yeabm25


Nameyeabm25 JSON
Version 0.1.2 PyPI version JSON
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SummaryYet Another BM25 algorithm implementation with update method, so that you dont need to fit on old + new documents.
upload_time2024-04-05 10:11:24
maintainerNone
docs_urlNone
authorNone
requires_python>=3.10
licenseApache 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
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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.

```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 list[float]
# returns generator object
yeabm.iter_document_vectors()
# use it 
for vector in yeabm.iter_document_vectors():
    # 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.
```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(['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).
```

            

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\n\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 list[float]\r\n# returns generator object\r\nyeabm.iter_document_vectors()\r\n# use it \r\nfor vector in yeabm.iter_document_vectors():\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.\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(['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",
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    "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.  \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files.  \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types.  \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below).  \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof.  \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. 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Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form.  3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed.  4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions:  (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and  (b) You must cause any modified files to carry prominent notices stating that You changed the files; and  (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and  (d) If the Work includes a \"NOTICE\" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License.  You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License.  5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions.  6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file.  7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License.  8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages.  9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.  END OF TERMS AND CONDITIONS  APPENDIX: How to apply the Apache License to your work.  To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets \"[]\" replaced with your own identifying information. (Don't include the brackets!)  The text should be enclosed in the appropriate comment syntax for the file format. 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