How to start small in a very specific area: 1. Write down a list of 10 to 20 questions that you want your RAG to be able to answer. Then write down the correct answers next to each question. If you don't know the correct answers, ask a subject matter expert. Build a RAG chatbot that can answer those questions first. 2. You have to start out using tools that are capable of growing with you. For example, don't use ChromaDB because it can only fit in memory. Use services that can scale up. 3. I cover this in my book, you can use the 10 to 20 questions as integration tests and run them every time you commit code to make sure everything still works. 4. How to feed with data continuously... do you mean keeping your databases updated? You can write a script to check for regulations published in the last week, and only upload those regulations to your database.

I hope all that helps, let me know if you have any other questions!

EXCELLENT question. We tried about 5 different ways of retrieving data, and we found that what works best for us is a search-as-a-service. We use Azure AI Search, but there are lots of other ones out there, including Google Vertex AI Search, Algolia, Amazon CloudSearch, or ElasticSearch.

What tradeoffs? It is fast and accurate, but it does get expensive when you have over 50 million records.

We have tests in place to make sure every function, every search is working, otherwise things won't deploy. We do have a dashboard with usage etc., but I have not spent much time looking at it. I think we just count broad usage across the company as a success. And yes we consider one-stars and complaints to be a failure, but we get those less often, maybe once every 2 weeks, and it is almost always the user's fault. Does that answer your question?

First of all, great question.

Second, we use a search service, and vectors are treated as supplementary to the text search, so chunking doesn't matter as much. We will usually take an entire PDF page and embed that, no matter what structure the data on that page is. We do keep track of the name of the document and the page number. For SQL records, we just turn each record into a text string and embed that.

Ok so the company has like 20 databases, plus over 100,000 pages of PDF catalogs. We tried using agents to query the company's SQL databases, but that took 30 seconds each call, and that is unacceptable because we wanted to return an answer to the user in 10 to 30 seconds. So what we ended up doing is, we created an Azure AI Search service and we made a different search index (like a collection) for each data source, one for each database and one for our repository of 100,000 pdf pages.

Our stack was just Python, Autogen for the agents, and as I mentioned Azure AI Search. We use Azure Web Apps for the backend, and OpenAI models for the generation. Great questions!

Great, thank you!

The main program is hosted on Azure Web Apps, the search is Azure AI Search, we use AutoGen for the agents, and we use OpenAI for the generation. Azure has a lot of tools that support AI and search, so we use those too.

Ah, sorry, I forgot to mention it. The title will be "Enterprise RAG: Scaling Retrieval Augmented Generation"