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Chat with Free Knowledge Base is an innovative tool that enhances your conversational experience by leveraging external knowledge sources from ArXiv and Wikipedia, all powered by the MyScale vector database. Designed for researchers, students, and curious minds, this app allows users to seamlessly access a wealth of information. With the GPT Action MyScale Open Knowledge Base, you can retrieve related academic papers, such as those on Generative Adversarial Networks or NeRF developments post-2021, by simply entering prompts like "Please give me some papers on GANs." Additionally, the tool provides access to relevant Wikipedia pages, enabling users to explore topics in depth, such as the properties of mathematical rings. By utilizing structured schemas for ArXiv and Wikipedia, you can expect precise, meaningful responses tailored to your inquiries. This integration not only saves time but also enriches your understanding of complex subjects, making it an invaluable resource for anyone seeking to engage with knowledge more interactively. Discover more and enhance your learning journey at https://chat.openai.com/g/g-nwM8eIYfm-chat-with-free-knowledge-base.
Quickly find and retrieve relevant academic papers from ArXiv by using the get_related_arxiv function, enhancing your research and study capabilities with cutting-edge information.
Utilize the get_related_wiki feature to discover related Wikipedia articles, allowing you to gain broader context and insights on various topics, enriching your knowledge base effortlessly.
Ask for papers on specific topics like GANs or NeRF, and receive up-to-date research materials, ensuring you are informed about the latest advancements in your field of interest.
Use the extensive knowledge from both ArXiv and Wikipedia to facilitate comprehensive conversations, enhancing your understanding and ability to discuss complex subjects confidently.
Leverage the power of MyScale vector database for quick and accurate access to external knowledge, making it easier to find precise information without sifting through irrelevant data.
Enhance Your Journey with Cutting-Edge Features and Functions
Get some related papers.\nYou should use schema here:\n\nCREATE TABLE ArXiv (\n `id` String,\n `abstract` String, \n `pubdate` DateTime, \n `title` String, \n `categories` Array(String), -- arxiv category\n `authors` Array(String),\n `comment` String,\nORDER BY id
Get some related wiki pages.\nYou should use schema here:\n\nCREATE TABLE Wikipedia (\n `id` String,\n `text` String,\n `title` String,\n `view` Float32,\n `url` String, -- URL to this wiki page\nORDER BY id\n\nYou should avoid using LIKE on long text columns.
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