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New Python library Quanta unifies retrieval systems

By PulseAugur Editorial · [1 sources] ·

A new open-source Python library called Quanta has been developed to unify different retrieval systems into a single API. Quanta integrates dense vector search with 4-bit quantised embeddings, BM25 full-text retrieval, and knowledge graph traversal. It distinguishes itself by combining signals using weighted reciprocal rank fusion and by using the knowledge graph as a candidate expander rather than a relevance scorer. AI

IMPACT This library could streamline the development of advanced retrieval-augmented generation pipelines by unifying disparate systems.

RANK_REASON The item describes a new open-source library for information retrieval presented in an arXiv paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Python library Quanta unifies retrieval systems

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The item describes a new open-source library for information retrieval presented in an arXiv paper. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Ioannis E. Livieris ·

    Quanta: A Self-Contained Python Library for Hybrid Retrieval over Quantised Embeddings, Lexical Indexes, and Knowledge Graphs

    An advanced retrieval-augmented generation pipeline is typically assembled from three or four independently operated systems: an approximate nearest-neighbour index, a full-text search engine, a graph database, and a relational document store. Each contributes its own deployment …

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