What the data for this category looks like
State-owned industrial fund data primarily comes from capital contribution payment vouchers, investment project ledgers, post-investment management reports, bank fund statements, and similar sources. The system adjusts data update frequency based on project progress and monthly or quarterly accounting deadlines, and adds temporary new data when projects launch. Documents include structured tables and unstructured text. Structured fields include investor entity, project name, subscribed amount, paid-in progress, annualized rate of return, and others. Units include ten thousand yuan, calendar days, percentage, and similar categories. Unstructured text mostly consists of project due diligence descriptions and risk assessment content.
What constraints these characteristics impose on the document parsing and chunking link
State-owned industrial fund structured ledgers have merged cells across rows and columns, and fields are tightly bound to their corresponding units. During document parsing, the system must accurately identify the cell structure to avoid splitting associated fields and their units. Frequently updated monthly and quarterly ledgers, plus temporary project documents, require chunking to adapt to incremental upload and batch processing scenarios, avoiding repeated parsing of full documents. Long-text post-investment reports must retain the integrity of business logic, without splitting complete descriptions of individual projects. The system must also support uploaded files of different formats, to ensure parsed data can be used for subsequent ledger summary work.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the typical length of individual project ledgers or report paragraphs for state-owned industrial funds, avoiding splitting core business fields and their bound unit information |
chunkSplitter | Split by paragraph + title | Supports mixed document structures of structured ledgers and unstructured reports, retains natural associations of business logic, and avoids splitting complete descriptions of the same paragraph |
customPdfParse | Enabled | Accurately identifies merged cell tables in PDF documents for state-owned industrial funds, preventing structured field extraction errors or omissions |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports large file upload requirements where a single quarterly post-investment report includes multiple attachments, adapting to batch document submission scenarios |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Reserves sufficient processing time for parsing large PDF documents, avoiding parsing task interruptions due to timeout |
chunkSettingMode | Manual configuration | Adjusts chunking rules separately for structured ledgers and unstructured reports, adapting to parsing needs for mixed documents |
Three common mistakes
- Symptom: Chunk content is truncated or an abnormal error occurs during vectorization. Cause: The
chunkSizewas not adjusted based on the long document and field binding characteristics of state-owned industrial funds, resulting in a mismatch between single chunk length and vector model input limits. - Symptom: Parsed ledger fields have misplaced units or empty values. Cause:
customPdfParsewas not enabled; the default parsing function fails to identify merged cell tables in PDFs, leading to incorrect structured field extraction. - Symptom: Parsing times out during batch upload of large files. Cause:
PARSE_FILE_TIMEOUT_SECONDSwas not adjusted to a duration suitable for large files, resulting in forced interruption of the processing process.
How to confirm the configuration is complete
- Upload a standard monthly ledger PDF for state-owned industrial funds, and verify that the parsed text retains the field association of merged cells, with no misalignment or omissions.
- View the chunk list to confirm that single chunk lengths fall within the preset
chunkSizerange, with no cases of core business fields being split across chunks. - Upload a single large file under 1000 MB, confirm that no timeout errors occur during the parsing process, and the task can be completed normally.
- Test incremental upload of a single project ledger, confirm that the chunking rules can adapt to the parsing logic for new data, with no duplicates or omissions.
The values listed above are common starting points and should be evaluated against the user'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.