What the data for this category looks like
Data comes from various documents produced by internal office systems. These include talent information Excel spreadsheets, project feasibility study PDF reports, internal management system Word files, and monthly attendance reports. Three update frequencies apply: real-time, monthly, and quarterly. Talent spreadsheets update in real time when personnel changes occur. Project documents update as project approval progresses. System documents only update when revised.
Document structures include structured tables, multi-chapter long texts, and mixed-format files. Fields cover personnel numbers, job levels, investment amounts, execution cycles, and more. Units include ten thousand yuan, months, person-times, and more.
Constraints on document parsing and chunking
Structured Excel spreadsheets contain multiple columns of associated fields. Retain column mapping relationships during parsing. This avoids data failure caused by field misalignment.
Multi-chapter long text documents have clear semantic boundaries. Match chapter hierarchy during chunking. This avoids cross-chapter splitting that breaks semantic integrity.
Small real-time updated files, such as personnel change notifications, need fast parsing processes. This reduces unnecessary waiting time.
Mixed-format files include both tables and body paragraphs. Support parsing logic for multiple content types. Ensure complete extraction of table data and no truncation of text paragraphs.
How to set the configuration
This configuration is formulated for FastGPT v4.17.0. It is adjusted for the characteristics of internal office documents of state-owned capital investment companies.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the mixed structure of state-owned capital investment company documents, which contain both structured table fields and long text paragraphs, avoiding single chunks that are too long or too fragmented |
chunkSplitter | By paragraph + semantic boundary | Matches the chapter and paragraph divisions of internal documents, preserving semantic integrity |
customPdfParse | Enabled | Extracts complete content including charts and formulas in project feasibility study PDFs through custom parsing rules |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Meets the upload requirements for large project feasibility study documents, preventing parsing failure due to oversized files |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Reserves sufficient parsing time for large mixed-format documents, preventing timeout interruptions |
chunkSettingMode | Manual configuration | Adjusts chunking parameters for different types of documents, such as Excel spreadsheets and management system documents, to meet specialized scenario requirements |
Three common configuration errors
- Symptom: After importing a talent information Excel file, some fields are empty or misaligned. Cause: No matching parsing rules are configured for structured tables. The default chunking logic does not retain column mapping relationships, resulting in incorrect field splitting.
- Symptom: After uploading a large project feasibility study PDF, the parsing process times out and interrupts. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted. The default timeout duration is insufficient to complete full parsing of large mixed-format documents. - Symptom: When using a third-party deployed large model, file parsing returns an error. Cause:
customPdfParseis not enabled to adapt to third-party parsing interfaces. This causes format incompatibility that prevents normal extraction of document content.
How to verify correct configuration
- Upload a single standard-sized Excel talent spreadsheet. Check if parsed fields correspond one-to-one with the source file. Confirm the chunking rules retain the column structure.
- Upload a large PDF project report. Monitor the total parsing time. Confirm the
PARSE_FILE_TIMEOUT_SECONDSsetting covers the full parsing process. - Upload three test document types: plain text, table, and mixed format. Check if chunking results split correctly by content type. Confirm the
chunkSplitterconfiguration takes effect. - Trigger a plain text question-and-answer process. Confirm the system does not trigger document parsing logic. Confirm the
chunkSettingModeclassification rules correctly distinguish question-and-answer text from documents to be parsed.
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.