What the data for this category looks like
Data for engineering machinery declaration material and file review mainly comes from enterprise internal equipment ledgers, project approval documents, factory quality inspection reports, and approved case archives. The data update rhythm is adjusted based on project progress. It is updated in batches during project declaration, and synced in batches during daily quality inspections. Each single document has a fixed structure: basic equipment information (model, serial number, manufacturing date), operating parameters (rated lifting capacity, working radius, with units of tons and meters), declaration subject information, and approval process records. Fields and units are fixed with clear corresponding relationships.
What constraints these characteristics impose on model access and configuration
The data includes structured parameters with fixed units and unstructured approval text. This requires models to accurately identify the corresponding relationship between units and values, so model output formats must be constrained. A single declaration case may include multiple pages of quality inspection reports and cross-node approval records. The context length exceeds general configuration thresholds, so context-related parameters must be adjusted. Declaration materials are updated in project batches. Stable API connection configuration is required during batch processing to avoid request timeouts. Case fields vary slightly across different projects, so model parameters must be adjusted to accommodate format variations.
How to set configurations
The following configurations are formulated for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | A single case contains multiple pages of quality inspection reports and approval records, requiring sufficient context capacity |
maxTokens | 2000–3000 token | Detailed review conclusions must be generated, while avoiding overly long outputs that exceed processing limits |
temperature | 0.1–0.3 | Accuracy of field extraction and review judgments must be ensured, preventing randomized output |
AIPROXY_API_ENDPOINT | Configure according to the internal enterprise deployment proxy address | Securely access internal data sources and avoid exposing sensitive business information |
AIPROXY_API_TOKEN | Generate an exclusive token based on enterprise permissions | Verify request legitimacy and ensure the security of data access |
toolChoice | auto | Automatically call necessary tools based on review tasks to improve review processing efficiency |
Three common mistakes
- A 404 status code with no response body is returned after configuring
AIPROXY_API_ENDPOINT. The cause is an incorrect proxy address, which does not point to the correct model service port or path. - Only reranking models appear in the channel model list, and general language models cannot be selected. The cause is that
AIPROXY_API_TOKENis not configured correctly, or the token permissions are restricted, preventing access to the general language model interface. - Incorrect units are extracted for equipment parameters, such as identifying "tons" as "kilograms". The cause is that
temperatureis set too high, leading to randomized model output and failure to accurately match the corresponding relationship between fields and units.
How to verify successful configuration
- Manually upload a single engineering machinery declaration case, and verify that the equipment model, serial number, operating parameters and other fields extracted by the model match the original document.
- Call the test interface to confirm connectivity between
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, with no error responses returned. - Adjust the
temperatureparameter, then compare the consistency of two review results to confirm stable format output. - Batch import declaration cases of the same type, and check that the model processes them without timeouts or format errors.
The values provided 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 2026-09-28.