RAG Source Database Routing for Scalable Query Retrieval
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Solution Overview
Problem
RAG systems face challenges in disambiguating user intention across time and queries without increasing computational costs, selecting the best source databases for answers, and scaling multi-database searches efficiently.
Innovation Solution
Implementing a service that automatically selects source databases using a machine learning engine with a past query database and a forgetting strategy to improve scalability and relevance, leveraging embeddings and distance metrics to filter databases based on historical data and user intention drift.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the RAG system searches across an entire ecosystem of multiple databases, then the comprehensiveness of answer coverage is improved, but the computational cost and system complexity increase significantly
Solution Approach 1:
The patent segments the monolithic database search process into two distinct stages: (1) a routing stage that selects relevant source databases using a machine learning classifier, and (2) a retrieval stage that searches only within the selected databases. This segmentation reduces the search space from the entire database ecosystem to a focused subset, thereby maintaining answer comprehensiveness while significantly reducing computational complexity and system overhead.
Solution Approach 2:
The patent introduces a machine learning-based routing service as an intermediary component between the user query and the database ecosystem. This routing service analyzes the query, determines the most relevant source databases, and directs the search accordingly. The intermediary enables intelligent filtering without requiring changes to the underlying databases, thus maintaining comprehensive coverage while reducing the effective search space and system complexity.
2Reliability
If the RAG system uses multiple source databases, then the quality and relevance of answers are improved, but the computational overhead increases
Solution Approach 1:
The patent performs preliminary action by pre-training a machine learning routing model on historical query-data pairs to learn which source databases are most relevant for different types of queries. During runtime, the pre-trained model quickly classifies incoming queries and selects the appropriate databases, avoiding the need to search all databases for every query. This preliminary training phase enables efficient, low-overhead query routing that maintains high answer quality while reducing computational overhead during actual operations.
3Adaptability or versatility
If the RAG system maintains a large ecosystem of source databases, then the versatility and adaptability are improved, but the scalability of search operations deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the routing model is continuously trained and refined using historical query-data pairs. The system learns from past interactions which databases are most effective for different query types, and this knowledge is fed back into the routing model to improve future database selections. This feedback loop enables the system to maintain high versatility across diverse query types while improving search scalability through increasingly accurate routing decisions that reduce the effective search space.
Data Source
AI summary
A service accesses a RAG system having access to source databases. The source databases are accessible by an LLM tasked with answering user queries. The LLM relies on the source databases to answer the queries. The service accesses a current user query. The service accesses a PQ database of previous user queries that were successfully answered by the LLM. The service retrieves, from the PQ database, a select number of previous user queries that are similar to the current user query. The service identifies source databases used by the LLM to answer those similar user queries. These identified source databases are weighted and ranked. The service generates a subset of source databases by filtering the databases based on the ranked weighted scores. The service tags the subset of source databases as ones the LLM is to potentially use when generating a response to the current user query.