Document Parsing and Chunking for IVD Diagnostic Reagent

Data Characteristics

Data for IVD diagnostic reagent clinical trial pre-screening primarily comes from clinical trial protocols, investigator brochures, subject informed consent forms, and various laboratory test reports provided by sponsors. These documents are typically in PDF, DOCX, or XLSX formats, containing both structured and semi-structured data. While the core content of trial protocols remains relatively stable, amendments, updates to laboratory testing methods, and subject screening results may change dynamically as the trial progresses. Common fields in these documents include subject ID, age, gender, diagnostic results, specific biomarker concentrations, test method batch numbers, and reference ranges. Units include international units, standard concentration units (e.g., mg/dL, ng/mL), and qualitative results.

Constraints Imposed by These Characteristics on Document Parsing and Chunking

The heterogeneous nature of IVD diagnostic reagent clinical trial data requires document parsers to effectively handle different formats. Specifically, table data in laboratory test reports needs precise identification of column structures and row content to prevent data misalignment. For numerical fields like biomarker concentrations, unit consistency and value range validation are crucial. Parsing must either retain original unit information or standardize it. Long text descriptions in trial protocols, such as inclusion/exclusion criteria, require fine-grained chunking to maintain semantic integrity and prevent truncation of key conditions. The dynamic update frequency means the knowledge base must support incremental updates and version management, ensuring each pre-screening uses the latest available information. Furthermore, the prevalence of specialized terminology demands higher accuracy in word segmentation and entity recognition.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`Chunk Length`	`500–800 characters`	Inclusion/exclusion criteria and diagnostic descriptions in clinical trial protocols often contain multiple logical statements. This length helps maintain the integrity of a single logical unit.
`Chunk Overlap`	`50–100 characters`	Ensures contextual continuity, especially in conditional statements or descriptions spanning multiple paragraphs, reducing the risk of information loss.
`File Parsing Timeout`	`600 seconds`	Accounts for the time required to parse large clinical trial protocols (e.g., PDFs over 200 pages) or Excel files with numerous data rows.
`Vector Model`	`text-embedding-ada-002` or `bge-large-zh-v1.5`	Selects models with excellent pre-training performance for specialized terminology and complex semantics in the biomedical field, improving vectorization quality.
`Knowledge Base Type`	`Text Knowledge Base` + `Table Knowledge Base`	Addresses both unstructured text from clinical trial protocols and structured table data from laboratory reports.
`Parsing Mode`	`Smart Chunking` with `Table Recognition`	Comprehensively processes long text and table data, ensuring table content is effectively parsed and forms independent blocks.

Three Common Mistakes

When parsing lengthy Word documents or Excel files, File parsing failed or PARSE_FILE_TIMEOUT is reported. This usually occurs because the file size or complex internal structure causes parsing to exceed the File Parsing Timeout parameter setting.
In multi-turn conversations, queries involving specific biomarker values fail to accurately recall relevant content, or recalled numerical units do not match. This happens because document parsing did not sufficiently identify and retain the association between values and their units, or chunking was too coarse, separating values from their descriptions.
Results queried from a database node are JSON strings and cannot be directly used by the LLM for logical judgment. This is because database nodes return JSON by default, requiring explicit configuration of a code node for JSON parsing to extract the required field values.

How to Verify Correct Configuration

Upload a clinical trial protocol containing complex tables and multi-page text. Check the chunking results in the knowledge base to confirm that table data is correctly identified and converted into structured content, and that text chunks are semantically coherent.
Formulate a query with multiple conditions for specific subject inclusion/exclusion criteria. Observe whether the recall results cover all relevant conditions and check the completeness of the recalled chunks.
Simulate a real pre-screening scenario by inputting queries with ambiguous or abbreviated terminology. Verify if the system can recall correct content through semantic matching.
Through the API interface or management console, check the parsing accuracy of key fields in the knowledge base (e.g., biomarker names, reference ranges) and compare them with the original documents.

The values provided are common starting points and should be measured 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.