out to Andrew Kane and everyone else who worked on parallel index builds.

The pgvector extension adds vector similarity search capabilities to your PostgreSQL database. To use the extension, you have to first create it in your database. You can install the extension, by connecting to your database and running the CREATE EXTENSION command from the psql command prompt: