Semantic Tree Content Chunking for Low-Latency AI Retrieval
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Solution Overview
Problem
Existing AI content management platforms face inefficiencies in computational resources, high latency, inconsistency, and lack of reuse across use scenarios due to full document processing and inconsistent document segmentation.
Innovation Solution
A semantic-tree-based AI content management approach that decomposes content into discrete chunks, constructs a semantic tree, and uses a retrieval-augmented generation pattern to process these chunks, enabling efficient, consistent, and scalable use of generative models across scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full document is provided to LLM for processing, then complete content analysis is achieved, but computational resources increase significantly and latency increases
Solution Approach 1:
The patent segments the full document into multiple content chunks and builds a semantic tree structure where each node represents a chunk or group of chunks. The LLM processes only relevant chunks based on the query rather than the entire document, reducing computational load and latency while maintaining analysis completeness through the semantic tree's hierarchical organization.
2Measurement precision
If full document is provided to LLM for processing, then complete content analysis is achieved, but computational resources are consumed excessively
Solution Approach 1:
The document is divided into semantic chunks organized in a tree structure, allowing selective processing of only relevant portions based on user queries. This segmentation enables the system to avoid processing entire documents, significantly reducing computational resource consumption while maintaining analysis quality.
Solution Approach 2:
The semantic tree is pre-built and stored before queries are submitted. This preliminary organization of content into hierarchical chunks with metadata allows the system to quickly identify and retrieve only the relevant portions needed for each query, avoiding redundant computational work.
3Adaptability or versatility
If document segmentation is performed without standardized approach, then flexibility in processing is maintained, but consistency across different calls deteriorates
Solution Approach 1:
The patent implements a universal semantic tree structure that serves multiple functions: it organizes content hierarchically, stores metadata for retrieval, and provides a consistent framework for different query types. This universal structure ensures consistent segmentation and processing across different calls while maintaining the flexibility to handle various document types and queries.
4Ease of operation
If cached results are not reused across use scenarios, then each scenario can be processed independently, but computational efficiency decreases
Solution Approach 1:
The semantic tree structure serves as a universal cached representation that can be reused across multiple use scenarios including Q&A, summarization, and content transformation. Once built, the same semantic tree and its chunks can serve different queries and scenarios without requiring independent processing, thereby improving computational efficiency while maintaining scenario independence through the retrieval-augmented generation pattern.
Data Source
AI summary
A data processing system implements receiving a call requesting a generative model to generate a semantic tree for a source content; constructing a first prompt including the source content and instructions to the model to analyze a semantic structure of the source content and to generate a semantic outline and content chunks of the source content, the semantic outline including one or more topics each connected with one or more of the content chunks, to compute one summary for each of the content chunks, to apply indices to reference each topic node of the semantic tree to one of the topics, and to apply indices to reference each leaf node of the semantic tree to one of the content chunks and the respective summary; providing the first prompt to the model and receiving the semantic tree of the source content; and storing the semantic tree in a database.