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# pretty_name: "" # Example: "MS MARCO Terrier Index"
tags:
- pyterrier
- pyterrier-artifact
- pyterrier-artifact.corpus_graph
- pyterrier-artifact.corpus_graph.np_topk
task_categories:
- text-retrieval
viewer: false
---
# ragwiki-corpusgraph
## Description
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
```python
# Load the artifact
import pyterrier as pt
artifact = pt.Artifact.from_hf('astappiev/ragwiki-corpusgraph')
```
### 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
*TODO: Provide benchmarks for the artifact.*
## Reproduction
This graph was constructed using [PyTerrier Adaptive](https://github.com/terrierteam/pyterrier_adaptive).
```python
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",
"package_hint": "pyterrier-adaptive",
"doc_count": 21015324,
"k": 16
}
``` |