| Name | rerankers JSON |
| Version |
0.6.0
JSON |
| download |
| home_page | None |
| Summary | A unified API for various document re-ranking models. |
| upload_time | 2024-11-12 08:11:32 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| 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. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright 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 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. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright 2023 Thiago Laitz Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. |
| keywords |
reranking
retrieval
rag
nlp
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# rerankers
[](https://pepy.tech/project/rerankers)
[](https://twitter.com/bclavie)
_A lightweight unified API for various reranking models. Developed by [@bclavie](https://twitter.com/bclavie) as a member of [answer.ai](https://www.answer.ai)_
---
Welcome to `rerankers`! Our goal is to provide users with a simple API to use any reranking models.
## Recent Updates
_A longer release history can be found in the [Release History](#release-history) section of this README._
- v0.5.2: Minor ColBERT fixes
- v0.5.1: Minor change making RankedResults subscribable, meaning results[0] will return the result for the first document, etc... ⚠️ This is sorted by **passed document order**, not by results, you should use `.top_k()` to get sorted results!
- v0.5.0: Added support for the current state-of-the-art rerankers, BAAI's series of `BGE` layerwise LLM rerankers, based on [Gemma](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight) and MiniCPM. These are different from RankGPT, as they're not listwise: the models are repurposed as "cross-encoders", and do output logit scores.
## Why `rerankers`?
Rerankers are an important part of any retrieval architecture, but they're also often more obscure than other parts of the pipeline.
Sometimes, it can be hard to even know which one to use. Every problem is different, and the best model for use X is not necessarily the same one as for use Y.
Moreover, new reranking methods keep popping up: for example, RankGPT, using LLMs to rerank documents, appeared just last year, with very promising zero-shot benchmark results.
All the different reranking approaches tend to be done in their own library, with varying levels of documentation. This results in an even higher barrier to entry. New users are required to swap between multiple unfamiliar input/output formats, all with their own quirks!
`rerankers` seeks to address this problem by providing a simple API for all popular rerankers, no matter the architecture.
`rerankers` aims to be:
- 🪶 Lightweight. It ships with only the bare necessities as dependencies.
- 📖 Easy-to-understand. There's just a handful of calls to learn, and you can then use the full range of provided reranking models.
- 🔗 Easy-to-integrate. It should fit in just about any existing pipelines, with only a few lines of code!
- 💪 Easy-to-expand. Any new reranking models can be added with very little knowledge of the codebase. All you need is a new class with a `rank()` function call mapping a (query, [documents]) input to a `RankedResults` output.
- 🐛 Easy-to-debug. This is a beta release and there might be issues, but the codebase is conceived in such a way that most issues should be easy to track and fix ASAP.
## Get Started
Installation is very simple. The core package ships with just two dependencies, `tqdm` and `pydantic`, so as to avoid any conflict with your current environment.
You may then install only the dependencies required by the models you want to try out:
```sh
# Core package only, will require other dependencies already installed
pip install rerankers
# All transformers-based approaches (cross-encoders, t5, colbert)
pip install "rerankers[transformers]"
# RankGPT
pip install "rerankers[gpt]"
# API-based rerankers (Cohere, Jina, soon MixedBread)
pip install "rerankers[api]"
# FlashRank rerankers (ONNX-optimised, very fast on CPU)
pip install "rerankers[flashrank]"
# RankLLM rerankers (better RankGPT + support for local models such as RankZephyr and RankVicuna)
# Note: RankLLM is only supported on Python 3.10+! This will not work with Python 3.9
pip install "rerankers[rankllm]"
# To support LLM-Layerwise rerankers (which need flash-attention installed)
pip install "rerankers[llmlayerwise]"
# All of the above
pip install "rerankers[all]"
```
## Usage
Load any supported reranker in a single line, regardless of the architecture:
```python
from rerankers import Reranker
# Cross-encoder default. You can specify a 'lang' parameter to load a multilingual version!
ranker = Reranker('cross-encoder')
# Specific cross-encoder
ranker = Reranker('mixedbread-ai/mxbai-rerank-large-v1', model_type='cross-encoder')
