Intelligent hybrid search and retrieval

A system, comprising:

• at least one computer processor;
• a hybrid search engine, executing on the at least one computer processor;
• a result ranking engine, executing on the at least one computer processor;
• and
• a generative artificial intelligence (GenAI model), executing on the at least one computer processor,
• wherein the system is configured for:
• generating, by the hybrid search engine, a plurality of indexes based on a plurality of mappings, the plurality of indexes corresponding to a plurality of documents, each index of the plurality of indexes comprising a text field and an embedding field,
• processing a search query with an embedding model of the hybrid search engine to obtain a search query embedding, wherein the search query is received from a user application,
• performing a hybrid search by:
• performing a federated search with a plurality of hybrid search engine instances to obtain a plurality of federated search results, the plurality of hybrid search engine instances performing a lexical search of the text field of the plurality of indexes with the search query and a semantic search of the embedding field of the plurality of indexes with the search query embedding, and
• aggregating the plurality of federated searches results to obtain a search result set,
• ranking the search result set with the result ranking engine, based on hybrid search scores, user context data, and a user graph to obtain a ranked result set,
• processing the ranked result set by the GenAI model by summarizing the ranked result set and annotating the ranked result set with explanations of the ranking to obtain a ranking summarization,
• presenting the ranked result set in the user application,
• capturing, via a faceted navigation feature of the user application, user-preferred results of the ranked result set obtained by filtering the ranked result set based on user-selected facets, and
• updating the user context data and the user graph based on the user-preferred results and the user-selected facets.

A method for ranking a set of search results, comprising:

• generating, by a hybrid search engine, a plurality of indexes based on a plurality of mappings, the plurality of indexes corresponding to a plurality of documents. each index of the plurality of indexes comprising a text field and an embedding field;
• processing a search query with an embedding model of the hybrid search engine to obtain a search query embedding, wherein the search query is received from a user application and generated by a user;
• performing a hybrid search by:
• performing a federated search with a plurality of hybrid search engine instances to obtain a plurality of federated search results, the plurality of hybrid search engine instances performing a lexical search of the text field of the plurality of indexes with the search query to obtain lexical search results and corresponding lexical search scores and a semantic search of the embedding field of the plurality of indexes with the search query embedding to obtain semantic search results and corresponding semantic search scores, and
• aggregating the plurality of federated search results to obtain a search result set;
• determining, by a result ranking engine, hybrid search scores of the search result set based on the corresponding lexical search scores and the corresponding semantic search scores;
• extracting, by the result ranking engine, from user context data, at least one feature of the user context data to obtain a feature set of the user context data, wherein the user context data corresponds to the user that generated the search query;
• processing the feature set of the user context data by a ranking model of the result ranking engine, and outputting, by the ranking model, a scaling factor based on the feature set of the user context data;
• applying the scaling factor to the hybrid search scores of the search result set to obtain scaled hybrid search scores of the search result set;
• obtaining, by a graph-based adjuster of the result ranking engine, a set of first entities occurring in the search result set,
• wherein the set of first entities is a subset of a set of second entities occurring in a user graph, and
• wherein the user graph corresponds to the user of the user application that generated the search query;
• for each first entity in the set of first entities,
• performing a connection analysis on user-to-entity connections and entity-to-entity connections of the user graph, wherein a first node of the user graph corresponds to the user, and remaining nodes of the user graph correspond to the set of second entities, to obtain multiplying factors for the set of first entities;
• applying the multiplying factors to the scaled hybrid search scores of the search result set to obtain ranking scores;
• ordering the search result set based on the ranking scores to obtain a ranked result set;
• and
• processing the ranked result set by a generative artificial intelligence (GenAI) model by summarizing the ranked result set and annotating the ranked result set with explanations of the ranking to obtain a ranking summarization.