Hot and Cold Embeddings for Cost-Aware Vector Search
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Solution Overview
Problem
Generative AI systems like LLMs face limitations in processing large volumes of data due to context window constraints, leading to inefficient data processing and integration, particularly in network service provisioning scenarios.
Innovation Solution
Implementing a data storage and search system using hot and cold embeddings, where hot embeddings are kept up-to-date and stored in a high-cost vector storage device, while cold embeddings are moved to a lower-cost long-term storage, facilitating efficient data processing and search functionality by maintaining a semantic index.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If all data is stored in hot embeddings vector storage device, then data access speed is improved, but storage cost increases
Solution Approach 1:
The patent segments the vector storage system into two distinct parts: hot embeddings vector storage device for frequently accessed data and cold embeddings vector storage device for less frequently accessed data. This segmentation allows the system to optimize for both speed (hot data) and cost (cold data) simultaneously, resolving the contradiction between access speed and storage cost.
2Reliability
If embeddings are maintained up-to-date in hot storage, then data freshness is improved, but computational cost increases
Solution Approach 1:
The patent applies local quality by maintaining high data freshness only for hot embeddings that are frequently accessed, while allowing cold embeddings to have lower freshness requirements. This selective approach ensures data reliability where needed while reducing unnecessary computational overhead for less critical data.
3Loss of information
If LLM processes all data directly, then processing completeness is improved, but processing efficiency deteriorates due to context window limits
Solution Approach 1:
The patent performs preliminary action by pre-processing data into embeddings and organizing them in vector storage devices before LLM processing. This preparation work is done in advance, allowing the LLM to work with pre-structured embeddings rather than raw data, thereby improving processing efficiency while maintaining completeness.
Solution Approach 2:
The patent introduces embeddings as an intermediary representation between raw data and LLM processing. These embeddings serve as a compressed, structured intermediate form that fits within context windows while preserving essential information, enabling efficient LLM processing without losing processing completeness.
Data Source
AI summary
Novel tools and techniques are provided for implementing data storage and search functionality using hot and cold embeddings. In examples, a first vector data storage device is implemented to store hot embeddings, which are vector representations of data that has been determined to likely be used within a succeeding timeframe and that is associated with a plurality of network services each of which is one of being provisioned, in a process of being provisioned, or being ordered for provisioning, by a service provider, at a plurality of locations associated with a corresponding plurality of entities. The first vector data storage device is kept up to date with updates to any of the plurality of data. Embeddings of data that has been determined to not likely be used within the succeeding timeframe are moved to a second vector data storage device as cold embeddings.