# FlashRank default. You can specify a 'lang' parameter to load a multilingual version!
ranker = Reranker('flashrank')
# Specific flashrank model.
ranker = Reranker('ce-esci-MiniLM-L12-v2', model_type='flashrank')
# Default T5 Seq2Seq reranker
ranker = Reranker("t5")
# Specific T5 Seq2Seq reranker
ranker = Reranker("unicamp-dl/InRanker-base", model_type = "t5")
# API (Cohere)
ranker = Reranker("cohere", lang='en' (or 'other'), api_key = API_KEY)
# Custom Cohere model? No problem!
ranker = Reranker("my_model_name", api_provider = "cohere", api_key = API_KEY)
# API (Jina)
ranker = Reranker("jina", api_key = API_KEY)
# RankGPT4-turbo
ranker = Reranker("rankgpt", api_key = API_KEY)
# RankGPT3-turbo
ranker = Reranker("rankgpt3", api_key = API_KEY)
# RankGPT with another LLM provider
ranker = Reranker("MY_LLM_NAME" (check litellm docs), model_type = "rankgpt", api_key = API_KEY)
# RankLLM with default GPT (GPT-4o)
ranker = Reranker("rankllm", api_key = API_KEY)
# RankLLM with specified GPT models
ranker = Reranker('gpt-4-turbo', model_type="rankllm", api_key = API_KEY)
# ColBERTv2 reranker
ranker = Reranker("colbert")
# LLM Layerwise Reranker
ranker = Reranker('llm-layerwise')
# ... Or a non-default colbert model:
ranker = Reranker(model_name_or_path, model_type = "colbert")
```
_Rerankers will always try to infer the model you're trying to use based on its name, but it's always safer to pass a `model_type` argument to it if you can!_
Then, regardless of which reranker is loaded, use the loaded model to rank a query against documents:
```python
> results = ranker.rank(query="I love you", docs=["I hate you", "I really like you"], doc_ids=[0,1])
> results
RankedResults(results=[Result(document=Document(text='I really like you', doc_id=1), score=-2.453125, rank=1), Result(document=Document(text='I hate you', doc_id=0), score=-4.14453125, rank=2)], query='I love you', has_scores=True)
```
You don't need to pass `doc_ids`! If not provided, they'll be auto-generated as integers corresponding to the index of a document in `docs`.
You're free to pass metadata too, and it'll be stored with the documents. It'll also be accessible in the results object:
```python
> results = ranker.rank(query="I love you", docs=["I hate you", "I really like you"], doc_ids=[0,1], metadata=[{'source': 'twitter'}, {'source': 'reddit'}])
> results
RankedResults(results=[Result(document=Document(text='I really like you', doc_id=1, metadata={'source': 'twitter'}), score=-2.453125, rank=1), Result(document=Document(text='I hate you', doc_id=0, metadata={'source': 'reddit'}), score=-4.14453125, rank=2)], query='I love you', has_scores=True)
```
If you'd like your code to be a bit cleaner, you can also directly construct `Document` objects yourself, and pass those instead. In that case, you don't need to pass separate `doc_ids` and `metadata`:
```python
> from rerankers import Document
> docs = [Document(text="I really like you", doc_id=0, metadata={'source': 'twitter'}), Document(text="I hate you", doc_id=1, metadata={'source': 'reddit'})]
> results = ranker.rank(query="I love you", docs=docs)
> results
RankedResults(results=[Result(document=Document(text='I really like you', doc_id=0, metadata={'source': 'twitter'}), score=-2.453125, rank=1), Result(document=Document(text='I hate you', doc_id=1, metadata={'source': 'reddit'}), score=-4.14453125, rank=2)], query='I love you', has_scores=True)
```
You can also use `rank_async`, which is essentially just a wrapper to turn `rank()` into a coroutine. The result will be the same:
```python
> results = await ranker.rank_async(query="I love you", docs=["I hate you", "I really like you"], doc_ids=[0,1])
> results
RankedResults(results=[Result(document=Document(text='I really like you', doc_id=1, metadata={'source': 'twitter'}), score=-2.453125, rank=1), Result(document=Document(text='I hate you', doc_id=0, metadata={'source': 'reddit'}), score=-4.14453125, rank=2)], query='I love you', has_scores=True)
```
All rerankers will return a `RankedResults` object, which is a pydantic object containing a list of `Result` objects and some other useful information, such as the original query. You can retrieve the top `k` results from it by running `top_k()`:
```python
> results.top_k(1)
[Result(Document(doc_id=1, text='I really like you', metadata={}), score=0.26170814, rank=1)]
```
The Result objects are transparent when trying to access the documents they store, as `Document` objects simply exist as an easy way to store IDs and metadata. If you want to access a given result's text or metadata, you can directly access it as a property:
```python
> results.top_k(1)[0].text
'I really like you'
```
And that's all you need to know to get started quickly! Check out the overview notebook for more information on the API and the different models, or the langchain example to see how to integrate this in your langchain pipeline.
