Layered Database Queries for Context Injection
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Solution Overview
Problem
Existing systems for automated technical support struggle to accurately and relevantly respond to user technical queries due to language model hallucinations and fabrications, and lack effective context injection methods.
Innovation Solution
The use of layered database queries that incorporate user-specific information from structured databases to augment vector database queries, allowing for context injection and reducing language model inaccuracies by providing relevant and accurate information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a language model is used to generate technical support responses, then the system can dynamically generate human-like text, but the model produces hallucinations and fabrications that reduce accuracy
Solution Approach 1:
The patent segments the context injection process into multiple layers: first querying a structured database for user-specific information, then using that information to query a vector database for additional context. This layered segmentation allows the system to progressively refine the context provided to the language model, reducing hallucinations while maintaining natural-language generation capabilities.
Solution Approach 2:
The patent performs preliminary actions by querying the structured database before the vector database, and by generating vector embeddings of user information in advance. This preliminary context preparation ensures that the language model receives accurate, pre-verified information first, which anchors the subsequent generation process and reduces fabrications.
2Reliability
If context is injected into the language model to improve response accuracy, then relevance improves, but computational resources and processing time increase
Solution Approach 1:
The patent divides context retrieval into two segmented stages: structured database queries for essential user information, then vector database queries for supplementary context. This segmentation allows the system to inject only the most relevant context into the language model, improving response relevance while controlling computational resource consumption through progressive refinement.
Solution Approach 2:
The patent applies partial action by selectively retrieving only the necessary portions of user information and context from the databases. Rather than injecting all available context, the system uses the structured database results to guide vector database queries, ensuring that only relevant context segments are processed and injected, thus optimizing the balance between response relevance and computational efficiency.
3Adaptability or versatility
If user-specific information is retrieved from structured databases to augment context, then response personalization improves, but system complexity increases
Solution Approach 1:
The patent segments the information architecture into two distinct databases: a structured database for user-specific information and a vector database for contextual knowledge. This segmentation enables personalized responses by systematically combining user data with relevant context, while managing system complexity through clear separation of data types and access methods.
Solution Approach 2:
The patent introduces vector embeddings as an intermediary mechanism that bridges the structured database and vector database. By converting user information into vector representations, the system enables efficient similarity searches and context matching without requiring complex direct integrations between databases, thus achieving personalization while controlling architectural complexity.
Data Source
AI summary
A method of automated technical support includes receiving a natural-language prompt from a user and a user identifier corresponding to the user, the natural-language prompt including at least one technical support query, querying a first database with the user identifier to retrieve first information, generating a vector embedding representative of the first information and the natural-language prompt, querying a second database using the vector embedding to retrieve second information, and generating a natural-language response text based on the natural-language prompt, the first information, and the second information. The second database is a vector database comprising a plurality of vectors, each vector of the plurality of vectors representative of a text segment of a plurality of text segments, the second information comprises at least one text segment of the plurality of text segments, and the natural-language response text responsive to the at least one technical support query.