FastGPT v4.17.0
What this type of data looks like
Documents and materials for power generation equipment manufacturing mainly come from technical specifications supporting product design drawings, bulk procurement contracts, unit operation logs, and factory quality inspection reports. Data is updated in batches when new model projects are initiated, and daily operation documents are updated monthly. Most documents have a fixed chapter structure, with fields such as rated power, rotor speed, and insulation grade. The corresponding units are kW, r/min, and %. Some long documents include parts lists and compliance attachments.
What constraints these characteristics impose on document parsing and chunking
Fixed chapter structures require retaining chapter boundaries during chunking. Cross-chapter content must not be split into the same chunk, as this will reduce subsequent retrieval accuracy. Long documents contain large numbers of professional parameters and parts lists. Chunk length must match the semantic completeness of professional terms. Over-splitting that breaks terms must be avoided. Fields with units will lose their parameter-unit correspondence if split. All parts of a complete parameter item must be included in a single chunk. Some documents include compliance attachments. Parsing logic must identify attachment types and handle chunking separately. Attachment content must not be mixed with main document content.
How to set the configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the length of professional terms in power generation equipment manufacturing documents, avoids splitting complete parameter items |
chunkSplitter | Split by chapter titles | Matches the fixed chapter structure of documents, retains semantic completeness of content |
customPdfParse | Enabled | Adapts to PDF documents with embedded parts lists and compliance attachments, retains original typesetting structure |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Addresses parsing time for long technical specifications, avoids timeout interruptions |
UPLOAD_FILE_MAX_SIZE | 500 MB | Supports batch upload of full technical documents for large units |
chunkSettingMode | Manual configuration | Makes targeted adjustments for professional fields and units, adapts to specialized scenario requirements |
Three common errors
- A 504 timeout error occurs when parsing large technical specifications. This happens because the
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted. The default timeout duration is insufficient to complete parsing of long documents. - Calls to MCP services fail when parsing documents with embedded complex graphics. Only some MCP services run normally. This happens because
customPdfParseis not configured to adapt to PDF parsing logic for documents with embedded parts lists and compliance attachments. Some services do not support documents with complex typesetting. - After uploading a Word document containing images, retrieval cannot recall content associated with the images. This happens because associated image parsing configuration is not enabled. Only text content is extracted, and image embedding information is not processed.
How to confirm the configuration is correct
- Upload a typical power generation equipment technical specification. Review the parsed chunk results to confirm that chapter boundaries are not disrupted.
- Test uploading a single document that does not exceed the configured size limit. Confirm that the upload and parsing processes do not interrupt.
- Trigger chunk configuration verification. Confirm that the
chunkSizevalue adapts to the length of professional terms, with no over-splitting occurring. - Upload a document with embedded images. Verify that image-associated information is retained after parsing.
The values listed are common starting points. They should be measured against target 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.