What this category of data looks like
Data for this category comes from the engineering construction project approval management system, construction drawing review system, enterprise qualification database, and credit platform. The update frequency is daily. Daily documents include construction permit applications, construction drawing review qualification certificates, quality and safety supervision procedures, completion record forms, and similar items. Data fields cover approval nodes, participating unit qualifications, personnel information, material submission status, and other categories. There are no additional custom statistical units. The length of individual documents varies widely, ranging from several pages of approval forms to dozens of pages of completion record materials. Mixed Q&A data sources must be formed by combining party building learning texts.
What constraints do these characteristics impose during model access and configuration
Scattered data sources with daily updates require model access to adapt to permission verification rules of multiple system interfaces, to avoid access restrictions triggered by cross-system calls. Large structural differences in daily documents require reserving flexible context processing space during configuration, to adapt to mixed input of different-length approval materials and party building learning content. Daily updated data requires configuring a reasonable recall refresh mechanism, to ensure Q&A content matches the latest approval processes and qualification requirements. Approval-related data involves government compliance, so the context scope of model calls must be strictly limited to prevent sensitive information leakage. This requires clearly defining context boundaries and call permissions during configuration.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context length of mixed party building learning texts and engineering approval documents, retains complete historical information for multi-turn conversations |
maxTokens | 2000–3000 token | Balances answer completeness and model call costs, avoids exceeding billing thresholds for single calls |
quoteMaxToken | 1500 token | Retains the original fragment length of approval documents and database returned content, achieves reference effects similar to RAG knowledge bases |
temperature | 0.3–0.5 | Adapts to the rigor requirements of government scenarios, reduces the probability of randomly generated content, ensures answers comply with official specifications |
toolChoice | auto | Triggers tool calls as needed, supports calling engineering approval data interfaces and party building knowledge bases, adapts to mixed Q&A scenarios |
AIPROXY_API_ENDPOINT | Fill in according to actual proxy address | Enables secure access to the model, ensures request forwarding complies with government network access rules |
AIPROXY_API_TOKEN | Fill in according to actual secret key | Completes permission verification for model interfaces, prevents access from being blocked |
Three common configuration mistakes
- The phenomenon: After locally deploying FastGPT, adding a new model for testing returns a 403 status code (no body). The cause:
AIPROXY_API_TOKENorAIPROXY_API_ENDPOINTis not configured correctly, leading to failed permission verification for the proxy interface. - The phenomenon: After calling MySQL data via Function CALL, the returned results do not include original database fragments. The cause: The
quoteMaxTokenparameter is not configured, so the effective reference length of database returned content is not retained. - The phenomenon: Multi-turn conversation content is lost when accessing the application via the API release channel. The cause: The
maxContextparameter is not set reasonably, leading to early truncation of old conversation history and inability to maintain context logic.
How to confirm the configuration is correct
- Test input including construction permit application content and party building learning-related questions is submitted, and returned results are checked for original fragments of approval documents.
- The configured model interface is called, and the returned status code is confirmed to be 200 with no abnormal error messages.
- Three consecutive conversations are initiated, and conversation context retention and answer logic consistency are verified.
- The
temperatureparameter is adjusted to different ranges, and the rigor of returned answers is verified to meet government scenario compliance requirements.
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.