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https://github.com/raphaelsty/cherche
Neural Search
https://github.com/raphaelsty/cherche
bm25 flashtext information-retrieval machine-learning natural-language-processing neural-networks neural-search nlp question-answering reader retrieval search searching semantic-search vector-search
Last synced: 8 days ago
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Neural Search
- Host: GitHub
- URL: https://github.com/raphaelsty/cherche
- Owner: raphaelsty
- License: mit
- Created: 2021-12-04T19:47:06.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-06-01T17:05:12.000Z (5 months ago)
- Last Synced: 2024-08-02T15:33:54.231Z (3 months ago)
- Topics: bm25, flashtext, information-retrieval, machine-learning, natural-language-processing, neural-networks, neural-search, nlp, question-answering, reader, retrieval, search, searching, semantic-search, vector-search
- Language: Python
- Homepage:
- Size: 41.6 MB
- Stars: 318
- Watchers: 8
- Forks: 14
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.bib
Awesome Lists containing this project
README
Cherche
Neural search
Cherche enables the development of a neural search pipeline that employs retrievers and pre-trained language models both as retrievers and rankers. The primary advantage of Cherche lies in its capacity to construct end-to-end pipelines. Additionally, Cherche is well-suited for offline semantic search due to its compatibility with batch computation.
Here are some of the features Cherche offers:
[Live demo of a NLP search engine powered by Cherche](https://raphaelsty.github.io/knowledge/?query=cherche%20neural%20search)
## Installation 🤖
To install Cherche for use with a simple retriever on CPU, such as TfIdf, Flash, Lunr, Fuzz, use the following command:
```sh
pip install cherche
```
To install Cherche for use with any semantic retriever or ranker on CPU, use the following command:
```sh
pip install "cherche[cpu]"
```
Finally, if you plan to use any semantic retriever or ranker on GPU, use the following command:
```sh
pip install "cherche[gpu]"
```
By following these installation instructions, you will be able to use Cherche with the appropriate requirements for your needs.
### Documentation
Documentation is available [here](https://raphaelsty.github.io/cherche/). It provides details
about retrievers, rankers, pipelines and examples.
## QuickStart 📑
### Documents
Cherche allows findings the right document within a list of objects. Here is an example of a corpus.
```python
from cherche import data
documents = data.load_towns()
documents[:3]
[{'id': 0,
'title': 'Paris',
'url': 'https://en.wikipedia.org/wiki/Paris',
'article': 'Paris is the capital and most populous city of France.'},
{'id': 1,
'title': 'Paris',
'url': 'https://en.wikipedia.org/wiki/Paris',
'article': "Since the 17th century, Paris has been one of Europe's major centres of science, and arts."},
{'id': 2,
'title': 'Paris',
'url': 'https://en.wikipedia.org/wiki/Paris',
'article': 'The City of Paris is the centre and seat of government of the region and province of Île-de-France.'
}]
```
### Retriever ranker
Here is an example of a neural search pipeline composed of a TF-IDF that quickly retrieves documents, followed by a ranking model. The ranking model sorts the documents produced by the retriever based on the semantic similarity between the query and the documents. We can call the pipeline using a list of queries and get relevant documents for each query.
```python
from cherche import data, retrieve, rank
from sentence_transformers import SentenceTransformer
from lenlp import sparse
# List of dicts
documents = data.load_towns()
# Retrieve on fields title and article
retriever = retrieve.BM25(
key="id",
on=["title", "article"],
documents=documents,
k=30
)
# Rank on fields title and article
ranker = rank.Encoder(
key = "id",
on = ["title", "article"],
encoder = SentenceTransformer("sentence-transformers/all-mpnet-base-v2").encode,
k = 3,
)
# Pipeline creation
search = retriever + ranker
search.add(documents=documents)
