Cherche allows creating a neural search pipeline using retrievers and pre-trained language models as rankers. We dedicated Cherche to small to medium-sized corpora. Cherche's main strength is its ability to build diverse and end-to-end pipelines.

Installation 🤖

pip install cherche --upgrade 

To install the development version:

pip install git+https://github.com/raphaelsty/cherche

Documentation 📜

Documentation is available here. It provides details about retrievers, rankers, pipelines, question answering, summarization, and examples.

QuickStart 💨

Documents 📑

Cherche allows findings the right document within a list of objects. Here is an example of a corpus.

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.

from cherche import data, retrieve, rank
from sentence_transformers import SentenceTransformer

# List of dicts
documents = data.load_towns()

# Retrieve on fields title and article
retriever = retrieve.TfIdf(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,
    path = "encoder.pkl"
)

# Pipeline creation
search = retriever + ranker

search.add(documents=documents)

search("Bordeaux")
[{'id': 57, 'similarity': 0.69513476},
 {'id': 63, 'similarity': 0.6214991},
 {'id': 65, 'similarity': 0.61809057}]

Map the index to the documents to access their contents.

search += documents
search("Bordeaux")
[{'id': 57,
  'title': 'Bordeaux',
  'url': 'https://en.wikipedia.org/wiki/Bordeaux',
  'article': 'Bordeaux ( bor-DOH, French: [bɔʁdo] (listen); Gascon Occitan: Bordèu [buɾˈðɛw]) is a port city on the river Garonne in the Gironde department, Southwestern France.',
  'similarity': 0.69513476},
 {'id': 63,
  'title': 'Bordeaux',
  'url': 'https://en.wikipedia.org/wiki/Bordeaux',
  'article': 'The term "Bordelais" may also refer to the city and its surrounding region.',
  'similarity': 0.6214991},
 {'id': 65,
  'title': 'Bordeaux',
  'url': 'https://en.wikipedia.org/wiki/Bordeaux',
  'article': "Bordeaux is a world capital of wine, with its castles and vineyards of the Bordeaux region that stand on the hillsides of the Gironde and is home to the world's main wine fair, Vinexpo.",
  'similarity': 0.61809057}]

Retrieve 👻

Cherche provides different retrievers that filter input documents based on a query.

• retrieve.Elastic
• retrieve.TfIdf
• retrieve.Lunr
• retrieve.BM25Okapi
• retrieve.BM25L
• retrieve.Flash
• retrieve.Encoder
• retrieve.DPR
• retrieve.Fuzz

Rank 🤗

Cherche rankers are compatible with SentenceTransformers models, Hugging Face sentence similarity models, Hugging Face zero shot classification models, and of course with your own models.

Summarization and question answering

Cherche provides modules dedicated to summarization and question answering. These modules are compatible with Hugging Face's pre-trained models and fully integrated into neural search pipelines.

Translation

Hugging Face's translation models can be fully integrated into the neural search pipeline to translate queries, documents, or answers.

Deploy

We provide a minimalist API to deploy our neural search pipeline with FastAPI and Docker; information is available in the documentation.

Hugging Face Space

A running demo is available on Hugging Face.

Contributors 🤝

Cherche was created for/by Renault and is now available to all. We welcome all contributions.

Acknowledgements 👏

The BM25 models available in Cherche are wrappers around rank_bm25. Elastic retriever is a wrapper around Python Elasticsearch Client. TfIdf retriever is a wrapper around scikit-learn's TfidfVectorizer. Lunr retriever is a wrapper around Lunr.py. Flash retriever is a wrapper around FlashText. DPR and Encode rankers are wrappers dedicated to the use of the pre-trained models of SentenceTransformers in a neural search pipeline. ZeroShot ranker is a wrapper dedicated to the use of the zero-shot sequence classifiers of Hugging Face in a neural search pipeline.

See also 👀

Cherche is a minimalist solution and meets a need for modularity. Cherche is the way to go if we start with a list of documents as JSON with multiple fields to search on and create pipelines. Also, Cherche is well suited for middle-sized corpora.

Do not hesitate to look at Jina, Haystack, or TxtAi, which offer advanced neural search solutions.

• Haystack
• Jina
• txtai

Dev Team 💾

The Cherche dev team is made up of Raphaël Sourty, François-Paul Servant, Nicolas Bizzozzero, Jose G Moreno. 🥳