I'll help you set up a Graph RAG system for your POC within the 6-hour timeframe. Let me first search for the best current Graph RAG frameworks to recommend the most suitable approach for your requirements.

Graph RAG System Design for Quick POC

Based on my research of the latest Graph RAG frameworks in 2025, I'll design a system that can be implemented within 6 hours while meeting your requirements for sensemaking and broader context questions.

System Architecture Overview

![System Architecture Diagram]

User Screen Content → Ingestion API → Knowledge Graph → Query Processing → Response Generation → User

Framework Selection

For your 6-hour POC timeline, I recommend using LlamaIndex with its graph store capabilities for these reasons:

1. Comprehensive ecosystem - LlamaIndex is designed specifically to connect LLMs with private data sources, making it ideal for your screen content ingestion requirements
2. Graph capabilities - LlamaIndex provides built-in graph store modules that can represent relationships between entities
3. Quick implementation - Well-documented and designed for rapid prototyping
4. Extensibility - Easy to extend for specific app categories as per your requirements

Detailed System Components

1. Data Ingestion Service

# app.py - FastAPI service
from fastapi import FastAPI, Request
from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
from llama_index.indices.knowledge_graph import KnowledgeGraphIndex
from llama_index.llms import OpenAI
import os, json, time

app = FastAPI()

# Initialize your indices
graph_index = None
vector_index = None

@app.post("/ingest")
async def ingest_data(request: Request):
    content = await request.json()
    screen_content = content.get("screen_content")
    app_type = content.get("app_type", "general")

    # Process and store the content in both vector and graph indices
    processed_data = preprocess_content(screen_content, app_type)
    update_indices(processed_data)

    return {"status": "success"}

2. Knowledge Graph Builder

# graph_builder.py
from llama_index.indices.knowledge_graph import KGExtractor
from llama_index.llms import OpenAI

def build_knowledge_graph(documents):
    llm = OpenAI(model="gpt-4-turbo")
    service_context = ServiceContext.from_defaults(llm=llm)

    # Extract entities and relationships
    kg_extractor = KGExtractor(llm=llm)
    kg_triplets = kg_extractor.extract_triplets(documents)

    # Build the knowledge graph
    graph_index = KnowledgeGraphIndex.from_documents(
        documents,
        kg_triplets=kg_triplets,
        service_context=service_context
    )

    return graph_index

3. App-Specific Processors

# processors.py
def preprocess_content(content, app_type):
    # Base preprocessing
    processed_data = extract_text_and_metadata(content)

    # App-specific processing
    if app_type == "messaging":
        processed_data = extract_chat_metadata(processed_data)
    elif app_type == "development":
        processed_data = extract_code_and_pr_info(processed_data)

    return processed_data

4. Query Engine