What the Data for This Category Looks Like
Core data is generated across the full lifecycle of individual assessment projects. Data sources include in-house assessment tools, on-site interview records, system lists from the assessed parties, and security device logs. Four types of standardized documents are produced regularly: classification determination reports, gap analysis reports, rectification plans, and assessment reports. Each document corresponds to one independent assessment project. Data updates are only triggered by project progress or annual reviews, with no fixed cycle. Data fields include control item numbers, rectification statuses, evidence file paths, and assessment scores. There is no unified fixed unit, as values vary by document type and business segment.
What Constraints These Characteristics Impose on Deployment and Upgrade
Since data is updated independently per project with no fixed cycle, the deployment phase must support isolated storage for individual project data to avoid cross-project data confusion. Daily documents include multiple types of structured and unstructured content, so deployment must support import and parsing of reports in different formats. Rectification evidence collection relies on multi-source logs and list data, so deployment must interface with the log APIs of assessment tools and security devices. The upgrade phase must ensure compatibility with old project data, preventing historical assessment reports from becoming unreadable after version updates. Additionally, since errors may lead to regulatory inspection risks, post-deployment stability verification must cover all data flow links.
How to Set Configurations
The following configurations are adapted for the cybersecurity level protection assessment consulting scenario using FastGPT v4.17.0:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | Configure as an independent cluster connection string, assign a dedicated database prefix for each project | Adapt to the requirement of isolated data per project, avoid cross-project data confusion |
STORAGE_S3_ENDPOINT | Configure as a S3-compatible object storage endpoint, divide storage paths by project ID | Adapt to independent storage for multiple projects, facilitate classified management of rectification evidence |
DB_MAX_LINK | Set to 100–200 | Match database connection requirements for parallel access across multiple projects, prevent connection exhaustion |
REDIS_URL | Configure as an independent instance with password authentication, set memory quota to 8 GB–16 GB | Ensure response speed for session caching and vector search, support concurrent operations across multiple projects |
FILE_TOKEN_KEY | Set to a 32-bit random string, configure expiration time according to the project cycle | Restrict access permissions for file uploads, ensure storage security of rectification evidence |
ROOT_KEY | Configure a dedicated key per project, store in encrypted environment variables | Restrict access scope of core permissions, reduce risks of permission leaks related to regulatory compliance |
Three Common Misconfigurations
- Symptom: The vector database connection returns a
504 Gateway Timeouterror, and external log data cannot be retrieved. Cause: No HTTP proxy configured for the log source of the assessment tool, preventing normal access to external data. - Symptom: After configuring an English prompt, the model output remains in Chinese, and English documents parsed by the knowledge base are also converted to Chinese. Cause: No default language parameter for multilingual parsing specified in the
FE_DOMAINconfiguration, so the system enforces Chinese output. - Symptom: After uploading a PDF rectification plan file, the parsing result is an empty field. Cause: No valid expiration time configured for
FILE_TOKEN_KEY, causing the file upload permission to expire early and preventing completion of parsing.
How to Verify a Correct Configuration
- Perform a single-project data import test: import documents such as the classification determination report and gap analysis report, verify that documents can be stored normally and associated with the corresponding project ID.
- Initiate parallel log pull requests across multiple projects, check that the database connection count does not exceed the configured limit of
DB_MAX_LINK. - Trigger a test request for an English prompt, verify that the output language matches the preset requirements.
- Upload rectification evidence files, verify that files can be stored normally in the path specified by
STORAGE_S3_ENDPOINT.
The values given 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 2026-10-19.