Data Characteristics
On-call handoff data in the biopharmaceutical sector primarily consists of shift schedules and contact information. Shift schedules are typically in Excel or CSV format. They include staff names, contact details, shift dates, specific time slots, and potentially specialized fields or projects. This data updates frequently, usually weekly or monthly, with immediate updates for temporary changes. Contact information originates from internal employee directories, containing fields such as name, department, title, mobile number, and WeChat Work ID. The document structure is standardized. Field names like On-Call Staff, Start Time, End Time, and Contact Phone are clear. Time units are precise to the minute, and phone numbers follow standard mobile phone formats.
Constraints from Data Characteristics on Deployment and Upgrade
High update frequency for shift data requires a flexible data synchronization mechanism in the deployment solution. This avoids delays from manual imports. The system needs to support scheduled fetching or Webhook-triggered updates to ensure real-time shift information. The Excel/CSV data format implies a need for built-in or external tools for data parsing and structuring. This also addresses potential format inconsistencies or missing values. The presence of WeChat Work IDs in contact information is crucial for automated WeChat Work group handoffs. Deployment must ensure FastGPT can securely access the WeChat Work API and correctly map internal user IDs to WeChat Work IDs. Furthermore, shift information involves personnel scheduling, making the data sensitive. Deployment requires particular attention to data encryption and access control, ensuring only authorized modules and personnel can access and modify it.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 5 MB | Shift schedules are typically small files; this value covers most cases. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Allows sufficient parsing time for schedules with multiple sheets or complex formulas. |
maxContext | 8000 | Ensures complete loading and understanding of context information for a single on-call handoff scenario. |
Chunk size | 500 characters | Preserves the integrity of shift information entries, preventing truncation of critical data. |
Recall count | Top 5 entries | Quickly identifies current and backup on-call personnel, reducing redundant information. |
Similarity threshold | 0.75 | Improves matching accuracy, ensuring query results are highly relevant to the shift schedule. |
Common Pitfalls
- Symptom: Model testing shows "connection failed" or "service unavailable." Reason: After local deployment, Docker containers or the Ollama service did not start correctly, or port mapping configuration was incorrect. This prevents FastGPT from accessing the large model service.
- Symptom: Shift information is not updated in a timely manner; the system still displays old schedule data. Reason: The data synchronization task was not configured correctly or failed to execute. It did not retrieve the latest shift schedule from the source at the preset frequency.
- Symptom: Messages cannot be correctly handed off to the corresponding on-call staff in the WeChat Work group, or the handoff target is incorrect. Reason: The mapping between internal contact IDs and WeChat Work IDs is configured incorrectly, or WeChat Work API permissions are insufficient.
Verification Steps
- Manually upload the latest shift schedule. Verify that the data in the knowledge base is correctly parsed and retrievable.
- Simulate an on-call query in FastGPT. Verify that the AI Agent accurately identifies the current on-call staff and their contact information.
- Send a test message through the WeChat Work group. Confirm that the system accurately hands off the message to the on-call staff from the schedule, according to the configured logic.
- Check system logs. Confirm that data synchronization tasks executed successfully as planned, without error messages.
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.