What the data for this category looks like
Automated equipment financial report data primarily comes from publicly disclosed periodic reports and temporary announcements of listed companies, as well as publicly available industry operation statistics documents. Data updates follow the schedule of periodic report release cycles, with relevant content updated immediately after temporary announcements are published. Document structures include core operating data fields such as revenue scale, production capacity scale, order amount, and equipment operation-related indicators. Field units are mostly currency units, unit counts, operation durations, and similar metrics. Some indicators require split statistics based on equipment model and batch.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The data sources for automated equipment financial reports are scattered, and update timelines are inconsistent. Multi-turn dialogue must guide users to clearly specify the financial report cycle and data source type, to avoid calling irrelevant data. Financial report documents can be lengthy, so the multi-turn dialogue context window must support long-text processing to prevent core indicators from being truncated. The industry has exclusive fields such as equipment production capacity and operation efficiency. Prompts must include preset industry term mapping rules to avoid misinterpretation of professional indicators. Additionally, temporary announcements may trigger data changes, so the dialogue flow must include a data timeliness confirmation step to ensure analysis is based on the latest disclosed content.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Adapts to the long-text structure of automated equipment financial reports, preventing core operating indicators from being truncated by context |
systemPrompt | Preset automated equipment financial report term mapping rules, limit analysis to only publicly disclosed financial report data | Calibrates terms for industry-exclusive fields such as equipment production capacity and operation efficiency, reducing misinterpretation of professional indicators |
recallTopK | Top 3–5 entries | Core indicators of automated equipment financial reports are concentrated, so no excessive recall entries are needed to cover analysis requirements |
similarityThreshold | 0.75–0.85 | Filters low-relevance financial report fragments, focusing on core content related to equipment operations |
fileParseChunkSize | 1000–1500 characters | Adapts to the paragraph length of financial report documents, ensuring a single chunk of text contains complete indicator context |
apiTimeout | 600 seconds | Complex calculations for long financial report parsing and multi-turn dialogue require a longer timeout period to avoid mid-process interruptions |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: The results of the AI dialogue node inserted into the workflow are appended to the final output content. Cause: No output filtering rules are configured for the dialogue node, and all intermediate step results are included in the final return by default.
- Phenomenon: Extra title content appears in the dialogue log during API calls, but no such issue occurs during front-end calls. Cause: The title splicing logic for dialogue context is not disabled in API request parameters, and system preset title fields are carried by default.
- Phenomenon: Parsing timeout errors occur frequently during multi-turn dialogue. Cause: The API timeout period is set too short, making it impossible to complete long financial report parsing and multi-turn calculations.
How to confirm correct configuration
- Upload a financial report document of a listed company in the automated equipment sector, initiate multi-turn dialogue, and verify that the system can accurately identify exclusive fields such as equipment production capacity and order amount.
- Trigger the AI dialogue node in the workflow, check that the final output only contains the target analysis results, with no redundant content from intermediate steps.
- Call the API interface, view the returned dialogue log, and confirm that no extra non-request-related title fields are carried.
- Adjust the context window parameter, initiate long-text analysis, and verify that core indicators are not truncated and the dialogue process proceeds normally.
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.