What the data for this category looks like
Power grid equipment industry financial report data mainly comes from listed company annual/quarterly reports, internal operational financial reports of grid enterprises, and industry association statistical documents. Data update cycles are divided into annual, quarterly, and monthly. Annual reports have longer length, while quarterly and monthly reports focus on short-term operational data. The document structure includes three core modules: financial statements, equipment ledger, and revenue composition analysis. Fields cover equipment model, unit count, revenue amount, kilowatt-hour metrics, and units such as ten thousand yuan, units, and kilowatt-hours.
What constraints these characteristics impose on document parsing and chunking
The multi-module structure of power grid equipment financial reports requires parsing and chunking to retain business boundaries, and avoid mixing equipment ledger and financial data. The characteristics of long documents and high-frequency updates require the parsing process to have sufficient stability to prevent parsing failures due to timeouts. Structured table data with multiple units and fields requires the chunking logic to retain cell-level associations, otherwise the binding information between equipment models and corresponding revenue will be lost. At the same time, the high-frequency monthly updated documents require the chunking configuration to quickly adapt to document content of different lengths.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxChunkSize | 800–1200 characters | Power grid financial reports contain both long-text analysis paragraphs and dense equipment table data. An overly large size will lose context association, while an overly small size will break the binding between equipment models and revenue data |
chunkOverlap | 150–200 characters | Equipment-related data often appears across paragraphs. The overlapping section retains the association between equipment models, parameters and corresponding financial data |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | A single annual financial report can be over 50 pages long and contains a large number of equipment ledger tables, resulting in long parsing time |
enableTableParse | Enabled | Power grid financial reports include structured tables such as equipment ledgers and revenue composition analysis. The cell-level structure must be retained to avoid data loss |
splitByHeadingDepth | 2 | The secondary headings of power grid financial reports correspond to business modules such as transformers and switchgear. Chunking by secondary headings ensures complete business context within a single chunk |
maxTableChunkSize | 1000–1500 characters | A single record in an equipment table includes model, parameters, and revenue data. The chunk size must be large enough to cover a complete single record |
The parameter values provided on this page are common recommendations used as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three common errors
- Phenomenon: The equipment model field is empty in the parsed chunks. Cause: The
enableTableParseconfiguration is not enabled, so structured fields such as equipment models in the table are not extracted. - Phenomenon: Calling the 4.8.10 version API to obtain the chunk index returns a
413 Request Entity Too Largeerror. Cause: TheUPLOAD_FILE_MAX_SIZEparameter is not adjusted, and the size of a single financial report document exceeds the system limit. - Phenomenon: The number of chunks after parsing a single financial report is much lower than expected. Cause: The
maxChunkSizevalue is too large, and the system does not split long paragraphs and table content according to the configuration.
How to confirm the configuration is correct
- Upload a standard power grid equipment financial report document, view the parsed chunk list, and confirm that each chunk contains complete business module content.
- Check whether fields such as equipment model and revenue amount in the chunks are complete, and verify that the table parsing configuration takes effect.
- Call the chunk index acquisition interface, compare the returned chunk data with the content of the uploaded document, and confirm that the matching degree meets expectations.
- After adjusting the
chunkOverlapparameter, compare the coverage of associated information in chunks before and after, and confirm that the overlap length adapts to business needs.
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.