Client-Side Embedding Cache for Conversational Search Latency
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Solution Overview
Problem
Conversational search systems face inefficiencies due to the lack of context in traditional keyword-based searches, leading to slower response times and reduced accuracy, as they fail to effectively utilize previous search queries and their relevance in multi-turn conversations.
Innovation Solution
Implementing a client-side document embedding cache that stores historical embeddings to reuse relevant documents from previous queries, leveraging dense retrieval models and nearest neighbor search algorithms to improve responsiveness and reduce backend index access, while assessing cache content quality to determine when to query the main index.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional keyword-based search is used, then the search system is simple to implement, but the response time is slow and accuracy is reduced due to lack of context understanding
Solution Approach 1:
The system performs preliminary actions by converting queries and documents to embeddings in advance and storing them in local caches. When a user submits a query, the system checks the local cache first to determine if relevant results are already available, avoiding the need to perform full vector similarity searches against the entire document corpus in real-time. This preliminary preparation of embedding data significantly accelerates response time while maintaining search accuracy through contextual understanding.
Solution Approach 2:
The system creates copies of document embeddings and stores them locally in the client device's cache. Instead of repeatedly accessing and processing the full document corpus from the server for every query, the system maintains local copies of embedding representations that can be quickly searched and compared. This copying approach reduces the computational burden on both client and server while preserving the ability to perform accurate similarity-based search.
2Measurement precision
If full vector similarity search is performed for every query, then search accuracy is maintained, but computational load on backend server increases and processing time extends
Solution Approach 1:
The search process is segmented into multiple stages: first, the system checks the local cache for potentially relevant results using quick embedding comparisons; second, only if the cache check yields insufficient results does the system proceed to perform a full vector similarity search against the complete document corpus on the server. This segmentation allows the system to maintain high search accuracy when needed while improving processing efficiency by avoiding full searches for queries that can be answered from cached data.
Solution Approach 2:
The system performs a partial search action by first conducting a limited check of locally cached embeddings before deciding whether to initiate a full-scale vector similarity search. This two-stage approach means that for many queries, only a partial search (cache check) is performed, which is computationally cheaper and faster. The full search is performed only when necessary, thus maintaining accuracy while improving overall processing efficiency.
3Productivity
If local caching of embeddings is implemented, then processing speed improves and server load reduces, but system complexity and memory requirements increase
Solution Approach 1:
The client device is empowered to autonomously manage its own local cache of document embeddings, including decisions about what to cache, when to update cached data, and how to query the cache for relevant results. The system implements self-service mechanisms where the client independently determines cache hit/miss conditions and manages local embedding storage without requiring complex centralized cache management infrastructure. This self-service approach simplifies the overall system architecture while maintaining processing speed benefits.
Data Source
AI summary
A method and system are described for improving the speed and efficiency of obtaining conversational search results. A user may speak a phrase to perform a conversational search or a series of phrases to perform a series of searches. These spoken phrases may be enriched by context and then converted into a query embedding. A similarity between the query embedding and document embeddings is used to determine the search results including a query cutoff number of documents and a cache cutoff number of documents. A second search phrase may use the cache of documents along with comparisons of the returned documents and the first query embedding to determine the quality of the cache for responding to the second search query. If the results are high-quality then the search may proceed much more rapidly by applying the second query only to the cached documents rather than to the server.