Data Characteristics for This Category
Registration and declaration documents originate primarily from official regulatory guidelines, company submission materials, and internal R&D reports. These documents have a relatively low update frequency, typically updating with policy changes or R&D milestones. Document structures are highly standardized, such as the ICH E3 Common Technical Document (CTD) format for clinical study reports, which includes detailed sections, subsections, and appendices. Fields and units adhere to strict industry standards, for example, dosage units (mg/kg) and time units (hours/day) in clinical trial protocols, and P-values and confidence intervals in statistical reports. Documents frequently contain non-textual information like tables, charts, and flowcharts.
Constraints Imposed by These Characteristics on Vector Models and Indexing
The standardized structure of registration and declaration documents requires vector models to consider logical boundaries during segmentation, preventing content from different sections from mixing. The low update frequency means that after initial index construction, subsequent incremental update pressure is low. However, historical version management and traceability remain important. Strict field and unit standards, along with extensive non-textual information, demand multimodal processing capabilities from vectorization models. Pure text vector models may not fully capture all critical information. Furthermore, specialized terminology, abbreviations, and specific named entities common in these documents require vector models to possess strong domain knowledge understanding for accurate recall.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters | Registration and declaration documents often have high logical completeness within a single segment. Longer segments help preserve context and reduce information loss. |
Chunk overlap (Segment Overlap) | 100 characters | Ensures information continuity at segment boundaries, preventing critical information from being cut off. |
Vector Model (Vector Model) | General Multimodal Vector Model | Documents contain extensive non-textual information like tables and charts. Multimodal models can encode information more comprehensively. |
Recall count (Recall Count) | Top 5–8 entries (Top 5–8 items) | Registration and declaration content typically requires precise matching. Increasing recall count appropriately improves recall rate. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Adjust according to the specific business scenario's balance requirements for precision and recall, typically between 0.75–0.85. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing large declaration files requires longer parsing times. This prevents parsing failures due to timeouts. |
Common Pitfalls
- The indexing process is particularly slow, or timeout errors occur. This happens when
PARSE_FILE_TIMEOUT_SECONDSis set too low, and parsing large PDF or Word documents exceeds the expected time. - Retrieval results contain a large amount of irrelevant content, or critical information is missing. This occurs when
Chunk size(Segment Length) is set too short, leading to the fragmentation of logically complete semantic units and loss of contextual information. - Key data in tables or images cannot be effectively retrieved after text content vectorization. This indicates the use of a pure text vector model, which cannot process multimodal information.
Verification of Configuration
- Upload a typical registration and declaration document. Check if its segmentation is logical and if segment boundaries are reasonable.
- Perform retrieval tests for specific professional terms, regulatory clauses, or key data within the document. Check if the recall results include this information.
- Adjust the
Similarity threshold(Similarity Threshold). Observe changes in recall count and relevance until a balance is found that retrieves critical information without introducing excessive noise.
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.