This blog post discusses strategies for staying up-to-date on the rapidly evolving field of AI, covering resources, tools, and techniques for tracking news, research, and developments.
A post discussing new techniques developed for parsing and searching PDFs, focusing on turning them into a hierarchical structure for RAG search. The approach involves dynamically generating chunks for searches, sending headers and sub-headers to the Language Model along with relevant chunks.
LangChain's ElasticsearchRetriever enables full flexibility in defining retrieval strategies, allowing users to experiment with different approaches.
This article provides a step-by-step guide on building a generative search engine for local files using Qdrant, NVidia NIM API, or Llama 3. It includes system design, indexing local files, and creating a user interface.
This article discusses the integration of Large Language Models (LLMs) into Vespa, a full-featured search engine and vector database. It explores the benefits of using LLMs for Retrieval-augmented Generation (RAG), demonstrating how Vespa can efficiently retrieve the most relevant data and enrich responses with up-to-date information.
This article discusses the growing importance of search functionality LLM applications. The author highlights the potential of search engines to handle complex queries, understand context, and deliver relevant results. The use of AI and machine learning in search is also explored, with examples of current and potential applications.
Google Photos is about to undergo a radical change with the integration of Gemini AI models. A new 'Ask Photos' feature will turn the platform into a visual database, enabling users to search for photos and videos using everyday language.
