Reference only; verify against the target FastGPT release and deployment documentation.
What data in this category looks like
Data related to administrative inquiries and responses in the steel smelting field mainly comes from internal enterprise documents: smelting process specifications, equipment maintenance records, safety operation manuals, government service process documents, and user inquiry archived documents.
Update frequency varies by document type:
- Process specifications are updated irregularly alongside technical iterations
- Equipment maintenance records are updated daily or weekly
- Government service process documents are updated synchronously with policy adjustments
Document types include long-text PDFs, structured Excel spreadsheets, and standardized process documents. Fields include parameters with clear units: furnace number, smelting batch, temperature (degrees Celsius), material weight (tons), and operation duration (hours).
What constraints do these characteristics impose on document parsing and chunking
Long-text PDFs and multi-page Excel documents make up a large share of the dataset. Parsing and chunking logic must balance content integrity and retrieval accuracy, to avoid splitting that breaks the correlation between process steps or table fields.
Structured tables contain numerical fields with clear units. Parsing must retain the binding between fields and units, otherwise parameter confusion will occur in subsequent inquiry responses.
A large number of large-volume maintenance documents are uploaded in batches. Parsing services must have stable queue processing capabilities to avoid batch task interruptions.
Administrative service process documents have strong node coherence. Chunking must retain the contextual association of the process, and must not arbitrarily split cross-node operation instructions.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the length of process steps in steel smelting documents, retains semantic completeness of single chunks while avoiding overly large chunks that reduce retrieval matching effectiveness |
chunkSplitter | By paragraph + semantics | Adapts to documents with long paragraph process descriptions and structured tables, retains coherence of operation logic and table structure during splitting |
customPdfParse | Enabled | Adapts to embedded tables, encrypted forms, and complex layouts commonly found in steel smelting PDF documents, retains field and unit information from the original document |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports uploading large smelting archives containing multiple batches of test reports per file, meets batch document processing requirements |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Covers parsing time for large-volume PDFs and multi-page Excel documents, avoids parsing task interruptions due to timeout |
chunkSettingMode | Custom mode | Allows adjustment of chunking parameters for different types of smelting documents (specifications, maintenance records, inquiry archives) to adapt to diverse business scenarios |
Three common mistakes
- Phenomenon: When deploying
customPdfParseprivately to parse PDFs, FastGPT reports a timeout but the parsing service returns success. Cause:PARSE_FILE_TIMEOUT_SECONDSis set too short, failing to cover the full parsing time of large-volume smelting documents. - Phenomenon: After uploading an Excel smelting material consumption sheet, the parsing result has no table content. Cause: Corresponding table parsing configuration is not enabled, the default parsing logic does not support structured table field extraction and retention.
- Phenomenon: Locally deployed models do not reference parsed document content after calls. Cause:
chunkSizeexceeds the model's context window range, making it impossible to match valid chunked content during retrieval.
How to confirm the configuration is properly set
- Upload a typical smelting process specification PDF containing tables and long paragraphs, check if the parsed text retains the row and column structure of the table and unit fields.
- Adjust
chunkSize, then use FastGPT's chunk preview function to confirm that key process steps or cross-node operation instructions are not split in single chunks. - Upload a single large document that meets the
UPLOAD_FILE_MAX_SIZErequirement, verify that the parsing process does not experience timeout interruptions or task failures. - After configuring
PARSE_FILE_TIMEOUT_SECONDS, simulate batch uploads of multiple smelting maintenance documents, confirm that the parsing queue does not terminate abnormally.
The values provided here are common starting points, and should be evaluated against the user'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.