An Optimized Content Retriever from Web Articles using Large Language Models and FAISS Indexing

Natural Language Processing (NLP) has transformed the way unstructured textual data is processed and analyzed, enabling the extraction of meaningful insights from extensive information sources. Traditional keyword-based retrieval systems often fail to capture the semantic relationships between words, leading to suboptimal search accuracy. The existing retrieval methods explore various NLP techniques, including rule-based and machine learningbased approaches, to analyze skill acquisition, taxonomy creation, and future skill predictions. However, these methods face limitations in handling large-scale datasets, maintaining contextual coherence, and providing semantically relevant responses. The proposed system addresses these challenges by integrating Large Language Models (LLMs), similarity search using FAISS indexing, text segmentation, and optimized embeddings to enhance semantic understanding. The system implements vector search techniques to improve content analysis, intelligent retrieval, and response generation. By implementing an advanced dense retrieval model, the system ensures that retrieved content is not only relevant but also contextually accurate and semantically rich. Performance evaluation is conducted using Precision, Recall, F1-score, Hit@K, BERTScore and Embedding Similarity Scores, demonstrating a 91% accuracy when compared against manually curated ground-truth answers.