What data in this category looks like
The data and documents in this category primarily serve product operations, data analysts, and customer success operations staff. Daily produced documents include usage analysis reports, tracking plans, indicator caliber tables, and dashboard requirement tickets. Data sources cover tracking systems, data warehouses, CRM systems, and ticket systems. Update frequencies are mainly daily or weekly. Document structures include structured indicator fields, calculation logic descriptions, event trigger conditions, and customer grouping rules. Fields involve indicator names, statistical units, trigger thresholds, customer grouping tags, and more. Some documents contain nested tables and cross-system association descriptions.
What constraints these characteristics impose on the document parsing and chunking link
The diversity of multi-source data and document formats requires the parsing link to support multiple formats including Markdown, Word, and Excel. It must not split indicator association content across cells. Frequently updated documents require the parsing process to have high batch processing efficiency to avoid delays that impact subsequent analysis. Structured indicator and event fields require chunking to retain contextual associations. Splitting an indicator name and its calculation logic into different chunks will cause subsequent retrieval and analysis to fail. The presence of nested tables and cross-system descriptions requires the chunking logic to identify semantic boundaries within documents. Forced splitting only by character length may damage information integrity.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the typical semantic block length of documents in this category, avoids splitting indicator names and their corresponding calculation logic |
chunkSplitter | semantic | Splits documents based on semantic boundaries, retains complete associations between events, indicators, and associated descriptions |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Adapts to the parsing time required for large tracking plans and indicator caliber tables, avoids interrupting batch tasks due to timeout |
customPdfParse | Enabled | Preserves original layout and table structure for exported usage analysis report PDFs, improves chunking accuracy |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports batch uploading of weekly summary multi-document packages, adapts to frequently updated business requirements |
chunkSettingMode | advanced | Allows adjustment of chunking parameters for different document types, adapts to parsing needs for multi-format documents |
Three common mistakes
- When uploading a Word-format indicator caliber table, calculation logic text associated with images is split into different chunks. The default chunking logic only splits by character length or paragraph, and does not recognize the semantic binding relationship between images and surrounding text.
- When batch uploading weekly summary multi-document packages, some document parsing tasks time out. The
PARSE_FILE_TIMEOUT_SECONDSsetting value is too small, and does not adapt to the parsing time required for large documents. - When configuring
chunkSizeto 1500 characters, a single chunk contains multiple independent customer grouping rules and indicator definitions, leading to reduced matching accuracy during subsequent retrieval. The chunk length exceeds the typical semantic boundary of documents in this category, damaging information association.
How to confirm configurations are set correctly
- Upload a typical tracking plan document, check if the chunking results retain complete associations between event names, trigger conditions, and parameters.
- Upload the maximum size document supported by FastGPT v4.17.0, confirm that no timeout error is triggered for the parsing task.
- Adjust
chunkSettingModeto advanced mode, upload documents of different formats, verify that the chunking logic adapts to the corresponding document types. - Test chunk overlap parameters to ensure adjacent chunks contain overlapping content, preventing core indicator definitions from being split across two chunks.
The values listed are common starting points and should be assessed against appropriate sample documents.
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.