## Features
Legend:
- ✅ Supported
- 🟠 Implemented, but not fully fledged
- 📍 Not supported but intended to be in the future
- ⭐ Same as above, but **important**.
- ❌ Not supported & not currently planned
Models:
- ✅ Any standard SentenceTransformer or Transformers cross-encoder
- ✅ RankGPT (Available both via the original RankGPT implementation and the improved RankLLM one)
- ✅ T5-based pointwise rankers (InRanker, MonoT5...)
- ✅ LLM-based pointwise rankers (BAAI/bge-reranker-v2.5-gemma2-lightweight, etc...)
- ✅ Cohere, Jina, Voyage and MixedBread API rerankers
- ✅ [FlashRank](https://github.com/PrithivirajDamodaran/FlashRank) rerankers (ONNX-optimised models, very fast on CPU)
- ✅ ColBERT-based reranker - not a model initially designed for reranking, but does perform quite strongly in some cases. Implementation is lightweight, based only on transformers.
- 🟠⭐ RankLLM/RankZephyr: supported by wrapping the [rank-llm library](https://github.com/castorini/rank_llm) library! Support for RankZephyr/RankVicuna is untested, but RankLLM + GPT models fully works!
- 📍 LiT5
Features:
- ✅ Metadata!
- ✅ Reranking
- ✅ Consistency notebooks to ensure performance on `scifact` matches the litterature for any given model implementation (Except RankGPT, where results are harder to reproduce).
- ✅ ONNX runtime support --> Offered through [FlashRank](https://github.com/PrithivirajDamodaran/FlashRank) -- in line with the philosophy of the lib, we won't reinvent the wheel when @PrithivirajDamodaran is doing amazing work!
- 📍 Training on Python >=3.10 (via interfacing with other libraries)
- ❌(📍Maybe?) Training via rerankers directly
## Reference
If rerankers has been useful to you in academic work, please do feel free to cite the work below!
```
@misc{clavié2024rerankers,
title={rerankers: A Lightweight Python Library to Unify Ranking Methods},
author={Benjamin Clavié},
year={2024},
eprint={2408.17344},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2408.17344},
}
```
## Release History
- v0.4.0: ColBERT performance improvement! It should now be faster and result in stronger results following implementation of the JaColBERTv2.5 dynamic query length method. This version also now supports HuggingFace's Text-Embedding-Server (TEI) inference as an API reranker option, thanks to [@srisudarsan](https://github.com/srisudarsan).
- v0.3.1: T5 bugfix and native default support for new Portuguese T5 rerankers.
- v0.3.0: Many changes! Experimental support for RankLLM, directly backed by the [rank-llm library](https://github.com/castorini/rank_llm). A new `Document` object, courtesy of joint-work by [@bclavie](https://github.com/bclavie) and [Anmol6](https://github.com/Anmol6). This object is transparent, but now offers support for `metadata` stored alongside each document. Many small QoL changes (RankedResults can be itered on directly...)
- v0.2.0: [FlashRank](https://github.com/PrithivirajDamodaran/FlashRank) rerankers, Basic async support thanks to [@tarunamasa](https://github.com/tarunamasa), MixedBread.ai reranking API
- v0.1.2: Voyage reranking API
- v0.1.1: Langchain integration fixed!
