This document applies to FastGPT v4.17.0.
What data looks like for this category
Game server R&D and operations teams use four core document types in daily work: server architecture descriptions, server launch plans, server merge plans, and fault reports. Data sources include game operation logs, online player count monitoring reports, business databases, and player support tickets. Document update frequency is daily, or adjusted per game event cycles. Some documents include structured parameter tables and unstructured troubleshooting steps. Fields include online player count, server load thresholds, player IDs, and data check values. Units include person-times, MB, seconds, and others.
Constraints on document parsing and chunking
The above document characteristics impose three constraints on the parsing and chunking process. First, operation and maintenance documents contain a large number of professional parameters and step-related content. Chunking must retain paragraph hierarchy and contextual logic, and avoid splitting critical processes such as server launch capacity estimation and server merge data verification. Second, some documents have large file sizes, such as long-term archived fault reports and full-server online monitoring summary files. Parsing capabilities must adapt to large-volume files. Third, documents mix structured tables and unstructured text. Parsing must correctly extract operation and maintenance data from tables, and avoid parameter loss.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the length of a single operation step or parameter description in operation and maintenance documents, and avoids splitting critical capacity estimation paragraphs |
chunkSplitter | Split by paragraph + heading | Retains the hierarchical structure of server launch plans and server merge plans, and avoids splitting continuous verification steps |
customPdfParse | Enabled | Correctly extracts embedded tables and complexly formatted text from PDF-format server architecture descriptions and fault reports |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to large server merge plan documents and long-term archived fault report files |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Meets the full parsing time requirements for large-volume log export documents |
chunkSettingMode | Manual configuration | Adjusts chunking rules for professional fields in operation and maintenance documents, and adapts to different types of business documents |
Three common configuration errors
- After uploading a PDF, search test results are empty, and data processing shows no data. The cause is that the
customPdfParseconfiguration is not enabled, so server operation and maintenance PDF documents with tables or embedded text cannot be parsed correctly. - After local deployment, knowledge base training is unresponsive, and data processing times out. The cause is that
PARSE_FILE_TIMEOUT_SECONDSis set too low, so complete parsing of large server launch plan documents cannot be completed. - Some PDF files can be recognized, while others show no content. The cause is that custom parsing rules are not configured for the special formatting of server operation and maintenance documents, such as vertically arranged parameter tables, leading to loss of some text blocks.
How to verify correct configuration
- Upload a typical server merge plan PDF document, check whether the data processing status shows completed, and whether search tests can retrieve paragraphs related to server merge data verification.
- Upload a single server log export file with a size exceeding 500 MB, and confirm that no timeout error is triggered during the parsing process.
- Adjust the
chunkSizeparameter, and check whether the chunk preview retains complete server launch capacity estimation paragraphs, with no critical parameters split. - After enabling
customPdfParse, confirm that the server architecture description table in the PDF is fully extracted with no missing content.
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.