What data for this category looks like
Air pollution control financial report data is sourced from public monitoring ledgers released by ecological environment authorities, annual special audit reports of air pollution control enterprises, and archived project operation and maintenance logs. Data updates follow three cycles: monthly monitoring indicator updates, quarterly operation and maintenance cost updates, and annual compliance financial report updates. A single document includes fields such as basic project information, emission concentration monitoring data, emission reduction calculation records, compliance rectification status, and capital expenditure details. Field units use professional environmental measurement standards including mg/m³, tons, ten thousand yuan, and hours. There is no unified unstructured text template, and some fields have inconsistent names across different data sources.
What constraints do these characteristics impose on the model integration and configuration phase
The multi-dimensional professional fields and cross-data source name differences in air pollution control financial reports require field mapping rules during model integration to avoid deviations in professional indicator parsing. Frequently updated monitoring data requires the model call chain to support scheduled synchronization configuration, ensuring the timeliness of retrieved data. Single documents with multi-page long text require vector storage and segmentation configurations adapted to long text parsing, preventing key indicators from being truncated. Measurement fields with varying units require the model to support semantic alignment of multiple data types, otherwise parsing errors with mismatched values and units will occur.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
llmModels | Sort by business priority, place compliance verification models first | Air pollution control financial reports require priority verification of emission compliance indicators. Calling the corresponding model first can improve analysis efficiency |
chunkSize | 800–1200 characters | Adapts to the length of a single monitoring record plus associated context, preventing professional indicators from being truncated during segmentation |
recallCount | Top 6–8 entries | Air pollution control financial reports involve multi-dimensional indicators of emissions, costs, and compliance. Sufficient associated data must be recalled to support analysis |
similarityThreshold | 0.72–0.80 | Professional environmental terminology has high semantic similarity differentiation. This range can filter irrelevant non-compliance search results |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | A single air pollution control financial report usually contains multi-page monitoring data, which takes a long time to parse. The timeout threshold needs to be extended |
embeddingModelConfig | Configure open-source vector models that support multi-field encoding | Adapts to the vector storage requirements of multi-unit and multi-type measurement fields, improving retrieval matching accuracy |
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 configuration errors
- Phenomenon: The interface displays "model not found", and the log returns the
model_not_founderror code. Cause: Only thellmModelsfield inconfig.jsonwas modified, and the bound configuration was not refreshed in the FastGPT model management interface, so the effective configuration was not synchronized. - Phenomenon: Vector search returns empty results, and the console shows a
504 Gateway Timeouterror. Cause: The access address and authentication parameters of the vector model were not configured inembeddingModelConfig, leading to cross-service call timeout. - Phenomenon: A "unit field is empty" parsing exception occurs when parsing financial reports. Cause: No field mapping rules were configured, and cross-data source field name differences were not aligned, so professional measurement units were not correctly identified.
How to verify a successful configuration
- Enter the FastGPT model management interface, confirm that the bound model list matches the content of the
llmModelsfield inconfig.json. - Upload a single air pollution control financial report sample, run the parsing task, and check if the parsed fields include preset professional fields such as emission concentration, emission reduction amount, and operation and maintenance cost.
- Initiate a financial report analysis request, check if the number of vector search results matches the
recallCountconfiguration parameter. - Check the OneAPI authentication log to confirm that the model call interface returns a
200 OKstatus code, with no authentication failure prompts.
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.