- v0.1.0: Initial release
Raw data
{
"_id": null,
"home_page": null,
"name": "rerankers",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": "Ben Clavi\u00e9 <bc@answer.ai>",
"keywords": "reranking, retrieval, rag, nlp",
"author": null,
"author_email": "Ben Clavi\u00e9 <bc@answer.ai>",
"download_url": "https://files.pythonhosted.org/packages/02/15/c0dffd1f95408b86b7544caa56b10fa4361712ba3ac70fd6a5c146e28f70/rerankers-0.6.0.tar.gz",
"platform": null,
"description": "\n# rerankers\n\n\n[](https://pepy.tech/project/rerankers)\n[](https://twitter.com/bclavie)\n\n\n_A lightweight unified API for various reranking models. Developed by [@bclavie](https://twitter.com/bclavie) as a member of [answer.ai](https://www.answer.ai)_\n\n---\n\nWelcome to `rerankers`! Our goal is to provide users with a simple API to use any reranking models.\n\n## Recent Updates\n_A longer release history can be found in the [Release History](#release-history) section of this README._\n\n- v0.5.2: Minor ColBERT fixes\n- v0.5.1: Minor change making RankedResults subscribable, meaning results[0] will return the result for the first document, etc... \u26a0\ufe0f This is sorted by **passed document order**, not by results, you should use `.top_k()` to get sorted results!\n- v0.5.0: Added support for the current state-of-the-art rerankers, BAAI's series of `BGE` layerwise LLM rerankers, based on [Gemma](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight) and MiniCPM. These are different from RankGPT, as they're not listwise: the models are repurposed as \"cross-encoders\", and do output logit scores.\n\n## Why `rerankers`?\n\nRerankers are an important part of any retrieval architecture, but they're also often more obscure than other parts of the pipeline.\n\nSometimes, it can be hard to even know which one to use. Every problem is different, and the best model for use X is not necessarily the same one as for use Y.\n\nMoreover, new reranking methods keep popping up: for example, RankGPT, using LLMs to rerank documents, appeared just last year, with very promising zero-shot benchmark results.\n\nAll the different reranking approaches tend to be done in their own library, with varying levels of documentation. This results in an even higher barrier to entry. New users are required to swap between multiple unfamiliar input/output formats, all with their own quirks!\n\n`rerankers` seeks to address this problem by providing a simple API for all popular rerankers, no matter the architecture.\n\n`rerankers` aims to be:\n- \ud83e\udeb6 Lightweight. It ships with only the bare necessities as dependencies.\n- \ud83d\udcd6 Easy-to-understand. There's just a handful of calls to learn, and you can then use the full range of provided reranking models.\n- \ud83d\udd17 Easy-to-integrate. It should fit in just about any existing pipelines, with only a few lines of code!\n- \ud83d\udcaa Easy-to-expand. Any new reranking models can be added with very little knowledge of the codebase. All you need is a new class with a `rank()` function call mapping a (query, [documents]) input to a `RankedResults` output.\n- \ud83d\udc1b Easy-to-debug. This is a beta release and there might be issues, but the codebase is conceived in such a way that most issues should be easy to track and fix ASAP.\n\n## Get Started\n\nInstallation is very simple. The core package ships with just two dependencies, `tqdm` and `pydantic`, so as to avoid any conflict with your current environment.