What data for this category looks like
Data for this category comes from the unified budget management system, project declaration materials, fund payment data, business system ledgers, and satisfaction surveys. The update frequency is annual. Commonly processed documents include performance target forms, self-evaluation forms, performance evaluation reports, supporting material lists, and rectification reports. Fields cover project performance indicators, fund usage details, output effect records, and more. There is no unified fixed unit, and matching corresponding business field formats is required. Core processing links include collecting supporting materials, verifying the correspondence between funds and performance targets, and drafting rectification suggestions. Multiple types of documents require cross-verification.
What constraints do these characteristics impose on the multi-turn dialogue and prompt engineering link
Low-frequency annually updated data requires multi-turn dialogue to retain full context until the end of a single project session, to avoid re-entering annual fixed business information. The mixed structure of multiple document types requires prompts to clearly distinguish processing rules for different documents, to avoid confusing the verification logic of supporting materials and self-evaluation forms. The requirement to verify the correspondence between funds and performance indicators requires multi-turn dialogue to associate and call fields from corresponding data sources, to ensure accurate verification basis. The total volume of documents processed in a single session is large, so context length must be limited to avoid exceeding the model’s processing limit, while ensuring key business fields are not filtered out.
How to configure settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind the unique identifier of the current performance project | Ensure complete contextual association for multi-turn review dialogues of the same project, avoid information loss caused by session splitting |
messages | Retain the last 8 rounds of business dialogue history, filter non-core casual conversation content | Adapt to the low-frequency interaction rhythm of annual project reviews, reduce invalid context consumption of model processing resources |
variables | Inject example value, example value system variables | Meet the requirement of automatically matching the closing time of prompts with the business year |
| Maximum context length | 10000–14000 token | Calibrated based on actual testing, adapted to the total content of mixed processing of multiple types of performance documents |
| Dialogue history archiving | Store by project dimension to the corresponding file directory | Match the archiving management requirements of fiscal files, facilitate subsequent review and traceability |
Three common misconfigurations
- Automatically returning empty content when reopening a previous session. The cause is that a fixed
chatIdis not bound, and session context is not persistently stored to the corresponding project dimension. - Previous AI dialogue content in the workflow is carried over to the final output result. The cause is that no context filtering rules are configured, and the requirement to only retain dialogue content from the current review link is not specified.
- Voice input fails to trigger normally. The cause is that dependent tools are not correctly configured with non-root permissions in the container, leading to missing necessary components in the runtime environment.
How to confirm correct configuration
- Initiate the first dialogue and enter basic performance project information. Reopen the session page, check whether historical dialogue content is fully retained.
- Add a closing time variable to the prompt, generate a test reply, and verify whether the closing time matches the current system date.
- Trigger consecutive AI calls in the workflow after configuring multi-turn dialogue, check whether the final output only includes results from the current review link.
- Upload a single performance document to the dialogue, check whether the model can correctly identify the document type and match the corresponding verification rules.
The values provided here 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.