# Search documents for 3 queries.
search(["Bordeaux", "Paris", "Toulouse"])
[[{'id': 57, 'similarity': 0.69513524},
{'id': 63, 'similarity': 0.6214994},
{'id': 65, 'similarity': 0.61809087}],
[{'id': 16, 'similarity': 0.59158516},
{'id': 0, 'similarity': 0.58217555},
{'id': 1, 'similarity': 0.57944715}],
[{'id': 26, 'similarity': 0.6925601},
{'id': 37, 'similarity': 0.63977146},
{'id': 28, 'similarity': 0.62772334}]]
```
We can map the index to the documents to access their contents using pipelines:
```python
search += documents
search(["Bordeaux", "Paris", "Toulouse"])
[[{'id': 57,
'title': 'Bordeaux',
'url': 'https://en.wikipedia.org/wiki/Bordeaux',
'similarity': 0.69513524},
{'id': 63,
'title': 'Bordeaux',
'similarity': 0.6214994},
{'id': 65,
'title': 'Bordeaux',
'url': 'https://en.wikipedia.org/wiki/Bordeaux',
'similarity': 0.61809087}],
[{'id': 16,
'title': 'Paris',
'url': 'https://en.wikipedia.org/wiki/Paris',
'article': 'Paris received 12.',
'similarity': 0.59158516},
{'id': 0,
'title': 'Paris',
'url': 'https://en.wikipedia.org/wiki/Paris',
'similarity': 0.58217555},
{'id': 1,
'title': 'Paris',
'url': 'https://en.wikipedia.org/wiki/Paris',
'similarity': 0.57944715}],
[{'id': 26,
'title': 'Toulouse',
'url': 'https://en.wikipedia.org/wiki/Toulouse',
'similarity': 0.6925601},
{'id': 37,
'title': 'Toulouse',
'url': 'https://en.wikipedia.org/wiki/Toulouse',
'similarity': 0.63977146},
{'id': 28,
'title': 'Toulouse',
'url': 'https://en.wikipedia.org/wiki/Toulouse',
'similarity': 0.62772334}]]
```
## Retrieve
Cherche provides [retrievers](https://raphaelsty.github.io/cherche/retrieve/retrieve/) that filter input documents based on a query.
- retrieve.TfIdf
- retrieve.BM25
- retrieve.Lunr
- retrieve.Flash
- retrieve.Encoder
- retrieve.DPR
- retrieve.Fuzz
- retrieve.Embedding
## Rank
Cherche provides [rankers](https://raphaelsty.github.io/cherche/rank/rank/) that filter documents in output of retrievers.
Cherche rankers are compatible with [SentenceTransformers](https://www.sbert.net/docs/pretrained_models.html) models which are available on [Hugging Face hub](https://huggingface.co/models?pipeline_tag=zero-shot-classification&sort=downloads).
- rank.Encoder
- rank.DPR
- rank.CrossEncoder
- rank.Embedding
## Question answering
Cherche provides modules dedicated to question answering. These modules are compatible with Hugging Face's pre-trained models and fully integrated into neural search pipelines.
## Contributors 🤝
Cherche was created for/by Renault and is now available to all.
We welcome all contributions.
## Acknowledgements 👏
Lunr retriever is a wrapper around [Lunr.py](https://github.com/yeraydiazdiaz/lunr.py). Flash retriever is a wrapper around [FlashText](https://github.com/vi3k6i5/flashtext). DPR, Encode and CrossEncoder rankers are wrappers dedicated to the use of the pre-trained models of [SentenceTransformers](https://www.sbert.net/docs/pretrained_models.html) in a neural search pipeline.
## Citations
If you use cherche to produce results for your scientific publication, please refer to our SIGIR paper:
```bibtex
@inproceedings{Sourty2022sigir,
author = {Raphael Sourty and Jose G. Moreno and Lynda Tamine and Francois-Paul Servant},
title = {CHERCHE: A new tool to rapidly implement pipelines in information retrieval},
booktitle = {Proceedings of SIGIR 2022},
year = {2022}
}
```
## Dev Team 💾
The Cherche dev team is made up of [Raphaël Sourty](https://github.com/raphaelsty), [François-Paul Servant](https://github.com/fpservant), [Nicolas Bizzozzero](https://github.com/NicolasBizzozzero), [Jose G Moreno](https://scholar.google.com/citations?user=4BZFUw8AAAAJ&hl=fr). 🥳