What Data Looks Like for This Category
Typical entities in this category are coatings, resin, additive, agrochemical intermediate, and specialty chemical enterprises. Daily documents include batch production instructions, feeding material verification slips, reaction process records, cleaning records, inspection reports, deviation and change orders. Data sources include formulation and process libraries, raw material batches, reactor DCS systems, weighing systems, LIMS, and warehouse systems. Update frequency follows per production batch or each critical step. Documents contain fields such as batch number, feeding amount (kg/L), reaction temperature (℃), time parameters, and cleaning verification items. Some formulation fields involve confidential content.
Constraints on Document Parsing and Chunking
The production mode that switches between multiple product variants per batch requires document parsing to split content by batch and critical steps, preventing cross-batch information mixing. The need for precise fields with units means parsing processes must retain original unit information, otherwise subsequent audit judgments will be affected. High-frequency updates of single-batch documents require parsing services to have efficient processing capabilities, avoiding excessive waiting time. The presence of confidential formulation fields requires filtering sensitive content during chunking, or only splitting content within authorized scope. Structural differences across document types require parsing and chunking rules to adapt to different formats such as instruction-type, record-type, and report-type documents.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Fine chemical documents contain precise fields with units and step descriptions. This length retains complete single-step context, avoiding splitting critical feeding or reaction records |
chunkSplitter | Split by paragraph + title | Documents in this category have different title structures such as batch production instructions and reaction records. Splitting by title ensures content from the same production step is concentrated in one chunk |
customPdfParse | Enabled | PDF documents in this category often include tabular content such as feeding slips and inspection reports. Enabling custom PDF parsing preserves table structure and original typesetting |
UPLOAD_FILE_MAX_SIZE | 1000 MB | A single production batch may include multiple associated documents like DCS logs, feeding material verification slips, and cleaning records. This upper limit supports batch document upload needs |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Batch parsing of multiple documents or parsing of long DCS logs requires sufficient time. This range covers parsing duration for most single-batch documents |
chunkSettingMode | Manual configuration | Documents in this category have large structural differences. Manual configuration allows adjusting chunking rules for different document types, adapting to different business scenarios |
Three Common Misconfigurations
- Symptom: GPU cannot be recognized after container deployment, and the log reports a memory overflow error. Cause: Host GPU resources were not correctly mapped to the FastGPT container, and there is a compatibility issue between PDF-Marker v0.1 and CUDA 12.8.
- Symptom: Images retain domain names when importing Word documents, and image paths are missing when importing after conversion to Markdown. Cause: Custom parsing image path replacement rules were not configured, and context information associated with images was not retained during chunking.
- Symptom: Timeout errors occur when batch parsing multiple batch documents. Cause: The value of
PARSE_FILE_TIMEOUT_SECONDSis set too low, which cannot cover the time required for multi-document parsing.
How to Verify Proper Configuration
The following verification steps are based on FastGPT v4.17.0.
- Upload a single typical reaction process record PDF, check whether the parsed Markdown retains fields with units such as feeding amount and reaction temperature.
- View the FastGPT container runtime logs, confirm that the CUDA device is properly recognized, and there are no memory overflow related errors.
- Upload multiple associated documents for a single production batch, check whether the parsing task is completed within the preset timeout period.
- Randomly select chunks of parsed documents, confirm that content from the same production step is not split across different chunks.
The values provided here are common starting points and should be evaluated against the user's own samples.