Dynamic Source Document Indexing for Automated QA
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Solution Overview
Problem
Automated question answering systems face challenges with stale and outdated information due to dynamic source documents, leading to administrative burdens and increased data security risks from caching secondary copies.
Innovation Solution
A system that generates an index of question-answer pairs based on ingested source documents with location metadata, allowing for real-time retrieval of current information from dynamic source documents, while transforming text-based index data into non-human readable formats for enhanced security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If source documents are cached for answering questions, then answer generation speed is improved, but information freshness deteriorates
Solution Approach 1:
The system pre-generates question-answer pairs from source documents and stores them in an index with location metadata before queries arrive. When a query is received, the system quickly retrieves relevant pre-generated answers using the location metadata to extract and return the appropriate text segments, eliminating the need for real-time document processing while maintaining answer freshness through selective retrieval.
2Loss of time
If cached copies of source documents are stored, then response time is improved, but storage burden increases
Solution Approach 1:
The system extracts only the essential location metadata (such as document identifiers, section markers, or position information) from the source documents and stores these compact references in an index, rather than caching entire document copies. This extraction approach dramatically reduces storage requirements while enabling rapid retrieval of specific answer segments when queries are processed.
3Productivity
If text-based index data is stored in readable formats, then retrieval efficiency is improved, but data security deteriorates
Solution Approach 1:
The system applies different quality characteristics to different parts of the index data structure. Location metadata that facilitates efficient retrieval (such as document IDs, section markers, or position information) is stored in human-readable formats for quick processing, while sensitive text content is transformed into non-human readable encrypted or obfuscated formats. This local differentiation maintains retrieval efficiency for structural metadata while protecting sensitive information.
Data Source
AI summary
A method including generating an index comprising (a) question-answer pairs generated based at least in part on a set of ingested source documents and (b) respective location metadata associated with each of the question-answer pairs. The respective location metadata can include a source document identifier of a first ingested source document of the set of ingested source documents and one or more location delimiters indicating a location of target text in the first ingested source document. The target text can correspond to a stored answer of a respective one of the question-answer pairs associated with the respective location metadata. The method also can include receiving a user question from a user. The user question can correspond to a question of a first question-answer pair of the question-answer pairs of the index. The method additionally can include determining a first answer to the user question based at least in part on the respective location metadata associated with the first question-answer pair. Other embodiments are disclosed.