What the Data for This Category Looks Like
Business data typically originates from art asset libraries, project management systems, engine resource packages, and outsourced delivery packages. Commonly used documents include art requirement sheets, asset specification sheets, outsourced feedback record sheets, and acceptance confirmation sheets. Most document fields include resolution, triangle count, delivery cycle, outsourcer information, and similar details. Units include pixels, pieces, days, and other standard units. Data update frequency is mostly daily or weekly. A single uploaded document may contain content related to one or multiple assets.
What Constraints These Characteristics Impose on Document Parsing and Chunking
Fixed fields and clear specification requirements for art asset-related documents require that field associations be retained during chunking, and that descriptive content across multiple assets not be split. Document sources are diverse and formats vary. Some outsourced delivery package PDFs include embedded tables and original art assets, so parsing must adapt to non-standard formats. The batch parsing requirement driven by data update frequency means the parsing process must handle single multi-page documents or combinations of multiple documents. Errors may lead to resource mismatches or performance overages, so chunking must fully retain key specification information.
How to Set the Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Single-paragraph specification descriptions in art documents are typically 300–500 characters. Chunking must retain field associations and avoid splitting complete requirements across multiple assets |
chunkSplitter | Split by paragraph + heading | Art documents mostly use headings to distinguish different asset types and specification requirements. Splitting by heading ensures each chunk corresponds to the complete content of a single asset |
customPdfParse | Enabled | Outsourced delivery package PDFs often have embedded tables and original art drafts. Custom parsing can retain table structure and text hierarchy |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | A single outsourced delivery package PDF may contain multiple acceptance records. Parsing takes a long time, so the timeout setting must cover batch parsing scenarios |
chunkSettingMode | Manual configuration | Art document fields are fixed but formats vary widely. Manual configuration allows adjusting chunking rules for different document types |
Three Common Mistakes
- Symptom: Parsing tasks return chunk results that include previously uploaded documents. Cause: The mode to only parse the currently uploaded file is not configured. By default, all associated documents in the knowledge base are loaded to participate in chunking.
- Symptom: After uploading a PDF with embedded original art, the model cannot recognize image content. Cause: Custom image parsing rules for art documents are not enabled. Default parsing only processes plain text content.
- Symptom: When asking a question about a single asset specification document, irrelevant chunk content is returned. Cause: Heading metadata from the document is not retained during chunking, leading to chaotic chunk associations.
How to Confirm the Configuration Is Correct
- Upload a single art requirement sheet PDF, view the parsed chunk list, and confirm each chunk corresponds to the specification description of a single asset.
- Upload an outsourced delivery package PDF with embedded original art, and check if the parsing result retains the text annotation content corresponding to the images.
- Upload multiple documents, select only one of them to initiate a question, and confirm the returned chunks only come from the target document.
- View the parsing task's timeout logs, confirm the task was not interrupted due to timeout, and falls within the range configured for
PARSE_FILE_TIMEOUT_SECONDS. - Confirm the currently used FastGPT version is v4.17.0 to avoid parameter configuration incompatibility.
The values provided 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 2026-10-19.