Federated Vector Retrieval With Cross-Database Relevance Normalization
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Solution Overview
Problem
Existing query-answering systems struggle to efficiently compare and combine results from multiple vector databases using different vectorization algorithms, leading to inconsistencies in relevance metrics across heterogeneous data sources.
Innovation Solution
A federated vector database system that applies a single vectorization algorithm to normalize and rank results from multiple vector databases, enabling unified and relevant data retrieval across databases with diverse vectorization methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple vector databases with different vectorization algorithms are queried independently, then data diversity and coverage are improved, but consistency and comparability of relevance metrics deteriorate
Solution Approach 1:
The patent introduces a normalization layer as an intermediary between the diverse vector databases and the query results. This normalization component transforms results from different vectorization algorithms into a common reference space, enabling consistent comparison while preserving access to diverse data sources. The normalization layer mediates the conflict between maintaining data diversity and achieving metric consistency.
Solution Approach 2:
The system dynamically changes the parameter space by applying different transformation operations (such as cosine similarity normalization, L2 normalization, or other metric adjustments) depending on the source database and query type. This allows the same query to be answered across multiple databases with different algorithms while adjusting the comparison parameters to ensure consistency in the final ranking.
2Measurement precision
If a single vectorization algorithm is applied to normalize results from multiple databases, then relevance metric consistency is improved, but processing time and computational complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing normalization parameters, reference vectors, or transformation matrices when data is ingested into the vector databases. This allows the normalization process during query time to be much faster, as the heavy computational work of establishing the reference framework has already been done in advance.
Solution Approach 2:
The patent applies partial normalization by selectively normalizing only the portions of results that need comparison, rather than re-vectorizing entire datasets. It may also use approximate normalization techniques that provide sufficient consistency for practical purposes without the full computational overhead of exact normalization, achieving an acceptable balance between consistency and speed.
3Productivity
If query results from multiple vector databases are combined without normalization, then retrieval speed is improved, but result accuracy and relevance ranking deteriorate
Solution Approach 1:
The system segments the query processing into distinct phases: rapid independent querying of multiple databases to maintain speed, followed by a separate normalization and ranking phase to ensure accuracy. This segmentation allows each phase to be optimized independently - the querying phase for speed and the normalization phase for precision - resolving the contradiction between retrieval speed and result accuracy.
Data Source
AI summary
A system and method including receiving, via a query interface, a query related to a medical topic, transmitting the query to one or more vector DBs, and retrieving result sets from the one or more vector DBs, each result set associated with a vector DB of the one or more vector DBs, each result set including result records, each result record being associated with content and a respective content vector. The system normalizes, based on a vectorization algorithm, one or more of the result records in the result sets to generate a normalized result set. The system generates, based on the normalized result set and one or more parameters, a unified result set, stores the unified result set, and returns the unified result set to a medical decision system that provides medical recommendations to a system user.