Semantic Search with Transformers

In this project, we'll build a semantic search engine for machine learning research papers using Transformer-based embeddings and vector similarity search. Unlike traditional keyword-based search, semantic search understands the meaning behind queries and retrieves relevant articles based on conceptual similarity, synonyms, and context rather than exact word matches. We'll use the sentence-transformers library to generate embeddings from research paper text and Facebook's Faiss library to perform efficient nearest-neighbor searches across thousands of documents.

We'll start by loading a dataset of machine learning research papers and retrieving a pre-trained Transformer model optimized for semantic text representations. Next, we'll generate vector embeddings for the entire corpus, capturing the semantic meaning of each paper's content in high-dimensional space. We'll then create a Faiss index that enables fast similarity searches and build helper methods for querying the database. Finally, we'll run experiments using both paper summaries and custom text prompts to demonstrate how semantic search retrieves contextually relevant results even when the exact search terms don't appear in the documents.

By the end, we'll have a working semantic search system demonstrating sentence-transformers for text embeddings, Faiss for vector indexing, k-nearest-neighbors search, and practical applications of Transformer models for information retrieval beyond traditional search engines.