What this category’s data includes
Data sources for this category cover local public data disclosure platforms, integrated big data platforms, departmental business databases, and electronic credential repositories. Daily maintained documents include public data resource catalogs, data disclosure lists, data item descriptions, desensitization rules, interface documentation, and update frequency descriptions. Data updates occur once per month. Documents contain structured fields: data item unique identifiers, open and sharing scopes, desensitization processing methods, interface call addresses, and update cycles. Some fields include unit annotations, such as interface concurrency limits and data storage capacity.
Constraints imposed by these characteristics on multi-turn dialogue and prompt engineering
The monthly data update requirement mandates that multi-turn dialogue explicitly limit queries to datasets that have completed monthly synchronization, to avoid requesting unupdated raw data. The variety of document types requires prompts to distinguish between usage scenarios such as public data resource catalogs and interface documentation, and guide calls to information from corresponding sources. The multi-source data characteristic requires multi-turn dialogue to gradually confirm the department owning each dataset, to prevent cross-departmental data mix-ups. The requirement for structured fields with unit annotations requires prompts to enforce that output results include matching units, such as marking interface concurrency limits as "times/second". The ledger summary scenario also requires multi-turn dialogue to gradually collect data item information from each department, to ensure complete summary content.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context | 8000–12000 token | The number of documents for public data catalog and disclosure is large, so the context must hold multi-turn dialogue history and multiple business document contents |
| Question Optimization | Enabled, calibrated via actual testing | Optimize vague user questions about government data catalogs and interfaces, to adapt to professional terminology scenarios |
chatId | Bound to the current ledger summary session | Ensure context association for multi-turn dialogue, avoid mixing data across sessions |
messages | Retain the most recent 20 dialogue records | Cover the multi-turn confirmation and information supplement steps needed for monthly ledger summary |
variables | Preset the data update cycle as "monthly" | Force queries to only datasets that have completed monthly synchronization, to ensure compliance |
| Single Search Limit | Top 10 data items | Match the single-page display specification of public data catalogs, avoid excessive redundant output |
Three common configuration errors
- Phenomenon: Multi-turn dialogue output includes the model's thinking process, rather than only business results. Cause: The prompt does not explicitly require hiding the thinking process, or the hidden logic is not preset via
variables. - Phenomenon: Data interface call results do not match expected counts, often exceeding 10 entries. Cause: No single search limit is set, or the limit exceeds the public data catalog's single-page display specification.
- Phenomenon: Dialogue queries data items that have not completed monthly updates. Cause: The data update cycle is not preset as "monthly" via
variables, so the model calls unsynchronized raw data.
How to verify correct configuration
- Initiate a query about "open data items of each department this month", and check that the dialogue history retains full context.
- Confirm that output results include unit annotations for corresponding fields, such as the unit for interface concurrency limits.
- Check that the configuration binds an exclusive
chatId, to prevent mixing session context with other tasks. - Trigger multi-round follow-up questions, and confirm that the model gradually supplements missing information required for the ledger, such as desensitization rules and update frequencies.
Note: The configuration values listed above are common starting points, and should be evaluated against relevant specific 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.