Reference only; verify against the target FastGPT release and deployment documentation.
What the Data for This Category Looks Like
Power generation equipment manufacturing business documents primarily originate from internal enterprise equipment operation and maintenance manuals, production process specifications, quality inspection reports, bidding compliance documents, and industry regulatory technical standard documents. Document update schedules adjust alongside new equipment launches, process iterations, or regulatory requirements, with no fixed cycle. Most documents feature fixed chapter modules, with embedded structured parameter tables, equipment parameter details, and long-form operation instructions. Fields often involve professional units such as megawatts, kilovolts, and operating hours. Some documents include equipment drawing annotations and bill of materials.
Constraints on Document Parsing and Chunking
The long text volumes and dense structured content of power generation equipment manufacturing documents impose multiple constraints on document parsing and chunking. Long paragraphs and coherent business process content require chunking to retain semantic context and avoid splitting operation steps or troubleshooting logic. Embedded parameter tables and detailed fields require parsing tools to preserve original formatting and row-column associations, preventing disconnection between parameter values and their corresponding descriptions. Individual documents can reach hundreds of megabytes in size, which may trigger upload, parsing timeout, or memory limit issues. Content with dense professional terminology requires chunk boundaries aligned with business semantics to ensure accuracy of extracted question-answer pairs.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Power generation equipment documents contain long paragraphs and professional parameters. This range preserves semantic integrity and avoids splitting parameters from their accompanying descriptions |
chunkSplitter | Split by semantic paragraphs | Adapts to documents such as equipment operation and maintenance manuals and process specifications that contain coherent business processes, preventing splitting of operation steps |
customPdfParse | Enable structured parsing mode | Adapts to parameter tables and equipment drawing annotations within documents, preserving original formatting and field associations |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Covers the size limit of large individual equipment manuals and prevents upload interruptions |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Reserves sufficient processing time for large file parsing and avoids premature termination due to timeout |
chunkSettingMode | Manual configuration | Allows adjustment of chunking parameters for different document types such as operation and maintenance manuals and quality inspection reports, to fit business scenarios |
Three Common Misconfigurations
- Uploading large PDF documents to the knowledge base results in an "offset out of range" error when parsing progress reaches approximately 90%. Cause: The
UPLOAD_FILE_MAX_SIZEandPARSE_FILE_TIMEOUT_SECONDSparameters were not adjusted, leading to large file parsing timeout or exceeded memory usage limits. - Uploading the same power generation equipment document via the file collection API and direct platform upload produces inconsistent chunking results. Cause: The
chunkSettingModeandchunkSplitterparameters were not unified, as the API and platform use different default chunking rules. - Executing chunking directly on equipment operation documents fails to extract valid business question-answer pairs. Cause: Structured parsing via
customPdfParsewas not enabled, and chunking was not performed by semantic paragraphs, splitting the context required for question-answer associations.
How to Verify Proper Configuration
- Upload a single power generation equipment manual larger than 500 MB, confirm that parsing progress completes without offset errors.
- Compare the chunking results of the same document uploaded via the API and direct platform upload, confirm that the chunking logic is consistent.
- Review the parsed chunk content, confirm that equipment parameter tables and operation step paragraphs are not split.
- Test extraction of business question-answer pairs from the document, confirm that associated context is fully retained within a single chunk.
The values listed are common starting points and should be evaluated against local 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.