2026· International journal of advanced engineering and management research· 0 citations
Abstract
This paper presents a Hebrew-first local LLM chat agent that combines Retrieval-Augmented
Generation (RAG), citation-aware document answering, controlled web search, and full right-toleft (RTL) user interaction. Unlike cloud-only assistants, the default response path operates
locally, supporting privacy, predictable operating costs, and deployment in environments where
documents should remain on-premises. The system integrates document upload and indexing,
hybrid semantic and lexical retrieval, reranking, source citation construction, streaming
responses, and PDF export. The revised manuscript positions the system against local RAG
baselines and Hebrew-capable cloud alternatives. It clarifies that the current evaluation is a
preliminary system-oriented assessment focused on grounded Hebrew document question
answering, lookup, summarization, citation behavior, and latency. The main contribution is a
practical architecture for private, transparent, Hebrew-oriented RAG assistance rather than a new
foundation model.
Retrieval-augmented generation (RAG) gives large language models (LLMs) access to external knowledge, but its conventional retrieve-concatenate-generate pipeline makes retrieval decisions on behalf of the model. As tool use and agent loops become more reliable, an agent can decide whether to retrieve, what to inspect,...
Overall, dense retrieval gives the best accuracy on this dataset, and additional reranking adds processing time without improving classification performance, while Category-level analysis shows strong performance on Shipping, Cancellation, and Return, while Unknown queries remain the main source of errors.
Conversational recommendation for e-commerce is increasingly mediated by large language models (LLMs), yet many real-world deployments operate under a stricter requirement: recommendations must be drawn only from a merchant's fixed catalog, without web search or unsupported product claims. In this setting, the main cha...
Ju-Li Huang, Hanna Clay, Sajjad Beygi et al.· 0 citations
Large language models (LLMs) increasingly rely on external sources when answering questions that require proprietary information or up-to-date live web content, through both traditional single-shot retrieval-augmented generation (RAG) and multi-turn agentic RAG. Yet today's web infrastructure is still built for human c...
Answering questions and completing tasks over large document collections often requires connecting evidence spread across multiple documents, such as a project's approval recorded in one, its requirements in another, and its latest status in a third. Recent LLM agents approach this by iteratively searching the full cor...
Soyeong Jeong, S. Jauhar, Sung Ju Hwang et al.· 0 citations
Large Language Models (LLMs) enable us to better understand text documents, including PDFs and Word documents. However, LLMs, as well as more modern LLM agents, i.e., those with tool-calling abilities, typically treat such documents as plain text, ignoring the fact that they are often organized hierarchically into sect...
Ruiying Ma, Yi-Ming Lin, Aditya G. Parameswaran· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
