At AIFD4 Google demonstrated Gemini 1.0 writing some code for a task that someone had. At CFD20 Google Lisa Shen demonstrated how easy it is to build a LLM-RAG from scratch using GCP Cloud Run and VertexAI APIs. (At press time, the CFD20 videos from GCP were not available but I am assured they will be up there shortly)
I swear in a matter of minutes Lisa Shen showed us two Python modules (indexer.py and server.py) that were less than 100 LOC each. One ingested Cloud Run release notes (309 if I remember correctly), ran embeddings on them and created a RAG Vector database with the embedded information. This took a matter of seconds to run (much longer to explain).
And the other created an HTTP service that opened a prompt window, took the prompt, embedded the text, searched the RAG DB with this and then sent the original prompt and the RAG reply to the embedded search to a VertexAI LLM API call to generate a response and displayed that as an HTTP text response.
Once the service was running, Lisa used it to answer a question about when a particular VPC networking service was released. I asked her to ask it to explain what that particular networking service was. She said that it’s unlikely to be in the release notes, but entered the question anyways and lo and behold it replied with a one sentence description of the networking capability.
GCP Cloud Run can do a number of things besides HTTP services but this was pretty impressive all the same. And remember that GCP Cloud Run is server less, so it doesn’t cost a thing while idle and only incurs costs something when used.
I think if we ask nicely Lisa would be willing to upload her code to GitHub (if she hasn’t already done that) so we can all have a place to start.
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Ok all you enterprise AI coders out there, start your engines. If Lisa can do it in minutes, it should take the rest of us maybe an hour or so.
My understanding is that Gemini 2.0 PRO has 1M token context. So the reply from your RAG DB plus any prompt text would need to be under 1M tokens. 1M tokens could represent 50-100K LOC for example, so there’s plenty of space to add corporate/organizations context.
There are smaller/cheaper variants of Gemini which support less tokens. So if you could get by with say 32K Tokens you might be able to use the cheapest version of Gemini (this is what the VertexAI LLM api call ends up using).
Also for the brave at heart wanting some hints as to what come’s next, I would suggest watching Neama Dadkhanikoo’s session at CFD20 with a video on Google DeepMind’s Project Astra. Just mind blowing.
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