What the data for this category looks like
Core data for metal cutting machining scenarios comes from the full production cycle of machine processing plants and precision parts factories. This includes process routes, operation cards, tool lists, CNC programs, first-piece inspection records, patrol inspection forms, and measuring tool calibration records. Data sources cover product drawings and 3D models, CAPP/CAM systems, machine tool collection data, tool management systems, MES systems, and measuring and inspection tool data. Document update frequency is triggered by work orders, single-piece production, spot checks, or tool changes. Single documents contain structured tables, code snippets, and text descriptions. Most fields have clear units: for example, process dimensions use millimeters, tool life uses minutes.
Constraints on Document Parsing and Chunking
Mixed multi-type documents have structural differences, so parsing rules must adapt to different content formats. For example, tabular operation steps on operation cards must retain row-level associations. G-code blocks in CNC programs must be fully extracted without splitting. Frequently updated documents must avoid re-parsing full content. Chunking must retain contextual associations to ensure the integrity of process steps. Documents from different sources have large format differences. For example, PDFs exported from CAD may have hidden layers, so parsing parameters need targeted configuration. Clear units on fields require that cross-field process requirements are not split during chunking. For example, clamping plans and corresponding dimension parameters must be in the same chunk.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the length of process content for a single operation or single tool, avoids splitting cross-step clamping plans and dimension requirements |
chunkSplitter | Split by title + paragraph | Adapts to structured documents with operation numbers and tool numbers, retains contextual associations of process steps |
customPdfParse | Enable table recognition + code block extraction | Adapts to table and G-code formats of CNC programs and tool lists, prevents loss of key machining parameters |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to full parsing duration for large CNC program manuals and multi-page process documents |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to upload requirements for full machine process document packages and batch tool lists |
chunkSettingMode | Customize by document type | Configure chunking rules separately for CNC programs and operation cards, balances parsing needs for different content |
Three Common Misconfigurations
- Phenomenon: PDF chunking preview returns that the file content cannot be read, or CNC program code blocks are parsed as empty. Cause: The code block and table recognition configuration of
customPdfParseis not enabled, or the deployment version is incompatible with the parsing plugin. - Phenomenon: Feishu knowledge base cannot sync PPT and PDF documents, or no parsing result appears after upload. Cause: Parsing rules for the corresponding format are not configured, or the uploaded file exceeds the
UPLOAD_FILE_MAX_SIZElimit. - Phenomenon: A single chunked section contains process requirements for two consecutive operations. Cause:
chunkSizeis set too small, orchunkSplitterdoes not use the operation title as the chunk boundary.
How to Verify Correct Configuration
- Upload a test document containing operation cards, tool lists, and CNC programs, check if the chunking preview retains complete single-operation content and G-code blocks.
- Test uploading a large process manual of around 100 MB, confirm no timeout errors occur during parsing.
- Deploy the parsing plugin for FastGPT v4.17.0, verify that complex formulas, tables, and code blocks can be parsed normally.
- Sync PPT process documents stored in Feishu, confirm chunked content generates normally after sync.
The values provided are common starting points and should be measured against the reader's own samples.