Characteristics of this data category
This data category is sourced from the unified budget management system, project declaration materials, fund payment data, business system ledgers, and satisfaction surveys. Updates occur annually. Commonly processed documents include performance target forms, self-evaluation forms, performance evaluation reports, supporting material lists, and rectification reports. Data fields focus on fiscal fund use performance, project output effects, and rectification implementation status. No additional custom units are used, and the data structure is updated in line with the annual performance evaluation cycle.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Batch ledger data updated on an annual fixed cycle requires multi-turn dialogue systems to support loading historical data in batches, to avoid overloading single-turn context. When multiple types of heterogeneous documents are input together, prompts must clearly distinguish association rules for performance targets, supporting materials, and self-evaluation data, to ensure unified summary standards. Differences in data fields across multiple business systems require multi-turn dialogue to gradually guide alignment of data formats, to ensure the accuracy of report generation. Corresponding verification between rectification reports and supporting materials requires multi-turn questioning to gradually confirm the matching relationship between fund expenditures and performance targets, to prevent missing associated items.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Context window size | 8000–12000 token | In FastGPT v4.17.0, annual performance evaluation data volume is large, so this size accommodates ledger and report fragments from multi-turn dialogue |
variables variable configuration | Preset core field variables for performance evaluation | Unify data call calibers and avoid field ambiguity during multi-turn dialogue |
| Multi-turn dialogue round limit | First 10 rounds of dialogue context | Annual evaluation data does not require excessive historical context backtracking, to avoid interference from redundant information |
messages message format validation | Enforce the format of "system prompt + user input + tool call" | Ensure consistency of prompts during multi-turn dialogue and standardize model output logic |
| Single data load volume | 500–1000 ledger records | Balance loading speed and context capacity, calibrated based on actual testing |
chatId session isolation configuration | Isolate sessions by annual performance evaluation project | Avoid data confusion across projects and ensure data security |
Three Common Misconfigurations
- Issue: Performance supporting materials uploaded via the dialogue window are not included in the model's statistical summary. Cause: The reference variable for uploaded files is not configured in
variables, causing the prompt to fail to call the corresponding data. - Issue: Timeout and lag occur when loading annual ledger data via multi-turn dialogue. Cause: The number of ledger records loaded in a single batch exceeds the system's carrying limit, and batch loading is not implemented.
- Issue: The configured workflow runs normally within FastGPT, but only the first dialogue node can be triggered after connecting to other frontends. Cause: The session transfer logic for
chatIdis not configured correctly, leading to loss of session context across frontends.
How to Verify Proper Configuration
- Upload a single annual performance self-evaluation form, check whether the dialogue output includes the core fields of the uploaded file, and adjust the prompt matching scope according to actual business needs.
- Initiate multi-turn questioning, check whether the context retains references to ledger data from previous conversations, and adjust the context window size according to the session scenario.
- Call the API to test the session, confirm that the
chatIdparameter remains consistent across each request, and verify the continuity of sessions across frontends. - Upload multiple supporting materials, check whether the model can associate materials with performance targets, and adjust the coverage scope of variable configuration according to data type.
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.