Data Characteristics in this Category
Quality documentation in nursing management primarily includes nursing regulations, operational Standard Operating Procedures (SOPs), checklists, risk assessment scales, nursing record templates, and training materials. Data sources are diverse; hospital nursing departments or individual units typically create and update these documents. The update frequency is relatively stable, usually quarterly or annually, with ad hoc adjustments for policy changes or major events. Document structures are often hierarchical Word, PDF, or Excel files. Content includes extensive professional terminology, process descriptions, and standard values. Fields include "patient identification code," "nursing level," "risk score," "operational steps," and "observation indicators." Units often involve time (minutes, hours), quantity (times, milliliters), and scores (0-5 points).
Constraints Imposed by these Characteristics on Tool Calling and Plugins
The hierarchical structure and specialized terminology of nursing management quality documents require tool calls to precisely target specific information segments, avoiding general responses. The relatively fixed document update frequency means the knowledge base update mechanism must balance efficiency and accuracy, ensuring that called information is always the latest version. Extensive process descriptions and standard values necessitate that plugins can parse complex instructions and execute multi-step operations, such as automatically recommending appropriate nursing SOPs based on risk assessment results. Furthermore, sensitive patient information in documents demands robust permission management and data anonymization for tool calls to ensure compliance. Recognizing specialized fields and units requires plugins to accurately understand their meaning, preventing misinterpretation or incorrect execution.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 2000 characters | Ensures complete transmission of complex processes and standard descriptions, preventing information truncation. |
similarityThreshold | 0.78 | Balances recall rate and accuracy, filtering out irrelevant nursing document fragments. |
recallCount | top 8 | Covers multiple highly relevant potential matches, improving recall quality. |
pluginTimeout | 60 seconds | Allows plugins sufficient time to execute complex queries or data processing tasks. |
UPLOAD_FILE_MAX_SIZE | 50 MB | Accommodates the upload of large PDF or Word documents containing images and tables. |
PARSE_FILE_TIMEOUT | 300 seconds | Ensures large or complex quality documents are fully parsed. |
Common Pitfalls
- Symptom: Tool calls return incomplete nursing operation SOPs or missing critical steps. Reason: The
maxContextparameter is set too low, truncating document segments and failing to capture the entire SOP content. - Symptom: When uploading Excel format checklists in a custom workflow, tool calls cannot correctly extract numerical variables. Reason: When referencing variables in file upload parameters during tool calls, ensure variable types match. The file parsing plugin must also support structured data extraction for that specific file format.
- Symptom: Installing a database connection plugin results in a "server internal error" or connection timeout. Reason: The database connection string or credentials configured for the plugin are incorrect, or the FastGPT server cannot access the target database service port.
Verification Steps
- Upload a typical nursing management SOP document. Use question-and-answer testing to verify that critical steps and standard values are accurately recalled.
- Simulate a scenario requiring complex logical judgment. Observe whether tool calls correctly execute multi-step operations and return expected results based on input parameters.
- Check FastGPT system logs to confirm no abnormal errors or timeout warnings occur during tool calls and plugin execution.
- Perform permission testing to verify that users with different roles correctly adhere to preset data access and operation permissions when calling tools.
The values provided are common starting points. Measure against specific samples to determine optimal configurations.
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.