Data Characteristics for This Category
Solid tumor-related protocols and SOP data primarily come from drug inserts and clinical guidelines published by the National Medical Products Administration (NMPA), as well as diagnostic and treatment norms and operating procedures developed internally by medical institutions. These documents are typically in PDF, Word, or internal knowledge base page formats. Data update frequency is relatively low, mainly occurring with new drug approvals, clinical research advancements, or policy and regulatory changes. Document structures are complex, containing extensive specialized terminology, dosage units (e.g., mg/kg, IU), time units (e.g., weeks, courses of treatment), and structured and unstructured information such as disease staging, treatment plans, and adverse reactions. Treatment plans often appear as flowcharts or tables, posing challenges for text comprehension and information extraction.
Constraints on Deployment and Upgrade from These Characteristics
The complexity of solid tumor protocol data imposes specific requirements on deployment and upgrade. Specialized terminology and abbreviations in documents require customized dictionary support to ensure accurate text segmentation and embedding. The low data update frequency means more resources can be allocated to fine-grained processing during initial deployment. Subsequent incremental update mechanisms must efficiently identify document version differences and integrate them. Diverse document formats demand robust file parser compatibility, especially for PDF files with embedded tables and flowcharts. The presence of dosage and time units necessitates pairing values with units during information extraction. This prevents comprehension errors due to missing or incorrect units, directly impacting vector database index construction and query recall effectiveness.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 200 MB | Solid tumor SOP documents may contain many images and charts, leading to large file sizes. Sufficient upload space is necessary. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Solid tumor protocol texts often have long paragraphs with closely related specialized content. Longer segments help maintain semantic integrity. |
Recall count (Recall Count) | Top 5 entries (top 5) | Protocol Q&A requires high accuracy. Increasing the recall count appropriately improves coverage, and reranking then enhances relevance. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Solid tumor terminology is extensive. Semantic similarity calculations need adjustment based on actual Q&A performance to avoid irrelevant recalls. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Parsing complex PDF documents and large Word files can be time-consuming. This prevents parsing failures due to timeouts. |
Rerank result count (Reranked Return Count) | Top 3 entries (top 3) | Based on high recall, reranking precisely filters the most relevant few items to improve answer accuracy. |
Three Common Mistakes
- After uploading knowledge base documents, the log shows
Exception: 'usage' Keye. This typically indicates incorrect or missing critical authentication information (e.g., API Key) in certain API calls, leading to interface authentication failure. - After deployment, Q&A results regarding drug dosages or treatment cycles are inaccurate. This often happens when document parsing fails to correctly identify and extract the association between numerical values and units, or the vectorization model inadequately understands specialized units in the domain.
- After creating a new knowledge base, certain key protocol clauses are not recalled during queries. This might be because the default segmentation strategy splits closely related clauses, breaking context, or the vector embedding model lacks sufficient sensitivity to specialized terminology in the solid tumor domain.
How to Confirm Correct Configuration
- Upload multiple solid tumor SOP documents containing complex tables and flowcharts. Check if all file parsing statuses are successful. Randomly select documents and preview their segmented content to confirm semantic integrity.
- Test the system's ability to accurately provide answers with correct units for questions involving specific drug dosages, treatment cycles, and other numerical and unit-based information. Cross-reference these answers with the original documents.
- Use a series of test questions containing specialized terminology related to solid tumor disease staging, treatment plans, and adverse reactions. Verify the recall quality and accuracy of the Q&A results, ensuring that the recalled knowledge points are highly relevant to the questions.
The values provided are common starting points and should be measured against the reader's 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.