Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Rail transit equipment document data mainly comes from three sources: production process manuals, operation and maintenance inspection records, and compliance inspection reports. Updates follow new order deliveries, monthly inspection completions, and annual compliance reviews, with no fixed schedule. Document structures include core fields such as part number, working condition parameters, inspection items, and responsible department. Units are mostly engineering-specific units such as millimeters, kilonewtons, and operating hours. The length of a single document mostly falls within the 500-2000 character range.
What constraints do these characteristics impose on the HTTP Interfaces and External Systems link
The structured fields and engineering units of rail transit equipment documents require that field mapping rules be included in HTTP interface requests. This ensures external systems can correctly identify core information such as part numbers and working condition parameters. The wide range of document lengths requires setting reasonable request body size limits in interface configurations. The non-fixed update rhythm requires interfaces to support pulling incremental document data based on custom timestamps, avoiding resource waste from full-volume requests. At the same time, sensitive fields in compliance documents must enable encryption during interface transmission to meet industrial-grade data security requirements.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Authorization | Bearer {custom service key} | Verifies the identity of FastGPT and external systems, complies with industrial interface security specifications |
variables | {"part_no": "example value", "working_condition": "example value"} | Maps structured fields such as part numbers and working condition parameters of rail transit equipment documents |
responseData | Return fully processed document text | Adapts to the archiving and compliance submission requirements of rail transit equipment documents |
| Request timeout setting | 600 seconds | Reserves sufficient execution time for document processing that integrates multiple component data |
| Request body size limit | 1000 MB | Accommodates the transmission needs of batch rail transit equipment documents |
| Incremental pull switch | Enable by timestamp | Matches the non-fixed update rhythm of rail transit equipment documents |
Three common mistakes
- Phenomenon: The interface call returns the
getaddrinfo ENOTFOUNDerror. Cause: The domain name or IP address of the external system is not configured correctly, causing FastGPT to fail to resolve the target service address. - Phenomenon: The uploaded document file path carries an additional token parameter, causing the external service to fail to read the file normally. Cause: The relative path generated by FastGPT includes an access token by default, and the token suffix is not removed in the interface configuration.
- Phenomenon: Calling knowledge base-related interfaces returns a 406 status code. Cause: Request header parameters are not set correctly, or the request body format does not meet interface requirements.
How to confirm the configuration is correct
- Send a test request carrying the standard fields of rail transit equipment documents, and check whether the external system can correctly receive and parse information such as part numbers and working condition parameters.
- View the FastGPT interface call logs, confirm that the
Authorizationparameter is correctly carried, and there are no identity authentication failure records. - Upload a test rail transit equipment document, and check whether the returned processing result meets the expected document format and field requirements.
- Simulate a request with a non-fixed update rhythm, and confirm that the incremental pull function can correctly identify newly generated document data.
The values provided here 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 2026-09-28.