Research By Vector
API DocumentationResearch By Vector is a revolutionary tool designed to simplify the process of academic research. Utilizing the advanced technology of vector embeddings, it refines the search for relevant research papers on ArXiv, a popular platform for scholarly articles, making it a breeze for academics and researchers alike. The tool operates on a dual-query mechanism—a human query and an API query. Users can input their research question in natural language (the human query) which the tool ingeniously translates into a more detailed API query. The API query is essentially a hypothetical title and abstract, crafted to be as specific as possible, enhancing the relevance and accuracy of the search results. For instance, a general interest in the recent advancements of convolutional neural networks for image recognition can be transformed into a detailed API query focusing on the state-of-the-art techniques, optimization strategies, and novel training methodologies developed in a particular year. This groundbreaking approach, combined with the tool's precision and efficiency, sets Research By Vector apart in the realm of academic research tools.
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Example Prompts
"Find me recent papers on the impact of AI on climate change."
"Search for research studies related to the COVID-
"Look for articles about quantum computing advancements after
"Search for papers on the role of machine learning in healthcare."
"Find me the latest research on the effects of social media on mental health."
"Search for studies on blockchain technology in the financial sector."
"Look for articles about the use of AI in autonomous vehicles."
"Find me research papers on the development of renewable energy sources."
"Search for recent studies on the impact of climate change on marine life."
"Look for articles about the advancements in gene editing technologies."
"Find me the latest research on the Mars Rover mission."
"Search for papers on the application of neural networks in image recognition."
"Look for research studies on data privacy issues in social media platforms."
Description for AI
This tool employs vector embeddings to search for relevant academic research papers on ArXiv. The process involves two distinct types of queries: the human query and the API query. The human query is what the user initially asks in natural language. For example, a user might ask, 'What are the recent advancements in convolutional neural networks for image recognition?' You, as the AI, then translate this human query into an API query.
The API query consists of a hypothetical title and abstract that you generate based on the human query. This title and abstract should be as detailed and specific as possible to yield the most relevant search results. For instance, a well-crafted API query could be: title - 'Innovations and Evolution in Convolutional Neural Networks (CNNs) for Enhanced Image Recognition: A 2023 Perspective', abstract - 'An exhaustive review of the state-of-the-art techniques developed in 2023 for convolutional neural networks, focusing on advancements in architecture design, optimization strategies, and novel training methodologies. It pays special attention to the impact of these advancements on image recognition tasks, including but not limited to object detection, image classification, and semantic segmentation. The review also highlights emerging trends and the potential future trajectory of CNNs in the field of image recognition.'
In essence, it's your job as the AI to translate the user's general interest expressed in the human query into a more specific and detailed API query. Remember, detailed and specific API queries will result in more accurate search results.