Data Characteristics in This Category
Regulatory and SOP documents in the autoimmune disease field originate from drug regulatory agencies, industry associations, and pharmaceutical companies' internal quality management systems. These documents typically update quarterly or semi-annually, with more frequent updates during major regulatory changes. Regulatory documents often structure content by chapters and clauses. SOPs usually contain operational steps, responsibility assignments, and risk controls. Data fields include disease names, drug mechanisms of action, indications, contraindications, adverse reactions, dosage and administration, and storage conditions. Units involve dosage (mg, μg), concentration (%), time (hours, days), and temperature (°C). Document lengths range from a few pages to hundreds of pages, often containing extensive specialized terminology and cross-references.
Constraints Imposed by These Characteristics on Vector Models and Indexing
The specialized and complex nature of autoimmune documents demands high semantic understanding from vector models. Dense specialized terminology and frequent abbreviations require models to accurately capture contextual semantics, avoiding recall bias due to lexical ambiguity. Cross-references and hierarchical structures in documents mean vector indexes must consider both individual text block semantics and their relation to chapters or clauses, supporting traceability. High update frequency necessitates efficient incremental update capabilities in the indexing system to ensure knowledge base timeliness. Furthermore, structural differences between document types, such as the rigor of regulatory files and the operational nature of SOPs, impact text chunking strategies. This requires fine-grained processing to ensure appropriate chunk granularity, avoiding both loss of critical information and introduction of excessive noise.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 500–800 characters | Balances semantic completeness with indexing efficiency, reduces cross-segment context loss |
Chunk Overlap Rate | 10–20% | Ensures semantic continuity between adjacent segments, improves recall accuracy |
Vector Model | DeepSeek-v2-32k | Adapts to long texts and complex semantics, supports multilingual specialized terminology |
Recall count | 8–12 entries | Covers potentially relevant information, balances recall volume with subsequent processing burden |
Similarity threshold | 0.75–0.85 | Filters low-relevance results, reduces interference from irrelevant information |
Rerank result count | 3–5 entries | Selects the most relevant results, improves the accuracy of the final answer |
Three Common Mistakes
- Poor relevance of query results: This occurs when text chunking is too fine or too coarse, causing individual vector blocks to either fail to represent complete semantics or contain too much irrelevant information.
- Inability to trace answers back to specific document sources: This happens when the indexing process does not retain mapping relationships between original documents and chunks, or when generating answers without citing corresponding source text blocks.
- Query results remain outdated after knowledge base updates: This is due to the indexing system not being configured for scheduled incremental update tasks, or update mechanisms having delays.
How to Verify Correct Configuration
- Select complex questions from typical regulatory and SOP documents. Perform multiple rounds of questioning to check consistency between answers and source documents.
- Verify that the traceability information cited in answers accurately points to specific paragraphs or sections of the original document.
- After a knowledge base update, immediately conduct query tests to confirm that the latest content has been correctly indexed and can be recalled.
Note: 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.