Update README.md

README.md

@@ -14,7 +14,8 @@ viewer: false
 
 ## Description
 
-*TODO: What is the artifact?*
+A corpus graph for Wikipedia corpus (`rag:nq_wiki`), built using `ragwiki-terrier` sparse index.  
+The graph encodes semantic relationships between documents by connecting each document to its `k=16` nearest neighbors based on `bm25` retriever.
 
 ## Usage
 
@@ -22,7 +23,27 @@ viewer: false
 # Load the artifact
 import pyterrier as pt
 artifact = pt.Artifact.from_hf('astappiev/ragwiki-corpusgraph')
-# TODO: Show how you use the artifact
+```
+
+### Usage with GAR
+
+```python
+import pyterrier as pt
+from pyterrier_adaptive import GAR, NpTopKCorpusGraph
+from pyterrier_t5 import MonoT5ReRanker
+
+sparse_index: pt.terrier.TerrierIndex = pt.Artifact.from_hf("pyterrier/ragwiki-terrier")
+retriever = pt.rewrite.tokenise() >> sparse_index.bm25(include_fields=["docno", "text", "title"]) >> pt.rewrite.reset()
+
+get_text = sparse_index.text_loader(["docno", "text", "title"])
+prepare_text = pt.apply.generic(lambda df: df.assign(qid=df["qid"].map(str), docno=df["docno"].map(str))) >> get_text
+scorer = prepare_text >> MonoT5ReRanker(verbose=False, batch_size=64)
+
+graph: NpTopKCorpusGraph = pt.Artifact.from_hf("astappiev/ragwiki-corpusgraph").to_limit_k(8)
+
+pipeline = retriever >> GAR(scorer, graph) >> get_text
+pipeline.search("hello world")
+
 ```
 
 ## Benchmarks
@@ -31,13 +52,33 @@ artifact = pt.Artifact.from_hf('astappiev/ragwiki-corpusgraph')
 
 ## Reproduction
 
+This graph was constructed using [PyTerrier Adaptive](https://github.com/terrierteam/pyterrier_adaptive).
+
 ```python
-# TODO: Show how you constructed the artifact.
+import pyterrier as pt
+from pyterrier_adaptive import NpTopKCorpusGraph
+
+dataset: pt.datasets.Dataset = pt.get_dataset("rag:nq_wiki")
+sparse_index: pt.terrier.TerrierIndex = pt.Artifact.from_hf("pyterrier/ragwiki-terrier")
+bm25 = pt.rewrite.tokenise() >> sparse_index.bm25(include_fields=["docno", "text", "title"], threads=slurm_cpus) >> pt.rewrite.reset()
+
+graph = NpTopKCorpusGraph.from_retriever(
+    bm25 % (build_graph_k + 1),
+    dataset.get_corpus_iter(),
+    "../index/ragwiki-corpusgraph",
+    k=build_graph_k,
+    batch_size=65_536,
+)
+graph.to_hf('astappiev/ragwiki-corpusgraph')
 ```
 
+However the code above will take 2 months to run on a single machine. The graph was built using modified version of `from_retriever` to enable parallel compute on the cluster.
+
+It took around 14340 CPU-hours to build the graph on our cluster.
+
 ## Metadata
 
-```
+```json
 {
   "type": "corpus_graph",
   "format": "np_topk",
@@ -45,4 +86,4 @@ artifact = pt.Artifact.from_hf('astappiev/ragwiki-corpusgraph')
   "doc_count": 21015324,
   "k": 16
 }
-```
+```