\nYou may then install only the dependencies required by the models you want to try out:\n\n```sh\n# Core package only, will require other dependencies already installed\npip install rerankers\n\n# All transformers-based approaches (cross-encoders, t5, colbert)\npip install \"rerankers[transformers]\"\n\n# RankGPT\npip install \"rerankers[gpt]\"\n\n# API-based rerankers (Cohere, Jina, soon MixedBread)\npip install \"rerankers[api]\"\n\n# FlashRank rerankers (ONNX-optimised, very fast on CPU)\npip install \"rerankers[flashrank]\"\n\n# RankLLM rerankers (better RankGPT + support for local models such as RankZephyr and RankVicuna)\n# Note: RankLLM is only supported on Python 3.10+! This will not work with Python 3.9\npip install \"rerankers[rankllm]\"\n\n# To support LLM-Layerwise rerankers (which need flash-attention installed)\npip install \"rerankers[llmlayerwise]\"\n\n# All of the above\npip install \"rerankers[all]\"\n```\n\n## Usage\n\nLoad any supported reranker in a single line, regardless of the architecture:\n```python\nfrom rerankers import Reranker\n\n# Cross-encoder default. You can specify a 'lang' parameter to load a multilingual version!\nranker = Reranker('cross-encoder')\n\n# Specific cross-encoder\nranker = Reranker('mixedbread-ai/mxbai-rerank-large-v1', model_type='cross-encoder')\n\n# FlashRank default. You can specify a 'lang' parameter to load a multilingual version!\nranker = Reranker('flashrank')\n\n# Specific flashrank model.\nranker = Reranker('ce-esci-MiniLM-L12-v2', model_type='flashrank')\n\n# Default T5 Seq2Seq reranker\nranker = Reranker(\"t5\")\n\n# Specific T5 Seq2Seq reranker\nranker = Reranker(\"unicamp-dl/InRanker-base\", model_type = \"t5\")\n\n# API (Cohere)\nranker = Reranker(\"cohere\", lang='en' (or 'other'), api_key = API_KEY)\n\n# Custom Cohere model? No problem!\nranker = Reranker(\"my_model_name\", api_provider = \"cohere\", api_key = API_KEY)\n\n# API (Jina)\nranker = Reranker(\"jina\", api_key = API_KEY)\n\n# RankGPT4-turbo\nranker = Reranker(\"rankgpt\", api_key = API_KEY)\n\n# RankGPT3-turbo\nranker = Reranker(\"rankgpt3\", api_key = API_KEY)\n\n# RankGPT with another LLM provider\nranker = Reranker(\"MY_LLM_NAME\" (check litellm docs), model_type = \"rankgpt\", api_key = API_KEY)\n\n# RankLLM with default GPT (GPT-4o)\nranker = Reranker(\"rankllm\", api_key = API_KEY)\n\n# RankLLM with specified GPT models\nranker = Reranker('gpt-4-turbo', model_type=\"rankllm\", api_key = API_KEY)\n\n# ColBERTv2 reranker\nranker = Reranker(\"colbert\")\n\n# LLM Layerwise Reranker\nranker = Reranker('llm-layerwise')\n\n# ... Or a non-default colbert model:\nranker = Reranker(model_name_or_path, model_type = \"colbert\")\n\n```\n\n_Rerankers will always try to infer the model you're trying to use based on its name, but it's always safer to pass a `model_type` argument to it if you can!_\n\nThen, regardless of which reranker is loaded, use the loaded model to rank a query against documents:\n\n```python\n> results = ranker.rank(query=\"I love you\", docs=[\"I hate you\", \"I really like you\"], doc_ids=[0,1])\n> results\nRankedResults(results=[Result(document=Document(text='I really like you', doc_id=1), score=-2.453125, rank=1), Result(document=Document(text='I hate you', doc_id=0), score=-4.14453125, rank=2)], query='I love you', has_scores=True)\n```\n\nYou don't need to pass `doc_ids`! If not provided, they'll be auto-generated as integers corresponding to the index of a document in `docs`.\n\n\nYou're free to pass metadata too, and it'll be stored with the documents. It'll also be accessible in the results object:\n\n```python\n> results = ranker.rank(query=\"I love you\", docs=[\"I hate you\", \"I really like you\"], doc_ids=[0,1], metadata=[{'source': 'twitter'}, {'source': 'reddit'}])\n> results\nRankedResults(results=[Result(document=Document(text='I really like you', doc_id=1, metadata={'source': 'twitter'}), score=-2.453125, rank=1), Result(document=Document(text='I hate you', doc_id=0, metadata={'source': 'reddit'}), score=-4.14453125, rank=2)], query='I love you', has_scores=True)\n```\n\nIf you'd like your code to be a bit cleaner, you can also directly construct `Document` objects yourself, and pass those instead. In that case, you don't need to pass separate `doc_ids` and `metadata`:\n\n```python\n> from rerankers import Document\n> docs = [Document(text=\"I really like