What data for this category looks like
This category’s declaration materials and file data primarily come from internal enterprise R&D archives, third-party testing institution verification records, and compliance declaration documents. Data updates follow this rhythm: batch import when a project is initiated, small updates with supplementary review materials on an ongoing basis, and centralized annual archiving. Document structures include a cover page, declaration subject information page, equipment parameter table, multi-page test reports, approval process records, and scanned attachments. Fields have clear units: rated power is marked in kW, manufacturing batches use number formats, and test dates use standard date formats.
Constraints during model access and configuration
The long document structure and specific field formats for this category require models to use a long context window to accommodate complete file segments. They also require enabling a vision model for text extraction from scanned attachments. Fixed field units and format rules mean configured models must accurately identify and match core parameters, to avoid review conclusions that deviate from business standards. For non-public internal enterprise data, interface calls must use a dedicated proxy node to prevent data from leaving the internal network. The unpredictable data update rhythm requires configurations to support both batch and incremental data synchronization modes.
How to set the configuration
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 10000–14000 token | Power generation equipment declaration materials often include multi-page test reports and complete approval processes. A long context window can accommodate complete file segments and avoid truncation of key information |
maxTokens | 2200–2800 token | A single review response needs to cover parameter verification and compliance judgment. This value range can support complete conclusion output |
quoteMaxToken | 1200–1600 characters | It is necessary to fully quote the original content of core fields such as equipment model and rated power. This range can cover the length of a single-page core parameter table |
toolChoice | auto | It is necessary to automatically select whether to call a retrieval tool to match enterprise-specific file data based on review tasks, to avoid relying on a general knowledge base |
temperature | 0.1–0.3 | Review scenarios require strict alignment with compliance standards. This temperature range ensures the rigor and consistency of output results |
AIPROXY_API_ENDPOINT | OneAPI proxy address deployed on the enterprise internal network | Ensure that confidential equipment manufacturing data does not leave the internal network, and comply with enterprise data security specifications |
Three common configuration errors
- Setting
AIPROXY_API_ENDPOINTto a public network proxy address. This causes the interface call to return a 403 status code, as permission verification fails when the enterprise internal dedicated proxy node is not used. - Omitting the interface signature parameter when configuring
AIPROXY_API_TOKENin FastGPT v4.9.7-fix2. This triggers a request timeout error in logs, as the OneAPI interface specification is not followed during token configuration. - Failing to enable the
visionconfiguration. This causes uploaded scanned test report attachments to fail parsing, with the interface showing empty attachment content, as the vision model adaptation flow for scanned document text extraction is not enabled.
How to confirm successful configuration
- Initiate a simulated declaration material review task in FastGPT v4.17.0. Check if the interface return logs include successful model call records, to verify that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations are valid. - Upload a test material containing a scanned test report. Check if the model output includes text from the attachment, to confirm that the
visionconfiguration is active. - Input a review question with a specific equipment model and parameter. Check if the returned result references core fields from the corresponding file, to confirm that
maxContextandquoteMaxTokenvalues fit the document length. - Adjust the
temperatureparameter and run the test again. Observe changes in the rigor of the review opinion, to confirm that the parameter value meets the current review scenario requirements.
The values provided here 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 2026-09-28.