What this category of data looks like
Data for nuclear power operation internal office and employee services comes primarily from reactor operation logs, equipment operation and maintenance ledgers, employee training manuals, internal approval documents, and office collaboration files. Update rhythms vary by data type: operation logs update in real time per shift, maintenance ledgers update when equipment overhauls are completed, and employee manuals and training materials only update when policies are revised. Most documents are in PDF or DOCX format. Some documents include structured fields such as equipment IDs, operation parameters with units like MPa or ℃, operator identifiers, and timestamps. Most documents have fixed chapter divisions and hierarchical headings.
What constraints these characteristics impose on the document parsing and chunking step
Reactor operation logs update in real time per shift. The parsing process must support incremental parsing to avoid reprocessing full historical datasets. Equipment operation and maintenance ledgers contain structured parameters with professional units. The parsing process must accurately extract the binding relationship between equipment IDs, operation parameters, and their units. This prevents broken associations after chunking. Long documents such as annual operation reports have nested hierarchical chapter headings. Chapter associations must be retained to maintain context integrity. Internal office approval documents have circulation traces and approval node information. This information must be fully captured during parsing. Otherwise, process tracing requirements for employee service scenarios cannot be met.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Nuclear power operation documents often contain professional parameters and long sentences. This range preserves the association between parameters and context, avoiding truncation of critical information during chunking |
chunkSplitter | Split by headings and paragraphs | Nuclear power documents typically have fixed chapter divisions. Splitting by headings preserves chapter context integrity, which works well for long operation reports and training manuals |
customPdfParse | Enable structured parsing mode | Some equipment ledger documents include tables and structured fields. Enabling this mode accurately extracts the binding relationship between fields and their units |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Nuclear power annual operation reports can be hundreds of pages long. This upper limit supports complete uploads of large documents |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large PDF documents take longer to parse. This duration prevents timeout interruptions to the parsing process |
chunkSettingMode | Manual configuration | Adjustment of chunking parameters is needed for different document types. Manual mode supports diverse scenarios including operation logs, ledgers, and training documents |
Three common configuration mistakes
Phenomenon: After deploying the frontend to a server, uploading a document returns a 404 error from the document parsing node. File content cannot be read. Cause: The server environment is not properly configured with file upload path permissions. Or the FastGPT service does not map the local file storage directory to the corresponding server path.
Phenomenon: RAG retrieval results only return chunked main text. Auxiliary information such as chapter headings or equipment IDs is not included. Cause: chunkSplitter is configured to only split by paragraphs. Hierarchical heading associations are not retained. Or the structured extraction function of customPdfParse is not enabled.
Phenomenon: After parsing a large annual operation report, an empty result is returned. Or the parsing process times out and terminates. Cause: The PARSE_FILE_TIMEOUT_SECONDS configuration value is lower than the actual parsing time. Or the UPLOAD_FILE_MAX_SIZE setting is too small. This causes the file to be truncated before parsing fails.
How to confirm the configuration is correct
Upload a typical nuclear power operation ledger document. Check if the parsed result includes the binding relationship between equipment IDs, operation parameters, and their corresponding units. This confirms that the customPdfParse configuration takes effect. Upload a long training manual document with chapters. Check if the chunked result retains the chapter heading association between adjacent chunks. This confirms that the chunkSplitter and chunkSettingMode configurations meet expectations. Upload an annual report document with more than 500 pages. Check that the parsing process does not show timeout errors and completes within a reasonable duration. This confirms that the PARSE_FILE_TIMEOUT_SECONDS configuration adapts to the document scale. Check the upload limit prompt in the file upload interface. Confirm that it matches the UPLOAD_FILE_MAX_SIZE configuration value. This verifies that the upload limit meets expectations.
The values provided here are common starting points, and should be evaluated 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.