What the Data for This Category Looks Like
Data for survey and design documents and materials mainly comes from project survey reports, structural calculation documents, construction drawing design files, change negotiation records, and submission and approval documents. Data is updated alongside project design phases, with version iterations for preliminary design, construction drawing, and as-built drawing stages. Most documents use a mixed format of structured and semi-structured content, including dedicated fields such as survey hole coordinates, load parameters, and standard reference numbers. Units involved include engineering professional units such as meters, kilonewtons, and megapascals.
Constraints on Model Access and Configuration
The unique data characteristics of survey and design impose multiple constraints on model access and configuration. A high share of engineering professional terms and dedicated units requires model parameters to align with text understanding needs in the engineering field, preventing unit identification errors or term misunderstandings. Documents update alongside project design phases. Model calls must link to the correct version of data sources to avoid calling expired content. Individual documents such as structural calculation books have long lengths. Context window parameters must be adjusted to fully load key paragraphs, stopping content truncation that leads to incomplete generation results.
Configuration Settings
Configuration items under FastGPT v4.17.0 are as follows:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
AIPROXY_API_ENDPOINT | Enter the deployment address of a model dedicated to engineering scenarios, such as the interface of an enterprise internally deployed engineering large model | Adapt to the call path of survey and design dedicated models, ensuring correct request routing |
AIPROXY_API_TOKEN | Enter the authorization key for the corresponding model, configured according to internal enterprise permission rules | Ensure the identity legitimacy of model calls, preventing unauthorized access |
maxContext | 8000–12000 token | Adapt to the long length of survey and design documents, fully loading key paragraphs and historical context |
maxTokens | 2000–3000 token | Control the length of document content generated per round, avoiding exceeding the model output limit |
temperature | 0.2–0.4 | Reduce generation randomness, ensuring that the format and professional expression of engineering documents comply with specifications |
quoteMaxToken | 1500 token | Limit the length of referenced knowledge base content, avoiding redundant information interfering with document drafting |
Three Common Configuration Mistakes
- Model calls return a
401 Unauthorizederror, even after checking theAIPROXY_API_TOKENconfiguration. The cause is that the access whitelist for the enterprise internal model has not been added to FastGPT's authorization rules, leading to identity verification failure. - Model configuration address test fails, returning a connection timeout prompt. The cause is that the outbound port of the model interface has not been opened in the FastGPT deployment environment, or network policies restrict access paths to internal engineering models.
- After multiple model calls in a workflow, the generated survey report has unit confusion issues. The cause is that the
temperatureparameter has not been configured to a low range, leading the model to randomly output expressions using different units, failing to match the fixed unit specifications for survey and design.
How to Confirm Proper Configuration
- Perform a model interface test, check if the returned results include engineering professional terms and correct unit formats, confirming that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations are valid. - Import a typical survey and design document, test whether the document generated by the model fully loads key fields from the source document, confirming that the
maxContextparameter adapts to the document length. - Trigger multiple model calls for the same scenario, check whether the units and formats of the generated results are consistent, confirming that the
temperatureparameter settings meet scenario requirements. - View workflow logs, confirm that the context rounds of model calls match the preset configurations, confirming that the
maxContextassociation rules take effect.
The values provided 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.