Abstract: Implementations are provided for organizing and/or managing files and/or folders within filesystem hierarchies. In various implementations, an input prompt for a generative model may be assembled to include data indicative of: one or more files to be filed within a given filesystem hierarchy, and one or more filesystem hierarchies. The input prompt may be processed using the generative model to generate one or more recommendations of one or more folders within the given filesystem hierarchy for storing one or more of the files. Output may be rendered to convey one or more of the recommendations, which may be accepted to automatically implement the recommendations in some cases.

Abstract: Systems and methods include pre-processing documents in cloud storage using query embeddings, providing personalized prompts to users based on documents in cloud storage, real-time anticipation of user interest in information contained in documents in cloud storage, and providing generative answers including citation to source documents in cloud storage. The system and methods generate generative machine learning model (MLM) prompts based on document portions of documents in a cloud-based content management platform. The systems and methods use the generative MLM to generate responses to prompts, and the responses include citations to the document portions used to generate the responses in order for users to verify the responses.

Abstract: Implementations are provided for organizing and/or managing files and/or folders within filesystem hierarchies. In various implementations, an input prompt for a generative model may be assembled to include data indicative of: one or more files to be filed within a given filesystem hierarchy, and one or more filesystem hierarchies. The input prompt may be processed using the generative model to generate one or more recommendations of one or more folders within the given filesystem hierarchy for storing one or more of the files. Output may be rendered to convey one or more of the recommendations, which may be accepted to automatically implement the recommendations in some cases.

Abstract: Implementations relate to leveraging a generative model in summarizing a folder having different files and/or sub-folder(s). Various types of information associated with the folder, one or more files within the folder, user metadata associated with a user that requests summarization of the folder, and/or other types of information can be utilized to generate a folder summary request. The folder summary request can be processed using the generative model, to generate a model output reflecting a folder summary. The folder summary request can include one or more instructions that prompt the generative model, so that the folder summary generated for the folder using the generative model can include, for instance, an overview of the folder, key topics of the folder, and/or key files of the folder. The folder summary can also vary in dependence on the user (e.g., a first-time user vs. a frequent user frequently visits the folder, etc.).

Abstract: A method is disclosed that includes obtaining a generative machine learning model (MLM) prompt that prompt includes an indication of a user request to generate content based on one or more of a plurality of documents stored in a cloud-based content management platform, selecting a subset of the plurality of documents based on the generative MLM prompt and a first query embedding corresponding to the generative MLM prompt, inputting the generative MLM prompt and the subset of the plurality of documents into a first generative MLM, and generating, using the first generative MLM, a response, wherein the response comprises content generated by the first generative MLM, and one or more citations to one or more documents of the subset.

Abstract: Systems and methods include pre-processing documents in cloud storage using query embeddings, providing personalized prompts to users based on documents in cloud storage, real-time anticipation of user interest in information contained in documents in cloud storage, and providing generative answers including citation to source documents in cloud storage. The system and methods generate generative machine learning model (MLM) prompts based on document portions of documents in a cloud-based content management platform. The systems and methods use the generative MLM to generate responses to prompts, and the responses include citations to the document portions used to generate the responses in order for users to verify the responses.

Abstract: Systems and methods include pre-processing documents in cloud storage using query embeddings, providing personalized prompts to users based on documents in cloud storage, real-time anticipation of user interest in information contained in documents in cloud storage, and providing generative answers including citation to source documents in cloud storage. The system and methods generate generative machine learning model (MLM) prompts based on document portions of documents in a cloud-based content management platform. The systems and methods use the generative MLM to generate responses to prompts, and the responses include citations to the document portions used to generate the responses in order for users to verify the responses.

Abstract: Systems and methods include pre-processing documents in cloud storage using query embeddings, providing personalized prompts to users based on documents in cloud storage, real-time anticipation of user interest in information contained in documents in cloud storage, and providing generative answers including citation to source documents in cloud storage. The system and methods generate generative machine learning model (MLM) prompts based on document portions of documents in a cloud-based content management platform. The systems and methods use the generative MLM to generate responses to prompts, and the responses include citations to the document portions used to generate the responses in order for users to verify the responses.

Abstract: Systems and methods include pre-processing documents in cloud storage using query embeddings, providing personalized prompts to users based on documents in cloud storage, real-time anticipation of user interest in information contained in documents in cloud storage, and providing generative answers including citation to source documents in cloud storage. The system and methods generate generative machine learning model (MLM) prompts based on document portions of documents in a cloud-based content management platform. The systems and methods use the generative MLM to generate responses to prompts, and the responses include citations to the document portions used to generate the responses in order for users to verify the responses.

Abstract: Systems and methods include pre-processing documents in cloud storage using query embeddings, providing personalized prompts to users based on documents in cloud storage, real-time anticipation of user interest in information contained in documents in cloud storage, and providing generative answers including citation to source documents in cloud storage. The system and methods generate generative machine learning model (MLM) prompts based on document portions of documents in a cloud-based content management platform. The systems and methods use the generative MLM to generate responses to prompts, and the responses include citations to the document portions used to generate the responses in order for users to verify the responses.

Abstract: Techniques are described herein for enabling more computationally efficient organization of files within a cloud storage system. A method includes: receiving information identifying a document and a set of folders; for each folder in the set of folders, using a trained model to predict a similarity measure between the folder and the document; for each folder in the set of folders, determining a score for the folder based on the predicted similarity measure for the folder; selecting a candidate folder from the set of folders using the scores of the folders within the set of folders; and providing, on a user interface, a selectable option to associate the document with the candidate folder.

Abstract: Techniques are described herein for enabling more computationally efficient organization of files within a cloud storage system. A method includes: receiving information identifying a document and a set of folders; for each folder in the set of folders, using a trained model to predict a similarity measure between the folder and the document; for each folder in the set of folders, determining a score for the folder based on the predicted similarity measure for the folder; selecting a candidate folder from the set of folders using the scores of the folders within the set of folders; and providing, on a user interface, a selectable option to associate the document with the candidate folder.