What data for this category looks like
Data sources for financial reports related to air pollution control include public pollutant discharge permit data from ecological environment departments, operation and maintenance logs of corporate air pollution control projects, environmental protection investment disclosures in annual reports of listed companies, and real-time online monitoring data. Update cycles cover monthly online monitoring values, quarterly project progress updates, and annual concentrated financial report disclosures. Document structures typically include fields such as unique project identifier, governance facility type, pollutant emission concentration (unit: mg/m³), pollutant treatment efficiency, single operation and maintenance duration, and total project investment. Each financial report document often contains associated data for multiple projects.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Decentralized data sources require multi-turn dialogue to clearly define the analysis scope, to avoid confusing fields from different data sources. The presence of multiple fields and professional units means prompts must specify standardized field extraction and output formats, to prevent unit misuse or field misalignment. The combination of long documents and multi-project data requires multi-turn dialogue to retain sufficient context to link analysis results across different projects, while avoiding context overflow that causes loss of key information. Different update frequencies require multi-turn dialogue to clearly distinguish between historically archived data and real-time updated monitoring data, to ensure analysis timeliness.
How to set configurations
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
maxContext | 12000–15000 characters | A single air pollution control financial report document often contains multi-project data, so sufficient context must be retained to avoid truncation of key fields |
recallTopK | Top 6–8 entries | Fields such as pollutant concentration and treatment efficiency in financial reports are strongly correlated; recalling too many entries will introduce irrelevant data |
similarityThreshold | 0.75–0.85 | Distinguish between professional terminology and ordinary expressions, to avoid confusing operation and maintenance duration and cost fields |
workflow_output_hide | Disabled by default, enabled after concatenation | Air pollution control data needs to be output after multi-step concatenation; hiding intermediate AI-generated content aligns with business processes |
dialogue_log_mark | Mark by data source type | Differentiate dialogue contexts between official monitoring data and corporate ledger data, to facilitate subsequent verification |
PARSE_FILE_TIMEOUT_SECONDS | 300–400 seconds | Parsing long financial report documents takes a long time; a reasonable timeout must be set to avoid interruptions |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Intermediate outputs of AI dialogue in the workflow are displayed directly on the page, without outputting after concatenation according to business processes. The cause is failing to enable the
workflow_output_hideconfiguration, and failing to configure the trigger output logic after the text concatenation component. - Analysis content from different data sources cannot be distinguished in the dialogue log, leading to difficult traceability. The cause is failing to enable the
dialogue_log_markconfiguration, and failing to specify data source marking rules in the prompt. - After passing knowledge base retrieval results to an HTTP request, the AI dialogue fails to correctly identify fields, leading to incorrect analysis results. The cause is failing to pass fields such as
pollutant_concentrationandunitin a standardized format, and failing to specify field mapping rules in the prompt.
How to confirm the configuration is correct
- Upload a single air pollution control financial report document, trigger multi-turn dialogue, check whether intermediate generated content is hidden, and only the concatenated final result is displayed.
- View the dialogue log, confirm that each analysis content is marked with the corresponding data source type, such as "official monitoring data" or "corporate ledger data".
- Pass standardized knowledge base retrieval results, check whether the AI dialogue can correctly identify fields such as
pollutant_concentrationandunitand complete the analysis. - Test multi-turn dialogue for long documents, confirm that no timeout errors occur, and key fields are not truncated.
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.