Semantic Retrieval Pipeline for Grounded LLM Summaries
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Solution Overview
Problem
Traditional search engines rely on keyword-based searches that fail to capture semantic nuances and context, leading to inaccurate results, while advanced techniques like LLMs suffer from hallucination and fragmented summaries, making it difficult to retrieve and synthesize relevant information accurately.
Innovation Solution
A multistage process involving query vectorization, parallel document retrieval, independent note-taking, synthesis, and optional verification, using machine learning algorithms and LLMs to transform queries into vectors, retrieve relevant documents, generate comprehensive summaries, and verify accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword-based search is used, then search speed is fast, but retrieval accuracy deteriorates due to inability to capture semantic nuances
Solution Approach 1:
The patent replaces traditional keyword-matching mechanical search with vector-based semantic representation. Queries and documents are transformed into vector embeddings that capture semantic meaning, allowing the system to retrieve relevant information based on conceptual similarity rather than exact keyword matches, thereby improving retrieval accuracy while maintaining operational efficiency
Solution Approach 2:
The system changes the fundamental parameter of information representation from discrete keywords to continuous vector embeddings in high-dimensional space. This parameter transformation enables semantic search by comparing vector similarities, allowing the system to capture nuanced meanings and contextual relationships that keyword-based approaches miss
2Reliability
If LLMs are used for information synthesis, then understanding of context and semantics is improved, but hallucination occurs leading to inaccurate information
Solution Approach 1:
The patent introduces vector embeddings as an intermediary between the query and the LLM processing. By first transforming queries and documents into vector representations and retrieving relevant documents based on vector similarity, the system provides the LLM with pre-filtered, semantically relevant information, thereby reducing hallucinations while maintaining the contextual understanding capabilities of LLMs
Solution Approach 2:
The system segments the information retrieval and synthesis process into distinct stages: vector-based document retrieval, selection of relevant documents, and LLM-based synthesis. This segmentation allows each component to perform its specialized function optimally, with the vector system handling retrieval accuracy and the LLM handling coherent synthesis, thereby improving overall reliability
3Reliability
If traditional summarization methods are used, then processing speed is fast, but summary coherence deteriorates resulting in fragmented information
Solution Approach 1:
The patent replaces traditional rule-based or template-based summarization with LLM-based generative summarization. The LLM synthesizes information from multiple retrieved documents by understanding their semantic content and relationships, producing coherent and integrated summaries rather than fragmented concatenations, thereby improving summary quality
Data Source
AI summary
A method for multistage information processing includes receiving a user query from a user device; transforming the user query into semantic vectors in a high-dimensional space using a machine learning algorithm; comparing the semantic vectors to a database of pre-vectorized documents; ranking documents by closeness to the vectors to select a subset; generating metadata from the selected documents via a large language model; synthesizing the metadata into a comprehensive summary; and transmitting the summary to the user device in response to the user query.