What the data for this category looks like
The core data for this category comes from risk control systems, audit records, user behavior logs, and report evidence. Update frequency is mostly daily or real-time. Daily produced documents include ban records, appeal materials, review opinions, and user notifications. Ban records contain account identifiers, violation types, disposal time, and disposal levels. Appeal materials contain appeal subject identifiers, appeal time, and associated supporting information. Review opinions contain review subjects, review results, and adjustment basis. User notifications contain notification recipients, sending time, and notification content.
What constraints do these characteristics impose on the document parsing and chunking link
Most data in this category is structured or semi-structured content bound to accounts, and some records are in table format. When parsing, the contextual association between account identifiers and related events must be retained to avoid splitting critical information due to chunk breaks. Real-time or high-frequency updated data requires the parsing process to have efficient processing capabilities to avoid repeated parsing of full data. At the same time, details such as supporting materials and review basis included in documents require retaining sufficient contextual length during chunking, to ensure that subsequent retrieval can fully match the association logic of appeals and reviews.
How to configure settings
For FastGPT v4.17.0, adaptation can be completed through the following configuration items:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Most documents in this category contain multiple pieces of associated information bound to accounts. This length can retain the complete context of violation behaviors and appeal reasons, avoiding splitting of critical information |
chunkSplitter | Split by paragraph + title | Daily documents mostly contain structured paragraphs such as ban reasons and appeal content. Splitting by title and paragraph can retain the logical structure of the document |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Some documents in this category contain multiple supporting materials, so sufficient parsing time must be reserved to complete full content loading |
customPdfParse | Enabled | If appeal materials include PDF format supporting files, enabling custom parsing can retain the original typesetting of tables and text, avoiding field misalignment |
UPLOAD_FILE_MAX_SIZE | 500 MB | Some supporting materials in this category contain multiple high-definition screenshots or log files. Setting a reasonable upper limit can avoid parsing failures |
chunkSettingMode | Manual configuration | Chunk parameters need to be adjusted according to the average length of a single document to adapt to ban records and appeal materials in different formats |
Three common misconfigurations
- Issue: After custom chunking, duplicate chunks in the knowledge base are automatically deleted, causing the pre-configured index order to not match the actual stored chunks. Cause: The global deduplication configuration is enabled by default, and no separate settings are made for the associated context of account ban-related documents, resulting in multiple segments of associated information under the same account being mistakenly deleted.
- Issue: When parsing appeal materials containing tables, table content is split into scattered text chunks, and the complete table structure cannot be restored. Cause: The
customPdfParseconfiguration is not enabled, and the default parsing engine has insufficient ability to retain table structure. - Issue: After uploading supporting files, the parsing process times out without completing and provides no clear feedback. Cause: The
PARSE_FILE_TIMEOUT_SECONDSconfiguration is not adjusted, and the default timeout period is insufficient to process documents containing multiple materials.
How to verify correct configuration
- Upload a single typical ban record document, check the parsed chunk results, and confirm that account identifiers and associated violation information are not split into different chunks.
- Call the knowledge base creation API, poll to obtain the parsing status, and confirm that status information for parsing in progress, ready, or failed can be returned correctly.
- Upload a PDF appeal material containing tables, and confirm that the parsed chunks retain the complete row and column structure of the table, with no scattered text or content loss.
- Upload a test file exceeding the conventional document size, and confirm that the system triggers a file size limit prompt matching the configured limit, with no abnormal errors.
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.