What this category of data looks like
The policy and regulatory basis data for engineering consulting supervision originates from engineering supervision specifications, industry standards, and local supervision implementation guidelines released by the Ministry of Housing and Urban-Rural Development of China and provincial housing construction authorities, as well as exclusive system documents archived by project supervision teams. National-level specifications are revised every 2 to 3 years. Local guidelines are updated alongside regional policy changes. Project-level systems are generated alongside project approval. Documents mostly follow a chapter-based structure, including sections such as general provisions, terminology, supervision scope, work procedures, and professional guidelines. Fields include document number, issuing authority, effective date, applicable scope, and specific clause content. Clause numbers use the standard format of "Article X".
Constraints imposed by these characteristics on multi-turn dialogue and prompt configuration
Policy data for engineering consulting supervision has hierarchical differences between national, local, and project levels, and falls under different professional domains such as housing construction and municipal engineering. Multi-turn dialogue must track contextual details such as the user’s requested region, project type, and effective date. This prevents returning policy clauses that cross hierarchical levels or do not match the query context. Individual policy documents can be lengthy. Context window capacity must be limited to avoid exceeding model processing limits. The supervision field contains many professional terms. Prompts must guide users to supplement necessary scenario information to reduce ambiguity.
How to configure settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to user session unique identifier | Distinguish multi-turn dialogue contexts across different users to avoid mixing session content |
messages | Retain the most recent 10 rounds of dialogue content | Track key information previously mentioned by the user, such as region and project type |
variables | Pass in region code, project specialty, effective date | Accurately match policy documents corresponding to the correct hierarchy, domain, and timeliness |
| Single recall count | Top 3 entries | Reduce interference from redundant policy entries and focus on core clause content |
| Question optimization | Enabled | Resolve ambiguity in supervision professional terms and optimize the expression accuracy of user questions |
| Maximum context length | 8000–12000 characters | Adapt to the conventional length range of individual engineering supervision policy documents |
Three common configuration errors
- Phenomenon: Policy clauses returned during multi-turn dialogue do not match the region and project type specified in prior questions. Cause: The
chatIdparameter is not correctly bound, andmessagescontext content across different sessions is not isolated. - Phenomenon: Multiple AI dialogue node policy query tasks cannot be executed in parallel. Cause: Parallel workflow nodes are not configured, and node trigger rules are not correctly set.
- Phenomenon: JPG-format supervision policy scan files cannot be uploaded directly in the AI dialogue module. Cause: Multimodal input configuration is not enabled, and a file parsing tool adapted to JPG format is not configured.
How to verify correct configuration
- Initiate multi-turn questions that include region and project type, and check if returned policy clauses match the preset
variablesparameter content. - View FastGPT v4.17.0 session logs to confirm that each dialogue is bound to a unique
chatIdidentifier. - Upload a JPG-format supervision policy screenshot, and check if the AI can correctly parse clause content from the image.
- Test parallel calls to multiple AI dialogue nodes, and confirm that contexts across each node do not interfere with one another, and returned results are independent and accurate.
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.