What Data for This Category Looks Like
Data is primarily sourced from the national integrated government service platform's "One Task" special zone, local integrated government service platforms, electronic certificate repositories, and departmental approval systems, with daily updates. Commonly used documents include joint task lists, one-time notice sheets, joint application forms, material transfer forms, parallel approval opinions, and completion receipts. Document structures typically include fields such as cross-departmental task associations, material reuse rules, and parallel approval nodes. There is no unified fixed format, so adaptation to heterogeneous document formats from multiple sources is required.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
Because data is updated daily, multi-turn dialogue must support dynamic loading of the latest joint task rules and approval node information, to avoid using outdated review standards. Heterogeneous document structures require prompts to have adaptive parsing capabilities, without fixed field mapping in advance, to adapt to material transfer forms, approval opinions and other documents from different sources. Cross-departmental task association logic is complex, so multi-turn dialogue must gradually guide the sorting of preceding and following relationships, to ensure no missing nodes during review. At the same time, full-process traceability of file review requires dialogue context to retain complete material transfer and approval records, to ensure review basis is traceable.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
messages | Retain 1000–1500 characters of context per conversation turn, automatically archive excess content to history | Joint declaration review conversations include multi-department task decomposition, material verification and other multi-turn interactions. Retaining a reasonable length of context ensures review logic remains coherent, and avoids losing key approval node information |
variables | Preset joint task lists and electronic certificate repository mapping rules as global variables | Allows quick calls to daily updated joint data, without repeatedly entering basic rules in each conversation, adapting to the daily data update feature |
| Maximum Context | 800–1200 token | Government joint review conversations contain many approval terms and cross-departmental fields. A reasonable context length balances information completeness and model response speed |
| Single Search Limit | Top 3–5 results | Joint review materials involve multiple documents from multiple departments. Limiting search results avoids redundant information interference, improving review efficiency |
chatId | Bind the unique business number of the joint task as chatId | Allows binding conversation records to specific joint case files, facilitating subsequent case traceability and review archiving, in compliance with government data management requirements |
Three Common Misconfigurations
- Symptom: The conversation interface displays the prompt "No permission to operate this conversation record". Cause: The joint business number is not correctly bound to
chatId, causing a mismatch between conversation context and case data, triggering permission verification failure. - Symptom: The AI cannot read the joint document interpretation content configured in the system prompt, and prompts that no file has been uploaded. Cause: Joint task lists, one-time notice sheets and other documents are not correctly mounted as global
variables, so the model cannot directly call preset document content. - Symptom: After refreshing the conversation page, joint review history records disappear, showing a brand new conversation. Cause:
chatIdis not persistently stored with the joint business number. After version upgrades, local session cache becomes invalid, causing context to be unrecoverable.
How to Verify Successful Configuration
- Initiate a simulated joint declaration conversation, input the material list for cross-departmental tasks, and verify whether the model automatically matches the preset joint task global variable content.
- Check whether the
chatIdof the conversation matches the simulated joint business number, to confirm the context binding relationship is correct. - Trigger the export action in the conversation, and check whether downloadable content conforming to the joint case file format is generated.
- Simulate multi-turn conversation until the preset context length limit is exceeded, check whether old content is automatically truncated and archived to history.
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.