What the data for this category looks like
Internal office documents for non-ferrous metal smelting mainly come from production operation logs, equipment maintenance manuals, safety compliance regulations, employee attendance ledgers, and raw material purchase reports. Production logs and real-time operation data are updated per shift or daily. Safety regulations and process manuals are revised quarterly or annually. Document types include multi-page long PDF process specifications, structured tables with multiple columns of numerical values such as material composition tables and furnace temperature parameter tables, and plain text employee service notices. Fields include smelting furnace temperature, raw material ratio, operation duration, and more. Common corresponding units are ℃, kilograms, and hours.
What constraints these characteristics impose on the document parsing and chunking link
The presence of long PDF process specifications means chunks cannot be overly fragmented. Otherwise, the contextual connection of process steps will be broken. If field and unit information from structured tables is lost, subsequent searches cannot match accurate business data. The volume of real-time updated operation logs fluctuates greatly. Parsing must adapt to different file sizes. In scenarios with mixed document types, unified chunking rules cannot cover all business needs. Parsing and chunking parameters must be adjusted for different document types.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to document scenarios with both long process paragraphs and short operation records, balancing semantic integrity and retrieval accuracy |
chunkSplitter | Split by paragraph + title | Matches the hierarchical structure of documents containing process chapters and equipment descriptions, retains title information to improve the semantic accuracy of chunks |
customPdfParse | Enable structured parsing mode | Retains complete field and unit information for structured content such as material composition tables and equipment parameter tables in documents |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the parsing needs of long PDF documents with more than 50 pages per file, avoids parsing failure caused by timeout |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Covers the upload needs of large-volume files such as annual safety compliance documents |
chunkSettingMode | Manual configuration mode | Adapts to chunking differences across different types of documents including logs, regulations, and ledgers, allowing flexible parameter adjustment |
Three common misconfiguration issues
- Phenomenon: marker-pdf call request error or local deployment connection failure. Cause: The network port of the custom PDF parsing service is not configured correctly, or docker container port mapping does not take effect.
- Phenomenon: Table fields are missing after uploading and parsing documents. Cause: The structured parsing mode of
customPdfParseis not enabled, causing table content to be flattened into plain text, losing field and unit information. - Phenomenon: Parsing task times out and fails. Cause: The value set for
PARSE_FILE_TIMEOUT_SECONDSis lower than the actual parsing time of a single long document.
How to confirm the configuration is properly set
- Upload a smelting process PDF containing a material composition table, check whether complete table structure and field information are retained in the parsing results of FastGPT v4.17.0.
- Upload a long document with more than 50 pages, confirm that the parsing task completes within 600 seconds with no timeout error prompts.
- Adjust
chunkSizeto different ranges, test the semantic coherence of chunking results, and confirm that it meets the contextual requirements of business retrieval. - Upload a test file close to 1000 MB, confirm that the parsing task can be submitted normally with no file size limit error.
The values provided 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.