FastGPT v4.17.0
What the data for this use case looks like
The party building learning-related data of state-owned capital investment companies mainly comes from official documents issued by superior party committees, internal party building work ledgers, party member learning courseware, and annual party building summary documents. The update rhythm is that superior documents are updated synchronously with their release cycle, while internal ledgers and learning materials are updated regularly on a quarterly or monthly basis. Document types include red-headed official PDF documents, multi-column Excel ledgers, Word-format learning materials, and PPT courseware. Fields include document number, issuing unit, learning date, participant information, etc. There is no unified fixed unit; adjustments are made flexibly based on the document type.
What constraints do these characteristics impose on the document parsing and chunking link
The multi-type characteristics of party building documents impose multiple constraints on the document parsing and chunking link. Red-headed official documents contain non-body content in headers and footers, which must be accurately filtered before chunking to avoid mixing irrelevant information. Excel ledgers contain multi-column structured fields, and the field association of each row must be retained during chunking to prevent splitting cross-row and cross-column data. Batch-uploaded quarterly ledger files have large volumes, requiring reasonable adaptation of upload and parsing limits. Long documents such as annual party building summaries require controlling chunk length to retain contextual coherence and avoid splitting policy statements.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Party building documents of state-owned capital investment companies are mostly official documents. A single chunk length adapts to contextual coherence and avoids splitting key policy statements |
chunkSplitter | Split by paragraph + title | Party building documents have clear chapter titles and paragraph structures. Chunking this way preserves the logical integrity of content |
UPLOAD_FILE_MAX_SIZE | 500 MB | Batch-uploaded quarterly party building ledger Excel files, annual summary PDF files, and other similar documents usually have large volumes, reserving sufficient upload space |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large multi-page PDF or complex Excel files takes a long time. This avoids mid-process interruptions |
customPdfParse | Enabled | Red-headed documents contain header and footer document numbers. Custom parsing can filter non-body content and improve chunking accuracy |
chunkSettingMode | Manual configuration | Party building documents have diverse formats. Manual adjustment can adapt to different types of files, including official documents, ledgers, and courseware |
Three common misconfigurations
- Symptom: After uploading an Excel party building ledger, the parsing result only shows the header row with no content. Cause: No chunking rules adapted for multi-column tables were configured, and the default chunking logic cannot recognize the field association of table rows.
- Symptom: When batch uploading monthly party building documents, parsing interrupts mid-process, and the task cannot be recovered after restarting the container. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted. Large file parsing timed out without triggering a retry mechanism, causing the task to get stuck. - Symptom: After uploading a red-headed document, the chunking result includes header document numbers and issuing units. Cause: The
customPdfParseconfiguration was not enabled, and the default PDF parsing retained non-body content from headers and footers.
How to confirm the configuration is properly set
- Upload a single party building red-headed document, review the parsed text content, and confirm that non-body content from headers and footers has been filtered.
- Upload a multi-column Excel party building learning ledger, check whether the parsed chunks retain complete field information for each row.
- Upload a batch of small party building documents, observe the completion status of parsing tasks, and confirm there are no mid-process interruptions.
- Adjust the
chunkSizeparameter, re-parse the long document, and check whether the chunked content retains complete policy statements.
The values provided are common starting points and should be measured against applicable 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.