### Putting it all together You can use Doc2Query or Doc2Query-- in an indexing pipeline to build an index of the expanded documents:
D
Doc2Query[−−]
D
Indexer
IDX
```python import pyterrer as pt pt.init() import pyterrier_doc2query doc2query = pyterrier_doc2query.Doc2Query(append=True) dataset = pt.get_dataset('irds:msmarco-passage') indexer = pt.IterDictIndexer('./msmarco_psg') indxer_pipe = doc2query >> indexer indxer_pipe.index(dataset.get_corpus_iter()) ``` Once you built an index, you can retrieve from it using any retrieval function (often BM25):
Q
BM25 Retriever
IDX
R
```python bm25 = pt.BatchRetrieve('./msmarco_psg', wmodel="BM25") ``` ### References & Credits - Rodrigo Nogueira and Jimmy Lin. [From doc2query to docTTTTTquery](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf). - Mitko Gospodinov, Sean MacAvaney, and Craig Macdonald. Doc2Query--: When Less is More. ECIR 2023. - Craig Macdonald, Nicola Tonellotto, Sean MacAvaney, Iadh Ounis. [PyTerrier: Declarative Experimentation in Python from BM25 to Dense Retrieval](https://dl.acm.org/doi/abs/10.1145/3459637.3482013). CIKM 2021.