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I want to setup graph rag within 6 hours Here is the requirement. How would I do it with Llamaindex

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

POST /chat_completion - when user asks sth

Example queries

• 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
• 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

some POC - jupyter notebook, python script - decide it for yourself. - code clarity and writing non trivial code

Answer

Setting Up GraphRAG with LlamaIndex in 6 Hours

Here's a step-by-step guide to implement a GraphRAG system using LlamaIndex that can handle your specific requirements within a 6-hour timeframe.

1. Project Structure (15 minutes)

Let's set up a clear project structure:

graphmem/
├── app.py               # FastAPI application
├── ingest.py            # Ingestion logic
├── query.py             # Query processing
├── parsers/             # Application-specific parsers
│   ├── __init__.py
│   ├── base.py          # Base parser class
│   ├── whatsapp.py      # WhatsApp parser
│   ├── github.py        # GitHub parser
│   └── generic.py       # Generic content parser
├── storage/             # Storage management
│   ├── __init__.py
│   ├── graph_store.py   # Graph database interface
│   └── vector_store.py  # Vector database interface
├── utils/               # Utility functions
│   ├── __init__.py
│   └── helpers.py       # Helper functions
├── config.py            # Configuration
├── requirements.txt     # Dependencies
└── demo.ipynb           # Demo notebook

2. Setting Up Dependencies (15 minutes)