Another commonly used feature in Pyserini is to fetch a document (i.e., its text) given its docid.
A sparse (Lucene) index can be configured to include the raw document text, in which case the doc() method can be used to fetch the document:
from pyserini.search.lucene import LuceneSearcher
searcher = LuceneSearcher.from_prebuilt_index('msmarco-v1-passage')
doc = searcher.doc('7157715')From doc, you can access its contents as well as its raw representation.
The contents hold the representation of what's actually indexed; the raw representation is usually the original "raw document".
A simple example can illustrate this distinction: for an article from CORD-19, raw holds the complete JSON of the article, which obviously includes the article contents, but has metadata and other information as well.
The contents contain extracts from the article that's actually indexed (for example, the title and abstract).
In most cases, contents can be deterministically reconstructed from raw.
When building the index, we specify flags to store contents and/or raw; it is rarely the case that we store both, since that would be a waste of space.
In the case of the pre-built msmacro-passage index, we only store raw.
Thus:
# Document contents: what's actually indexed.
# Note, this is not stored in the pre-built msmacro-v1-passage index.
doc.contents()
# Raw document
doc.raw()As you'd expected, doc.id() returns the docid, which is 7157715 in this case.
Finally, doc.lucene_document() returns the underlying Lucene Document (i.e., a Java object).
With that, you get direct access to the complete Lucene API for manipulating documents.
Since each text in the MS MARCO passage corpus is a JSON object, we can read the document into Python and manipulate:
import json
json_doc = json.loads(doc.raw())
json_doc['contents']
# 'contents' of the document:
# A Lobster Roll is a bread roll filled with bite-sized chunks of lobster meat...Every document has a docid, of type string, assigned by the collection it is part of.
In addition, Lucene assigns each document a unique internal id (confusingly, Lucene also calls this the docid), which is an integer numbered sequentially starting from zero to one less than the number of documents in the index.
This can be a source of confusion but the meaning is usually clear from context.
Where there may be ambiguity, we refer to the external collection docid and Lucene's internal docid to be explicit.
Programmatically, the two are distinguished by type: the first is a string and the second is an integer.
As an important side note, Lucene's internal docids are not stable across different index instances.
That is, in two different index instances of the same collection, Lucene is likely to have assigned different internal docids for the same document.
This is because the internal docids are assigned based on document ingestion order; this will vary due to thread interleaving during indexing (which is usually performed on multiple threads).
The doc method in searcher takes either a string (interpreted as an external collection docid) or an integer (interpreted as Lucene's internal docid) and returns the corresponding document.
Thus, a simple way to iterate through all documents in the collection (and for example, print out its external collection docid) is as follows:
for i in range(searcher.num_docs):
print(searcher.doc(i).docid())