What the Data for This Category Looks Like
Papermaking industry intelligent due diligence data sources include publicly available statistics from industry associations, internal production ledgers of papermaking enterprises, raw material purchase contracts, and public environmental protection monitoring data. Update cadence is categorized by data type: weekly raw material purchase prices, monthly production capacity and output data, quarterly financial reports and environmental protection monitoring data. Documents are centered on structured tables, with supplementary unstructured text such as production logs and compliance reports. Fields include raw material category, purchase unit price, daily output, unit energy consumption, emission concentration, etc. Most units are tons, yuan, kilowatt-hours, milligrams/liter.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
The multi-source heterogeneous nature and differentiated update cadence of due diligence data require that the initial stage of multi-turn dialogue first clarifies the time period and statistical dimension of the data required by the user, to avoid confusion of cross-cycle data. The structured fields cover multiple dimensions such as raw materials, production capacity, and energy consumption. Prompts must pre-limit the range of paper types to prevent misuse of cross-paper-type data. Production logs and compliance reports account for a significant portion of long text, so context recall length must be limited to avoid exceeding the model's processing limit. At the same time, unit consistency requirements are strict. Prompts must mandate that output results include corresponding units to reduce the risk of data misuse.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Papermaking due diligence data includes long-text production logs and multi-field tables, and needs to cover the context of at least 3 full conversations, while adapting to the context limits of mainstream models |
recallTopK | Top 6–8 entries | Papermaking due diligence data has many finely divided fields. An appropriate amount of recall can cover business needs across different dimensions, and avoid context overload caused by too many recalled entries |
promptTemplate | Fixed question and answer template specifying paper type, time period and unit | Papermaking industry data has finely divided dimensions and strict unit requirements. Pre-limiting can reduce result deviations |
conversationHistoryMaxCount | Top 10–15 entries | Papermaking due diligence mostly involves multi-turn dimension confirmation. Retaining an appropriate amount of history can avoid repeated questions, while preventing excessive historical data from occupying resources |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Papermaking due diligence documents include multi-page tables and long text, requiring sufficient time to complete structured parsing |
similarityThreshold | 0.75–0.85 | Papermaking industry data has high requirements for field accuracy, and low-match irrelevant data needs to be filtered out |
The parameter values provided on this page are all conventional recommendations used as starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to conduct actual tests on relevant samples before finalizing the settings.
Three Common Mistakes
- Symptom: A 404 status code is returned when calling the API to initiate a dialogue. Cause: The API address and port of the embedding model are not configured correctly, preventing normal invocation of the model service.
- Symptom: All session records are returned when retrieving session history, instead of only sessions for the specified customUid. Cause: The customUid parameter is not included in the history query interface for filtering, or the parameter format does not meet requirements.
- Symptom: Thinking content wrapped in think tags is retained in dialogue generation results. Cause: The code execution node only takes effect in the debug environment, and is not correctly mounted to the output processing link of the dialogue flow during actual operation, or the thinking block in the model's native output is not handled.
How to Verify Correct Configuration
- Initiate a test query specifying a specific paper type and time period, verify that the returned results match the preset category and unit requirements.
- Call the history interface with a custom customUid, verify that the returned results only include content from the corresponding session.
- Run a workflow that includes a code processing node, check that the output results have removed thinking block content generated by the model.
- Upload a papermaking due diligence document, verify that the parsed fields fully cover the business-required dimensions.
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.