What the Data for This Category Looks Like
Party building learning data for the port and shipping industry mainly comes from official red-headed documents issued by internal party committees, monthly branch study minutes, party lesson courseware, and sorted content from online learning columns. The update cycle is monthly, with special study materials released quarterly. The main document format is PDF, including standard red-headed documents, party lesson PPT converted files with embedded images and tables, and some historical materials are scanned PDF files. Document fields include file number, issuing unit, release date, study topic and corresponding study hours, with no complex nested non-standard formats.
Constraints on Document Parsing and Chunking
The fixed format of port party building documents requires that metadata such as the header and footer of red-headed documents be retained during parsing, to avoid damaging the integrity of official files through chunking. The presence of embedded images and tables requires the parsing link to support OCR recognition and table structure retention, otherwise key information in study materials will be lost. The monthly batch upload update cycle requires the parsing link to support high-concurrency batch task processing to avoid queue delays. The presence of fixed fields requires that chunking be prioritized by chapter titles, ensuring that content on the same topic is not forcibly split into multiple unrelated chunks based on character length.
How to Set Configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 1000–1500 characters | Adapts to the average paragraph length of port party building documents, avoids losing context association from overly long chunks, and reduces retrieval redundancy from overly short chunks |
chunkSplitter | Split by title + paragraph | Party building documents often have clear chapter titles, splitting by title preserves the logical integrity of content |
customPdfParse | Enable OCR mode | Covers historical party building study materials in scanned document form, recognizes text content embedded in images |
UPLOAD_FILE_MAX_SIZE | 500 MB | Adapts to large special courseware containing multi-page image materials, meets batch upload requirements |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Avoids timeout interruptions during parsing of large scanned PDF files, adapts to processing duration for complex documents |
Three Common Mistakes
- Phenomenon: When image files or PDFs with embedded images are uploaded, the knowledge base parsing result has no corresponding text for the images. Cause: The OCR mode of
customPdfParseis not enabled, and only plain text content is parsed by default. - Phenomenon: When party building study files are batch uploaded, some large document parsing tasks time out and fail. Cause:
PARSE_FILE_TIMEOUT_SECONDSis not adjusted to a duration suitable for large documents, and the default duration is insufficient for scanned document OCR parsing. - Phenomenon: Parsed chunk content has misplaced or missing table cells. Cause:
chunkSplitteris not configured to prioritize splitting by table structure, causing table content to be forcibly split based on character length.
How to Verify the Configuration
- Upload a party learning PDF with embedded images, confirm that the parsing result includes the text content from the images.
- Upload a party lesson document containing tables, check that the chunking result retains the complete row and column structure of the tables.
- Upload a large PDF file within 500 MB, confirm that the parsing task completes within the preset duration without timeout errors.
- Batch upload 10 party building documents of different formats, check that all files successfully enter the parsing queue and generate valid chunks.
The values provided are common starting points and should be evaluated against sample data relevant to the specific deployment.
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.