What this category of data looks like
The typical users for this category are growth operations specialists, user operations specialists, and data analysts. Documents processed daily include growth experiment plans, user segmentation tables, outreach copy, and retention analysis reports. Data sources include user behavior logs, push systems, experiment platforms, and CRM systems. Update frequencies are mostly daily or weekly. Document structures fall into two categories: structured tables and natural paragraph text. Fields include user identifiers, behavior timestamps, outreach records, experiment groups, and more. There is no fixed unified unit format.
What constraints do these characteristics impose on the document parsing and chunking stage
Documents in this category contain multi-column structured tables and scattered natural text. Chunking must accurately match business units, and avoid splitting cross-column user segmentation data. Daily or weekly batch data updates require the parsing process to support batch processing and avoid timeout interruptions. Differences in document formats across multiple sources require the parsing module to adapt to multiple file types, while also supporting custom chunking rules. Incorrect chunking will lead to errors in user segmentation and experiment attribution, which in turn cause user dissatisfaction or budget waste. Therefore, chunking must strictly align with the boundaries of business fields.
How to set configurations
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the typical paragraph length of growth experiment plans and retention analysis reports, avoiding splitting core business units |
chunkSplitter | Split by row (Excel), Split by paragraph (text) | Matches the structural characteristics of different documents. For example, user segmentation tables must be split by row to preserve the integrity of individual user data |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the time required for batch parsing of weekly user behavior logs, avoiding interruptions during large file parsing |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports uploading large-scale daily user behavior datasets to meet batch parsing requirements |
chunkSettingMode | Manual configuration | Allows custom chunking rules for structured documents, avoiding chaotic results from automatic chunking |
Three common mistakes
- Multi-column Excel documents have chaotic automatic chunking results, with each row's content split into different segments. This occurs because the row-wise chunking rule is not configured for structured tables, and the default paragraph splitting logic is used instead.
- When parsing in bulk, the system loads previously uploaded documents, and the configuration to only process the currently uploaded attachment does not take effect. This occurs because the single-file independent parsing mode is not enabled, and the system defaults to associating parsing batches with historical knowledge bases.
- After uploading a test document in image format, the model cannot recognize the image content. This occurs because the image parsing configuration for FastGPT v4.17.0 is not enabled, or the corresponding parsing parameters are not configured.
How to confirm that configurations are set correctly
- Upload a test Excel document containing multiple columns, check the chunking results, and confirm that each row of content corresponds to an independent segment.
- Upload a single test document, check the parsing logs, and confirm that only the currently uploaded file is loaded, with no association with historical documents.
- Call the API to upload a test document in image format, initiate a corresponding Q&A request, and verify that the model can return valid content related to the image.
- Upload a single PDF document, configure single-document retrieval parameters, initiate a Q&A request, and verify that only the content of this document is used to answer the query.
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.