VisQueryPDF processes PDF files by splitting them into chunks, generating vector embeddings via OpenAI or compatible models, and storing those embeddings in a local vector store. Users can submit natural language queries to retrieve the most relevant chunks. Search hits are displayed with highlighted text on the original PDF pages and plotted in a two-dimensional embedding space, allowing interactive exploration of semantic relationships between document segments.

Ask Screenshot for ChatGPT revolutionizes the way you interact with information online. This Chrome extension allows you to effortlessly capture screenshots of any page and submit them to ChatGPT for analysis. By combining visual data with AI insights, users can extract knowledge, generate summaries, or clarify content quickly. Whether you’re a researcher, student, or professional, the tool saves time and enhances learning by providing direct answers and insights based on the captured visual content.

Mapify is an AI-powered tool that converts YouTube videos, PDFs, and webpages into interactive mind maps. Designed to enhance comprehension and learning, it extracts key insights from various sources and presents them in a structured visual format. Whether you're researching, summarizing, or learning new topics, Mapify helps you grasp key points quickly and effectively. By leveraging AI technologies like Claude and ChatGPT, Mapify organizes complex content visually, boosting productivity and facilitating faster learning.

SciPhi is an open-source platform designed to simplify the building, deploying, and scaling of Retrieval-Augmented Generation (RAG) systems. It provides an end-to-end solution for developers, enabling them to focus on AI innovation without worrying about the underlying infrastructure. With tools for automated knowledge graph extraction, document and user management, and robust observability, SciPhi ensures efficient and optimized RAG system deployment.

GPT Researcher leverages state-of-the-art AI algorithms to assist users in conducting comprehensive literature reviews. It can analyze vast databases to locate pertinent studies and articles, summarize findings, and facilitate better understanding of complex topics. Users can integrate it into their workflow for efficient knowledge extraction, saving valuable time and effort while ensuring thoroughness in their research processes.

Starlight Search is an AI agent designed to improve online research by understanding natural language queries and returning concise summaries paired with source links. It aggregates data from multiple websites, extracts key points, and formats responses in readable paragraphs or bullet points. Users can refine queries in real time, delve deeper into topics, and verify information through direct citations. The tool supports multi-turn dialogue, enabling clarifications and follow-up questions without restarting the search process.

The RAG-based Intelligent Conversational AI Agent combines a vector store-backed retrieval layer with Google’s Gemini LLM via LangChain to power context-rich, conversational knowledge extraction. Users ingest and index documents—PDFs, web pages, or databases—into a vector database. When a query is posed, the agent retrieves top relevant passages, feeds them into a prompt template, and generates concise, accurate answers. Modular components allow customization of data sources, vector stores, prompt engineering, and LLM backends. This open-source framework simplifies the development of domain-specific Q&A bots, knowledge explorers, and research assistants, delivering scalable, real-time insights from large document collections.