Document Enrichment via Source and Enrichment Index Synchronization
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Solution Overview
Problem
Current document indexing systems face challenges in efficiently enriching documents with additional data without significantly increasing pre-processing and indexing time, while ensuring accuracy and relevance of search results.
Innovation Solution
A method and system for enriching documents by generating enriched documents through determining reference data, applying an enrichment policy to add additional data fields, and indexing these enriched documents, which includes remote batch searches to retrieve additional data from a source index and store it in an enrichment index.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If documents are enriched with additional data from external sources, then the informativeness and accuracy of search results improve, but the pre-processing and indexing time increases substantially
Solution Approach 1:
The system performs enrichment operations in advance during indexing, before search queries are executed. By pre-fetching and caching additional data from external sources during the indexing phase, the system prepares enriched document representations ahead of time, so that search operations can directly utilize these pre-enriched documents without incurring additional network latency or processing delays during actual search execution
Solution Approach 2:
The system creates enriched copies of documents by combining original document content with additional data from external sources. Instead of modifying the original documents, the system generates enriched versions that include supplementary information such as entity descriptions, geographical data, or metadata from external APIs. These enriched copies are then indexed and used for search operations, allowing the original documents to remain unchanged while providing enhanced search results through the copied enriched versions
2Measurement precision
If remote searches are performed for each document to retrieve additional data, then the accuracy of enrichment improves, but the processing speed and efficiency decrease
Solution Approach 1:
The system merges multiple individual remote search operations into a single batched enrichment process. Instead of querying external data sources separately for each document, the system collects multiple document identifiers and performs a consolidated batch search against the external source. This merging of operations reduces the total number of network round-trips and leverages batch processing capabilities of external APIs, thereby maintaining accurate enrichment while significantly improving processing throughput and efficiency
Solution Approach 2:
The system implements continuous enrichment processing by maintaining persistent connections and session state with external data sources. Rather than establishing new connections for each search operation, the system keeps enrichment processes running continuously, allowing multiple documents to be enriched in an uninterrupted workflow. This continuous action eliminates the overhead of repeated connection establishment and maintains steady-state processing efficiency
Data Source
AI summary
Provided are systems and methods for enriching documents for indexing. An example method can include receiving a plurality of documents and generating a plurality of enriched documents. The generation of the plurality of enriched documents can include determining, based on a document of the plurality of documents, reference data, determining, based on the reference data and an enrichment policy, additional data, and adding the additional data to the document. Prior to the generation of the plurality of enriched documents, the method may index the reference data of plurality of documents to obtain a source index and generate, based on the enrichment policy and the source index, an enrichment index. The determination of the additional data may include reading the additional data from the enrichment index.