What data for this category looks like
Data for declaration materials and case review in basic telecommunications operations primarily comes from network access approval archives, spectrum declaration documents, annual inspection materials, and business work orders from business halls. Data updates are triggered by project initiation, annual inspection cycles, or business modifications, with no fixed uniform update frequency. Document types include structured tables with fixed fields, multi-page long-text application descriptions, and PDF files converted from scanned documents. Fields include operator codes, frequency band numbers, business license numbers, user scale, and more. Units include MHz, ten thousand households, natural years, and similar units.
What constraints these characteristics impose on document parsing and chunking
Documents with a high proportion of structured tables can cause general text chunking tools to split table rows, losing field associations. Long-text application descriptions contain communications industry-specific terminology. Chunking must avoid truncating these terms mid-sentence. PDF files converted from scanned documents have layout shifts and misaligned text recognition, requiring custom PDF parsing logic to be adapted. Large single case file sizes will increase parsing time. Documents with multiple associated fields require retaining contextual connections during chunking, to avoid damaging the integrity of review logic.
How to configure the settings
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the mixed content length of structured tables and long text in single case files, avoiding splitting of professional terms or field associations |
chunkSplitter | By paragraph + table boundary | For mixed document structures, prioritize splitting by natural paragraphs, and automatically treat an entire table as one chunk unit when a table is encountered |
customPdfParse | Enabled | Adapts to text recognition misalignment and layout shifts in scanned PDF files, improving parsing accuracy |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses parsing time for large single case files, avoiding parsing failures due to timeout |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Covers the upload and parsing requirements for large single case files |
chunkSettingMode | Manual configuration | For the mixed structure of documents in this category, chunking rules and parameters can be flexibly adjusted to adapt to different types of declaration materials |
Three common mistakes
- Phenomenon: After uploading a single case file exceeding the configured limit, the interface shows upload failure or parsing timeout. Cause: The
UPLOAD_FILE_MAX_SIZEandPARSE_FILE_TIMEOUT_SECONDSparameters are not adjusted, exceeding the default configuration thresholds. - Phenomenon: After uploading structured table declaration materials, some fields are empty or data is missing in the parsing results. Cause:
customPdfParseis not enabled, and the general parsing logic cannot accurately identify associated fields within tables, leading to data loss. - Phenomenon: When parsing long-text application descriptions, communications professional terms are split across two chunks, resulting in subsequent retrieval semantic breaks. Cause: The
chunkSizevalue is too small, andchunkSplitteris not configured to use paragraph + table boundary.
How to confirm the configuration is correct
- A typical structured table case file is uploaded, and the parsing results are checked for retention of all fields and table integrity.
- The
chunkSizeparameter is adjusted, then a long-text application description is uploaded, and professional terms are confirmed to not be split midway. - A file with a size close to the configured maximum limit is uploaded, and the parsing process is verified to not trigger timeout errors.
- After enabling
customPdfParse, a PDF case file converted from a scanned document is uploaded, and the consistency between the recognized text and the original document layout is compared.
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.