Speaker:In this session of "Introduction to RAG and AI Search" we cover the following;
-Traditional vs. AI Search: The session begins with an overview of keyword-based search (BM25, TF-IDF) and its limitations, leading to the introduction of AI-powered search using Transformer-based models like BERT.
-Neural Search & Semantic Embeddings: AI Search leverages neural networks to generate vector embeddings, allowing for context-aware retrieval instead of relying solely on exact keyword matches.
-Efficiency in Large-Scale Search: Neural search is computationally expensive (O(N)), but techniques like Approximate Nearest Neighbor (ANN) and FAISS reduce complexity, making large-scale search feasible.
-Multimodal & Multilingual Search: AI search extends beyond text, enabling retrieval across different modalities, including images, video, and multilingual documents.
Image-to-Text & Visual Search: Advances in multimodal models (e.g., CLIP, BLIP) enable searches where images can retrieve related text and vice versa, demonstrating deeper AI comprehension.
-Real-World Applications: Examples include searching Tamil poetry using English queries, retrieving images based on abstract concepts (e.g., "love a dog"), and interpreting semantic relationships.
-Upcoming Topics – Retrieval-Augmented Generation (RAG): The session concludes with a preview of RAG, which will be covered in-depth in future sessions to enhance AI search capabilities.