Data Characteristics in This Category
Blood glucose data interpretation relies on several data sources: patient daily blood glucose monitoring records, physical examination reports, hospital medical records, and relevant medical guidelines and literature. Patient blood glucose monitoring data is typically structured time-series data, including fields such as date, time, blood glucose value (mmol/L or mg/dL), and measurement method (fasting, post-meal, etc.). Physical examination reports and medical records often exist as unstructured text, containing diagnostic results, medication details, and descriptions of complications, potentially mixed with tables and charts. Medical guidelines and literature are usually PDF or Word documents, containing extensive medical terminology, clinical pathways, and drug dosages. These documents have a relatively low update frequency but high authority. Data update frequency varies by source; self-monitored patient data can update multiple times daily, while medical guidelines might update annually or every few years.
Constraints Imposed by These Characteristics on "Document Parsing and Chunking"
The diversity of blood glucose data sources places complex demands on document parsing. For structured blood glucose monitoring data, precise identification of timestamps and unit values is necessary to ensure data integrity. In unstructured medical records, extracting key information from tables and images is challenging, especially when table structures are irregular or image quality is low, which can lead to data loss. Medical terminology and complex sentences in medical guidelines require the parser to have high semantic understanding capabilities to avoid misinterpretation. Furthermore, blood glucose data involves sensitive health information, requiring careful attention to privacy protection during chunking to avoid over-associating patient identity information with blood glucose data. The differing data update frequencies also require the system to flexibly handle incremental and full document updates, maintaining the timeliness of the knowledge base.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
chunk_size | 500-800 characters | Balances context completeness and recall efficiency, avoiding semantic fragmentation due to chunks that are too long or too short. |
chunk_overlap | 50-100 characters | Ensures contextual continuity at chunk boundaries, preventing critical information from being split. |
max_file_size_mb | 100 MB | Accommodates the size of medical literature and reports, preventing upload or parsing timeouts for large files. |
image_ocr_enabled | True | Ensures blood glucose charts and key text within images in physical examination reports and medical records are recognized. |
table_parsing_strategy | auto or ocr_only | Handles various table formats that may appear in medical reports, prioritizing data extraction. |
parse_timeout_seconds | 300-600 seconds | Addresses the time required to parse large PDF medical guidelines or complexly structured documents. |
Three Common Mistakes
- Poor recognition of tables and images in imported Word documents, manifested as jumbled table content or unextracted image text. This usually occurs when OCR and table parsing capabilities are not enabled or correctly configured, or when table structures in the document are overly complex or image clarity is low.
- Specific fields (e.g.,
references) are empty after parsing, making it impossible to retrieve original citation information. This might be because the default parser does not specifically handle this field, or the field's identifier in the document structure is unclear. - After document chunking, semantically closely related content is split into different chunks, leading to incomplete recall results. This typically happens when
chunk_sizeis set inappropriately, failing to adequately consider the paragraph logic and contextual relationships of medical texts.
How to Verify Configuration
- Select a typical blood glucose report or medical guideline containing complex tables, images, and medical terminology. Import it into the knowledge base and check the completeness and accuracy of the parsed text, especially critical blood glucose values and diagnostic information.
- For the parsed documents, randomly select multiple chunks and manually assess their semantic coherence. Ensure that each chunk can independently express a complete or relatively complete concept.
- Use test questions to simulate patient inquiries, asking questions that involve tables, images, or information spanning multiple chunks in the document. Observe whether the smart customer service's responses accurately cite and integrate relevant content, and adjust
chunk_sizeandchunk_overlapbased on the results.
The values provided are common starting points and should be measured against specific 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.