Systems and methods for providing improved retrieval augmented generation (RAG) for query response

A method for providing improved retrieval augmented generation (RAG) for query response, comprising:

• retrieving, by a processor, a plurality of question-and-answer pairs from a question-and-answer pairs database;
• indexing, by the processor, the plurality of question-and-answer pairs into a plurality of vector databases, wherein a first vector database contains the questions from the plurality of question-and-answer pairs, a second vector database contains the answers from the plurality of question-and-answer pairs, and a third vector database contains a plurality of answer chunks of the answers;
• processing, by the processor, a query through the first vector database and the third vector database to generate a list of questions from the first vector database and a list of answer chunks from the third vector database;
• retrieving, from the second vector database, corresponding answers to the questions in the list of questions from the first vector database;
• composing, by the processor, an augmented prompt based on an aggregation of the query, the list of questions, the corresponding answers, and at least a portion of the answer chunks from the list of answer chunks;
• generating, by the processor, a proposed response by feeding the augmented prompt into a large language model (LLM);
• retrieving, by the processor, from the second database, a revised list of answers based on the proposed response;
• comparing, by the processor, a highest ranked answer from the revised list of answers with the proposed response;
• and
• based on a similarity threshold, outputting, by the processor, one of the highest ranked answer from the revised list of answers or the proposed response as a final response.

A system for providing improved retrieval augmented generation (RAG) for query response, comprising:

• memory storing computer program instructions;
• and
• one or more processors configured to execute the computer program instructions to:
• retrieve a plurality of key-value pairs from a key-value pairs database;
• index the plurality of key-value pairs into a plurality of vector stores, wherein a first vector store contains the keys from the plurality of key-value pairs, a second vector store contains the values from the plurality of key-value pairs, and a third vector store contains a plurality of value chunks of the values;
• process a query through the first vector store and the third vector store to generate a list of keys from the first vector store and a list of value chunks from the third vector store;
• retrieve, from the second vector store, corresponding values to the keys in the list of keys from the first vector store;
• compose, an augmented prompt based on an aggregation of the query, the list of keys, the corresponding values, and at least a portion of the value chunks from the list of value chunks;
• generate a proposed response by feeding the augmented prompt into a large language model (LLM);
• retrieve, from the second vector store, a revised list of values based on the proposed response;
• compare a highest ranked value from the revised list of values with the proposed response;
• and
• based on a similarity threshold, output one of the highest ranked value from the revised list of values or the proposed response as a final response.