What this type of data looks like
Data for policy and regulatory basis questions related to building construction general contracting comes primarily from publicly released official documents of the Ministry of Housing and Urban-Rural Development, housing and urban-rural development authorities of all provinces and cities, and compliance guidelines issued by industry associations. This data updates whenever new policies are released, with no fixed update cycle. A single document typically includes fields such as policy document number, issuing authority, effective date, applicable scenarios, and specific clause content. Clauses follow a hierarchical structure of chapters, articles, and paragraphs. Some documents include quantitative description fields for project pricing, qualification requirements, and other details.
What constraints these characteristics impose on model access and configuration
The complex hierarchical structure and high proportion of long text in policy documents require models to support long-context processing to avoid truncating key clauses. Policies vary across different provinces and cities, so recall logic that can flexibly switch data sources must be configured. Policy updates have no fixed cycle, so a fast configuration process for incremental data access must be supported. Some policies include quantitative fields such as project pricing and qualification requirements. Models must accurately associate these fields with question-and-answer scenarios, so semantic matching parameters need to align with the semantic recognition accuracy of long text.
How to configure these settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long-text structure of building construction policy documents to avoid truncation of key clauses |
maxTokens | 2000–3000 token | Balances answer length and complete reference to policy clauses |
quoteMaxToken | 1500–2000 characters | Matches the length of core clauses in a single policy document to ensure complete referenced content |
temperature | 0.1–0.3 | Reduces answer randomness to ensure accuracy of policy references |
AIPROXY_API_ENDPOINT | Fill in according to the deployed proxy address | Adapts to local or private deployment model call links |
AIPROXY_API_TOKEN | Fill in according to the key assigned by the platform | Ensures identity verification security for model calls |
Three common configuration errors
- Attempting to switch vector models without correctly binding a dedicated dataset results in recalled policy documents that do not match the building construction general contracting scenario. This occurs because the policy data source released by the Ministry of Housing and Urban-Rural Development is not associated with the vector model call configuration.
- Testing model calls returns a 403 status code (no body). This happens because
AIPROXY_API_TOKENor the proxy address is configured incorrectly, leading to identity verification failure. - Conversation prompts fail to accurately recognize space-separated clauses in policy text, resulting in disordered clause hierarchies in question-and-answer results. This occurs because the prompt does not explicitly require the model to retain space formatting in text to distinguish clause hierarchies.
How to confirm successful configuration
- Upload a single long-text building construction policy document, trigger a recall test, and check whether the returned referenced content is complete and not truncated.
- Call the model to test policy question-and-answer, and check whether the returned results accurately reference the hierarchy and document number of policy clauses.
- View model call logs to confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations have taken effect, with no identity verification errors. - Adjust the
temperatureparameter, then run multiple question-and-answer tests to confirm that result consistency meets expected standards.
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.