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| Autores principales: | , , |
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
| Publicado: |
Zenodo
2025
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| Acceso en línea: | https://doi.org/10.5281/zenodo.15450517 |
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| _version_ | 1866901513057599488 |
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| author | D. Anjaneyulu P. Bhaskar S. Harini Yadav |
| author_facet | D. Anjaneyulu P. Bhaskar S. Harini Yadav |
| contents | <p>The "RAG Bot for Random Documents (Offline)" is an intelligent AI-powered system designed to efficiently extract, understand, and respond to queries based on offline documents. It utilizes the power of Retrieval-Augmented Generation (RAG) to combine the strengths of document retrieval with natural language generation, offering accurate, context-aware answers without the need for an internet connection. The bot supports multiple file formats such as PDF and TXT, making it highly flexible and practical for various user needs, including researchers, students, legal professionals, and business analysts. Unlike traditional document search tools that rely solely on keyword matching, the RAG Bot uses sentence embeddings and semantic search to understand the true meaning of queries. This is achieved using transformer models like Sentence-BERT for generating embeddings and Flan-T5 for generating natural language answers. The backend incorporates modules for extracting document content, embedding textual data, and indexing with FAISS for efficient similarity-based retrieval. The frontend, built using Flask and HTML/CSS, provides a user-friendly interface for uploading documents and asking questions</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15450517 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | RAG Bot for Random Documents D. Anjaneyulu P. Bhaskar S. Harini Yadav RAG, offline, document processing, embeddings, semantic search, Flask, file formats, transformer models. <p>The "RAG Bot for Random Documents (Offline)" is an intelligent AI-powered system designed to efficiently extract, understand, and respond to queries based on offline documents. It utilizes the power of Retrieval-Augmented Generation (RAG) to combine the strengths of document retrieval with natural language generation, offering accurate, context-aware answers without the need for an internet connection. The bot supports multiple file formats such as PDF and TXT, making it highly flexible and practical for various user needs, including researchers, students, legal professionals, and business analysts. Unlike traditional document search tools that rely solely on keyword matching, the RAG Bot uses sentence embeddings and semantic search to understand the true meaning of queries. This is achieved using transformer models like Sentence-BERT for generating embeddings and Flan-T5 for generating natural language answers. The backend incorporates modules for extracting document content, embedding textual data, and indexing with FAISS for efficient similarity-based retrieval. The frontend, built using Flask and HTML/CSS, provides a user-friendly interface for uploading documents and asking questions</p> |
| title | RAG Bot for Random Documents |
| topic | RAG, offline, document processing, embeddings, semantic search, Flask, file formats, transformer models. |
| url | https://doi.org/10.5281/zenodo.15450517 |