Data Characteristics in This Category
Registration and declaration documents in the biomedical field primarily include clinical trial data, non-clinical study reports, manufacturing processes, quality control, stability studies, and drug labels. Data originates from diverse sources, involving hospitals, laboratories, production workshops, and research institutions. Update frequency typically correlates with R&D progress and regulatory requirements; for example, clinical trial data updates periodically, while manufacturing process changes may trigger unscheduled updates. Document structure is highly standardized, adhering to guidelines issued by national drug regulatory agencies (e.g., FDA, EMA, NMPA), such as the Common Technical Document (CTD) format. Field names are standardized and highly specialized, involving terminology from pharmacology, toxicology, pharmacokinetics, and statistics, with units specified precisely (e.g., micrograms, milliliters, moles).
Constraints Imposed by These Characteristics on Tool Calling and Plugins
The standardized document structure of registration and declaration materials imposes clear format requirements for tool calling. For example, parsing CTD-formatted PDF files requires precise identification of section headings, table contents, and image captions. The irregular nature of data updates demands tools capable of flexible incremental data import and version control to prevent errors due to outdated data. Highly specialized fields and units mean that data extraction or conversion requires a large pre-loaded industry dictionary and unit conversion rules to avoid semantic misunderstandings or unit errors. Additionally, due to data sensitivity and compliance requirements, tool calls must ensure data flow security and traceability, such as audit logs for external API calls. Tools require extremely high accuracy and robustness; even minor data discrepancies can affect submission outcomes.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 4000–8000 tokens | Addresses context requirements for complex reports while balancing processing efficiency. |
Chunk size (Segment Length) | 500–800 characters | Balances semantic integrity of text with recall accuracy, reducing information loss. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Improves accuracy of relevant information recall, avoiding interference from irrelevant content. |
Rerank result count (Reranked Results Count) | 5–10 items | Provides sufficient reference information while maintaining relevance. |
API_TIMEOUT_SECONDS | 60 seconds | Accommodates potential network latency when querying external tools or databases. |
MAX_RETRIES | 3 times | Enhances tool call stability, handling transient network or service failures. |
Three Common Mistakes
- Tool calls return empty results or error codes, often due to parameters passed to the tool not conforming to expected formats, such as incorrect date formats or missing required fields.
- When parsing lengthy clinical trial reports, the tool fails to correctly identify all table data, leading to the omission of critical statistical results. This often occurs because the segmentation strategy does not adequately account for tables spanning multiple pages or complex nested structures.
- Querying an external database for drug approval numbers returns results that do not match expectations. This may be because query parameters are not standardized, such as a drug name having multiple aliases, but the tool only recognizes one.
How to Confirm Proper Configuration
- Simulate submissions with test cases containing common fields and various data types to verify the tool can correctly parse and extract all key information.
- Review tool call logs to confirm that each API call returns the expected status code and that no timeouts or error retries occurred.
- Randomly select multiple declaration documents in different formats, run the tool, and manually compare its output with the original documents to ensure data extraction completeness and accuracy. For example, verify that p-values for critical experimental data match.
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.