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Embedding-based vector search marks a
transformative shift in information Re-trieval,
particularly for large-scale textual datasets where
conventional keyword-based methods fall short in
capturing semantic relevance. This approach reframes
text retrieval as a semantic similarity task, representing
documents—such as legal or academic texts—as high-
dimensional vector embeddings using advanced natural
language processing (NLP) models like BERT or
RoBERTa. These embeddings encapsulate the
contextual and conceptual essence of the documents,
enabling re-trieval based on meaning rather than exact
keyword matches.
Keywords:
Vector Search ,Semantic Similarity, Embedding Generation ,Natural Language Processing (NLP) ,FAISS ,Retrieval-Augmented Generation (RAG) , Generative Al , Information Retrieval
Cite Article:
"Embedding-Based Vector Search for Large Scale Text Retrieval", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 8, page no.b448-b450, August-2025, Available :http://www.ijrti.org/papers/IJRTI2508158.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator