What data for this category looks like
Data typically comes from national public resource trading platforms, natural resource trading systems, property right exchange systems, and state-owned assets supervision systems, with a daily update frequency. Commonly handled daily documents include transfer announcements, trading guidelines, bid application forms, margin payment vouchers, transaction confirmation letters, listing result announcements, and property right transfer specifications. Most documents contain fixed fields such as subject ownership, trading terms, amount, and process nodes. Some documents include structured content like plot coordinates and property right details.
What constraints do these characteristics impose on the document parsing and chunking stage
Batch documents updated daily usually require stable parsing efficiency to avoid single-task timeouts disrupting the overall workflow. Multiple document types have distinct structural differences, so different chunking logic is typically used to preserve contextual connections. The parsing accuracy of core trading fields such as ownership and amount directly impacts the effectiveness of content for party building learning Q&A. Chunking that splits critical information must be avoided. Some documents include structured attachments, so the link between attachment content and main text must be maintained to ensure full association of policy requirements and trading scenarios during Q&A.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1024 MB | Adapt to batch-uploaded collections of trading documents in a single batch, avoid interruptions from large file uploads |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handle long documents with multi-page ownership descriptions and bidding processes, ensure complete parsing |
chunkSize | 800–1200 characters | Balance contextual integrity of ownership clauses and process descriptions in trading documents, avoid splitting critical trading terms |
chunkSplitter | By paragraph + custom delimiter | Adapt to the fixed paragraph structure of transfer announcements and property right transfer specifications, retain the binding relationship between policies and trading rules |
customPdfParse | Enabled | Parse structured PDF documents containing plot coordinates and property right details, restore original layout and field associations |
chunkSettingMode | Manual configuration | Adjust chunking parameters for different document types, meet the requirement of linking policy clauses and trading cases in party building learning Q&A |
Three common misconfigurations
- Uploading a 3 MB PDF results in an error, with the interface returning
{"detail":"Error message}. The cause is incorrect configuration ofUPLOAD_FILE_MAX_SIZEorPARSE_FILE_TIMEOUT_SECONDS, leading to large file parsing timeouts or exceeding upload limits. - After setting a custom delimiter, chunking results either merge multiple paragraphs or split a single paragraph. The cause is that the
chunkSizevalue does not match the document paragraph length, and chunking parameters were not adjusted based on the length of ownership clauses in trading documents. - Deploying Marker via Docker results in PDF processing errors. The cause is incorrect configuration of environment variables related to
customPdfParse, or incompatibility between the parsing tool and the current FastGPT version, leading to failed structured attachment parsing.
How to confirm the configuration is properly set
- Use a service deployed with FastGPT v4.17.0, upload a single property right transfer specification under 1 MB, check the parsed chunking results to confirm that ownership clauses are not split.
- Batch upload three different types of trading documents, check that no parsing tasks time out, confirm that the
PARSE_FILE_TIMEOUT_SECONDSconfiguration is effective. - Adjust the
chunkSizeparameter, compare the contextual integrity of chunking results to confirm that it meets the requirement of linking policies and trading scenarios in party building learning Q&A. - After enabling
customPdfParse, check whether structured fields in the PDF are correctly extracted, with no missing or misaligned content.
Note: The values listed above are common starting points. Adjustments should be made based on the reader's own specific 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.