What the data for this category looks like
The data sources for railway operation party building learning mainly include official study documents released by internal party committees, branch meeting minutes, online party lesson courseware, and party building knowledge base documents stored on shared platforms. Update rhythm aligns with quarterly study plans. There is no fixed cycle, but new content is added each month. Most documents are in PDF or Word format. Some contain embedded PPT screenshots, event photos, and assessment progress tables. Fields include document number, issuing department, release date, and study topic. Some documents include unit information such as study hours and assessment counts.
Constraints on Document Parsing and Chunking
Document characteristics of railway operation party building learning impose multiple constraints on parsing and chunking. Formal official red-headed documents have fixed title hierarchies and numbering logic. Parsing must retain structural integrity to avoid breaking the document’s logical flow through chunking. Embedded PPT screenshots, event photos, and assessment tables need to be identified and linked to their corresponding context. This ensures the AI agent can access image-text associated information. Dispersed documents from multiple sources require batch parsing support. They must also adapt to parsing rules for different formats. Content with unit information such as study hours and assessment counts must retain the binding between units and their corresponding text. This avoids semantic breaks.
How to Configure
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Railway party building documents are mostly formal writings. Chunk length adapted to conventional semantic units avoids embedding failures from overly long chunks, or broken context association from overly short chunks. |
chunkSplitter | Split by headings and paragraphs | Documents have fixed title hierarchies. Chunking by headings retains semantic integrity and adapts to the structural characteristics of red-headed documents. |
customPdfParse | Enabled | Most railway party building documents are in PDF format. Enabling custom parsing retains associated information for embedded images and tables, solving the problem of separated images and text. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | A single party building document may contain multiple pages of courseware and images. Sufficient timeout time is required to complete full parsing and avoid mid-process interruptions. |
UPLOAD_FILE_MAX_SIZE | 1000 MB | The size of single railway party building documents usually falls within this range. Setting this value avoids excessive parsing resource usage from oversized files. |
chunkSettingMode | Manual configuration | Adjustment of chunking rules is required based on the structural characteristics of railway party building documents. Manual configuration adapts to specific format requirements. |
Three Common Mistakes
- Phenomenon: No embedded image associated text appears in the parsed knowledge base results, or images cannot be called by the AI agent. Cause: The
customPdfParseconfiguration is not enabled. The default parsing logic does not retain the binding relationship between embedded images and context. - Phenomenon: Parsing fails when importing a publicly shared document link, or an empty knowledge base content is returned. Cause: The link is not publicly accessible, or the link is not recognized as a valid knowledge base data source format by the system.
- Phenomenon: When calling a tool to initiate an HTTP request, parameter parsing is normal but the corresponding operation is not executed. Cause: Tool permission parameters are not configured, or the request timeout setting is too short, leading to early interruption.
How to Verify Proper Configuration
- Upload a party building PDF document containing embedded images, and check whether the association between images and their corresponding text is retained in the parsed results.
- Import a publicly accessible party building document sharing link, and confirm that the parsing task completes normally and valid chunks are generated.
- Adjust the
chunkSplitterconfiguration, then check whether the chunked results retain the document’s title hierarchy and logical structure. - Upload a Feishu multi-dimensional document, and confirm that the parsing system generates corresponding knowledge base index entries.
The values given 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 2026-09-28.