This page is written using the document parsing and chunking features of FastGPT v4.17.0.
What this category’s data looks like
Coal-electric integration ledger data comes from full-process records of coal mining and power generation. Update cadences cover shift, daily, and monthly cycles. Document formats include three types: structured Excel ledgers, PDF daily operation reports, and scanned archived paper reports. Single documents contain fields such as coal extraction volume, unit generator power, coal consumption rate, and equipment runtime. Units include tons, kilowatt-hours, grams per kilowatt-hour, hours, and others. Some scanned documents have no natively extractable text.
What constraints do these characteristics impose on the document parsing and chunking workflow
Mixed document formats require parsing workflows to support structured table extraction, PDF text parsing, and scanned document OCR conversion. This prevents field splits from being broken. High-frequency updated ledger data requires reasonable timeout settings for parsing. This stops single-document parsing delays from disrupting subsequent ledger summarization and report generation workflows. Specific industry fields and units require retaining contextual associations during chunking. Truncating solely by fixed character count will separate units and values, harming subsequent data association analysis. Scanned document text extraction relies on OCR capabilities. Corresponding parsing modes must be configured to adapt to files without native text.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Coal power ledgers have dense fields per document. This range preserves complete data associations for a single generator unit or single production batch. |
chunkSplitter | Paragraph + fixed character truncation | Ledger documents include structured tables and paragraph-style explanations. Mixed splitting balances field integrity and chunking efficiency. |
customPdfParse | Enabled | Some PDF ledgers contain embedded tables and scanned pages. Custom parsing optimizes table extraction and OCR trigger logic. |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Large monthly ledger documents have substantial content. This duration covers the full parsing process. |
chunkSettingMode | Manual configuration | Coal power ledger formats are inconsistent. Manual configuration allows flexible adjustment of chunking rules to adapt to different document types. |
Three common mistakes
- Knowledge base search returns no results, with the interface showing 0 matching entries. This occurs when chunking breaks the association between core fields and values, preventing full query keyword matching during searches.
- Scanned ledger documents have no text content after parsing, with an upload status showing successful parsing but empty fields. This occurs when the OCR mode corresponding to
customPdfParseis not enabled, making it impossible to recognize printed text in scanned documents. - Parsing tasks time out and fail, returning status code 504. This occurs when
PARSE_FILE_TIMEOUT_SECONDSis set too short, failing to cover the full parsing process for large monthly ledger documents.
How to confirm proper configuration
- Upload a single structured Excel ledger, check that the parsed data table retains all fields and units completely.
- Upload a scanned archived paper report document, confirm that the parsed result contains retrievable text content.
- Adjust the
chunkSizeparameter, compare chunking results across different configurations to ensure core production data is not truncated. - Upload a large monthly ledger document, confirm that no parsing timeout errors occur.
The values provided are common starting points, and should be evaluated against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.