Product Database Virtual Assistant for Semantic Query Retrieval
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Solution Overview
Problem
Existing systems fail to provide users with accurate and relevant information from product databases due to varying report formats and the inability of simple textual queries to retrieve data based on vector similarities, leading to inefficient and inaccurate retrieval of information.
Innovation Solution
A virtual assistant system that utilizes natural language processing to generate customized queries using indexes and few-shot prompts, processing user queries to retrieve relevant data from a database based on semantic similarity and vector indexing, and displaying results in a predetermined format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple textual queries are used to retrieve data from database, then ease of operation is improved, but measurement precision of information retrieval deteriorates
Solution Approach 1:
The patent introduces an intermediary processing layer between the user's simple text query and the database. This layer includes query understanding modules that analyze the intent, relevance modules that match query semantics with database content using vector similarity, and response generation modules that format results. This intermediary system enables users to operate with simple text while achieving precise information retrieval through automated semantic matching and filtering.
2Productivity
If database stores data with indexes and vector similarities, then productivity of data organization is improved, but ease of operation for retrieval deteriorates
Solution Approach 1:
The system implements self-service retrieval by automatically performing query analysis, semantic matching against vector indexes, and result formatting without requiring users to understand the underlying database structure. The automated pipeline includes: (1) extracting query intent and entities, (2) computing vector similarities with stored data, (3) filtering and ranking results based on relevance, and (4) presenting formatted outputs. This allows users to retrieve information easily while the system handles the complex indexed search operations autonomously.
3Ease of operation
If simple text query is used, then ease of operation is improved, but loss of information increases due to inability to retrieve relevant information
Solution Approach 1:
The system employs feedback mechanisms to minimize information loss while maintaining simple query operation. The automated retrieval pipeline provides feedback at multiple stages: (1) query understanding feedback that confirms interpretation of user intent, (2) relevance feedback that matches query semantics with database content using vector similarity scoring, and (3) result feedback that presents formatted, relevant information. This feedback loop ensures that simple text queries yield comprehensive and relevant results by continuously refining the retrieval process based on semantic matching and relevance assessment.
Data Source
AI summary
The present disclosure provides a system for providing virtual assistant for generating customized query for retrieving data from a database. The system comprises a processor and a memory storing program instructions, which, when executed by the processor, causes the processor to provide one or more index of categories of the data, for efficient retrieval of the data from the database based on a customer's query. The processor is also configured to provide one or more few-shot prompts for use by the customer for generating an output in a predetermined format corresponding to the customer's query. The processor is configured to receive a query from the one or more customer, wherein said query is a text and is based on at least one of index of categories and the few-shot prompt. The processor, based on indexes and few-shot prompt processes the query using natural language processing to extract one or more textual content from the query and sends the query to the database for retrieval of the data. The system retrieves the relevant data based on identified textual content, said relevant data is based on semantic similarity between the vector index and displays the retrieved data to the one or more customer based on the predetermined format specified by few-shot prompt.