What the data for this category looks like
Air governance-related financial report data comes primarily from special environmental protection chapters in listed companies’ annual or quarterly reports, publicly available emission reduction monitoring ledgers from local ecological environment departments, and compliance reports from third-party environmental service institutions. Data updates follow an annual core cycle, with some key emission reduction projects updated quarterly. Most documents combine structured tables and paragraphs, with fields including monitoring point codes, pollutant concentrations, annual emission reduction volumes, and compliance status. Units mostly use standard environmental industry units such as mg/m³, tons/year, and cubic meters/hour.
What constraints do these characteristics impose on the document parsing and chunking link
The mixed document structure of air governance financial reports requires parsing tools to support semantic extraction from both structured tables and natural paragraphs, and avoid semantic breaks across table rows. The presence of multiple fields with standardized units requires the parsing process to automatically associate fields with their corresponding units, preventing confusion between concentration and emission reduction units. The batch data feature, updated quarterly and annually, requires chunking to aggregate data by cycle, and avoid splitting cross-cycle monitoring data into the same chunk. Some ledger documents include repeated header information across pages. This requires automatic filtering of redundant headers to reduce duplicate content after chunking.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Air governance financial reports often contain long table paragraphs. This range preserves complete semantics for a single set of monitoring data or a single page of compliance explanations |
chunkOverlap | 100–150 characters | Prevents semantic breaks across table rows and cross-cycle data, and ensures contextual continuity between chunks |
PARSE_TABLE_ENABLE | true | Most core data in air governance financial reports exists in structured tables. Enabling this option fully extracts cell content and associated fields |
PARSE_REMOVE_REPEAT_HEADER | true | Multi-page ledger documents have repeated headers. Enabling this option automatically filters redundant header information to reduce invalid chunks |
PARSE_TIMEOUT_SECONDS | 300 seconds | Compatible with FastGPT 4.9.0 and above versions, and covers parsing time requirements for most large environmental protection ledger documents |
SIMILARITY_THRESHOLD | 0.75 | Fields in air governance data have strong relevance. This threshold filters low-relevance redundant chunks and improves recall accuracy |
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 errors
- Phenomenon: The number of chunk results differs between a file uploaded via the Create File Collection API and the same file uploaded directly on the platform. Cause: When the
parse_configparameter is not included in the API upload, the platform uses its default parsing configuration. Direct platform upload retains the user's previously saved custom configuration, leading to differences in chunking rules. - Phenomenon: After configuring custom URL parsing for documents, the returned result is empty with no error message. Cause: The custom URL does not point to a publicly accessible structured document, or the document format is not covered by the enabled parsing rules, so the parsing engine cannot extract valid content.
- Phenomenon: After configuring the document parsing node, the model cannot read the uploaded file. Cause: The
PARSE_FILE_ENABLEparameter is not enabled, or the uploaded file size exceeds theUPLOAD_FILE_MAX_SIZElimit, so the parsing node fails to generate valid chunk data.
How to confirm the configuration is correct
- Upload a typical air governance financial report document, enter the parsing log page, and check whether core fields such as pollutant concentrations and emission reduction volumes in the table are fully extracted.
- Compare the chunk details of the same file uploaded via the API and directly on the platform to confirm that the chunking rules are consistent.
- Test the custom URL link to check whether the parsing result contains the core content of the target document, with no empty returns.
- View the output fields of the parsing node to confirm that standard chunk fields such as
chunk_contentandchunk_metadataare included and not missing.
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.