What this category of data looks like
This data typically involves entities such as finance bureau budget departments and budget unit finance departments. Daily processed documents include department budget drafts, project application forms, performance target forms, personnel expense calculation forms, and budget approvals. Data mainly comes from the unified budget management system, department staff establishment system, prior year final accounts, project library, and Ministry of Finance budget preparation notices. The update frequency is annual. Most documents contain structured tables, hierarchical titles, and fixed fields, such as the number of personnel for personnel expenses and calculation basis for project expenses. Some documents embed performance supporting images or scanned copies.
What Constraints These Characteristics Impose on Document Parsing and Chunking
Annual concentrated document updates create bulk parsing pressure. Documents from multiple sources exhibit large format differences. Some documents are scanned copies or contain embedded images, requiring adaptation for non-native text parsing. Structured fields and hierarchical relationships in budget documents are closely linked. Chunking must retain contextual connections of fields such as department, project, and amount to avoid breaking data logic. A large number of nested tables and long paragraph descriptions exist in documents. Chunking rules must balance content completeness and retrieval accuracy, preventing single chunks from being too long or too fragmented to affect subsequent ledger aggregation and report generation.
Configuration Settings
The following configurations apply to FastGPT v4.17.0:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Department budget documents contain multi-field tables and project descriptions. This range retains complete information for a single budget entry |
customPdfParse | Enable OCR mode | Budget approval documents often embed performance supporting images and scanned copies. OCR extracts text within images |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Annual aggregated budget documents are usually large in size. This duration prevents large file parsing timeouts |
chunkSplitter | Split by paragraph + title | Budget documents have clear department and project hierarchical titles. Splitting by title retains data contextual connections |
chunkSettingMode | Manual configuration | Document formats vary widely across different budget units. Manual configuration adapts to dedicated parsing rules |
Three Common Mistakes
- Uploading a PDF with images results in parsed output without image text:
customPdfParseOCR mode is not enabled. Only the native text layer of the PDF is extracted, and embedded image content cannot be recognized. - The knowledge base cannot select table datasets: The custom parsing table extraction switch is not enabled, or the uploaded dataset format does not comply with the structured table field specifications of budget documents, causing the system to fail to recognize valid tables.
- Parsing task shows timeout failure: The set value of
PARSE_FILE_TIMEOUT_SECONDSis lower than the actual required parsing duration, or the document contains too many nested tables and long paragraphs, leading to parsing time exceeding the preset threshold.
How to Confirm Proper Configuration
- Upload a single-page budget approval PDF containing tables and embedded images, check if the parsed result includes handwritten annotations or printed text within the images.
- Import a batch of department budget documents, view the parsing task progress logs, confirm there are no timeout errors or empty field exception records.
- Adjust
chunkSizeto the preset range, randomly extract parsed chunks, check if complete project budget entries and hierarchical title connections are retained. - Enter the knowledge base settings page, confirm the table dataset option is displayed normally and can be selected.
The values given are common starting points and should be evaluated against the relevant 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.