What this category's data looks like
The processing subjects for this category are typically game customer service, community operations, and publishing operations teams. Daily processed documents include player tickets, compensation applications, public opinion daily reports, and standardized response scripts. Data sources cover customer service system backends, community forums, app store comment sections, and game runtime logs. Update frequencies are mostly daily batch processing or real-time streaming. Most documents are semi-structured, containing fields such as player account identifiers, problem descriptions, and processing progress. There is no unified fixed format specification, and some documents include embedded images or tables.
What constraints these characteristics impose on the document parsing and chunking step
Since data sources are scattered and formats vary, the parsing link must adapt to multiple types of text, embedded image documents, and semi-structured content, and avoid splitting key business fields. Real-time updated data sources require the parsing process to have low latency. Batch-processed historical tickets also need support for large-volume file parsing. The semi-structured document characteristics require the chunking strategy to retain contextual associations. This prevents key information such as account verification and compensation rules from being split into independent chunks, which would affect subsequent compliance reviews and problem handling. At the same time, errors directly impact player experience and community reputation. The parsing link must ensure key information is not omitted or misaligned.
How to configure settings
This guide is written based on the configuration logic of FastGPT v4.17.0. The specific configuration items are listed below:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the per-chunk information density of player tickets and public opinion daily reports, avoiding splitting key business content such as account verification and compensation rules |
chunkSplitter | Chunk by paragraph + semantic meaning | Adapts to structured text in scripts and daily reports, retains contextual associations, and ensures chunked content can be independently used for compliance reviews |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Covers the parsing time of large-volume public opinion daily reports and Feishu multi-dimensional documents, preventing batch tasks from terminating early |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports batch import of historical ticket packages exported from customer service systems, meeting compliance review requirements for full historical data |
chunkSettingMode | Manual configuration | Adjusts chunk granularity for special fields in compensation applications and account tickets, ensuring key information is not truncated |
Three common mistakes
- Importing a Yuque public link into the knowledge base results in a parsing failure, with the interface prompting unsupported data source type. The cause is that the share link is not set to publicly accessible, or the Yuque parsing rules adapted to the platform are not used.
- After parsing embedded images in tickets, the large language model cannot obtain the text content of the images. The cause is that the image OCR configuration item in
customPdfParseis not enabled, resulting in images only being uploaded as attachments without text extraction. - When calling an HTTP tool, parameter parsing succeeds but the actual request is not triggered. The cause is that the correct execution trigger condition is not bound in the tool configuration, or the request timeout setting is too short, leading to early termination.
How to confirm the configuration is correct
- Upload a test player ticket document, check if the parsed chunks retain key content such as account information and compensation application items.
- Import a publicly shared script document from Yuque, verify that the parsing result includes complete text content without truncation or loss.
- After setting
PARSE_FILE_TIMEOUT_SECONDSto 600 seconds, upload a large-volume public opinion daily report package, confirm that the parsing task does not time out early. - Enable the image OCR switch in
customPdfParse, upload a ticket screenshot containing embedded images, and verify that the large language model can obtain the text information in the images.
The values provided are common starting points and should be evaluated 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.