POST /ingest -> called every 5 seconds -
    client calls it with whatever is there on user's screen

POST /chat_completion - when user asks sth

type of questions(not answerable by RAG) -

- which people are in my team?
- what message i haven't yet replied to on whatsapp? give a draft reply for
   those chats.
- what PRs need my reviews
- add 3 more examples

• you control the client - and can parse in whatever ways you want
• schema of ingest is for you to decide
• main objective - zoomed out view of the whole system rather than code.
• first principles, the only thing you have will be databases of your choice, an embedding model(if needed), and a LLM
• solution has to be as generic as possible
  • but you can do specific things for a category of apps - e.g. conversational apps
  • overall framework should still be easy to extend
• topics which might help - knowledge graph, memory - grafiti, mem0, GraphRag(you should read research paper)
• talk with AI, and maybe get more topics
• write dfs and graph stuff

Outcome -

• assuming you’re leading the effort for building above backend - a clear proposal for what you would you do and how would you do it. - written doc.
• some POC - jupyter notebook, python script - decide it for yourself. - code clarity and writing non trivial code

Requirements: Research, proposal, POC, System Design

Bonus: create multiple POC for different architecture. and give a comparison

As our requirements are only to fulfil broad contextual question

we will have a filter that check if the question is a contextual question on not.

neo4j for Graph Rag

fast API for API service