What this category of data looks like
This category draws core data from local public data open platforms, integrated big data platforms, departmental business databases, and electronic credential repositories. Daily documents include public data resource catalogs, data open lists, data item descriptions, desensitization rules, interface documents, and update frequency descriptions, all in structured formats. These documents contain fields such as data item identifiers, open permissions, desensitization rule versions, interface call addresses, and update cycles. Data updates follow a monthly schedule, with no large volumes of unstructured free text. Fields and units adhere to unified government data governance specifications.
Constraints imposed on multi-turn dialogue and prompt engineering
Since data updates occur monthly, multi-turn dialogue must prioritize calling the latest catalog data to avoid referencing expired information that causes review errors. Since data primarily uses structured fields, prompt engineering must clearly define extraction rules to ensure accurate identification of fields such as data item codes, open conditions, and desensitization requirements. Since data sources are scattered across multiple platforms, multi-turn dialogue must gradually guide users to clarify parameters including data providing entities and interface types, to avoid ambiguous queries. Since desensitization rules and interface documents are core review content, conversation context must fully store relevant fields to enable traceable file reviews. This configuration uses FastGPT v4.17.0.
Configuration Settings
| Configuration Item | Recommended Practice | Rationale |
|---|---|---|
variables | Predefine 12 core field variables (data item code, open entity, desensitization level, etc.) | Match structured fields in daily documents to reduce variable extraction errors |
messages | Retain the most recent 20 turns of conversation context | Ensure the latest monthly updated data is referenced in conversations, avoiding expired information |
| Context window size | 8000–12000 token | Accommodate content from multiple interface documents and desensitization rules, supporting full process recording for file review |
chatId | Enable session ID binding and log storage | Ensure conversation records for file review are traceable, facilitating issue troubleshooting |
| Maximum retry attempts | 3 | Address query delays from cross-departmental data sources, preventing dialogue interruptions |
Three common configuration errors
- Issue: Extracted public data item fields (such as data item code, desensitization rules) are empty, or expired catalog data is matched. Cause: No
variablesextraction rule is preset for structured fields in public data catalogs, and prompt engineering does not clearly specify extraction requirements for structured fields. - Issue: No corresponding text is generated after voice input, and the session does not respond. Cause: No voice-to-text binding parameters are configured, and session state verification for
chatIdis not enabled. - Issue: Complete open condition parameters still cannot be obtained after multi-turn dialogue, leading to review failure. Cause: No guided prompt engineering is implemented, and parameters such as data providing entities and interface permissions are not gradually confirmed in dialogue via
variables.
How to verify configuration completion
- Initiate a test session, input "Query the public data open catalog of XX department", and verify that extracted
variablesinclude preset fields such as data item name and open entity. - Upload a voice test file, verify that corresponding text content is generated in the session, and that voice-to-text records are correctly stored in
messages. - Simulate a monthly data update scenario, input "Query the latest public data open list", and verify that returned content matches the preset latest catalog data.
- Confirm that the configuration enables
chatIdsession log storage, to ensure conversation records for file review are traceable.
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.