AI Query Vectorization Using Pre-Generated Questions
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Solution Overview
Problem
AI pipelines face inefficiencies due to repetitive queries, leading to excessive resource consumption and latency, especially in token-based architectures, and lack a scalable method to anticipate user queries accurately.
Innovation Solution
A system that segments datasets into chunks, generates pre-generated questions using vector embeddings, and stores them for efficient retrieval, allowing semantic matching of user inputs to these questions, thereby optimizing response generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the AI pipeline processes each query individually through vector search and AI service, then response accuracy is maintained, but computational resources and tokens are excessively consumed
Solution Approach 1:
The system performs preliminary actions by generating and storing anticipated questions with their corresponding data chunks before actual user queries arrive. When a user query comes in, the system matches it against pre-generated questions rather than performing full vector search and AI service execution from scratch, thereby reducing computational resource consumption while maintaining response accuracy.
Solution Approach 2:
The system creates copies of anticipated questions and their associated data chunks in advance and stores them for rapid retrieval. Instead of processing every query through the complete AI pipeline, the system retrieves pre-generated question-data chunk pairs that match user queries, reducing the need for repeated computational processing while maintaining accurate responses.
2Reliability
If the AI pipeline executes full vector search and AI service for each query, then response completeness is ensured, but processing time and latency increase
Solution Approach 1:
The system performs preliminary action by pre-generating questions and retrieving associated data chunks before actual user queries arrive. This anticipation mechanism ensures that when a query comes in, the relevant information is already prepared and can be retrieved quickly, reducing processing time while maintaining response completeness through the pre-fetched data chunks.
Solution Approach 2:
The system skips the lengthy vector search and AI service execution steps by matching user queries against pre-generated questions. This allows the system to rush through the processing pipeline by retrieving pre-prepared data chunks directly, significantly reducing processing time while ensuring response completeness through the pre-fetched content.
3Adaptability or versatility
If the system uses token-based architecture for responses, then scalability is improved, but repetitive queries lead to inefficient token usage
Solution Approach 1:
The system performs preliminary action by generating and storing anticipated questions with their data chunks before user queries arrive. When queries are repetitive, the system can retrieve pre-generated content instead of generating new responses through token-consuming AI service calls, thereby improving token efficiency while maintaining scalability through the pre-computed question-data chunk pairs.
Solution Approach 2:
The system uses copies of pre-generated questions and data chunks to handle repetitive queries efficiently. Instead of consuming tokens to generate identical responses multiple times, the system retrieves copied pre-generated content, significantly improving token efficiency while preserving the scalability benefits of the token-based architecture.
4Reliability
If the system processes similar queries repeatedly, then response relevance is maintained, but system latency increases
Solution Approach 1:
The system performs preliminary action by pre-generating and storing anticipated questions with their data chunks. When similar queries arrive, the system can quickly match them against pre-generated questions and retrieve the associated data chunks, maintaining response relevance while significantly improving response speed by avoiding repeated processing of the same information.
Solution Approach 2:
The system uses copies of pre-generated questions and data chunks to handle similar queries. By retrieving copied pre-generated content instead of processing queries de novo, the system maintains response relevance through accurate matching while improving response speed by eliminating redundant computational operations.
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.