Knowledge Base Retrieval and Recall for Laboratory Service Registration and Declaration Document Preparation

Data Characteristics

Laboratory service registration and declaration document preparation primarily uses data from experiment reports, testing methods, instrument validation records, quality control documents, SOPs (Standard Operating Procedures), regulations, standards, and guidelines. These documents are typically in PDF, DOCX, and XLSX formats. Their content structure varies. Experiment reports contain fields such as batch numbers, test items, parameters, results, units, and analysis conclusions. These reports update frequently with new experiment batches or method optimizations. Regulations, standards, and guidelines update less frequently, but an update has a broad impact. Documents often include specialized terminology, abbreviations, and complex tabular data, covering biology, chemistry, and physics.

Constraints on Knowledge Base Retrieval and Recall

The diversity and complexity of laboratory service data impose specific requirements on knowledge base retrieval and recall. High-frequency numerical data and specialized terminology in experiment reports require the knowledge base to support fine-grained word segmentation and numerical range retrieval. This ensures timely and accurate retrieval results. The semi-structured nature of many SOPs and regulatory documents demands that the knowledge base supports semantic understanding, not just keyword matching. It must identify different expressions for the same concept across documents. For example, a single test indicator might have different units in different reports. The retrieval system must handle unit conversions to prevent recall failures due to unit inconsistencies. Embedded charts and tabular data in documents increase the difficulty of text extraction, affecting segment completeness and contextual relevance.

Configuration Settings

Common Pitfalls

• Retrieval results contain many irrelevant or outdated experimental data points. This indicates incomplete data cleaning or a knowledge base that is not updated promptly, leading to a mix of old and new data.
• Retrieval time fluctuates significantly for the same query. Document parsing parameters are not optimized, resulting in inefficient processing of large documents, or an unreasonable index reconstruction strategy.
• Knowledge base answers fail to accurately cite regulatory clauses or cite incorrect versions. This suggests insufficient document metadata management, a failure to differentiate regulatory versions effectively, or overly coarse-grained segmentation leading to missing context.

Verification of Configuration

• Perform multiple queries for core information, such as key regulatory clauses and testing methods. Check the completeness and relevance of the recall results. Compare them with manually verified results to assess the reasonableness of the relevance threshold.
• Select different types and sizes of experiment reports and SOP documents. Test the knowledge base's document parsing and index construction speed. Observe for timeouts or parsing errors. Adjust parameters like PARSE_FILE_TIMEOUT_SECONDS accordingly.
• Simulate users with different permission levels. Attempt to create, modify, or query knowledge base content. Verify that permission control functions as expected. For example, confirm if team members can create content normally.
• Randomly select a batch of queries. Record the response time for each query. Analyze for abnormal delays. Check logs to identify the cause of delays, such as whether maxContext is too small, leading to multiple fragmented retrievals.

The values provided are common starting points. Measure them against your own samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.