Follow-up Reminders for Private Domain Consultation

Data Characteristics

Follow-up reminder data for private domain consultation conversion in the biomedical sector originates from internal Customer Relationship Management (CRM) systems, Sales Force Automation (SFA) platforms, and compliant online consultation records. Data updates occur in real-time or near real-time. For example, a follow-up task generates immediately after a customer submits a consultation, or a reminder triggers when a sales representative updates communication records.

Data is typically stored in structured JSON or XML format. Key fields include:

patient_id: Unique identifier for the patient or consultant.
consultation_id: Consultation session ID.
consultation_time: Consultation time, in ISO 8601 format.
followup_due_date: Follow-up due date.
priority_level: Priority level (e.g., "High", "Medium", "Low").
assigned_agent: ID of the AI or human agent assigned to the follow-up.
consultation_summary: Text summary of the consultation.

Time fields are typically precise to the second. The priority field uses enumerated values.

Constraints from "HTTP Interface and External Systems"

The real-time or near real-time update frequency of follow-up reminder data requires the HTTP interface to have high availability and fast response capabilities to prevent reminder delays. Structured data simplifies FastGPT's parsing of incoming data. However, it requires precise mapping of key business identifiers like patient_id and consultation_id to ensure reminder accuracy and uniqueness.

The consultation_summary text field means the interface must handle variable-length strings. This may involve text summarization or keyword extraction for FastGPT to better understand the follow-up context. The priority_level field determines the urgency of the reminder. It must be passed as a parameter during interface calls and may influence FastGPT's internal task scheduling strategy. Additionally, pharmaceutical industry compliance requirements, such as data anonymization or encryption, may impose extra constraints on HTTP request headers or body format.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`HTTP Method`	`POST`	Follow-up reminders typically involve creating or updating data; `POST` is semantically appropriate.
`Request URL`	`https://api.example.com/followup/reminders`	Clearly points to the external system's endpoint for receiving follow-up reminders.
`Content-Type`	`application/json`	External system data is commonly in JSON format, facilitating parsing and processing.
`Timeout`	`6000 ms`	Balances network latency and external system processing time, preventing premature termination due to excessive waiting.
`Max Retries`	`3`	Addresses transient external system failures, improving the success rate of reminder delivery.
`Rate Limit`	`100 requests/minute`	Aligns with the external system's API rate limiting policy, preventing blocking due to request overload.

Common Pitfalls

HTTP 502 Error: This occurs due to connection issues between FastGPT's configured proxy or gateway and the external target service. This could be incorrect proxy configuration or network routing problems.
Follow-up Reminder Not Triggered On Time: This happens when the followup_due_date field in the data returned by the external system has an unexpected format. This leads to FastGPT parsing failures or incorrect timestamp calculations.
Empty assigned_agent Field: This occurs when the HTTP interface's returned JSON structure lacks this field, or the field path mapping is incorrect. This prevents FastGPT from identifying the assigned follow-up agent.

Verification Steps

Manually trigger a follow-up reminder in the FastGPT interface. Then, query the external system's logs or database to confirm data reception and correct field content.
Use FastGPT's debugging tools to check if the HTTP request's Request URL, HTTP Method, and Content-Type match the external system's API documentation.
Simulate follow-up reminders with different priority_level values returned by the external system. Observe if the priority of related tasks in FastGPT is set as expected.
Review FastGPT's request history. Check if the HTTP interface's response status codes are all 200 OK or other success codes, and if the response body content matches expectations.

The values provided are common starting points. Measure them against your own samples to determine the most suitable configuration.

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.