What the data for this category looks like
Engineering consulting financial report data primarily comes from internal project management ledgers, financial accounting systems, and third-party audit reports. Update rhythm is adjusted based on individual project node progress or monthly cycles, with no unified fixed frequency. Documents center on structured tables, with corresponding written descriptions attached. Fields include project identifiers, work classifications, labor/material/machinery cost items, settlement amounts, and similar fields. Units for costs and work volume are mostly yuan, hours, cubic meters, and other standard units.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Engineering consulting financial report data comes from dispersed sources. Multi-turn dialogue must recall information across multiple ledgers and accounting systems. Prompts must explicitly bind the current conversation’s project ID to avoid mixing data across projects. Data updates follow project nodes, with no fixed cycle. Multi-turn dialogue must support dynamic supplementation of the latest progress data. Prompts must include trigger rules for real-time data calls. Documents primarily use structured tables. Multi-turn dialogue must enable the AI to accurately match fields and work types. Prompts must preset field mapping logic. Individual financial report datasets have many entries. Multi-turn context must limit retention to recent interactions and associated data to prevent window overflow.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Engineering consulting financial reports have many individual data entries. Multi-turn interaction project context must be retained to prevent window overflow |
RECALL_TOP_N | Top 8–12 entries | Engineering consulting financial report fields are highly segmented and closely linked. Sufficient associated data must be recalled while controlling redundant information |
PROMPT_TEMPLATE | Bind the current project ID, only respond using financial report data from this project, prompt to upload corresponding ledgers when data needs supplementation | Engineering consulting project data has strong binding properties. Clarifying the data source scope avoids mixing data across projects |
UPLOAD_FILE_MAX_SIZE | 500 MB | Engineering consulting financial reports may include multi-page ledgers or accompanying drawings. Larger file uploads must be supported |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing large structured ledgers takes significant time. Timeout duration must be extended to ensure parsing completes |
global.workerPoll.countGptMes | Enable statistics per project cycle | Engineering consulting project cycles have large spans. Token consumption must be counted per cycle to align with budgets |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: Project data added during multi-turn dialogue is not added to the knowledge base, and cannot be recalled in subsequent conversations. Cause: No trigger rule for automatically synchronizing dialogue content to the knowledge base is configured. Only files are manually uploaded without associating them with the current conversation’s project context.
- Phenomenon: The token statistics entry cannot be found, or the statistics data does not match actual call consumption. Cause: The
global.workerPoll.countGptMesparameter is not correctly enabled, or the statistics switch is not configured per project cycle. - Phenomenon: SQL query results returned by the workflow cannot be displayed in the AI dialogue box, or Markdown formatting is abnormally hidden on WeChat. Cause: No message output node of the workflow is bound to the AI dialogue channel, and Markdown format compatibility settings for the target channel are not enabled.
How to confirm configurations are set correctly
- Initiate a multi-turn dialogue bound to a specified project ID, verify that the AI only calls financial report data for that project, and does not involve information from other unrelated projects.
- Navigate to the system configuration page, locate the statistics module corresponding to
global.workerPoll.countGptMes, confirm that the parameter is enabled and the statistics rules are configured per project cycle. - Upload an engineering consulting financial report ledger of the corresponding scale, verify that the file parsing process completes normally, with no timeouts or format errors.
- Test the workflow to call SQL to query financial report data, confirm that query results can be synchronized to the AI dialogue box, and that the message format meets expectations on the target channel.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.