Structured Data RAG Pipeline for Accurate LLM Query Responses
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Solution Overview
Problem
Existing data analytics approaches for video conferencing platforms require technical expertise and resources, leading to inefficiencies and delays in extracting insights from structured data, as non-technical stakeholders cannot directly query persisted data due to the complexity of SQL queries and tool development.
Innovation Solution
Stakeholders can query persisted data using natural language questions through a chat-like interface, leveraging a computing system that generates embedded representations of structured data, uses an encoder model to convert queries into embedded vectors, and employs a vector database for efficient retrieval-augmented generation (RAG) to provide accurate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If SQL queries and data analysis tools are used to extract insights from structured data, then measurement precision and data analysis capability are improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The patent introduces an intermediary system comprising an encoder model, vector database, and LLM that translates between natural language queries and structured data queries. This intermediary layer enables non-technical users to interact with complex data systems using simple natural language, resolving the contradiction between maintaining high data analysis capability and reducing system complexity.
Solution Approach 2:
The patent replaces the mechanical interaction model of SQL queries and technical tools with a natural language-based interaction model. By substituting the traditional query interface with conversational natural language processing, the system maintains precise data analysis capabilities while dramatically improving ease of operation for non-technical users.
2Measurement precision
If SQL queries and data analysis tools are used to extract insights from structured data, then measurement precision and data analysis capability are improved, but ease of operation deteriorates
Solution Approach 1:
The intermediary natural language processing system acts as a bridge between users and the complex data analysis backend. It translates user-friendly natural language questions into precise SQL queries and data analysis operations, enabling non-technical stakeholders to perform sophisticated data analysis without learning complex query languages or tools.
Solution Approach 2:
The patent substitutes the mechanical skill-based interaction with SQL and data tools with a natural conversational interface. This replacement eliminates the need for users to learn technical query languages while preserving the precision and power of structured data analysis capabilities.
3Measurement precision
If manual data scientist intervention is used to query persisted data, then measurement precision and data analysis accuracy are improved, but loss of time and productivity deteriorate
Solution Approach 1:
The patent implements a self-service data analysis system where non-technical users can independently query persisted data using natural language without requiring manual intervention from data scientists. The system automatically processes queries, retrieves relevant data, and generates accurate responses, eliminating wait times while maintaining analysis quality through the encoder-LLM architecture.
Solution Approach 2:
The system performs preliminary actions by pre-processing structured data into embedded representations and organizing it in a vector database before queries are submitted. This preliminary preparation enables rapid retrieval and analysis when users submit natural language queries, eliminating the need for manual data scientist intervention while maintaining high accuracy.
4Measurement precision
If manual data scientist intervention is used to query persisted data, then data analysis accuracy is improved, but productivity deteriorates
Solution Approach 1:
The system empowers non-technical users to perform data analysis independently through natural language queries, eliminating the bottleneck of manual data scientist intervention. Users can immediately query persisted data and receive accurate responses without scheduling meetings or waiting for data scientist availability, dramatically improving organizational productivity while maintaining analysis accuracy through the sophisticated encoder-LLM pipeline.
Data Source
AI summary
Techniques for optimized LLM inference from structured data via intermediate documents (“LLMs”) are disclosed. In an example method, a computing device accesses a database including a set of collections. The computing device generates one or more documents based on a first collection of the set of collections. The computing device determines one or more portions of the one or more documents based on at least one topic of the one or more documents. The computing device generates an embedded representation of each portion. The computing device receives, from a client device, a first query, including at least a first topic. The computing device determines a first portion of the one or more portions based on the first topic. The computing device generates a response based on the first query and the first portion and outputs the response to the client device.