What this category of data looks like
Data for engineering consulting supervision policy and regulatory basis Q&A comes from industry standards, supervision procedures, project approval policy documents issued by the Ministry of Housing and Urban-Rural Development and local housing and urban-rural development authorities, as well as national-level engineering construction-related laws and regulations. Updates are triggered when new policies are released or existing policies are revised, with no fixed cycle. Document structures include fields such as document number, issuing authority, effective date, applicable scenario, clause text, and attachment list. The length of a single document ranges from hundreds to tens of thousands of words. Most fields are text-based, and some include tabular clause details.
What constraints do these characteristics impose on the model integration and configuration link
The data source consists of official authoritative documents, so returned results must include accurate source citations. Model integration therefore requires configuring parameters that support traceability binding. Document lengths vary widely and include nested clause structures, with some documents containing tabular details. Integration must support long-text parsing to avoid truncating key clause content. Updates have no fixed cycle, so flexible data source synchronization configuration must be supported to ensure timely synchronization of policy document updates to the knowledge base. Most fields are text-based, with some including structured tables, so configuration must support accurate extraction and restoration of tabular content.
How to set configurations
All configurations below follow the specifications for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Engineering consulting supervision policy documents are mostly long texts, requiring space for complete policy clauses and context to avoid truncation of key information |
maxTokens | 2000–3000 token | Single-round Q&A requires outputting policy interpretations and corresponding clauses. Output length must cover complete responses without exceeding model limits |
quoteMaxToken | 1500 token | Reserve sufficient quota for traceability citations to ensure complete display of cited original policy text |
temperature | 0.1–0.3 | Policy Q&A must strictly follow original text expressions. Lower temperature reduces irrelevant generation and improves answer accuracy |
toolChoice | auto | Automatically select whether to call retrieval tools based on user questions, adapting to different Q&A scenarios |
AIPROXY_API_TOKEN | Exclusive security token generated during deployment | Used for interface identity verification to ensure call security |
AIPROXY_API_ENDPOINT | Exclusive interface address corresponding to the deployment environment | Used to specify the routing address for model requests, ensuring proper call routing |
Three common configuration errors
- Symptom: Empty content returned after calling the model, or retrieved policy clauses not displayed properly. Cause:
maxContextorquoteMaxTokenparameters not configured correctly, leading to truncation of long texts and failure to extract complete policy content. - Symptom: Chaotic format of Q&A results, with nested clauses unable to be split correctly. Cause: Not adapted to the nested structure of engineering consulting supervision policy documents, long-text parsing configuration not enabled, leading to loss of clause hierarchy.
- Symptom: Frequent timeouts or failures in interface calls. Cause:
AIPROXY_API_TOKENorAIPROXY_API_ENDPOINTnot configured correctly, leading to identity verification failure or routing errors, unable to connect to the model service normally.
How to confirm successful configuration
- Upload a locally stored engineering consulting supervision policy document, initiate a Q&A targeting the document content, and verify whether the returned result includes accurate original text citations.
- Adjust the
temperatureparameter and initiate multiple rounds of Q&A for the same question, observe whether the answer content remains consistent to verify the stability of the parameter configuration. - Replace the test policy document to verify whether the model can correctly parse file content of different lengths and structures.
- Check the interface call logs to confirm that there are no format errors in the configuration of
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINT, and that the call path is normal.
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.