What the Data for This Category Looks Like
The data used for internal office and employee services of state-owned industrial funds originates from internal investment research files, project due diligence reports, office approval forms, employee management system documents, and monthly operation reports. Updates occur alongside project progress, system revisions, and daily office work, with no fixed cycle. Document formats include dozens-of-page PDF due diligence reports, monthly operation reports with structured tables, plain-text internal notices, and approval forms. Fields include project code, investment amount, duration, approval nodes, and more. Units involved include ten thousand yuan, year, person-time, and others.
Constraints Imposed on Document Parsing and Chunking
The existence of long-text due diligence reports requires that chunking preserves core semantic integrity, and avoids splitting cross-page project logic. Operation reports with structured tables require the parsing process to retain row and column associations of tables, preventing table data from breaking after chunking. Fields with clear units such as project code and investment amount require accurate identification of the binding relationship between fields and corresponding values, preventing separation of units and values. The diverse formats of daily office documents require parsing to adapt to internal notices and approval forms with different layouts, avoiding format loss that impacts subsequent use.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the semantic unit length of dozens-of-page due diligence reports, avoids single chunks that are too long or too short |
chunkSplitter | Split by paragraph + title | Matches the title hierarchy and paragraph logic of internal documents, preserves semantic coherence |
customPdfParse | Enabled | Targets PDF-format due diligence reports and operation reports, retains table and layout structures |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the parsing time of long PDF documents, prevents parsing interruptions due to timeout |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Accommodates single dozens-of-page due diligence report files |
chunkSettingMode | Manual configuration | Flexibly adjusts chunking parameters for different types of fund documents |
Three Common Mistakes
- Symptom: The PDF parsing process returns error logs, indicating parsing failure. Cause: The
customPdfParseparameter is not enabled, and the default parsing logic cannot adapt to the long PDF due diligence report format used by industrial funds. - Symptom: Uploaded files are not directly passed to the large model node, but are automatically parsed into plain text by the system before being sent. Cause: Relevant parameters for skipping default text parsing are not correctly configured, causing files to be processed via the default workflow.
- Symptom: When calling the API to create a file collection, the specified PDF parsing parameters do not take effect. Cause: The parameter value of
customPdfParseis not correctly included in the API request, causing the default parsing logic to be used.
How to Verify Successful Configuration
- Upload a single-page PDF operation report, check whether the parsed text retains the row and column structure of the table.
- Check the file parsing log to confirm that no timeout interruption prompt appears, matching the configured duration of
PARSE_FILE_TIMEOUT_SECONDS. - Initiate a create file collection request in the API debugging tool, include the
customPdfParseparameter, and confirm that the parsing configuration has been applied in the returned result. - Test the chunked text fragments, confirm that the single chunk length falls within the configured range of
chunkSize, and no chunking across semantic units occurs.
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 2026-09-28.