Real-Time Contextual Retrieval with Dynamic Knowledge Graphs
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Solution Overview
Problem
Existing retrieval augmented generation (RAG) systems are unsuitable for real-time applications due to the time-consuming process of fetching, indexing, and converting static external data, which can lead to missed key events and inaccurate descriptions in real-time data streams like videos.
Innovation Solution
A method and system that updates a knowledge graph in real-time using streaming information input, processes queries based on this graph, and performs actions responsive to those queries, utilizing lightweight models to prioritize key aspects for rapid response and maintain contextual accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static external data is fetched, indexed, and converted into structured formats for RAG, then the system can provide comprehensive knowledge base, but the process is time-consuming and unsuitable for real-time applications
Solution Approach 1:
The patent transforms the static RAG approach into a dynamic system that continuously updates the knowledge graph in real-time as new data arrives from streaming sources. Instead of periodic batch processing, the system adapts the knowledge structure dynamically, allowing it to respond immediately to new information while maintaining comprehensive coverage.
Solution Approach 2:
The system performs preliminary indexing and structuring of data as it arrives in real-time streams, rather than waiting to accumulate large volumes of static data before processing. This preliminary action on incoming data eliminates the time-consuming batch processing step while ensuring data is ready for immediate retrieval and use.
2Adaptability or versatility
If comprehensive external knowledge bases are used in RAG, then the system can answer complex queries, but key real-time events may be missed due to outdated data
Solution Approach 1:
The knowledge graph updates continuously as new data arrives in real-time streams, ensuring the system always has access to the most current information. This continuous update mechanism prevents information loss by immediately incorporating new events while maintaining the comprehensive knowledge base needed for complex queries.
Solution Approach 2:
The system implements feedback loops where query results and streaming data continuously refine and update the knowledge graph structure. This feedback mechanism ensures the system adapts to new information and maintains both comprehensiveness and currentness, preventing missed events while preserving complex query capabilities.
3Quantity of substance
If all data from streaming input is processed and stored in the knowledge graph, then complete information is available, but system complexity and processing overhead increase significantly
Solution Approach 1:
The system extracts only the essential elements and relationships from streaming data that are relevant to the knowledge graph structure, rather than processing and storing all raw data. This selective extraction maintains information completeness for query purposes while significantly reducing processing overhead and system complexity.
Solution Approach 2:
The knowledge graph is segmented into modular components that can be independently updated and managed. This segmentation allows the system to process and store information in manageable units, reducing overall system complexity while maintaining complete information availability through the interconnected modular structure.
4Use of energy by moving object
If traditional RAG systems process data in batch, then resource usage is efficient, but real-time response capability is compromised
Solution Approach 1:
The system uses periodic updates at optimized intervals based on data arrival rates and query patterns, rather than continuous processing or long batch cycles. This periodic action maintains real-time responsiveness while optimizing resource usage by processing data at efficient intervals that balance speed and energy consumption.
Data Source
AI summary
Methods and systems for query processing include updating a knowledge graph based on information extracted from a streaming information input. One or more queries relating to the streaming information input are processed based on the knowledge graph. An action is performed responsive to the one-or-more queries.