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DocuSearch system enhances enterprise search with hybrid RAG and KG expansion

By PulseAugur Editorial · [1 sources] ·

A new research paper details DocuSearch, an enterprise document search system designed to improve accuracy and grounding for complex queries. DocuSearch integrates semantic search with BGE-Large embeddings, BM25 full-text search, and knowledge graph expansion. These retrieval methods are combined using Reciprocal Rank Fusion, followed by cross-encoder reranking and Maximal Marginal Relevance pruning. A key innovation is its per-chunk evaluation loop, where an LLM assesses context, answer completeness, and grounding, ensuring only reliable information is returned. AI

IMPACT This system could significantly improve the reliability and accuracy of information retrieval in enterprise settings by integrating multiple search techniques and an LLM-based grounding evaluation.

RANK_REASON Research paper detailing a new system for enterprise document search. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

DocuSearch system enhances enterprise search with hybrid RAG and KG expansion

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Research paper detailing a new system for enterprise document search. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Harish Saragadam, Sudhanshu Sharma, Meghana Pujari ·

    Hybrid Retrieval-Augmented Generation with Knowledge Graph Expansion, RRF Fusion, and Per-Chunk Grounded Evaluation for Enterprise Document Search

    arXiv:2609.01617v1 Announce Type: cross Abstract: Getting accurate, grounded answers out of large enterprise document repositories is a difficult problem. Dense vector retrieval alone frequently performs poorly on queries that mix technical terminology, vendor-specific acronyms, …

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