Reference only; verify against the target FastGPT release and deployment documentation.
What the Data for This Use Case Looks Like
Internal office documents for metal material processing originate from four main sources: process specifications, raw material quality inspection reports, equipment operation and maintenance logs, and employee onboarding training manuals. Data covers the full production workflow. Process specifications are updated quarterly. Quality inspection reports are generated for each batch of incoming raw materials. Equipment operation and maintenance logs are updated per shift. Documents include structured tables (such as composition percentages and dimensional tolerances), long-text process steps, and records with fixed fields. Fixed fields include furnace number, batch number, and hardness value. Common units are MPa, mm, %, and tons.
Constraints on Parsing and Chunking
Metal material processing document characteristics create multiple constraints for parsing and chunking:
- High proportions of structured tables include multi-unit fields. Standard chunking logic may split individual cells, breaking associations between composition and tolerance data.
- Long-text process steps have continuous operation logic. Chunking must retain contextual links between preceding and following steps to avoid invalid process guidance.
- Batch-generated quality inspection reports have large file sizes, which may trigger upload and parsing capacity limits.
- Fixed fields such as furnace number and batch number must retain metadata alongside each chunk, to ensure retrieval can link to corresponding production batches.
How to Configure
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the length of long-text process steps and structured tables in metal material processing documents, avoiding splitting key logic units |
chunkSplitter | Split by paragraph + header | Retains contextual links between tables and corresponding process descriptions, preventing structured data from being split |
customPdfParse | Enabled | Adapts to quality inspection reports and process specifications with complex tables and parameters, retaining original layout and field structure |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Accommodates batch upload requirements for single-batch quality inspection reports, preventing large file upload failures |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses parsing time for complex process specifications, avoiding parsing interruptions due to timeouts |
chunkSettingMode | Manual mode | Customizes chunking rules to match business needs for documents with high proportions of structured data |
Three Common Misconfigurations
- Phenomenon: After uploading process specification files larger than 10 MB, some chunks in the generated chunk list fail vectorization, and the interface displays a "vectorization exception" prompt. Cause: The
UPLOAD_FILE_MAX_SIZEandchunkSizeconfigurations were not adjusted. When splitting large files, the vector model’s input limits for long text were not accommodated, causing some chunks to exceed the model’s supported length range. - Phenomenon: When parsing structured records from equipment operation and maintenance logs, some fields are empty or incorrectly associated after chunking. Cause:
customPdfParsewas not enabled, or the chunking rule split by header was not configured. Structured data was incorrectly split, resulting in lost field associations. - Phenomenon: Vectorization exceptions persist after multiple retries. Cause: The exception is caused by unreasonable chunking rules. Retries only resolve temporary upload or parsing interruptions, and cannot fix chunking logic errors. This type of exception is unrelated to temporary network failures.
How to Confirm Proper Configuration
- Upload a single typical quality inspection report file, view the parsed chunk list, and confirm that tables and corresponding descriptions are not split.
- Check the values of
UPLOAD_FILE_MAX_SIZEandPARSE_FILE_TIMEOUT_SECONDSin system configuration items, to match current business file size and parsing time requirements. - Randomly sample chunked content, confirm that fixed fields such as furnace number and batch number are fully retained alongside each chunk.
- Submit test data after chunking, and verify that the vector generation process has no abnormal errors.
The values provided are common starting points and should be validated against the relevant use case’s sample data.
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.