reorg of readme

examples/llm/g_retriever_utils/README.md

@@ -2,10 +2,7 @@
 
 | Example                                                          | Description                                                                                                                                                               |
 | ---------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| [`rag_feature_store.py`](./rag_feature_store.py)                 | A Proof of Concept Implementation of a RAG enabled FeatureStore that can serve as a starting point for implementing a custom RAG Remote Backend                           |
-| [`rag_graph_store.py`](./rag_graph_store.py)                     | A Proof of Concept Implementation of a RAG enabled GraphStore that can serve as a starting point for implementing a custom RAG Remote Backend                             |
-| [`rag_backend_utils.py`](./rag_backend_utils.py)                 | Utility functions used for loading a series of Knowledge Graph Triplets into the Remote Backend defined by a FeatureStore and GraphStore                                  |
-| [`rag_generate.py`](./rag_generate.py)                           | Script for generating a unique set of subgraphs from the WebQSP dataset using a custom defined retrieval algorithm (defaults to the FeatureStore and GraphStore provided) |
 | [`benchmark_model_archs_rag.py`](./benchmark_model_archs_rag.py) | Script for running a GNN/LLM benchmark on GRetriever while grid searching relevent architecture parameters and datasets.                                                  |
+| [`minimal_demo.py`](./minimal_demo.py) | Minimal demo for WebQSP dataset comparing GNN+LLM vs LLM                                                |
 
 NOTE: Evaluating performance on GRetriever with smaller sample sizes may result in subpar performance. It is not unusual for the fine-tuned model/LLM to perform worse than an untrained LLM on very small sample sizes.