AI Agent Output Caching via Predicted Input Vectors
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Solution Overview
Problem
AI pipelines face inefficiencies due to repetitive queries, leading to excessive resource consumption and increased costs, especially in token-based architectures. Additionally, users often struggle with semantically mismatched queries, requiring repeated attempts to obtain relevant results.
Innovation Solution
The system employs data chunking, vector embeddings, pre-generated questions, and third-party AI models to process user queries efficiently. By segmenting datasets into coherent chunks, generating potential questions, and storing them in a vectorized form, the system semantically matches user inputs to pre-generated questions, reducing unnecessary processing and improving response accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the AI system processes each user query through the complete pipeline including vector search and AI service analysis, then the response accuracy is maintained, but computational resources and token usage increase significantly due to repetitive queries
Solution Approach 1:
The system performs preliminary processing by generating embeddings for all dataset chunks upfront and storing them in a vector database. When a user query arrives, the system retrieves pre-computed embeddings and performs semantic similarity comparison, bypassing the need to reprocess the entire dataset through the AI service. This preliminary action separates the computationally intensive embedding generation from the query processing, reducing token consumption during actual user interactions.
Solution Approach 2:
The system extracts the embedding generation step from the main query processing pipeline. By pre-computing and storing embeddings independently, the system removes the redundant embedding generation and AI service analysis from repetitive query handling, keeping only the essential semantic similarity comparison and response retrieval steps that maintain accuracy while reducing token usage.
2Stability of the object's composition
If the AI system reprocesses identical or similar queries multiple times, then consistent responses are provided, but system latency increases and user experience deteriorates
Solution Approach 1:
The system creates a copy of the dataset representations in the form of pre-computed embeddings and stores them in a vector database. Instead of reprocessing the original data through the AI service for each query, the system copies the essential semantic information into embedding vectors that can be rapidly compared and retrieved, maintaining response consistency while dramatically reducing processing time and latency.
3Ease of operation
If users rephrase queries multiple times to achieve semantic matching with dataset content, then relevant results are obtained, but processing costs and time consumption increase
Solution Approach 1:
The system transforms the query parameter from raw text to semantic embedding vectors, enabling the comparison to be based on meaning rather than exact wording. This parameter change allows the system to match queries with dataset content based on semantic similarity, so users obtain relevant results regardless of how they phrase their questions, eliminating the need for multiple rephrasing attempts and reducing processing time.
Data Source
AI summary
The present invention provides a system for processing user queries through an artificial intelligence (“AI”) pipeline, utilizing data chunking, question generation, and AI models to deliver contextually relevant responses. The system includes a server that ingests and chunks datasets, generates vector embeddings, and stores the data in one or more vector databases. A pipeline engine sends the chunked data to an AI model that generates potential questions tailored to different user personas. These questions, along with their corresponding data chunks, are stored in the database for future retrieval. When a user submits a query, the system semantically compares the query to the pre-generated question vectors and retrieves the most relevant question and associated data chunk. The query is then sent to an external AI model for final response generation. The system provides seamless interaction, delivering optimized, context-aware responses to user queries in real-time.