What this category of data looks like
This category of data is generated across the full lifecycle of data collection tasks. Sources include customer requirement documents, public compliance data sources, collection tool logs, and internal sample libraries. Daily output documents cover collection plans, sample specification lists, authorization description files, and task scheduling tables. Update frequency follows individual project cycles or weekly iterations. Data fields typically include sample scope, field format, compliance check items, scheduling nodes, and similar items. There are no unified fixed units. Matching the corresponding format requires consideration of specific collection scenarios.
What constraints these characteristics impose on the "model access and configuration" link
This category of data has scattered sources, and update rhythms fluctuate with projects. Model access must support format validation for multi-source data. Daily document structures vary widely. The configuration link must support parsing and adaptation of non-standardized text. Collection legality verification relies on accurate identification of compliance fields. The model must support matching verification between authorization descriptions and collection scopes. Single-project data volume fluctuates significantly. Configuration options must support flexible adjustment to adapt to task design reviews of different scales.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context length requirements of splicing multiple documents such as collection plans and authorization descriptions, and avoids truncating key information for compliance checks |
maxTokens | 2000–3000 token | Matches the output length of a single compliance review result, ensuring complete coverage of explanations for sample legality and authorization status |
temperature | 0.1–0.3 | Reduces output randomness, ensures consistency of compliance review results, and avoids misjudgments of authorization status |
AIPROXY_API_TOKEN | Non-empty administrator token consistent with AI Proxy server configuration | Environment variable for the App/Admin server, used for AI Proxy service authentication. Not an application call credential or model vendor key |
toolChoice | auto | Automatically triggers tool calls related to compliance verification, adapting to format validation and field extraction requirements for multi-source data |
Three common mistakes
- Symptom: The model directly returns a rejection reply stating that no matching content exists in the knowledge base, and cannot trigger custom compliance inference. Cause:
maxContextor context splicing logic is not configured correctly, causing historical authorization documents related to compliance to not be included in the retrieval scope. - Symptom: An identity verification failure error is returned when calling an external model channel. Troubleshooting items: Check whether the vendor credentials and channel address configuration of the upstream model channel are correct. Check whether the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENenvironment variables are correctly configured on the server. - Symptom: Random deviations appear in compliance review results, with inconsistent conclusions obtained for the same sample submitted multiple times. Cause: The
temperaturevalue is set too high, causing fluctuations in the model's judgment of authorization status and sample scope.
How to confirm configuration effectiveness
In FastGPT v4.17.0, configuration effectiveness can be verified through the following actions:
- Upload a single copy of the collection plan and authorization description document, and check whether the compliance review result returned by the model covers the key fields in the document.
- Adjust the value of
maxContext, and verify that key compliance check items can still be fully identified after long documents are spliced, with no critical information truncated. - Submit test samples containing abnormal compliance fields, and confirm that the results returned by the model are consistent with the preset verification logic.
- Verify that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENenvironment variables are correctly configured on the server. After the service startup verification is passed, initiate a call to confirm that normal model responses can be obtained, with no identity verification related errors.
The values provided are common starting points and should be evaluated against the deployer’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.