| Name | yeabm25 JSON |
| Version |
0.1.4
JSON |
| download |
| home_page | None |
| Summary | Yet Another BM25 algorithm implementation with update method, so that you dont need to fit on old + new documents. |
| upload_time | 2024-07-03 11:04:00 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. 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| keywords |
text
bm25
dense vector embeddings
information retrieval
hybrid search
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
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|
# yeabm25
Yet Another BM25 algorithm implementation with helpful implementation of:
1. functionallity to update the index with .update() method. In fack you can use just update.
2. per document vector.
3. sparse vector support
Installation:
```
pip install yeabm25
```
### Quickstart
```python
from yeabm25 import YeaBM25
import nltk
nltk.download('stopwords', quiet=True)
stopwords_en = set(stopwords.words('english'))
def normalize_for_bm(text: str):
text = re.sub("[^a-zA-z1-9]", " ", text)
words = text.lower().split()
return [word for word in words if word not in stopwords_en]
corpus = ["The quick brown fox jumps over the lazy dog",
"The lazy dog is brown",
"The fox is brown",
"Hello there good man!",
"It is quite windy in London",
"How is the weather today man?",
]
normalized_corpus = [normalize_for_bm(txt) for txt in corpus]
# fitting the whole corpus
yeabm = YeaBM25(epsilon=0.25)
yeabm.fit(normalized_corpus)
# fit and then update
bm_update = YeaBM25(epsilon=0.25)
bm_update.fit(normalized_corpus[:3])
bm_update.update(normalized_corpus[3:])
assert yeabm.doc_len == bm_update.doc_len
assert yeabm.average_idf == bm_update.average_idf
assert yeabm.idf == bm_update.idf
assert yeabm.get_scores(['fox', 'jump']) == bm_update.get_scores(['fox', 'jump'])).all()
```
This work is inspired(and uses some code and ideas) by this great package - https://github.com/dorianbrown/rank_bm25/tree/master.
The main focus is creating document and query vectors (supports sparse vectors). Then using the vectors with your favourite Vector DB.
How to get the document and query vectors:
```python
# recommended approach for large corpus, returns iterator. Each element is sparse vector.
# To represent a sparse vector we can use:
# - Dict[int, float] <--- This is currently the sparse format in YeaBM25
# - Any of the scipy.sparse sparse matrices class family with shape[0] == 1
# - Iterable[Tuple[int, float]]
# this method returns generator object
yeabm.iter_document_vectors() # or
yeabm.iter_document_vectors_sparse() # <--- recommended for usage with Vector DB
# use it in loop
for vector in yeabm.iter_document_vectors_sparse():
# dostuff could be put in DB.
dostuff(vector)
query = ...
yeabm.encode_query(query)
```
Why would you want to do that? Essentially the BM25 score formula is a sum, so it is a perfect candidate for one of the metrics any DB
supports - **inner product (IP)**.
```python
#
bm_index.get_scores(['quick', 'fox'])
# ~ [1.30, 0.0, 0.72, 0.0, 0.0, 0.0]
# you get the same scores like so:
yeabm.get_embeddings() @ np.asarray(yeabm.encode_query_dense(['fox', 'quick']))
# ~ [1.30, 0.0, 0.72, 0.0, 0.0, 0.0]
```
Of course you would like to leave the last calculation to the Vector DB.
One more opinionated implementation is that words that are found in more than half of the corpus would not have idf of 0. It would be small
but still positive. For example in other implementations:
```python
from rank_bm25 import BM25Okapi
okapi = BM25Okapi(normalized_corpus)
okapi.get_scores(['brown'])
# [0. 0. 0. 0. 0. 0.]
# where
yeabm.get_scores(['brown'])
#[0.18 0.28 0.33 0. 0. 0.]
