Declaration Materials and File Review for State-owned Capital Operation Companies: Document Parsing and Chunking

What this category of data looks like

Data for declaration materials and file review at state-owned capital operation companies comes primarily from three sources: structured ledgers exported from state-owned assets supervision systems, electronically scanned paper case files archived internally, and annual business budget declaration materials. Data updates follow declaration cycles, covering scenarios such as monthly or quarterly asset declarations, and annual property right transfer case archiving. Document types include multi-sheet structured Excel files (with fields including staffing establishment, total assets, and more), long-form PDF documents (including approval processes, audit reports, and more), and scanned PDF files (electronized paper approval forms). Fields include unified social credit code, shareholding ratio, approval document number, and more. Some fields have fixed associated units.

What constraints these characteristics impose on document parsing and chunking

Structured multi-sheet Excel files depend on headers to link fields. If chunking fails to retain the binding relationship between headers and corresponding rows, split segments will lose field correspondence logic. This affects subsequent review matching. Long case PDF files include cross-page approval processes and detailed entries. Chunk length must align with a single approval node or single asset entry. This avoids broken splitting logic that causes incomplete semantics. Scanned electronic case files have layout shifts and blurry fonts. General parsing logic cannot accurately extract fields, so additional parsing rules are required. Frequently updated declaration materials require limiting single-file parsing duration to avoid timeouts interrupting batch processing workflows. Retaining fixed unit fields affects semantic accuracy after chunking. Splitting must avoid breaking the association between fields and their units.

Configuration Settings

Reference only; verify against the target FastGPT release and deployment documentation.

Three Common Configuration Mistakes

• After uploading a multi-sheet Excel file containing personnel information, the system shows empty data processing results. Cause: chunkSplitter is not configured to adapt to structured multi-sheet data. The default chunking logic does not recognize field associations between sheets, leading to loss of valid segments.
• After bulk uploading long case PDF files with more than 10 pages, vectorization takes longer than expected. Cause: chunkSize is set too large. A single chunk contains excessive redundant content, increasing the computational load of the embedding model.
• When entering non-document-related questions in real-time conversations, the document parsing process is triggered. Cause: trainingType is configured incorrectly. Real-time conversation content is mistakenly categorized as training data, triggering unnecessary parsing steps.

How to Verify Configurations Are Correct

• Upload an Excel file containing a single asset detail. Check if parsed segments retain headers and corresponding field values to confirm that chunkSplitter and chunkSize configurations are active.
• Upload a single-page scanned PDF case file. Verify that parsed text restores the field order of the original layout, confirming that customPdfParse is enabled correctly.
• Upload a 100MB long PDF case file. Wait for parsing to complete, confirm no timeout errors, verifying the validity of the PARSE_FILE_TIMEOUT_SECONDS configuration.
• Test entering non-document-related content in a real-time conversation. Check if the document parsing process is triggered, confirming that trainingType is configured correctly.

The values listed here are common starting points. They 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.