LLM Knowledge Retrieval With Hybrid Lexical-Semantic Ranking

The patent introduces an LLM-based embedding model as an intermediary between the query and the document corpus. This model transforms both queries and documents into vector embeddings, enabling semantic similarity search that captures the meaning and temporal relevance of information. The embedding model acts as a mediator that filters and ranks documents based on semantic relevance rather than just keyword matching, thereby improving relevance accuracy in large corpora.

The patent transforms the retrieval problem by changing the parameter space from keyword-based text matching to vector-based semantic space. By converting documents and queries into high-dimensional vector representations, the system can measure relevance through distance metrics in the embedding space, which captures temporal and contextual nuances that traditional algorithms miss.

The patent replaces traditional mechanical keyword-matching algorithms with an LLM-based embedding system that captures semantic meaning. Instead of relying on exact word matches, the system uses neural network embeddings to understand the semantic relationships between queries and documents, enabling the system to handle nuanced business queries with temporal aspects.

The LLM-based embedding model serves multiple functions: it transforms text to vectors, captures semantic meaning, handles temporal relevance, and works across different domains and query types. This universal approach replaces multiple specialized algorithms, making the system more adaptable to various business contexts while managing complexity through a unified framework.