Abstract: Methods and systems are presented for providing a knowledge bot configurable to interact with users across multiple domains. The knowledge bot includes at least a text-based search engine and a semantic-based search engine. Each of the search engine is configured to retrieve documents from a corpus of documents based on the user query. The user query is in a natural language format. The retrieved documents may be ranked according to how relevant the documents are to the user query. A subset of the documents is used as the search results based on the ranking. The search results from the search engine are combined with the user query to generate a prompt for an artificial intelligence model. Based on the prompt, a response in the natural language format is generated by the artificial intelligence model.

Abstract: Methods and systems are presented for providing a knowledge bot configurable to interact with users across multiple domains. The knowledge bot includes at least a text-based search engine and a semantic-based search engine. Each of the search engine is configured to retrieve documents from a corpus of documents based on the user query. The user query is in a natural language format. The retrieved documents may be ranked according to how relevant the documents are to the user query. A subset of the documents is used as the search results based on the ranking. The search results from the search engine are combined with the user query to generate a prompt for an artificial intelligence model. Based on the prompt, a response in the natural language format is generated by the artificial intelligence model.

Abstract: There are provided systems and methods for building a chunked text as input for a machine learning model. An example system may receive an input text in at least one language. The system may determine a character limit corresponding to the language for deriving a segment from the input text, a character number of the segment does not exceed the character limit. The system may divide the input text into one or more segments sequentially based on a set of characteristics of input text, such as conjunction and punctuation. The system may generate a list of segments including the one or more segments by appending segment sequentially. The system may generate a list of combined segments as the input for the machine learning model by appending each segment in the list of segments sequentially.

Abstract: Techniques for generating training datasets for machine learning algorithms are disclosed. An initial labelled dataset may be a noisy dataset with multiple misclassifications in labelling of the data. Human-based annotation and the application of historical data information are implemented to refine labels for a subset of data from the initial labelled dataset. After refinement of the subset of data, data with existing labels is extracted from the initial labelled dataset to add to the refined subset and generate a training dataset. The data that is extracted from the initial labelled dataset is data that is similar to data in the refined subset with the same label as the extracted data. The extraction of data according to similarities in the data is applied to scale the subset of data to a larger dataset while maintaining quality in order to provide a large, high-quality training dataset for the machine learning algorithm.