What data in this category looks like
Data in this category primarily originates from databases, data dictionaries, masking tools, and access approval systems. Updates are performed once per batch task or project. Daily associated documents include masking plans, field mapping tables, masking validation reports, and approval records. Data fields cover sensitive information types, mapping rules, validation results, and approval statuses. There are no fixed units, with adjustments made flexibly based on project configuration. Data volume fluctuates according to project scale.
What constraints these characteristics impose on model integration and configuration workflows
Scattered data sources and per-project updates require model integration to adapt to multi-source data formats, and avoid hardcoding for single data sources. Daily associated document structures include configuration items such as field mappings, requiring models to support custom field recognition and mapping rule parsing. The sensitive field recognition, masking rule configuration, and availability validation steps account for a large share of total time. This requires model integration to provide a lightweight rule debugging entry, while the validation step must interface with output results from masking tools to ensure configuration takes effect. Approval records must be synchronized to model configuration logs for compliance traceability, so model integration must support associating approval status fields to enable full-process traceability.
How to set configurations
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context requirements of multi-field mapping in masking plans, avoiding truncation of critical rule information |
maxTokens | 2000–3000 token | Matches the output length of masking validation reports, ensuring complete return of validation results |
AIPROXY_API_ENDPOINT | Fill in the AI Proxy service root address according to the deployment environment, e.g. http://aiproxy:3000 | An environment variable for the App/Admin server side, adapting to access paths for local or cloud deployments to ensure reachable AI Proxy service calls. Do not append /v1 or /chat/completions; this is not a single-model address for VLLM/Ollama/Xinference, etc. |
AIPROXY_API_TOKEN | Non-empty string matching the AI Proxy server-side configuration | An environment variable for the App/Admin server side, serving as the AI Proxy administrator token. This is not an application API Key, model vendor secret, or token issued per application |
temperature | 0.1–0.3 | Reduces randomness in rule parsing, ensuring consistency of field mapping and masking rule configurations |
toolChoice | auto | Automatically select the appropriate masking rule verification tool without manual specification |
quoteMaxToken | 500 token | Limits the length of referenced field mapping tables, avoiding redundant information interfering with configuration parsing |
Three common configuration errors
- Symptom: New vendor options such as Volcano Ark cannot be found in the model channel configuration. Cause: Custom vendor access permissions were not enabled in the channel management page of FastGPT v4.17.0.
- Symptom: No image indexing model option appears when uploading documents for local deployment versions. Cause: The extension module required for this function was not deployed with the base package; the corresponding plugin must be installed manually.
- Symptom: Garbled characters are returned by the knowledge base thinking model. Cause: The configured
temperatureparameter is outside the reasonable range, themaxContextparameter was not set correctly during model integration leading to context truncation, or the server-side environment variablesAIPROXY_API_ENDPOINTandAIPROXY_API_TOKENwere not configured correctly leading to startup verification failure and inability to call the model normally. Normal operation resumes after replacing with a non-thinking model.
How to confirm configuration is complete
- View the new vendor list on the channel management page to confirm the target vendor has been added to the configuration items.
- Upload a batch of test masking documents and check whether the field recognition results match the preset field mapping table.
- Run a masking validation task and check whether the output result contains the complete validation report content.
- Check the server-side environment variable configuration to confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENhave been set correctly. - Check the model call logs to confirm that the request parameters fully match the configured values.
The values listed are common starting points and should be evaluated 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.