What the data for this category looks like
Data sources for power industry investment research include grid operation reports released by industry regulatory agencies, electricity price policy documents, unit operation logs and monthly operation reports from power generation enterprises, technical briefings from industry associations, and technical white papers from equipment manufacturers. Update rhythms vary significantly: policy documents are updated irregularly, operation logs and operation reports are updated daily or hourly, and annual industry reports are released per calendar year. Common document formats are PDF and structured Excel spreadsheets. Some enterprises store internal operation data in Feishu multi-dimensional tables. Documents contain standardized fields including power generation (unit: megawatt-hours), installed capacity (unit: kilowatts), and coal consumption rate (unit: grams per kilowatt-hour), alongside long-form technical analysis paragraphs and parameter comparison tables.
What constraints do these characteristics impose on the document parsing and chunking link
The mixed format and diverse unit systems of power industry investment research documents create multiple constraints for the parsing and chunking process. First, document structures that combine structured tables, long technical paragraphs, and real-time operation data require the parsing workflow to support both non-text content extraction and structured field recognition. Second, the unique unit system such as megawatt-hours and grams per kilowatt-hour requires parsing tools to accurately match fields with their corresponding units to avoid unit confusion during retrieval. Third, some enterprises store operation data in Feishu multi-dimensional tables, so the chunking process must retain associations between fields and adapt to structured data formats pulled via APIs. Fourth, power policy documents often use multi-column layouts, so text line break misalignment during parsing must be prevented.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
pdf_parse_mode | enhanced | Power industry PDF documents often contain nested tables and multi-column layouts. The enhanced mode improves the accuracy of structured content extraction |
chunk_size | 800–1200 characters | Power investment research documents often contain long technical paragraphs and parameter tables. This range preserves complete information for a single topic and avoids chunk breakage |
max_paragraph_depth | 3 | The paragraph hierarchy of power operation documents is mostly body text under third-level headings. This setting accurately identifies paragraph boundaries |
structured_table_extract | enabled | A large number of standardized parameter tables exist in power industry documents. Enabling this option retains the association between fields and units |
pdf_marker_version | v2 | Version v2 optimizes parsing performance for multi-column PDFs and nested tables, adapting to common formats of power industry documents |
api_parse_timeout | 120 seconds | Large operation log documents in the power industry take longer to parse. This duration prevents parsing timeout failures |
The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material format, 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 mistakes
- Issue: Calling the
/v2/parse/fileAPI returns a 404 error, and the PDF enhanced parsing function cannot be enabled in the interface. Cause: The pdf-marker v2 parsing service was not deployed correctly, or the service access port was not opened. - Issue: Pulling documents from the library via API returns empty data, or the extracted fields do not match the original data. Cause: The correct API pull target was not specified, FastGPT internal interfaces were misused, and the enterprise's own business interface (such as the Feishu multi-dimensional table official API) was not used.
- Issue: Unit confusion appears in retrieved results after chunking, or long technical paragraphs are incorrectly truncated. Cause: The
structured_table_extractparameter was not enabled, or themax_paragraph_depthsetting does not match the paragraph hierarchy of power industry documents.
How to confirm the configuration is correct
- Upload a power industry PDF policy document, and check if the parsed structured tables retain exclusive fields such as power generation and coal consumption rate along with their corresponding units.
- Call the
/v2/parse/fileAPI to test parsing a single power operation log, verify that the API returns a 200 status code, and that the parsed content has no line break misalignment. - Configure a test task to pull Feishu multi-dimensional tables via API, and confirm that the returned field names exactly match the column names in the multi-dimensional table.
- View the chunked document fragments, confirm that long technical paragraphs are not excessively truncated, and that paragraph boundaries align with the third-level headings in the document.
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.