What This Type of Data Looks Like
Data for query result reports is sourced from internal archived system documents and regularly generated business statistical reports of central government-owned enterprises. Update frequency follows the business reporting cycle, covering monthly, quarterly, or on-demand updated compliance reports. Documents use structured table format with fixed header fields including report number, responsible department, generation date, core indicator values, and statistical caliber description. Most fields are text or numeric types, with units marked per business standards, commonly ten thousand yuan, person-times, and natural days. No unified global format exists; files must be read exactly as archived.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
The multi-field association feature of structured tables requires multi-turn dialogue to track report dimensions specified by users, to avoid cross-dimension confusion. The combination of fixed headers and numeric fields means prompts must clearly define the scope of extracted fields to prevent returning irrelevant content. Inconsistent update cycles require adding data timeliness checks in prompts, requiring returned results to include generation dates. For long-text statistical caliber descriptions, multi-turn dialogue must allow users to ask follow-up questions about caliber details to avoid missing information. No unified standard exists for internal data field naming, so prompts must adapt to the field naming rules of different archived files, requiring higher adaptability.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Fixed value bound to user session, valid within 24 hours | Matches the duration of a single business consultation for internal users, to avoid session confusion |
messages | Retain the most recent 10 rounds of dialogue content | Covers the context needs of user multi-turn follow-up questions, while controlling token consumption |
variables | Pass the current report's responsible department and statistical cycle parameters | Allows prompt engineering to accurately match the field rules of the corresponding data source |
| Question optimization toggle | Enabled | Adapts to complex questions about internal systems and reports, automatically breaks down long questions into executable query instructions |
| Maximum context length | 8000–12000 characters | Accommodates the fields and statistical caliber descriptions of structured reports, to avoid truncation of critical information |
| Prompt engineering version lock | Use the latest version of archived files | Ensures each call uses prompt engineering matching the current report format, adhering to the management rules of FastGPT v4.17.0 |
Three Common Configuration Errors
- Phenomenon: After calling report data in multi-turn dialogue, the result is not displayed in the dialogue box, and only becomes visible after re-entering the session. Cause: The session persistence configuration for
chatIdis not correctly bound, causing tool call results to not be associated with the current dialogue context. - Phenomenon: Irrelevant non-report field content is returned after prompt engineering is called. Cause: The business dimension parameters of the report are not passed in
variables, causing prompt engineering to fail to limit the query scope. - Phenomenon: Context truncation occurs after multiple follow-up questions, and critical statistical caliber is omitted. Cause: The maximum context length is set too low, failing to accommodate complete report fields and historical dialogue content.
How to Verify Proper Configuration
- Initiate a single-turn query, input the query instruction for the specified report, and verify that the returned result includes the preset fields and units.
- Initiate two rounds of follow-up questions, such as first querying a monthly report, then asking about the statistical caliber, and verify that the dialogue context is correctly retained.
- Check the session log to confirm that in the session bound to
chatId, tool call results are associated with the current dialogue, with no cross-session data confusion. - Adjust the maximum context length parameter, input a long-text query containing complete statistical caliber, and verify that the result is not truncated.
The values provided here 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.