What the data for this category looks like
Growth operations, user operations, and data analysts are the typical processing subjects for this category. Daily handled documents include four types: growth experiment plans, user segmentation tables, outreach copy, and retention analysis reports. Data sources cover user behavior logs, push systems, experiment platforms, and CRM systems. Most updates occur on a daily or weekly basis. Document structures have both structured and unstructured features. User segmentation tables include fields such as segmentation tags and outreach thresholds. Outreach copy includes parameters such as channels and trigger timing. Growth experiment plans and retention analysis reports primarily consist of paragraph-style analysis content. This document is written based on FastGPT v4.17.0.
What constraints do these characteristics impose on the document parsing and chunking link
Diverse data sources mean document formats cover multiple types including Word, Excel, TXT, and PDF. The parsing link must support multi-format files. The daily or weekly update frequency creates a need for batch processing. Total parsing and chunking time must be controlled to avoid disrupting operational workflows. Structured document table fields and unstructured text require separate parsing logic to ensure key compliance fields are not split or omitted. Security and compliance review requirements also mandate that the parsing link filter sensitive content. When chunking, complete context related to compliance must be retained to avoid splitting key review nodes.
How to configure settings
| Configuration Item | Recommended Value | Rationale for this value |
|---|---|---|
chunkSize | 800–1200 characters | Daily documents in this category mostly consist of short paragraphs and structured tables. This length retains complete context while avoiding excessively long chunks that reduce retrieval efficiency |
chunkSplitter | Paragraph-based + custom rules | Balances paragraph splitting for unstructured text and field extraction for structured tables, adapting to parsing needs for multiple document types |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to batch uploads of daily/weekly reports and log files, preventing upload failures due to oversized files |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Meets parsing time requirements for large user segmentation tables and retention analysis reports, preventing parsing tasks from being interrupted due to timeout |
customPdfParse | Enabled | Adapts to parsing of compliance-related PDF documents. Custom extraction of sensitive fields and text content meets security review requirements |
Three common mistakes
- Phenomenon: Calling the knowledge base API returns a file access link. Clicking the link prompts the error "Only support .txt, .m"; Cause: The uploaded file format is not supported by the system, and the parsing configuration for the corresponding format is not enabled.
- Phenomenon: After uploading a Word or Excel file, the parsing result is normal, but the large model's generated response does not include image content from the document; Cause:
customPdfParseis not enabled, or image parsing and extraction rules are not configured, and image text description information is not associated during chunking. - Phenomenon: When setting a custom chunk length, the actual meaning of the parameter value is unclear, leading to chunks that are too long or too short; Cause: The parameter is not configured based on the average length of category documents, and the conversion logic between character count and token count is confused.
How to confirm the configuration is correct
- Upload typical documents for this category, such as user segmentation tables and outreach copy, and check if the parsing result fully extracts table fields and text content.
- Call the knowledge base API to obtain chunk results, and verify that the chunk length falls within the preset
chunkSizerange. - Upload a file with a compliant format, and confirm that the returned access link can be opened normally without format error prompts.
- Batch upload multiple daily updated documents, and check if parsing tasks complete within
PARSE_FILE_TIMEOUT_SECONDSwithout timeout interruptions.
The values provided are common starting points and should be validated against samples appropriate for the intended use.
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.