Add vectors to Hub datasets and do in memory vector search.
A private and powerful multimodal AI chatbot that runs local
Answer questions about images in natural language
Answer questions based on images and text
Chat about images using text prompts
Ask questions about text or images
Fetch and display crawler health data
Display a loading spinner and prepare space
Rank images based on text similarity
Display current space weather data
View and submit results to the Visual Riddles Leaderboard
Ask questions about images and get detailed answers
Explore a virtual wetland environment
Vectorsearch Hub Datasets is a tool designed to enhance datasets on Hugging Face Hub by enabling vector-based search capabilities. It allows users to add vector embeddings to their datasets and perform in-memory vector similarity searches, making it easier to find relevant data points within large datasets. This tool is particularly useful for applications that require efficient and accurate visual question answering (Visual QA) tasks.
What does vectorization mean in this context?
Vectorization refers to converting data (e.g., text, images) into numerical vector representations, enabling similarity-based searches.
What types of datasets are supported?
Vectorsearch Hub Datasets primarily supports text-based datasets but can be extended to other data types with appropriate vectorization.
How do I ensure data privacy?
Data remains on Hugging Face Hub, and Vectorsearch Hub Datasets only processes data in-memory during search operations.