you\", doc_id=0, metadata={'source': 'twitter'}), Document(text=\"I hate you\", doc_id=1, metadata={'source': 'reddit'})]\n> results = ranker.rank(query=\"I love you\", docs=docs)\n> results\nRankedResults(results=[Result(document=Document(text='I really like you', doc_id=0, metadata={'source': 'twitter'}), score=-2.453125, rank=1), Result(document=Document(text='I hate you', doc_id=1, metadata={'source': 'reddit'}), score=-4.14453125, rank=2)], query='I love you', has_scores=True)\n```\n\nYou can also use `rank_async`, which is essentially just a wrapper to turn `rank()` into a coroutine. The result will be the same:\n\n```python\n> results = await ranker.rank_async(query=\"I love you\", docs=[\"I hate you\", \"I really like you\"], doc_ids=[0,1])\n> results\nRankedResults(results=[Result(document=Document(text='I really like you', doc_id=1, metadata={'source': 'twitter'}), score=-2.453125, rank=1), Result(document=Document(text='I hate you', doc_id=0, metadata={'source': 'reddit'}), score=-4.14453125, rank=2)], query='I love you', has_scores=True)\n```\n\nAll rerankers will return a `RankedResults` object, which is a pydantic object containing a list of `Result` objects and some other useful information, such as the original query. You can retrieve the top `k` results from it by running `top_k()`:\n\n```python\n> results.top_k(1)\n[Result(Document(doc_id=1, text='I really like you', metadata={}), score=0.26170814, rank=1)]\n```\n\nThe Result objects are transparent when trying to access the documents they store, as `Document` objects simply exist as an easy way to store IDs and metadata. If you want to access a given result's text or metadata, you can directly access it as a property:\n\n```python\n> results.top_k(1)[0].text\n'I really like you'\n```\n\nAnd that's all you need to know to get started quickly! Check out the overview notebook for more information on the API and the different models, or the langchain example to see how to integrate this in your langchain pipeline.\n\n\n## Features\n\nLegend:\n- \u2705 Supported\n- \ud83d\udfe0 Implemented, but not fully fledged\n- \ud83d\udccd Not supported but intended to be in the future\n- \u2b50 Same as above, but **important**.\n- \u274c Not supported & not currently planned\n\nModels:\n- \u2705 Any standard SentenceTransformer or Transformers cross-encoder\n- \u2705 RankGPT (Available both via the original RankGPT implementation and the improved RankLLM one)\n- \u2705 T5-based pointwise rankers (InRanker, MonoT5...)\n- \u2705 LLM-based pointwise rankers (BAAI/bge-reranker-v2.5-gemma2-lightweight, etc...)\n- \u2705 Cohere, Jina, Voyage and MixedBread API rerankers\n- \u2705 [FlashRank](https://github.com/PrithivirajDamodaran/FlashRank) rerankers (ONNX-optimised models, very fast on CPU)\n- \u2705 ColBERT-based reranker - not a model initially designed for reranking, but does perform quite strongly in some cases. Implementation is lightweight, based only on transformers.\n- \ud83d\udfe0\u2b50 RankLLM/RankZephyr: supported by wrapping the [rank-llm library](https://github.com/castorini/rank_llm) library! Support for RankZephyr/RankVicuna is untested, but RankLLM + GPT models fully works!\n- \ud83d\udccd LiT5\n\nFeatures:\n- \u2705 Metadata!\n- \u2705 Reranking \n- \u2705 Consistency notebooks to ensure performance on `scifact` matches the litterature for any given model implementation (Except RankGPT, where results are harder to reproduce).\n- \u2705 ONNX runtime support --> Offered through [FlashRank](https://github.com/PrithivirajDamodaran/FlashRank) -- in line with the philosophy of the lib, we won't reinvent the wheel when @PrithivirajDamodaran is doing amazing work!\n- \ud83d\udccd Training on Python >=3.10 (via interfacing with other libraries)\n- \u274c(\ud83d\udccdMaybe?) Training via rerankers directly\n\n## Reference\n\nIf rerankers has been useful to you in academic work, please do feel free to cite the work below!