# this is helpful if the user is looking for a term that is abundant in the corpus and would still get somewhat useful results
# where with BM25Okapi you would get essentially random results (or no results).
```
### Usage examples:
- [Use sparse vectors with Milvus DB](examples/sparse_vector_milvus.ipynb)
Raw data
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"description": "# yeabm25\r\nYet Another BM25 algorithm implementation with helpful implementation of:\r\n1. functionallity to update the index with .update() method. In fack you can use just update. \r\n2. per document vector.\r\n3. sparse vector support\r\n\r\nInstallation:\r\n```\r\npip install yeabm25\r\n```\r\n\r\n### Quickstart\r\n```python\r\nfrom yeabm25 import YeaBM25\r\nimport nltk \r\nnltk.download('stopwords', quiet=True)\r\nstopwords_en = set(stopwords.words('english'))\r\n\r\ndef normalize_for_bm(text: str):\r\n text = re.sub(\"[^a-zA-z1-9]\", \" \", text)\r\n words = text.lower().split()\r\n return [word for word in words if word not in stopwords_en]\r\n\r\ncorpus = [\"The quick brown fox jumps over the lazy dog\",\r\n \"The lazy dog is brown\",\r\n \"The fox is brown\",\r\n \"Hello there good man!\",\r\n \"It is quite windy in London\",\r\n \"How is the weather today man?\",\r\n ]\r\nnormalized_corpus = [normalize_for_bm(txt) for txt in corpus]\r\n\r\n# fitting the whole corpus\r\nyeabm = YeaBM25(epsilon=0.25)\r\nyeabm.fit(normalized_corpus)\r\n\r\n# fit and then update \r\nbm_update = YeaBM25(epsilon=0.25)\r\nbm_update.fit(normalized_corpus[:3])\r\nbm_update.update(normalized_corpus[3:])\r\n\r\nassert yeabm.doc_len == bm_update.doc_len\r\nassert yeabm.average_idf == bm_update.average_idf\r\nassert yeabm.idf == bm_update.idf\r\nassert yeabm.get_scores(['fox', 'jump']) == bm_update.get_scores(['fox', 'jump'])).all()\r\n```\r\n\r\nThis work is inspired(and uses some code and ideas) by this great package - https://github.com/dorianbrown/rank_bm25/tree/master.\r\nThe main focus is creating document and query vectors (supports sparse vectors). Then using the vectors with your favourite Vector DB.\r\n\r\nHow to get the document and query vectors: \r\n```python\r\n# recommended approach for large corpus, returns iterator. Each element is sparse vector. \r\n# To represent a sparse vector we can use:\r\n# - Dict[int, float] <--- This is currently the sparse format in YeaBM25\r\n# - Any of the scipy.sparse sparse matrices class family with shape[0] == 1\r\n# - Iterable[Tuple[int, float]]\r\n\r\n# this method returns generator object\r\nyeabm.iter_document_vectors() # or\r\nyeabm.iter_document_vectors_sparse() # <--- recommended for usage with Vector DB\r\n# use it in loop\r\nfor vector in yeabm.iter_document_vectors_sparse():\r\n # dostuff could be put in DB. \r\n dostuff(vector)\r\n\r\nquery = ...\r\nyeabm.encode_query(query)\r\n```\r\n\r\nWhy would you want to do that? Essentially the BM25 score formula is a sum, so it is a perfect candidate for one of the metrics any DB\r\nsupports - **inner product (IP)**.\r\n```python\r\n# \r\nbm_index.get_scores(['quick', 'fox'])\r\n# ~ [1.30, 0.0, 0.72, 0.0, 0.0, 0.0]\r\n\r\n# you get the same scores like so:\r\nyeabm.get_embeddings() @ np.asarray(yeabm.encode_query_dense(['fox', 'quick']))\r\n# ~ [1.30, 0.0, 0.72, 0.0, 0.0, 0.0]\r\n```\r\nOf course you would like to leave the last calculation to the Vector DB.\r\n\r\nOne more opinionated implementation is that words that are found in more than half of the corpus would not have idf of 0. It would be small \r\nbut still positive. For example in other implementations:\r\n\r\n```python\r\nfrom rank_bm25 import BM25Okapi\r\nokapi = BM25Okapi(normalized_corpus)\r\nokapi.get_scores(['brown']) \r\n# [0. 0. 0. 0. 0. 0.]\r\n# where \r\nyeabm.get_scores(['brown'])\r\n#[0.18 0.28 0.33 0. 0. 0.]\r\n# this is helpful if the user is looking for a term that is abundant in the corpus and would still get somewhat useful results\r\n# where with BM25Okapi you would get essentially random results (or no results).\r\n```\r\n\r\n### Usage examples:\r\n- [Use sparse vectors with Milvus DB](examples/sparse_vector_milvus.ipynb)\r\n",
"bugtrack_url": null,
"license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. 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