What the data for this category looks like
The daily documents for this category include technical proposal documents, customer requirement research forms, quotation lists, and demo scripts. Data sources include customer interview records, tender documents, vendor product materials, and historical project proposals. Documents are updated per order or weekly, and are typically structured by project phase, requirement items, and implementation boundaries across chapters. Fields include project identifiers, requirement items, product parameters, quotation details, etc. Units adjust flexibly based on document type, with no unified fixed format.
What constraints these characteristics impose on the document parsing and chunking workflow
Document sources cover multiple formats such as PDF and Word, and some documents contain complex tables and embedded images. This requires parsing tools to support multiple formats and retain core layout structures. Documents are updated frequently and vary widely in single-document size, so a balance must be struck between parsing efficiency and accuracy. Core content is concentrated in chapters such as requirement items and implementation boundaries. Chunking must accurately extract this content and avoid mixing in irrelevant information. Errors can lead to consequences such as losing bidding points or making overscope commitments, so parsed results must fully retain key fields and paragraph structures.
How to set configurations
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Aligns with the paragraph length of single chapters in pre-sales proposals, avoiding splitting individual requirement items or implementation boundary content |
chunkSplitter | Split by heading hierarchy | Matches the chapter-based structure of pre-sales documents, preserving the integrity of core modules such as requirements and solution roadmaps |
customPdfParse | Enabled | Adapts to PDF formats of tender documents and vendor materials, retaining original layout and table structures |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses parsing time for large tender documents or multi-page vendor materials, preventing mid-process interruptions |
chunkSettingMode | Manual mode | Allows adjustment of chunking rules for special documents such as quotation lists and demo scripts, adapting to unstructured content |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Meets upload requirements for large historical project proposals or multi-page tender documents |
Three common errors
- Symptom: Image links are lost after importing a Word document, only image placeholders remain. Cause: Custom parsing configuration is not enabled, and the original path and domain information of images are not retained.
- Symptom: Timeout errors occur when parsing large tender documents. Cause: The
PARSE_FILE_TIMEOUT_SECONDSsetting value is too small, failing to cover the full parsing time of large documents. - Symptom: A single requirement item is split into two separate chunks after chunking. Cause: The
chunkSizesetting value is too small, failing to match the full character length of a single requirement.
How to confirm configurations are properly set
- Upload a typical tender document in PDF format, check if the parsed content retains the original table and heading hierarchy structure.
- Upload a Word-format requirement research form that includes images, check if the parsed results contain complete domain information in image links.
- Upload a historical project proposal with more than 100 pages, check if parsing completes within 600 seconds with no error prompts.
- Adjust the
chunkSizeparameter, verify that a single requirement item is fully retained in a single chunk and not split.
The values provided are common starting points and should be evaluated against samples relevant to the specific use case.
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.