RAG Knowledge Base Caching for Low-Latency Query Response
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Solution Overview
Problem
Large and frequently updated data stores in chatbot environments using foundation models with Retrieval Augmented Generation (RAG) pose challenges due to high latencies and resource inefficiencies, necessitating improved techniques for building and maintaining knowledge bases to respond to user queries efficiently.
Innovation Solution
A knowledge base manager populates a database with pre-generated query-answer pairs using a foundation model, updates answers based on document changes, and uses embeddings for efficient similarity searches, leveraging a caching layer to conserve resources and reduce real-time generation latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If pre-generated query-answer pairs are stored in a knowledge base, then response latency is reduced and resource consumption is minimized, but the system complexity increases due to the need for embedding generation and similarity search infrastructure
Solution Approach 1:
The system performs preliminary actions by pre-generating query-answer pairs and storing them in a knowledge base before actual queries arrive. Embeddings for these pre-generated queries are created and stored in advance, enabling rapid similarity-based retrieval when real queries are received, thus reducing response latency while managing complexity through automated pipelines.
Solution Approach 2:
The system creates copies of queries and their corresponding answers in the knowledge base. Instead of generating responses from scratch for each user query, the system stores multiple query-answer pairs and their embeddings as copies, allowing efficient retrieval through similarity matching without repeating the full generation process.
2Use of energy by moving object
If a knowledge base with pre-generated query-answer pairs is implemented, then resource consumption during real-time operations is reduced, but the initial setup and maintenance overhead increases
Solution Approach 1:
The system implements self-service mechanisms where the knowledge base automatically updates itself. When new documents are added to the datastore, the system automatically generates new query-answer pairs from these documents and adds them to the knowledge base, reducing manual maintenance overhead while minimizing real-time resource consumption through automated embedding generation and storage.
Solution Approach 2:
The system establishes feedback loops where the knowledge base is continuously monitored and updated based on changes in the underlying datastore. This feedback mechanism ensures that the knowledge base remains synchronized with the source documents, allowing efficient real-time operations while managing maintenance through automated update processes triggered by data changes.
3Productivity
If embeddings are generated and stored for all queries, then similarity search efficiency is improved, but the storage requirements and computational overhead for embedding generation increase
Solution Approach 1:
The system extracts only the essential embedding representations from the full query-answer pairs and stores these compact numerical vectors in the knowledge base. By separating the embedding extraction from the full text storage, the system achieves efficient similarity search through vector comparison while minimizing storage requirements compared to storing complete query texts and answers for every possible variation.
Data Source
AI summary
A knowledge base is built, maintained, and utilized for responding to user queries. The knowledge base is populated with queries and corresponding answers that have been generated based on prompting a foundation model with documents included in a datastore used for RAG. Embeddings of the queries are generated and stored in the knowledge base with their corresponding query-answer pairs. To manage changes to the documents in the datastore, updates, deletions, and/or additions to documents in the datastore are periodically identified, and the query-answer pairs that are associated with changes to documentation can be updated accordingly. As a support service receives user queries, the support service searches the knowledge base for queries that are sufficiently similar to the user query based on comparing the associated embeddings. The support service generates a response to the user query based on the answer that corresponds to the most similar query in the knowledge base.