Tool Calling and Plugins for Respiratory System Products

Data Characteristics

Data for respiratory system products and reagents comes from various sources. These include clinical trial reports, drug inserts, medical device registration certificates, academic papers, patent literature, and industry standards. Data update frequencies vary. New drug approvals, clinical research progress, and guideline revisions can cause rapid local data updates. Basic pharmacological and toxicological information remains relatively stable.

Document structures differ. Drug inserts typically include standardized fields like indications, dosage and administration, adverse reactions, and contraindications. Medical device registration certificates focus on product performance, technical specifications, and scope of application.

Common fields include Active Ingredient, Dosage, Mechanism of Action, Target, and respiratory physiology parameters such as FEV1 (Forced Expiratory Volume in 1 second) and FVC (Forced Vital Capacity). Units strictly follow pharmaceutical and medical standards, such as mg (milligrams), ml (milliliters), breaths/min (breaths per minute), and L (liters).

Constraints on Tool Calling and Plugins

The diverse sources and varying structural complexity of respiratory product data require robust data parsing capabilities for tool calling and plugins. These tools must handle various formats like PDF, HTML, and XML.

Some clinical data updates rapidly. This demands real-time data synchronization or periodic fetching. Plugin design must consider API access frequency limits and authentication mechanisms of data sources.

Standardized fields enable precise JSONPath or XPath extraction of key information. However, unstructured text content, such as adverse reaction descriptions, still relies on the LLM's understanding for summarization. Accurate recognition and contextual understanding of medical terminology and abbreviations (e.g., COPD, ARDS) are crucial for tool calling preprocessing.

Patient safety is paramount. This requires high accuracy and consistency for data. Any unit conversion and validation for numerical fields (e.g., dosage) must be strictly controlled before or after plugin execution.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`2000-3000 characters`	Ensures coverage of key sections of a complete product insert, reducing semantic understanding errors due to insufficient context.
`Chunk size` (Segment Length)	`800 characters`	Balances semantic completeness and model processing efficiency. Avoids diluting key information with overly long segments or losing context with overly short segments.
`Recall count` (Recall Count)	`Top 8`	Given the specialized and interconnected nature of respiratory product information, this increases recall to cover more relevant clinical data or product details.
`Similarity threshold` (Similarity Threshold)	`0.78`	Improves matching accuracy for medical terms and professional descriptions, ensuring high relevance between recall results and user queries.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Provides sufficient file parsing time for large PDF clinical trial reports or product manuals, preventing timeout interruptions.
`API_KEY_MANAGEMENT`	`Unified Key Management Platform`	Ensures centralized and secure storage and rotation of authentication keys for various third-party APIs (e.g., drug administration databases, professional literature search).

Common Pitfalls

Third-party API calls return 401 Unauthorized or 403 Forbidden errors. This typically indicates incorrect or expired API_KEY or token configurations, or insufficient access permissions.
Plugin execution returns empty or incomplete fields. This occurs when JSONPath or XPath expressions do not accurately match the target data structure, or when the original data source format changes.
The model provides incorrect or irrelevant suggestions after calling a plugin. This often happens when the model misunderstands the data returned by the plugin, potentially due to ambiguous medical terminology or unstandardized data units.

Verification Steps

Use FastGPT's debugging interface for core API calls. Verify the validity of each API_KEY and the response data structure.
Simulate typical respiratory product query scenarios. Check if key fields returned by the plugin (e.g., Active Ingredient, Dosage) match expectations. Verify unit accuracy.
Monitor plugin call success rates and average response times using FastGPT's logging system. Evaluate the reasonableness of parameters like PARSE_FILE_TIMEOUT_SECONDS.

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.