Introduction

This Repo will serve as my exploration and source code for generating a submission for https://azurecosmosdb.devpost.com/. I will be documenting my experience in my upcoming Medium article series

Phase 1: Calling Azure OpenAI

Using the endpoint, key and deployed models that the hackathon provides to its users, we were able to very easily throw together a simple node script to call an LLM for chat completion. This was shockingly easy!

Phase 2: Deploying some demo code

We are just deploying some demo code here, the hackathon provided us an example front-end to build on. I used azure cli instead of the weird powershell stuff they did

az login az group create --name mongo-devguide-rg --location eastus

before I actually deployed the template they gave me, Azure has not made OpenAI generally available and I don't have an edu email to request access. The hackathon organizers provided an open AI endpoint to use so I just commented out the OpenAI parts

az deployment group create --resource-group mongo-devguide-rg --template-file azuredeploy.bicep --parameters azuredeploy.parameters.json

PHASE 3: Playing with Cosmos DB

In this section we just explored some common API calls to make to Cosmos DB using the azure SDK, if you are familiar with any noSQL cloud database (like firebase or DDB) you could probably just skip this

PHASE 4: Bulk loading

In this section we updated our Cosmos db app to do all bulk operations

PHASE 5: Building a RAG

In this section we combine our database inserting skills and OpenAI calling to generate a text embedding vector for our collections, then we demonstrate how we can use that index to do simple cosine similarity search but also use the vector indexes to plug into an OpenAi chat completion to give our chat app better context when answering the questions

PHASE 6: LangChain

In this section we rebuilt our similarity search and RAG system using langchain. I had to make my own custom embedding class and deviated from the ChatModel class provided by the hackathon demo, since the library was not set up to use an openAI endpoint and was requiring an instance name (which I did not have). Once we completed the RAG, we used langchain to build a very cool AI agent with tools to query databases and plan its approach based on the query it received

PHASE 7: Backend

In this section, we encapsulated our agent in a class and created a swagger api to create two endpoints: one GET at root which serves as somewhat of a health check endpoint, and one at /ai POST which will take a session id (if we want stateful communication) and a message to send the agent (NOTE: usually an anti-pattern, APIs should be stateless any state should be stored in a redis cluster or passed in from user ). I did have a lot of issues with this stage, Azure container registry UI never showed that my image was present so I needed to run some cli to confirm, also I realized my initial deployment had failed to deploy the backend app env and container due to an issue with worker profiles, I and to go and re-deploy. At the end of this, we have our backend API running on the cloud

PHASE 8: Creating our own project

Here comes the fun part, we are going to take what we learned and build a submission for the Hackathon! I am going to build a 3D store with an AI salesperson, which is a nice marriage of my 3D core competency and this AI hackathon's goals