Query-Adaptive Embedding Weighting for Hybrid Search Relevance
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Solution Overview
Problem
Existing hybrid search engines face sub-optimal search outcomes due to fixed or static weights assigned to textual and image-based vector similarity searches, failing to leverage the unique advantages of different embedding models for varying query types.
Innovation Solution
A dynamic weighting module that uses a machine learning model to predict optimal weights for combining search result sets from multiple embedding generators, adjusting weights based on the search query and user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed or static weights are assigned to textual and image-based vector similarity searches, then the system structure is simple and easy to implement, but the search relevance and accuracy deteriorate for varying query types
Solution Approach 1:
The patent implements dynamic weighting by training a machine learning model to predict optimal weights for textual and image embedding generators based on the input query. The weight prediction model processes the query and outputs adaptive weights that adjust the contribution of each embedding type according to the query characteristics, transforming the static weighting system into a dynamic one that adapts to different search scenarios
Solution Approach 2:
The patent changes the weighting parameters from fixed values to dynamic predictions based on query analysis. The machine learning model learns optimal weight configurations from training data and applies these learned parameters to adjust the relative importance of textual versus image embeddings according to the specific query being processed, enabling parameter adaptation without changing the underlying system structure
2Measurement precision
If dynamic query-dependent weighting is implemented using machine learning models, then the search relevance and accuracy improve, but the device complexity and computational overhead increase
Solution Approach 1:
The patent applies preliminary action by training the machine learning weight prediction model offline using historical query data before deployment. The model learns optimal weighting strategies in advance during the training phase, so that during actual search operations, only lightweight inference is required. This pre-computation of weighting knowledge reduces the computational burden during real-time query processing
Solution Approach 2:
The patent introduces an intermediary machine learning model that sits between the query input and the embedding generation process. This weight prediction model acts as a mediator that analyzes the query and determines the appropriate weights for different embedding types, decoupling the complexity of weight optimization from the core search functionality and allowing each component to be optimized independently
3Adaptability or versatility
If multiple embedding generators are used to handle different query types, then the adaptability and search quality improve, but the computing resources and processing time increase
Solution Approach 1:
The patent applies partial action by using the machine learning model to predict weights that may heavily favor one embedding type over another depending on the query. For certain query types, the model may assign near-zero weight to one embedding generator, effectively disabling it for that particular query. This selective activation reduces computational resources by avoiding unnecessary embedding generation for queries where certain modalities are less relevant
Data Source
AI summary
Methods and systems for optimally weighting search results in a hybrid search framework are described. Responsive to a search query, query embeddings are obtained using corresponding embedding generators. The query embeddings are provided to search operators corresponding to the embedding generators to obtain corresponding search result sets having search results and associated scores. Optimal weights for each of the corresponding embedding generators are determined using a machine learning model, based on the search query. The search result sets are combined, based on the determined weights and the associated scores, yielding a combined search result set. The disclosed methods and systems dynamically optimize weights applied to search result sets that are retrieved using more than one vector-based search operator (e.g., where each search operator performs a vector-based search using embeddings generated by a corresponding embedding generator), for generating more relevant search results within the hybrid search framework.