What this category of data looks like
State-owned industrial fund document data mainly comes from internal project approval documents, capital contribution agreements, post-investment management ledgers, industry research drafts, and regulatory submission materials. Projects trigger data updates as they advance, with multiple updates occurring during a single project cycle. The scale of a single update ranges from several pages to dozens of pages. Documents include two categories: structured tables and unstructured long text. Structured tables often contain fields such as project number, investor name, subscribed amount, in-place progress, and industry classification. The unit of amount is mostly ten thousand yuan or hundred million yuan. Ledger-type documents are stored classified by project dimension.
What constraints these characteristics impose on document parsing and chunking
Structured tables have multiple columns and scattered fields. Direct chunking causes field association breaks within a single chunk, making it impossible to fully restore a single project’s information. Long text reports have inconsistent paragraph hierarchies. Fixed-length chunking may split associated content such as policy citations and project progress descriptions. Regulatory submission materials have fixed formats but include required field checks. Missing parsed fields cause subsequent retrieval failures. Ledger data imported from Feishu online tables often has merged cells and hidden columns. Default parsing cannot fully extract all valid fields, affecting semantic integrity after chunking.
How to configure settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | State-owned industrial fund documents include multi-column ledgers and long text reports. This range balances single-chunk semantic integrity and retrieval recall density |
chunkSplitter | Mixed paragraph + fixed length | Adapts to the paragraph hierarchy of existing long text reports, while supplementing ledger row content that remains too long after segmentation |
customPdfParse | Enabled | State-owned industrial fund regulatory submission PDFs often contain fixed-format tables. Enabling custom parsing fully extracts content from merged cells and hidden columns |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | A single large post-investment ledger may contain tens of thousands of rows of data. Sufficient parsing time must be reserved to avoid timeouts |
chunkSettingMode | Manual configuration | Chunking rules must be adjusted separately for structured tables and unstructured text. Automatic mode cannot adapt to all scenarios |
Three common misconfigurations
- Symptom: After uploading a Feishu online table or multi-column Excel ledger, the knowledge base only extracts the first two columns of content. Cause: The
customPdfParseconfiguration is not enabled. The default parsing rule only recognizes the first two valid fields. - Symptom: A timeout error occurs when parsing large regulatory submission PDFs. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted. The default timeout duration is insufficient to cover large file parsing. - Symptom: Retrieval after chunking cannot match complete project capital contribution information. Cause: The
chunkSizesetting is too small. Multiple column fields of a single project are split into different chunks, causing semantic association breaks.
How to verify successful configuration
- Upload a typical multi-column ledger document, view the parsed text content, and confirm that all fields are fully extracted.
- Adjust the
chunkSizeparameter, generate test chunks, and check whether a single chunk contains complete single project information. - Upload a single regulatory report with more than 500 pages, and verify that the parsing process does not experience timeout interruptions.
- For structured table documents, manually verify whether the chunked text retains the association between fields.
The values provided are common starting points and should be measured 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.