Abstract: A multimodal content management system having a block-based data structure can include a question and answer (Q&A) assistant engine. The Q&A assistant engine can capture a natural language prompt, which can be tokenized to generate a set of data source tokens and a set of parameter tokens. A parameter token in the set of parameter tokens can be indicative of a block property in the block-based data structure. A trained neural network can accept the data source tokens and/or parameter tokens as inputs to generate a query executable against the block-based data structure. The query can generate a result set, which can be visualized concurrently with the input control of the Q&A assistant engine. The input control can accept subsequent natural language prompts while maintaining result set visualizations. Items in result set visualizations generated in response to a particular prompt can include content in different modalities.

Abstract: A multimodal content management system having a block-based data structure can include a neural network trained to operate on the block-based data structure, such as by recognizing units (e.g., block titles, block identifiers, block content/content types, block properties, block types, block dependencies, and/or block format) within the block-based data structure. The neural network can be trained on tokens (e.g., tokens generated from a natural language prompt) that relate to the aforementioned units. Training operations can include causing the neural network to generate: (i) a first set of response tokens based on a keyword included in training data and (ii) a second set of response tokens based on an inference made regarding tokens in the training data. Training operations can further include generating a GUI that includes the keyword, the inference and at least one navigable link to a unit in the block-based data structure.

Abstract: A multimodal content management system having a block-based data structure can include a question and answer (Q&A) assistant engine. The Q&A assistant engine can capture a natural language prompt, which can be tokenized to generate a set of data source tokens and a set of parameter tokens. A parameter token in the set of parameter tokens can be indicative of a block property in the block-based data structure. A trained neural network can accept the data source tokens and/or parameter tokens as inputs to generate a query executable against the block-based data structure. The query can generate a result set, which can be visualized concurrently with the input control of the Q&A assistant engine. The input control can accept subsequent natural language prompts while maintaining result set visualizations. Items in result set visualizations generated in response to a particular prompt can include content in different modalities.

Abstract: A multimodal content management system having a block-based data structure can include a question and answer (Q&A) assistant (e.g., a chatbot). The system can receive a natural language prompt and generate a result set. The result set can include blocks (e.g., blocks that include responsive content, including content in different modalities). The system can apply a set of authority signals to items in the result set to generate a ranked result set. The authority signals can be generated using aspects of the block-based data structure, such as block properties. The system can cause the Q&A assistant to return a set of hyperlinks to the ranked result set items. The hyperlinks can be operable to enable navigation to block content without closing the Q&A assistant.

Abstract: A multimodal content management system having a block-based data structure can include an artificial intelligence (AI)-based code unit generator that can generate code units executable against the block-based data structure to provide information requested by users. For example, the code units can be generated in response to natural language prompts received via a question and answer Q&A assistant engine. A neural network can be trained on block types, block dependencies, block content values, block content types, and/or block format. The neural network can receive a set of tokens generated based on a natural language prompt and generate one or more query strings to be included in a particular code unit. The tokens can be indicative of block properties, content, or other items in the block-based data structure. The code unit can be structured to execute more than one query against the block-based data structure such that a particular result set can include content items of different modalities.

Abstract: A multimodal content management system having a block-based data structure can include an artificial intelligence (AI)-based embeddings generator and indexer. After receiving an item update instruction that includes an object (e.g., a block content, a block property, or a block schema) identifier and an update payload, the system can transform the update payload—for example, by generating a chunk to capture at least a portion of the update payload. The chunk can correspond to a particular content modality included in the update payload. The system can generate and retrievably store a vector comprising a set of embeddings corresponding to the chunk, where the embeddings represent a vectorized portion of block content, block property, or block schema.