Data Characteristics
Intelligent triage regulation data in the biomedical field comes primarily from internal hospital management systems, regulatory documents, Standard Operating Procedure (SOP) files, and medical service guidelines. This data is typically unstructured text, PDFs, or Word documents. Update frequency is relatively low, usually quarterly or annually, coinciding with policy adjustments or process optimizations. Document structures often include chapters, articles, and appendices. SOP files detail operational steps, responsible parties, required tools, and time limits (e.g., 30 minutes). Fields and units may involve department names, job responsibilities, risk levels, and approval processes, often containing specialized terminology and abbreviations.
Constraints Imposed by These Characteristics on Tool Calling and Plugins
The low update frequency of intelligent triage regulation data means real-time knowledge base requirements are not high for tool calling. However, the ability to trace historical versions may be necessary. Complex document structures and extensive specialized terminology require tool calling and plugins to accurately identify document hierarchies during parsing. Semantic disambiguation through specialized dictionaries or contextual understanding is also crucial.
Furthermore, regulations contain structured information like operational time limits and risk levels. Plugins need the ability to extract these key entities from unstructured text and process them structurally for subsequent logical judgments or process triggers. For job responsibilities and approval processes, plugins may need to integrate with external systems to verify personnel permissions or query process statuses.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8192 | Accommodates the long-text characteristics of complex regulatory documents, ensuring contextual completeness. |
Similarity threshold (Similarity Threshold) | 0.75 | Ensures precision of recall results when querying specialized terms and detailed clauses. |
Recall count (Number of Retrieved Items) | 5 | Balances coverage while avoiding the recall of too many irrelevant or duplicate regulatory entries. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds (300 seconds) | Addresses potentially long parsing times for large regulatory documents, preventing timeout issues. |
Chunk size (Segment Length) | 500 characters (500 characters) | Balances text semantic integrity and retrieval efficiency, suitable for clause-based regulatory texts. |
Rerank result count (Number of Reranked Items) | 3 | Optimizes the final regulatory entries presented to the user, improving relevance. |
Common Pitfalls
- Symptom: The model makes factual errors or omits critical clauses when answering regulatory details. Reason: The knowledge base segmentation strategy is unreasonable, leading to fragmentation of important regulatory context. Alternatively, the
Similarity threshold(Similarity Threshold) is set too high, failing to recall sufficient relevant information. - Symptom: The AI cannot automatically trigger tools to query external approval processes or personnel permissions based on user questions. Reason: Tool descriptions and input parameters are not accurately defined, failing to establish effective links with relevant entities in the regulations (e.g.,
approver,process status). - Symptom: Uploaded regulatory files fail to process, showing
File size exceeds limit. Reason: The file size exceeds FastGPT's default or customUPLOAD_FILE_MAX_SIZEparameter limit, preventing successful import and parsing into the knowledge base.
How to Confirm Correct Configuration
- Randomly select several complex regulatory queries. Check if the model's answers accurately cite the corresponding regulatory clauses and verify the completeness of the cited original text.
- For scenarios involving external system calls, simulate user questions. Observe tool trigger records in the logs and verify that tool input parameters are correctly passed.
- Upload regulatory documents of different sizes and formats. Confirm that file parsing and knowledge base construction processes are error-free and that the knowledge base content matches the original text.
- Ask questions about specific specialized terminology and abbreviations. Check if the model correctly understands and provides relevant regulatory explanations. Adjust the
Similarity threshold(Similarity Threshold) if necessary.
Note: The values provided are common starting points. Measure them against your own samples to determine the optimal configuration for your specific use case.
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.