\n\n```\n@misc{clavi\u00e92024rerankers,\n title={rerankers: A Lightweight Python Library to Unify Ranking Methods}, \n author={Benjamin Clavi\u00e9},\n year={2024},\n eprint={2408.17344},\n archivePrefix={arXiv},\n primaryClass={cs.IR},\n url={https://arxiv.org/abs/2408.17344}, \n}\n```\n\n## Release History\n\n- v0.4.0: ColBERT performance improvement! It should now be faster and result in stronger results following implementation of the JaColBERTv2.5 dynamic query length method. This version also now supports HuggingFace's Text-Embedding-Server (TEI) inference as an API reranker option, thanks to [@srisudarsan](https://github.com/srisudarsan).\n- v0.3.1: T5 bugfix and native default support for new Portuguese T5 rerankers.\n- v0.3.0: Many changes! Experimental support for RankLLM, directly backed by the [rank-llm library](https://github.com/castorini/rank_llm). A new `Document` object, courtesy of joint-work by [@bclavie](https://github.com/bclavie) and [Anmol6](https://github.com/Anmol6). This object is transparent, but now offers support for `metadata` stored alongside each document. Many small QoL changes (RankedResults can be itered on directly...)\n- v0.2.0: [FlashRank](https://github.com/PrithivirajDamodaran/FlashRank) rerankers, Basic async support thanks to [@tarunamasa](https://github.com/tarunamasa), MixedBread.ai reranking API\n- v0.1.2: Voyage reranking API\n- v0.1.1: Langchain integration fixed!\n- v0.1.0: Initial release\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. 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. For the purposes of this definition, \"submitted\" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as \"Not a Contribution.\" \"Contributor\" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright 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 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. We also recommend that a file or class name and description of purpose be included on the same \"printed page\" as the copyright notice for easier identification within third-party archives. Copyright 2023 Thiago Laitz Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.",
"summary": "A unified API for various document re-ranking models.",
"version": "0.6.0",
"project_urls": {
"Homepage": "https://github.com/answerdotai/rerankers"
},
"split_keywords": [
"reranking",
" retrieval",
" rag",
" nlp"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "aa20e458b4c3cca1347621ecea1c4ea25891987638f2ba6915091a424cd95820",
"md5": "109abc70d5a7e020ca4d43910cc21538",
"sha256": "2d70b4a20b040e87b94c46cc1dff817ddf86488949e477a2508eec08d79976f7"
},
"downloads": -1,
"filename": "rerankers-0.6.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "109abc70d5a7e020ca4d43910cc21538",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8",
"size": 41057,
"upload_time": "2024-11-12T08:11:30",
"upload_time_iso_8601": "2024-11-12T08:11:30.257198Z",
"url": "https://files.pythonhosted.org/packages/aa/20/e458b4c3cca1347621ecea1c4ea25891987638f2ba6915091a424cd95820/rerankers-0.6.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "0215c0dffd1f95408b86b7544caa56b10fa4361712ba3ac70fd6a5c146e28f70",
"md5": "1f6a370020fccfe629738750ca7ca88f",
"sha256": "3073456ecc6c704159b39138f30071c8cca15588315bfb3759ea8112ad5bcf1d"
},
"downloads": -1,
"filename": "rerankers-0.6.0.tar.gz",
"has_sig": false,
"md5_digest": "1f6a370020fccfe629738750ca7ca88f",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 39943,
"upload_time": "2024-11-12T08:11:32",
"upload_time_iso_8601": "2024-11-12T08:11:32.451228Z",
"url": "https://files.pythonhosted.org/packages/02/15/c0dffd1f95408b86b7544caa56b10fa4361712ba3ac70fd6a5c146e28f70/rerankers-0.6.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-11-12 08:11:32",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "answerdotai",
"github_project": "rerankers",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"lcname": "rerankers"
}
