What Data in This Category Looks Like
Data for this category primarily comes from local public data open platforms, integrated big data platforms, departmental business databases, and electronic credential repositories. Maintained documents include public data resource catalogs, data open lists, data item descriptions, desensitization rules, interface documents, and update frequency descriptions. Data updates occur once per month. Each document includes structured fields such as data item name, open conditions, desensitization requirements, call interfaces, and update cycle. Some documents include interface call examples and permission descriptions.
What Constraints Do These Characteristics Impose on Model Access and Configuration
The monthly update schedule requires model access configuration to align with the sync cycle, to avoid using expired catalog content. Structured document fields and interface descriptions require configuration to support accurate recall of specific field content, and to verify data open conditions and desensitization rules. Multi-source data requires unified context management, to avoid model output confusion caused by differing data formats across departments. Additionally, the parameter complexity of interface documents requires configuration to limit the length of single references, to prevent context overflow that affects recall effectiveness.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length of public data catalogs and open lists, avoids truncating core permission and interface information |
maxTokens | 2000 token | Covers the complete output of a single data item description, including desensitization rules and call parameters |
quoteMaxToken | 1200–1500 characters | Limits the length of a single referenced data piece, ensures multiple data items can be recalled simultaneously for comprehensive output |
temperature | 0.2–0.4 | Maintains accuracy of data output, reduces generation of irrelevant content, meets the strict requirements of government data |
AIPROXY_API_ENDPOINT | Fill in the official address provided by the FastGPT v4.17.0 platform | Ensures normal model call connectivity, adapts to access rules for public data interfaces |
AIPROXY_API_TOKEN | Fill in the exclusive key assigned by the platform | Ensures identity verification security for interface calls, prevents unauthorized access |
Three Common Configuration Mistakes
- Symptom: A connection error appears during debugging. Cause:
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENis not filled correctly, or the interface access permission of the public data open platform is not granted to the configuration node. - Symptom: The model only returns the single text block with the highest matching degree. Cause:
quoteMaxTokenis not configured properly, a single data item occupies all available context, making it impossible to recall multiple results for comprehensive output. - Symptom: Unable to polish and expand the public data description content output by the model. Cause: The
toolChoiceconfiguration is not enabled, or the complete public data catalog and open list are not included in the model context scope.
How to Confirm Proper Configuration
- Check whether
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatch the information issued by the FastGPT v4.17.0 platform, initiate a test call to confirm return of a normal structured data response. - After configuring
maxContextandquoteMaxToken, import a single data open list, test whether the number of recalled results meets the expected multi-block data output. - Set
temperatureto the 0.2–0.4 range, test whether generated public data description content remains accurate and free of redundancy. - Enable the
toolChoiceconfiguration, test whether the model can complete the specified polish and expansion operation based on the imported catalog content.
The values provided here are common starting points and should be evaluated against relevant 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.