What data for this category looks like
The data for this category mainly comes from tracking systems, data warehouses, CRM systems, and ticket systems. Update cycles are mostly daily or weekly. Commonly processed documents include usage analysis reports, tracking plans, indicator caliber tables, and dashboard requirement forms. Most document structures include structured fields such as indicator name, statistical cycle, corresponding function module, and unstructured explanatory content. Fields cover user behavior identifiers, function usage frequency, abnormal data identifiers, etc. There are no uniform mandatory units, and some indicators require supplementary unit descriptions based on business scenarios.
What constraints do these characteristics impose on the document parsing and chunking link
Tracking plans and indicator caliber tables mostly include structured tables and formula comments. During chunking, the integrity of table structures must be retained to avoid breaking indicator association relationships. Usage analysis reports have relatively high update frequencies, so fast parsing processes for batch documents must be supported. Documents exported from ticket systems are mostly in non-standard formats, so parsing logic compatible with multiple file types is required. Data field differences across sources are large, so field context must be retained during chunking to ensure consistent indicator caliber during subsequent retrieval.
How to set configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the information density per chunk of indicator caliber tables and usage analysis reports, avoiding including too much cross-indicator content in a single chunk |
chunkSplitter | Split by paragraph | Matches the natural paragraph structure of daily documents, retaining the contextual association between indicators and explanations |
customPdfParse | Enabled | Compatible with PDF-format tracking plans and dashboard requirement forms, retaining the parsing integrity of tables and formulas |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the parsing of large-volume batch usage analysis reports, avoiding parsing timeouts for long documents |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports uploading documents exported from large data warehouses, covering the parsing needs of full business data |
chunkSettingMode | Manual configuration | Adjusts chunking parameters for different document types, adapting to the differences between structured tables and unstructured reports |
Three common configuration errors
- Failing to enable table multi-vector support, resulting in failure to accurately match indicator calibers in tables during retrieval. This occurs because corresponding parsing rules are not configured for structured tables, and table content is processed only as ordinary text during chunking.
- Attempting to enable PDF enhanced parsing but the function does not trigger. This occurs because the
customPdfParseparameter is not configured correctly, or the uploaded PDF document does not meet format requirements. - Calling the API interface to configure chunking parameters but the configuration does not take effect, resulting in unexpected numbers of retrieval results. This occurs because the corresponding configuration interface is not called correctly, or the parameter values do not follow the system-required format.
How to confirm configuration is valid
- A typical indicator caliber table document is uploaded. Parsed chunking results are reviewed to confirm that table structures are fully retained without content breaks.
- Batch uploading of 1 to 2 usage analysis reports is performed. Parsing task completion status is checked to confirm completion within a reasonable time frame.
- The knowledge base configuration query API is called. Values of parameters such as
chunkSizeandcustomPdfParseare confirmed to match preset configurations. - A specific business indicator name is entered as a search term. Chunked content in returned results is reviewed to confirm inclusion of the complete caliber description and associated information for the indicator.
The values provided are common starting points and should be validated using sample data appropriate to the specific use case.
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.