Data Characteristics
Infectious disease registration data comes from diverse sources. These include clinical trial reports, epidemiological data, in vitro antimicrobial susceptibility test results, pharmacokinetic/pharmacodynamic (PK/PD) model data, and guidelines and regulations from various domestic and international regulatory bodies. Data update frequencies vary. Clinical trial data typically updates in phases, epidemiological data may update annually or quarterly, and regulatory documents can be revised at any time. Document structures are complex, often containing extensive unstructured text (e.g., clinical investigator brochures, case report forms) and structured data (e.g., laboratory test results, adverse event lists). Fields and units are highly specialized, such as microbial species names, minimum inhibitory concentration (MIC) units (μg/mL), and drug exposure (AUC, Cmax). Naming conventions or unit conversion requirements may differ across regions or studies.
Constraints on Tool Calling and Plugins
The multi-source and unstructured nature of infectious disease data requires robust document parsing capabilities for tool calling and plugins. This is particularly true for complex tables and mixed text-image content in PDF and Word formats. Rapidly updating epidemiological data and regulatory documents necessitate plugin support for periodic or on-demand data fetching and knowledge base update mechanisms. Variations in specialized fields and units demand advanced semantic understanding and entity recognition from tools to prevent data misinterpretation or calculation errors due to terminology confusion or inconsistent units. Furthermore, the strictness of registration documents makes traceability and confidence assessment of tool call results critical. Each call must clearly indicate data sources and processing logic to meet compliance requirements.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000 tokens | Infectious disease registration documents are extensive and context-dependent, requiring a larger context window for complex logical reasoning. |
Chunk size (Segment Length) | 500–700 characters (characters) | Balances semantic completeness and retrieval efficiency. Avoids overly long segments that dilute key information and overly short segments that break core concepts. |
Similarity threshold (Similarity Threshold) | 0.78–0.82 | Ensures precision of retrieved content, filtering out text snippets irrelevant to highly specialized infectious disease queries. |
Recall count (Number of Retrieved Items) | Top 8–12 entries (top 8–12 items) | Considering the depth and breadth of infectious disease knowledge bases, increasing the number of retrieved items improves coverage for complex queries. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Processing large clinical trial reports or regulatory documents can take a long time. This prevents processing failures due to timeouts. |
External Tool Call Frequency | Calibrate by actual measurement (calibrate based on actual measurements) | Access limits for external microbial databases or epidemiological data interfaces vary. Adjust based on actual API restrictions. |
Common Pitfalls
- A 422 error code from a model call typically indicates an unexpected API request body format. This could be due to incorrect parameter types or missing required fields.
- Missing critical specialized terms or data in knowledge base answers may result from an improper document segmentation strategy, causing relevant information to be split or overlooked during retrieval.
- Image processing failures in multimodal conversations, displaying a 400 invalid image error, may relate to incompatible image encoding or exceeding image file size limits.
Verification Steps
- For typical queries, verify the accuracy of knowledge points cited in generated answers and trace them back to the correct original document source.
- Simulate common questions from registration documents to confirm that tool calls correctly trigger external data interfaces and return expected microbiological or epidemiological data.
- Check knowledge base update logs to confirm that newly published infectious disease regulations or guidelines have been successfully ingested and indexed as planned.
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.