pamelafox

While there, I helped develop this middle school curriculum for Snap: https://bjc.berkeley.edu/bjc-r/course/sparks.html

It was really interesting to attempt a functions-first approach that was still fun - I ended up re-making similar projects for a functions-first Python course as well.

I have a repository here with similar examples across all those frameworks: https://github.com/Azure-Samples/python-ai-agent-frameworks-...

I started comparing their features in more details in a gist, but it's WIP: https://gist.github.com/pamelafox/c6318cb5d367731ce7ec01340e...

I can flesh that out if it's helpful. I find it fascinating to see where agent frameworks converge and diverge. Generally, the frameworks are converging, which is great for developers, since we can learn a concept in one framework and apply it to another, but there are definitely differences as you get into the edge cases and production-level sophistication.

That query generation approach does not extract structured data. I do maintain another RAG template for PostgreSQL that uses function calling to turn the query into a structured query, such that I can construct SQL filters dynamically. Docs here: https://github.com/Azure-Samples/rag-postgres-openai-python/...

I'll ask the search about SPLADE, not sure.

That's why I write blog posts like https://blog.pamelafox.org/2024/06/vector-search-is-not-enou...

AI Search team's been working with the Sharepoint team to offer more options, so that devs can get best of both worlds. Might have some stuff ready for Ignite (mid November).

Disclosure: I work at MS and help maintain our most popular open-source RAG template, so I follow the best practices closely: https://github.com/Azure-Samples/azure-search-openai-demo/

So few developers realize that you need more than just vector search, so I still spend many of my talks emphasizing the FULL retrieval stack for RAG. It's also possible to do it on top of other DBs like Postgres, but takes more effort.