What data this category handles
Typical processing entities for this category include platform governance, audit quality control, and customer service teams. Daily processed documents include ban records, appeal materials, reconsideration opinions, and user notifications. Data sources primarily include risk control systems, audit records, user behavior logs, and report evidence. Update frequency is typically daily or real-time. Each document typically includes fields such as account identifier, violation time, appeal reason, and evidence attachments. Core processing steps include violation evidence verification, appeal reason judgment, and disposition level adjustment. Processing errors may lead to incorrect bans, return of violating accounts, or legal complaints. Unlike community report processing categories, this category focuses on account-level punishment and reconsideration workflows.
Constraints on document parsing and chunking
Documents in this category are mostly structured text exported from systems or materials with attachments. Processing requires association with account identifiers. Therefore, chunking must retain the integrity of account-related fields, and avoid splicing content across accounts. Real-time updated data sources require parsing processes with high response speed. This avoids delaying reconsideration efficiency due to parsing delays. Core processing steps rely on accurate positioning of evidence and reasons. Chunk granularity must balance semantic completeness and retrieval accuracy. It must not be too fine to cause context breaks, nor too coarse to reduce retrieval matching efficiency. Multiple materials uploaded in batches must ensure parsing consistency. This avoids parsing failures or field loss for some documents.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Ban records and appeal materials usually contain multiple coherent paragraphs. 800–1200 characters can retain the complete semantics of a single appeal reason or violation evidence, avoid splitting account-related information, and align with the chunking logic of FastGPT v4.17.0 |
chunkSplitter | Split by paragraph + title | Document structures include structured fields such as account ID, violation time, and evidence attachments. Splitting by paragraph + title can retain field association and ensure logical coherence of information within chunks |
customPdfParse | Enabled | Appeal materials often include scanned documents or formatted PDFs. Enabling this option retains original typesetting and field information, and supports preliminary parsing of image content |
UPLOAD_FILE_MAX_SIZE | 500 MB | A single batch of reconsideration materials may include multiple report evidence files. 500 MB covers conventional batch upload requirements, and avoids upload failures due to capacity limits |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | For large-volume appeal materials or batch parsing scenarios, 300 seconds covers the parsing time of most documents, and avoids interrupting the parsing process due to timeout |
chunkSettingMode | Manual mode | Adjusting chunking rules for account-level documents is required. Manual mode allows precise control over chunk granularity, and adapts to different types of appeal materials |
Three common configuration errors
- Symptom: After uploading appeal materials with images, question answering tests cannot extract image content. Cause: The image parsing switch for
customPdfParseis not enabled, or the associated image parsing tool is not configured, resulting in image information not being effectively embedded into chunks. - Symptom: Some documents fail to parse when uploading ban records in batches. Cause:
UPLOAD_FILE_MAX_SIZEis set too low, exceeding the capacity limit for single files or batch uploads, resulting in some large-volume documents failing to complete parsing. - Symptom: Content across accounts is spliced after chunking. Cause:
chunkSplitterdoes not use account ID or appeal order number as the splitting boundary, resulting in materials from different accounts being merged into the same chunk, affecting subsequent account-level retrieval and reconsideration.
How to verify correct configuration
- Upload a test document containing account ID, violation records, and image attachments, and check if the parsed chunks retain account-related fields, with no cross-account splicing.
- Upload batch materials within 500 MB, confirm that there are no timeout errors in the parsing progress, the status code shows 200, and all documents have completed parsing.
- After enabling the image parsing switch for
customPdfParse, enter a question related to the image content in the question answering test, and confirm that the corresponding information can be extracted. - Adjust
chunkSizeto 800 characters, parse a single appeal material, and confirm that the chunk does not split core appeal reasons and has complete semantics.
The values listed are common starting points and should be evaluated against relevant 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.