Abstract: A system and method to receive a search query input from an end user system, wherein the search query input relates to information associated with a merchant system. In view of the search query, a document associated with the merchant system is identified. A set of portions of the document are identified. A keyword-matching density level is determined for each portion of the set of portions of the document. A selected portion of the set of portions having a highest relative keyword-matching density level is identified. A graphical user interface including a search result responsive to the search query is generated, the graphical user interface including the selected portion of the document.

Abstract: A method for a chat bot includes receiving multiple instruction sets corresponding to multiple goals, each instruction set defining a prompt to a large language model (LLM) to achieve a corresponding goal. The method further includes receiving a chat history from a user, comprising multiple messages, each message being one of a user-authored message or a chat bot-authored message, and further comprising metadata associated with said chat history. The method further includes identifying a particular goal of said user from the multiple goals, selecting a particular instruction set corresponding to said particular goal, and defining a particular prompt to the LLM. The method further includes executing said particular instruction set by providing said particular prompt to said LLM and providing a reply to said user with a result of executing said particular instruction set.

Abstract: A system and method to receive a search query input from an end user system, wherein the search query input relates to information associated with a merchant system. In view of the search query, a document associated with the merchant system is identified. A set of portions of the document are identified. A keyword-matching density level is determined for each portion of the set of portions of the document. A selected portion of the set of portions having a highest relative keyword-matching density level is identified. A graphical user interface including a search result responsive to the search query is generated, the graphical user interface including the selected portion of the document.

Abstract: A system and method to generate search results in response to a search query based on comparisons of embedding vectors. The system and method receive, from an end user system, a search query including a set of keywords associated with the entity. Using a neural network, an embedding vector is identified based on the set of keywords of the search query. The system and method compares the embedding vector associated with the search query to a set of embedding vectors associated with a set of structured data elements relating to the entity. Based on the comparison, a set of matching structured data elements is identified. The system and method generate a search result in response to the search query, wherein the search result includes at least a portion of the set of matching structured data elements. The search result is displayed via an interface of the end user system.

Abstract: A system and method to generate search results in response to a search query based on comparisons of embedding vectors. The system and method receive, from an end user system, a search query including a set of keywords associated with the entity. Using a neural network, an embedding vector is identified based on the set of keywords of the search query. The system and method compares the embedding vector associated with the search query to a set of embedding vectors associated with a set of structured data elements relating to the entity. Based on the comparison, a set of matching structured data elements is identified. The system and method generate a search result in response to the search query, wherein the search result includes at least a portion of the set of matching structured data elements. The search result is displayed via an interface of the end user system.

Abstract: A system and method to receive a search query including a set of search terms associated with a merchant system. A machine-learning model is executed to identify a first subset of one or more multi-term phrases associated with one or more named entity types. A set of tokens corresponding to the search query is generated, wherein the set of tokens comprises a token associated with each of the first subset of one or more multi-term phrases. A comparison of the set of tokens to a document index associated with the merchant system is executed to identify one or more matching documents. Based on the comparison, a set of search results comprising the one or more matching documents is generated.

Abstract: A system and method to receive a search query input from an end user system, wherein the search query input relates to information associated with a merchant system. In view of the search query, a document associated with the merchant system is identified. A set of portions of the document are identified. A keyword-matching density level is determined for each portion of the set of portions of the document. A selected portion of the set of portions having a highest relative keyword-matching density level is identified. A graphical user interface including a search result responsive to the search query is generated, the graphical user interface including the selected portion of the document.

Abstract: A system and method to train one or more components of a search algorithm based on feedback received from a user system. The system and method provide, via an interface, a first search query comprising one or more terms and a first query response generated by a component of the search algorithm. The system and method receive, via the interface, a label corresponding to the query response. An override action is generated in view of the label. In response to a second query comprising the one or more terms, generating a second query response in accordance with the override action.

Abstract: A system and method to generate search results in response to a search query based on comparisons of embedding vectors. The system and method receive, from an end user system, a search query including a set of keywords associated with the entity. Using a neural network, an embedding vector is identified based on the set of keywords of the search query. The system and method compares the embedding vector associated with the search query to a set of embedding vectors associated with a set of structured data elements relating to the entity. Based on the comparison, a set of matching structured data elements is identified. The system and method generate a search result in response to the search query, wherein the search result includes at least a portion of the set of matching structured data elements. The search result is displayed via an interface of the end user system.