What this category of data 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. Regularly maintained documents include public data resource catalogs, data open lists, data item descriptions, desensitization rules, interface documentation, and update frequency descriptions. Data updates occur on a monthly basis. Core fields include data item identifier, open subject, desensitization rules, call interface address, authorized usage conditions, and data update cycle. All fields use structured or semi-structured formats. They must comply with unified government data standards.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Data updates occur monthly, so multi-turn dialogue systems must regularly sync the latest public data catalogs and open lists. This ensures prompt references match currently active assets. Desensitization rules and authorized usage conditions are included, so prompts must actively verify user requests against open permissions during conversations. This avoids data leakage risks. Core fields include structured information such as data item identifiers and interface addresses, so multi-turn dialogue must guide users to explicitly specify specific data items. This prevents matching errors from vague requests. Documents from different sources must be sorted by priority. The most recently updated catalog content must be prioritized as the basis for dialogue.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Context window retention turns | 8–12 previous conversation turns | Adapts to the multi-turn interaction length of government business processes, prevents context overflow |
variables bound fields | Bind the unique identifier of the current month’s public data catalog | Ensures prompts always reference the latest updated catalog content |
messages system prompt rules | Require conversations to explicitly confirm the data item name before providing guidance | Matches the query requirements of structured data fields, reduces fuzzy matching |
chatId session permission isolation | Divide session groups by service scenario | Ensures data access permission isolation across different government service scenarios |
| Global data sync cycle | Execute once every 30 days | Matches the monthly update rhythm of this category of data, ensures content timeliness |
Three common misconfigurations
- Conversation response content does not match user questions, manifested as returning data item descriptions from other departments. The cause is that the prompt fails to explicitly limit the open subject of the current service scenario, resulting in matching irrelevant catalog content.
- Large model call response is empty, manifested as
chat:LLM_model_response_emptystatus. The cause is that no redundant protection for the context window is configured. When the number of conversation turns is too high, context overflow occurs, preventing the model from generating valid replies. - Global variables are not correctly bound, manifested as prompts referencing last month’s outdated data open list. The cause is that no scheduled task is set to sync the catalog version ID bound to
variables, resulting in delayed content updates.
How to confirm configurations are correct
- Trigger a test conversation, enter a specific data item name, and verify whether the returned content includes the current month’s update frequency description.
- View session logs to confirm that the
messagessystem prompt rules have been correctly injected at the start of the conversation and include clear scenario restriction requirements. - Check the global variable configuration page of FastGPT v4.17.0 to confirm that the catalog version ID bound to
variableshas an automatic sync task set with a 30-day sync cycle. - Simulate more than 8 consecutive turns of multi-turn dialogue, confirm that the system can correctly retain context and match corresponding data item information.
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.