RAG Knowledge Base Access Control for LLM Data Transformation
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Solution Overview
Problem
Existing systems face challenges in transforming unstructured data into a format consumable by Large Language Models (LLMs) and ensuring secure access control for sensitive information, which is crucial for maintaining data integrity and compliance with regulations.
Innovation Solution
The system employs Retrieval-Augmented Generation (RAG) to convert unstructured data into manageable chunks, embed them via LLMs, and store them in a vector database, while implementing access controls based on user permissions to ensure secure and relevant responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If unstructured data is transformed into LLM-consumable format using RAG, then data accessibility and relevance are improved, but data security and access control become more challenging
Solution Approach 1:
The patent segments data into discrete chunks with associated metadata before embedding and storage. This segmentation enables fine-grained access control where each chunk can be individually controlled, allowing the system to provide relevant data to authorized users while preventing unauthorized access to sensitive information.
Solution Approach 2:
The patent introduces an intermediary access control layer between the vector database and the LLM. This intermediary component filters and selects which embedded data chunks should be retrieved and provided to the LLM based on user authorization, ensuring that even though data is accessible through natural language queries, security constraints are enforced.
2Productivity
If data is split into manageable chunks and embedded, then processing efficiency and LLM consumption are improved, but data integrity and completeness may be compromised
Solution Approach 1:
The patent performs preliminary actions by creating embeddings and storing metadata alongside data chunks before they are needed for querying. This pre-processing enables efficient retrieval and ensures that when data is accessed, the complete context and metadata are available, maintaining data integrity while improving processing efficiency.
Solution Approach 2:
The patent implements feedback mechanisms where the system tracks which data chunks are accessed and can provide feedback loops to ensure complete and accurate data retrieval. This feedback ensures that the LLM receives complete information context, maintaining data integrity while enabling efficient processing through the RAG framework.
Data Source
AI summary
The disclosed methods and systems may transform existing datasets into a format that may be consumed by Large Language Models (LLMs). A Retrieval-Augmented Generation application may provide natural language (NL) answers to queries related to the existing data, which may be stored in a knowledge base following transformation. The knowledge base may generate NL responses for users according to their corresponding access rights.