What Data Looks Like for This Category
Core documents for this category include asset ledgers, monthly inventory sheets, loan and return forms, scrap application forms, and the like. Data sources cover enterprise asset management systems, procurement systems, financial fixed asset modules, and terminal management platforms. Most documents are structured tables or lists, with fields including asset number, device name, storage location, user, purchase date, status, and the like. Units are mostly physical measurement units such as unit, set, piece. Update frequency follows monthly, quarterly, or annual cycles.
Constraints Imposed by These Characteristics on Document Parsing and Chunking
Most documents in this category are structured tables or lists. During chunking, the association between fields must be preserved to avoid disconnecting core data such as ownership, location, and user caused by splitting cross-row asset information. The requirement for importing multi-source data requires the parsing link to adapt to different formats of ledgers, inventory sheets, and other documents, and support custom parsing rules to match format differences exported by different systems. Low update frequency but high data accuracy requirements require chunking to retain complete context, preventing loss of associated information across the entire asset lifecycle after splitting. The standardization requirement for physical measurement fields requires unifying unit and field naming rules during chunking to avoid matching deviations in subsequent retrieval.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapt to the length of single table blocks or associated field groups for structured asset documents, avoid splitting cross-row asset information |
chunkSplitter | Split by table rows and paragraphs | Match the characteristic that most documents in this category are structured tables, preserve field association integrity |
customPdfParse | Enable custom parsing rules | Adapt to different format ledger documents exported by multi-source systems, correct non-standard headers and field order |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Cover file volume requirements for batch import of quarterly/annual inventory sheets |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapt to parsing time of large ledger files, avoid interrupting the parsing process due to timeout |
chunkSettingMode | Manual configuration | Adjust chunking rules for different types of asset documents to meet personalized parsing needs |
The configurations on this page apply to FastGPT v4.17.0.
Three Common Configuration Mistakes
- Symptom: After uploading an asset ledger file, the parsing node shows not started or abnormal status. Cause:
UPLOAD_FILE_MAX_SIZEis not configured correctly, and the file volume exceeds the limit, causing parsing trigger failure. - Symptom: The parsed chunk content only includes partial asset entries, and complete data is not captured. Cause:
chunkSplitteris configured to split by fixed characters, which does not adapt to the table structure, causing cross-row asset entries to be forcibly split and omitted. - Symptom: After chunking the parsed scrap application form document, the user field is empty or matched incorrectly. Cause:
customPdfParsecustom parsing rules are not enabled, and it cannot identify non-standard format application form headers and field mapping relationships.
How to Verify Successful Configuration
- Upload a single quarterly inventory sheet file, check whether the parsing task starts normally and generates chunk results.
- Randomly select chunk content, verify that core fields such as asset number and user retain complete associated relationships.
- Adjust the value range of
chunkSize, confirm that the chunk length meets the adaptation requirements of the document structure. - Import ledger documents exported by multi-source systems, confirm that the custom parsing rules take effect and correctly match 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 2026-10-19.