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Quanta Python library unifies retrieval systems for RAG pipelines

By PulseAugur Editorial · [2 sources] ·

Researchers have developed Quanta, an open-source Python library designed to simplify the integration of disparate retrieval systems into a single retrieval-augmented generation pipeline. Quanta unifies dense vector search with quantised embeddings, BM25 full-text retrieval, and knowledge-graph traversal under one API. It distinguishes itself by using weighted reciprocal rank fusion for combining signals and by employing knowledge graphs as candidate expanders rather than relevance scorers. AI

IMPACT Simplifies the integration of complex retrieval systems, potentially accelerating RAG pipeline development.

RANK_REASON The cluster describes a new open-source library and associated research paper published on arXiv.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Quanta Python library unifies retrieval systems for RAG pipelines

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Ioannis E. Livieris ·

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

    arXiv:2609.18248v1 Announce Type: cross Abstract: 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 do…

  2. 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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