This document applies to FastGPT v4.17.0.
What this category of data looks like
Data for this category primarily comes from cloud billing systems, tag data, order systems, and resource monitoring tools. Commonly processed documents include billing details, cost allocation sheets, resource usage reports, and optimization suggestion forms. Data updates follow a daily or monthly cadence. Most documents are structured tables. Fields include resource identifiers, billing cycles, cost accounting items, associated tags, and resource usage parameters. Units include basic measurement dimensions such as yuan, hours, and GB. Fields are closely linked, with no overly complex nested levels.
Constraints on Document Parsing and Chunking
Structured table documents have strong inter-field connections. Chunking must retain contextual links between fields. This avoids splitting cross-row and cross-column accounting logic. Documents updated in daily or monthly batches need to adapt to batch parsing capacity and timeout limits. Merged documents from multiple sources require accurate identification of cross-system field ownership. This prevents mixing tags and cost items during parsing. Units of structured fields must be retained alongside the fields. This avoids unit mismatches in subsequent cost accounting. Large-volume monthly billing PDFs require adapted chunking parameters. This prevents content offset errors during parsing.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Most documents in this category are structured tables. Each chunk must cover complete accounting rows and associated tags. This avoids splitting critical accounting logic |
chunkSplitter | By paragraph + table boundary | This category of documents contains many structured tables and text explanations. Splitting by table boundaries preserves complete field association |
customPdfParse | Enabled | PDF bills in this category are mostly fixed-format cloud vendor exports. Custom parsing adapts to table recognition rules. It improves structured extraction accuracy |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Monthly batch billing PDFs have large file sizes. This setting adapts to large file upload requirements. It avoids upload interruptions |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large-volume monthly bill parsing requires longer processing time. This prevents timeout interruptions |
chunkSettingMode | Custom mode | Documents in this category have uniform structures but high field complexity. Custom mode allows flexible adjustment of chunking rules. It fits business needs |
Three Common Misconfigurations
- Phenomenon: When uploading large-volume monthly billing PDFs, an "offset out of range" error appears after parsing progress hits 90%. Cause:
UPLOAD_FILE_MAX_SIZEandchunkSizeparameters are not adjusted, causing parsing boundary breaches during large file chunking. - Phenomenon: Documents uploaded via the create file collection API have inconsistent chunking results compared to those uploaded directly via the platform. Cause:
chunkSettingModeandchunkSplitterconfigurations are not unified, resulting in differing default chunking rules between the API and interface. - Phenomenon: Valid accounting logic cannot be extracted after directly chunking billing documents. Cause:
chunkSplitteris not configured to split by table boundaries, leading to cross-row and cross-column accounting fields being split.
How to Verify Proper Configuration
- Upload a single monthly billing PDF under 1000 MB. Check that parsing progress completes without offset errors.
- Compare the same document uploaded via the create file collection API and directly via the platform. Confirm consistent chunking results.
- View parsed chunked content. Confirm each chunk contains complete table rows and associated tags, with no field splitting errors.
- Test the return data of the custom parsing link. Confirm structured fields of documents in this category can be extracted.
The values listed 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.