Interactive demo for GNN+LLM (chatgpt-like)

CHANGELOG.md

@@ -7,6 +7,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
+- Added G-retriever interactive demo ([#9417](https://github.com/pyg-team/pytorch_geometric/pull/9417))
+- Added `nn.models.GRetriever` ([#9167](https://github.com/pyg-team/pytorch_geometric/pull/9167))
+- Added the `WebQSPDataset` dataset ([#9481](https://github.com/pyg-team/pytorch_geometric/pull/9481))
 - Added the `GRetriever` model ([#9480](https://github.com/pyg-team/pytorch_geometric/pull/9480))
 - Added the `ClusterPooling` layer ([#9627](https://github.com/pyg-team/pytorch_geometric/pull/9627))
 - Added the `LinkPredMRR` metric ([#9632](https://github.com/pyg-team/pytorch_geometric/pull/9632))

examples/README.md

@@ -14,9 +14,9 @@ For examples on [Open Graph Benchmark](https://ogb.stanford.edu/) datasets, see
 - [`ogbn_papers_100m.py`](./ogbn_papers_100m.py) is an example for training a GNN on the large-scale `ogbn-papers100m` dataset, containing approximately ~1.6B edges.
 - [`ogbn_papers_100m_cugraph.py`](./ogbn_papers_100m_cugraph.py) shows how to accelerate the `ogbn-papers100m` workflow using [CuGraph](https://github.com/rapidsai/cugraph).
 
-For examples on using `torch.compile`, see the examples under [`examples/compile`](./compile).
+For examples on co-training LLM with GNN, see examples and README under [`examples/llm_plus_gnn`](./llm_plus_gnn).
 
-For examples on scaling PyG up via multi-GPUs, see the examples under [`examples/multi_gpu`](./multi_gpu).
+For examples on using `torch.compile`, see examples and README under [`examples/compile`](./compile).
 
 For examples on working with heterogeneous data, see the examples under [`examples/hetero`](./hetero).
 

examples/llm_plus_gnn/README.md

@@ -0,0 +1,6 @@
+# Examples for LLM and GNN co-training
+
+| Example                                                                | Description                                                                                                                                             |
+| ---------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| [`g_retriever.py`](./g_retriever.py)                                   | Example for Retrieval-Augmented Generation (RAG) w/ GNN+LLM by co-training LLAMA2 with GAT for answering questions based on knowledge graph information |
+| [`interactive_g_retriever_demo.py`](./interactive_g_retriever_demo.py) | Example of how to interact with a G Retriever model.                                                                                                    |