BM25

Our BM25 retriever relies on the sparse.BM25Vectorizer of LeNLP.

>>> from cherche import retrieve

>>> documents = [
...    {
...        "id": 0,
...        "article": "Paris is the capital and most populous city of France",
...        "title": "Paris",
...        "url": "https://en.wikipedia.org/wiki/Paris"
...    },
...    {
...        "id": 1,
...        "article": "Paris has been one of Europe major centres of finance, diplomacy , commerce , fashion , gastronomy , science , and arts.",
...        "title": "Paris",
...        "url": "https://en.wikipedia.org/wiki/Paris"
...    },
...    {
...        "id": 2,
...        "article": "The City of Paris is the centre and seat of government of the region and province of Île-de-France .",
...        "title": "Paris",
...        "url": "https://en.wikipedia.org/wiki/Paris"
...    }
... ]

>>> retriever = retrieve.BM25(key="id", on=["title", "article"], documents=documents, k=30)

>>> retriever("france")
[{'id': 0, 'similarity': 0.1236413097778466},
 {'id': 2, 'similarity': 0.08907655343363269},
 {'id': 1, 'similarity': 0.0031730868527342104}]

We can also initialize the retriever with a custom sparse.BM25Vectorizer.

>>> from cherche import retrieve
>>> from lenlp import sparse

>>> documents = [
...    {
...        "id": 0,
...        "article": "Paris is the capital and most populous city of France",
...        "title": "Paris",
...        "url": "https://en.wikipedia.org/wiki/Paris"
...    },
...    {
...        "id": 1,
...        "article": "Paris has been one of Europe major centres of finance, diplomacy , commerce , fashion , gastronomy , science , and arts.",
...        "title": "Paris",
...        "url": "https://en.wikipedia.org/wiki/Paris"
...    },
...    {
...        "id": 2,
...        "article": "The City of Paris is the centre and seat of government of the region and province of Île-de-France .",
...        "title": "Paris",
...        "url": "https://en.wikipedia.org/wiki/Paris"
...    }
... ]

>>> count_vectorizer = sparse.BM25Vectorizer(
...  normalize=True, ngram_range=(3, 7), analyzer="char_wb")

>>> retriever = retrieve.BM25Vectorizer(
...  key="id", on=["title", "article"], documents=documents, count_vectorizer=count_vectorizer)

>>> retriever("fra", k=3)
[{'id': 0, 'similarity': 0.15055477454160002},
 {'id': 2, 'similarity': 0.022883459495904895}]

Batch retrieval

If we have several queries for which we want to retrieve the top k documents then we can pass a list of queries to the retriever. This is much faster for multiple queries. In batch-mode, retriever returns a list of list of documents instead of a list of documents.

>>> retriever(["fra", "arts", "capital"], k=3)
[[{'id': 0, 'similarity': 0.051000705070125066}, # Match query 1
  {'id': 2, 'similarity': 0.03415513704304113}],
 [{'id': 1, 'similarity': 0.07021399356970497}], # Match query 2
 [{'id': 0, 'similarity': 0.25972148184421534}]] # Match query 3

Map keys to documents

We can map documents to retrieved keys.

>>> retriever += documents
>>> retriever("fra")
[{'id': 0,
  'article': 'Paris is the capital and most populous city of France',
  'title': 'Paris',
  'url': 'https://en.wikipedia.org/wiki/Paris',
  'similarity': 0.15055477454160002},
 {'id': 2,
  'article': 'The City of Paris is the centre and seat of government of the region and province of Île-de-France .',
  'title': 'Paris',
  'url': 'https://en.wikipedia.org/wiki/Paris',
  'similarity': 0.022883459495904895}]