The article provides a comprehensive tutorial on implementing vector search and retrieval systems using Hugging Face Hub, demonstrating multiple approaches from basic searching to creating a deployabl
e web application. It covers the entire pipeline from data loading and embedding generation to deployment as a microservice, using tools like DuckDB, vicinity, and Gradio.
Reasons to Read -- Learn:
how to implement a complete vector search system from scratch, with practical code examples using popular tools like DuckDB, vicinity, and Gradio
how to deploy AI applications as web services on Hugging Face Spaces, including both the front-end interface and API accessibility
different vector search approaches and their trade-offs, including direct Hub querying, indexed searches with DuckDB, and using the vicinity library for better performance
publisher: Hugging Face – The AI community building the future.
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