AmenRa/retriv

🔥 News

• [August 23, 2023] retriv 0.2.2 is out!
  This release adds experimental support for multi-field documents and filters. Please, refer to Advanced Retriever documentation.

• [February 18, 2023] retriv 0.2.0 is out!
  This release adds support for Dense and Hybrid Retrieval. Dense Retrieval leverages the semantic similarity of the queries' and documents' vector representations, which can be computed directly by retriv or imported from other sources. Hybrid Retrieval mix traditional retrieval, informally called Sparse Retrieval, and Dense Retrieval results to further improve retrieval effectiveness. As the library was almost completely redone, indices built with previous versions are no longer supported.

⚡️ Introduction

retriv is a user-friendly and efficient search engine implemented in Python supporting Sparse (traditional search with BM25, TF-IDF), Dense (semantic search) and Hybrid retrieval (a mix of Sparse and Dense Retrieval). It allows you to build a search engine in a single line of code.

retriv is built upon Numba for high-speed vector operations and automatic parallelization, PyTorch and Transformers for easy access and usage of Transformer-based Language Models, and Faiss for approximate nearest neighbor search. In addition, it provides automatic tuning functionalities to allow you to tune its internal components with minimal intervention.

✨ Main Features

Retrievers

• Sparse Retriever: standard searcher based on lexical matching. retriv implements BM25 as its main retrieval model. TF-IDF is also supported for educational purposes. The sparse retriever comes armed with multiple stemmers, tokenizers, and stop-word lists, for multiple languages. Click here to learn more.
• Dense Retriever: a dense retriever is a retrieval model that performs semantic search. Click here to learn more.
• Hybrid Retriever: an hybrid retriever is a retrieval model built on top of a sparse and a dense retriever. Click here to learn more.
• Advanced Retriever: an advanced sparse retriever supporting filters. This is and experimental feature. Click here to learn more.

Unified Search Interface

All the supported retrievers share the same search interface:

• search: standard search functionality, what you expect by a search engine.
• msearch: computes the results for multiple queries at once. It leverages automatic parallelization whenever possible.
• bsearch: similar to msearch but automatically generates batches of queries to evaluate and allows dynamic writing of the search results to disk in JSONl format. bsearch is handy for computing results for hundreds of thousands or even millions of queries without hogging your RAM. Pre-computed results can be leveraged for negative sampling during the training of Neural Models for Information Retrieval.

AutoTune

retriv automatically tunes Faiss configuration for approximate nearest neighbors search by leveraging AutoFaiss to guarantee 10ms response time based on your available hardware. Moreover, it offers an automatic tuning functionality for BM25's parameters, which require minimal user intervention. Under the hood, retriv leverages Optuna, a hyperparameter optimization framework, and ranx, an Information Retrieval evaluation library, to test several parameter configurations for BM25 and choose the best one. Finally, it can automatically balance the importance of lexical and semantic relevance scores computed by the Hybrid Retriever to maximize retrieval effectiveness.

📚 Documentation

• Sparse Retriever
• Dense Retriever
• Hybrid Retriever
• Text Pre-Processing
• FAQ

🔌 Requirements

python>=3.8

💾 Installation

pip install retriv

💡 Minimal Working Example

# Note: SearchEngine is an alias for the SparseRetriever
from retriv import SearchEngine

collection = [
  {"id": "doc_1", "text": "Generals gathered in their masses"},
  {"id": "doc_2", "text": "Just like witches at black masses"},
  {"id": "doc_3", "text": "Evil minds that plot destruction"},
  {"id": "doc_4", "text": "Sorcerer of death's construction"},
]

se = SearchEngine("new-index").index(collection)

se.search("witches masses")

Output:

[
  {
    "id": "doc_2",
    "text": "Just like witches at black masses",
    "score": 1.7536403
  },
  {
    "id": "doc_1",
    "text": "Generals gathered in their masses",
    "score": 0.6931472
  }
]

🎁 Feature Requests

Would you like to see other features implemented? Please, open a feature request.

🤘 Want to contribute?

Would you like to contribute? Please, drop me an e-mail.

📄 License

retriv is an open-sourced software licensed under the MIT license.