AI Document Chunking with Resolver-Based Semantic Retrieval
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Solution Overview
Problem
Existing AI systems for document chunking lack the ability to leverage domain-specific knowledge of administrators, leading to suboptimal query responses due to inadequate handling of semantic coherence and document structure.
Innovation Solution
A customizable system that allows knowledge administrators to define document structures, semantics, and intents, enabling user-friendly customization and precise retrieval of relevant document chunks through user-provided intents and resolvers that adapt to document types and domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated chunking methods (size-based, paragraph-based, recursive character splitting) are used, then processing speed and automation are improved, but semantic coherence and domain-specific relevance deteriorate
Solution Approach 1:
The system enables domain experts to define custom chunking rules and resolvers that automatically adapt to their specific document types. The resolver framework allows self-configurable chunking strategies where administrators can encode domain knowledge without manual intervention during actual chunking operations, combining automation with domain-specific accuracy
Solution Approach 2:
The system dynamically adjusts chunking parameters based on document type, domain, and administrator-defined characteristics. Different resolvers can specify different chunk sizes, splitting strategies, and semantic thresholds, allowing the same automated system to produce domain-appropriate chunks by changing configuration parameters rather than using fixed rules
2Measurement precision
If generic NLP-based semantic chunking is used, then semantic understanding is improved, but domain-specific accuracy deteriorates due to lack of specialized knowledge
Solution Approach 1:
Instead of applying uniform NLP chunking to all documents, the system identifies document types and domains, then applies specialized chunking strategies tailored to each. Different resolvers for different document types (e.g., legal, medical, technical) ensure that domain-specific semantic characteristics are preserved, with each chunking method optimized for its target domain's unique structure and terminology
Solution Approach 2:
The resolver acts as an intermediary layer between generic NLP processing and domain-specific requirements. Resolvers translate domain knowledge into chunking instructions, mediating between the capabilities of general NLP models and the needs of specific domains, thereby enhancing domain-specific accuracy while maintaining semantic understanding
3Stability of the object's composition
If fixed document structure templates are used, then processing consistency is improved, but adaptability to internal variations within the same format deteriorates
Solution Approach 1:
The resolver framework provides dynamic configuration capabilities where chunking parameters and strategies can be adjusted based on the specific document content and domain characteristics. Instead of rigid fixed templates, the system allows administrators to define adaptable rules that maintain consistency for standard documents while automatically adjusting to handle variations in document structure, ensuring both stability and versatility
Data Source
AI summary
Systems and methods are provided to execute chunking of documents in accordance with a resolver selected in accordance with one or more document elements of the document. A server or other computing device then chunks the document, which may be initially chunked in accordance with static rules, and re-chunked to maintain logical associations and meanings between the otherwise separate chunks.