What this category of data looks like
Data for this category originates primarily from project document libraries, configuration lists, meeting minutes, and test reports. Updates occur after each project phase delivery or when an individual project is finalized. Daily generated documents include completion documents, configuration manuals, training records, and acceptance certificates. Common data fields include project unique identifier, document version number, signature verification status, and archiving timestamp. Units are mostly standard document formats such as PDF, DOCX, and timestamp formats, with no custom special units.
Constraints Imposed by These Characteristics on HTTP Interfaces and External Systems
Data sources are scattered and updates have no fixed cycle. Interfaces must support on-demand pulling of documents from multiple sources, and must be compatible with document parsing for different formats. Single archiving data volume may fluctuate with project scale. Interfaces must support segmented transmission or batch submission. Documents include verification fields such as signature and version. Interfaces must pass verification-related parameters to ensure archived data compliance. Acceptance-related documents must be synchronized to client-side systems. Interfaces must support two-way data synchronization, and must retain operation logs for subsequent verification.
Configuration Settings
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
Authorization | Include a Bearer token with a validity period set to 7200 seconds | Matches the single operation duration for project document handover, preventing token expiration from interrupting the archiving process |
variables | Pass two core variables: project number and document type | Accurately locate archived documents for the corresponding project, preventing data confusion |
responseData | Only return archiving status codes and failed document lists | Reduce invalid data transmission, adapting to the lightweight requirements of operation report summary |
| Batch submission threshold | 100 items per request | Balances interface load and archiving efficiency, adapting to the batch handover scenario for project documents |
| Timeout period | 600 seconds | Covers transmission and verification time for large project documents, preventing mid-process timeout failures |
Three Common Misconfigurations
- After an interface is called, SQL query results cannot be displayed in the AI dialog box. The cause is incorrect configuration of field mapping for
responseData, and failure to bind query results to conversation context parameters, resulting in returned data not being loaded by the AI. - The archiving verification model generated by the interface changes. The cause is failure to fix the model parameter in
variablesin the configuration, causing the system to call the latest version model by default, which does not match the preset verification logic. - Timeout is triggered when batch submitting archived documents. The cause is that the total document size for a single submission exceeds 1000 MB, and batches are not split for transmission, exceeding the interface's default load limit.
How to Confirm Configuration is Correct
- A small test call is executed, passing a known project number and document type, and checking if
responseDatareturns correct archiving status and corresponding document list. - Interface logs are reviewed to confirm that the
Authorizationtoken is properly included in the request header, and that its validity period does not expire during the call. - Batch test data is submitted, the failed document list returned by the interface is compared with actual documents to be archived, and the batch submission threshold is adjusted to a value matching the current project scale.
- In AI conversation scenarios, SQL query results returned by the interface are verified to be properly loaded into the conversation context, with no missing fields or format errors.
The values provided are common starting points and should be evaluated against samples from the actual deployment environment.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.