Web Browser Vector Database for Low-Latency Model Inference
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine-learned models for real-time applications face challenges due to high parameter counts, demanding significant memory, processing power, and network resources, making them inefficient and impractical for real-time inference on user computing devices.
Innovation Solution
Implement a structured preprocessing and postprocessing framework that subdivides tasks into discrete operations, utilizing lightweight models to condition inputs for a primary machine-learned model, leveraging parallel processing accelerators like GPUs or ASICs to efficiently manage session data in a vector database for rapid retrieval and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a primary machine-learned model with high parameter count is used for real-time inference, then task processing capability is improved, but memory usage and processing time increase significantly
Solution Approach 1:
The patent segments the machine learning processing into multiple components: a lightweight preprocessing model that handles initial data processing, a vector database for efficient storage and retrieval of session data, and a primary model that only processes retrieved relevant data. This segmentation allows the system to maintain high task processing capability while reducing the memory footprint of any single component.
Solution Approach 2:
The patent introduces a vector database as an intermediary between the preprocessing model and the primary model. This intermediary stores embedded representations of session data and enables efficient retrieval of relevant information, reducing the amount of data that needs to be processed by the primary model and thereby reducing its memory requirements.
2Adaptability or versatility
If a primary machine-learned model with high parameter count is used for real-time inference, then task processing capability is improved, but processing speed decreases
Solution Approach 1:
The patent implements preliminary action by using a lightweight preprocessing model to process input data and generate embedded representations before the primary model is invoked. Session data is also pre-processed and stored in the vector database in advance. This preliminary processing reduces the computational burden on the primary model during real-time inference, improving processing speed.
Solution Approach 2:
The vector database acts as an intermediary that enables fast retrieval of relevant session data using vector similarity search. This intermediary layer allows the system to quickly access only the most relevant historical data, avoiding the need for the primary model to process all session data, thereby improving processing speed.
3Speed
If session data is stored in embedded representations in a vector database, then retrieval speed is improved, but computational resources for embedding generation increase
Solution Approach 1:
The patent segments the computational workload by using a lightweight preprocessing model to generate embedded representations of session data, rather than using the full primary model. This segmentation allows embedding generation to be performed with fewer computational resources while still maintaining the ability to retrieve relevant data quickly through vector similarity search.
Data Source
AI summary
A web browsing application can implement a vector database to store session data. A web browsing application can automatically embed loaded web content into an embedded data store that maintains session data for a number of web sessions. A user can query the web browser using simple instructions. The web browser can interpret the instructions and use the embedded data store to quickly search across multiple modalities of embedded data to retrieve relevant results. The web browser can use machine-learned models to answer queries or perform other tasks by performing vector-based queries over the content of visited web data.