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Search Autocomplete


AI model preview image
The "search-autocomplete" model is an API designed to predict and provide autocomplete suggestions based on the input prompt. It runs on the CPU and offers possible completions to sentence starters or queries. When it receives an input schema like {"prompt":"what do i "}, it generates an array of potential endings, such as questions or tasks related to the user's daily life, legal documents, dinner options, and future planning. Its output is a list of common searches that complete the given phrase.

Use cases

The search-autocomplete AI model can be used in several ways to enhance the user experience across various platforms. For instance, it can be integrated into search engines to speed up search queries and help users better formulate their search terminologies based on popular queries. It can also be implemented in e-commerce platforms where it can suggest products or services that shoppers may be interested in, based on their initial search terms, in turn streamlining the buying process. Similarly, travel and food platforms can utilise this model to better anticipate user intent, offering suggestions for destinations or meals users might be seeking. Further potential use is in academic or educational platforms where it can suggest potential topics or subjects of interest based on initial queries. In terms of practical product use, this could result in predictive keyboards, enhanced browser search bars, quick fill forms, and targeted user interface design tools that can anticipate and suggest based on user behaviour.



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Summary of this model and related resources.

Model NameSearch Autocomplete
an autocomplete api that runs on the cpu :)
Model LinkView on Replicate
API SpecView on Replicate
Github LinkNo Github link provided
Paper LinkNo paper link provided


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