What This Category of Data Looks Like
Typical data for this category originates from process and quality departments at foundries and die casting workshops. Daily documents include casting process cards, batching lists, heat records, mold histories, first-piece inspection records, material test reports, and nonconforming product review forms. Data sources include ERP material master data, melting furnaces and thermometers, spectrometers, MES heat records, mold management systems, customer drawings, and technical requirements. Update frequency follows per heat, per batch, or daily cycles. Documents contain specialized fields such as heat number, mold cycle, melting temperature, and alloy batching weight. Some documents are structured table files, while others are unstructured text.
Constraints Imposed on Document Parsing and Chunking
Mixed document types with differing structures require the parsing engine to support both table and text formats. High-frequency updated bulk data demands improved overall parsing and chunking efficiency. Specialized fields and units require retaining associations between fields during chunking, to avoid splitting critical parameter combinations. Data from cross-system sources must be bound to identifiers such as heat number and mold cycle, to ensure post-chunk content can be linked and matched during cross-review. Additionally, single document file sizes vary widely, from single-page batching lists to multi-page bulk heat records. Processing must support parsing and chunking for all file size ranges.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the length of core single-segment content in foundry heat records and process cards, avoids breaking the association between heat number and corresponding alloy batching |
chunkSplitter | Paragraph + semantic separation | Adapts to mixed document structures, preserves contextual integrity for batching list tables and process steps |
customPdfParse | Enable structured parsing mode | Supports table-style documents such as ERP-exported batching lists and heat records, fully extracts all fields and values within tables |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Covers parsing time for bulk batch heat records, prevents parsing timeouts for high-frequency updated documents |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports bulk uploads of complete mold histories and multi-heat summary records |
chunkSettingMode | Manual configuration | Adjusts chunking rules for foundry-specific fields such as heat number and mold cycle, ensures valid associations for cross-review |
Three Common Mistakes
- Symptom: Alloy batching data corresponding to a chunk index cannot be retrieved when calling the API. Cause: Contextual binding between heat number and associated parameters was not retained during chunking, leading to failed index association.
- Symptom: Parsing interrupts during bulk heat record uploads. Cause: The
PARSE_FILE_TIMEOUT_SECONDSconfiguration value is lower than the actual parsing time for bulk documents. - Symptom: Table content is split into scattered text blocks after uploading a batching list PDF. Cause: Structured parsing mode for
customPdfParsewas not enabled, causing tables to be parsed as plain text.
How to Verify Correct Configuration
Upload a standard heat record document, and check if the chunking result retains the contextual association between heat number, melting temperature, and alloy batching. Upload a mold history document larger than 500 MB, and confirm the upload and parsing process does not interrupt. Call the chunk retrieval interface of FastGPT v4.17.0, and verify that the specified index returns complete single heat parameter content. Upload a structured batching list PDF, and check if parsed chunks fully retain all fields and values from the original table.
The values provided are common starting points, and should be validated against the user’s own samples.