What this category of data looks like
The data related to building engineering general contracting service consultation mainly comes from approval guidelines issued by housing and urban-rural development authorities, project bidding documents, construction process logs, completion acceptance reports, and project contract documents. Data update frequency is adjusted according to project progress. Progress-related documents are updated monthly during the construction phase, and archived and locked after completion of acceptance. A single document usually includes fields such as project number, section scope, approval node requirements, material list, and acceptance standards. Units mostly use standard engineering industry units such as square meters, calendar days, ten thousand yuan, and tons.
What constraints do these characteristics impose on the model access and configuration link
The service consultation data for building engineering general contracting has the characteristics of long single-document length, dense professional terminology, and dynamic updates with project progress, which impose multiple constraints on model access and configuration. Long text content requires that the context window configuration be adapted to the total length after splicing multiple documents to avoid truncation of key approval requirements or construction standards. Documents with dense professional terminology require adjustments to the model's rigor parameters, while tool calls need to adapt to the procedural approval node query logic. Dynamically updated data requires the knowledge base synchronization frequency to align with project construction nodes to avoid using expired approval guideline content. The configuration logic of FastGPT v4.17.0 must match these characteristics, otherwise incomplete replies or invalid citations may occur.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Building project-related documents are mostly long texts, which need to accommodate the spliced content of multiple approval guidelines and construction logs to avoid truncation of key information |
maxTokens | 2000–3000 token | Service consultation replies need to cover specific approval requirements and operation steps, controlling output length to ensure response efficiency |
quoteMaxToken | 3000–4000 token | Complete document citation fragments need to be retained to ensure that the approval basis in the reply can be traced, meeting the rigor requirements of engineering consulting |
toolChoice | auto | Service consultation involves multiple types of tool call requirements such as approval node query and material list verification, and automatic tool selection can improve response accuracy |
temperature | 0.3–0.5 | Service consultation requires rigorous and accurate professional content, avoiding overly divergent non-standard expressions |
AIPROXY_API_TOKEN | Fill in the exclusive token generated during deployment | Ensure identity verification security for model calls and prevent unauthorized access |
Three common mistakes
- Phenomenon: Extra meaningless number 0 appears in tool call return results. Cause: The call scenario of
toolChoiceis not restricted, and the model accidentally triggers irrelevant numerical processing tools. - Phenomenon: The complete approval basis content cited in the knowledge base is not included in the reply. Cause: The
quoteMaxTokenconfiguration value is too small, resulting in truncation of the long document fragment being cited. - Phenomenon: A prompt indicating missing model permissions appears during model calls. Cause: The
AIPROXY_API_TOKENconfiguration is incorrect, and a valid verification token generated during deployment is not used.
How to confirm the configuration is correct
- Initiate a test query containing long-text approval requirements, check that no key content is truncated in the reply, and confirm that the
maxContextandquoteMaxTokenconfigurations are adapted to the current data length. - Initiate a test query involving multi-stage approval consultation, check that the model automatically triggers corresponding tool calls, and confirm that the
toolChoiceconfiguration meets the scenario requirements. - View the interface return log after submitting the test request, check that the
AIPROXY_API_TOKENand identity verification information in the call link are normal. - Adjust the
temperatureparameter and initiate a test, check that the rigor change of the reply meets expectations, and confirm that the parameter configuration is adapted to business 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.