Data Characteristics in This Category
Complaint ticket data in the biopharmaceutical industry originates from multiple channels. These include customer service call records, email exchanges, online form submissions, and third-party regulatory platforms. Data update frequencies vary; urgent complaints may be recorded in real-time, while routine inquiries or suggestions might be imported in batches daily or weekly. Ticket document structures typically include standardized fields and unstructured text. Standardized fields might cover ticket ID, patient ID (desensitized), drug name, complaint type (e.g., drug quality, adverse reaction, logistics issue), submission time, processing status, and handler. The unstructured text section contains detailed patient descriptions, customer service communication logs, and investigation reports. Field units are precise: time is usually recorded as "YYYY-MM-DD HH:MM:SS," quantity units involve "boxes," "tablets," "milliliters," and temperature or dosage units include "Celsius" or "milligrams."
Constraints on "Tool Calling and Plugins" Imposed by These Characteristics
The multi-source nature and varying update frequencies of complaint ticket data require tool calling to have flexible data interface adaptation capabilities. This integrates ticket information from different systems. High real-time requirements for complaint processing necessitate tools that can trigger and respond instantly. The mix of structured and unstructured information in ticket documents means that when extracting key information, tools must parse standardized fields. They also need to use natural language processing to extract important context such as drug batch numbers, specific symptoms, and medication history from patient descriptions, providing a basis for subsequent decisions. The standardization of fields and units places clear demands on data validation and conversion after tool calls. For example, ensuring consistent drug dosage units avoids processing errors due to unit confusion. Furthermore, the desensitization of patient IDs strictly requires tools to comply with data privacy regulations during calls.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 800–1200 characters | Complaint ticket descriptions are often long; sufficient context must be retained |
Recall count (Recall Count) | Top 10 | Ensures coverage of relevant drug information, historical tickets, and processing guidelines |
Similarity threshold (Similarity Threshold) | 0.75 | Balances recall precision and coverage, avoiding interference from irrelevant information |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles attachment documents that may contain large amounts of text, such as inspection reports |
enable_tool_call | True | Ensures external systems can be triggered for ticket queries or status updates |
Three Common Mistakes
- Symptom: AI response does not mention ticket ID or drug name, but this information is clearly present in the original text. Reason: Flawed parameter extraction logic before tool calling, failing to correctly parse or pass key structured fields.
- Symptom: System attempts to call a non-existent tool or plugin, resulting in a
ToolNotFoundExceptionerror. Reason: Incorrect tool definition or registration path configuration; the model cannot correctly match or access the declared tool. - Symptom: Ticket status update tool frequently times out, ultimately failing to modify the status. Reason: External ticket system interface responds slowly, or the
request_timeoutparameter is set too low, not allowing sufficient response time.
How to Verify Configuration
- For different types of simulated complaint tickets, observe whether the AI accurately identifies and extracts fields such as drug name, complaint type, and key timestamps.
- In a test environment, simulate triggering the ticket query tool. Check if the returned ticket details match the expected data source and verify field completeness.
- By simulating a call to the ticket status update plugin, observe whether the status changes in the external ticket system align with FastGPT's output, and check the API return status code.
The values provided are common starting points. Measure them against your 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.