What data for this category looks like
Daily documents for this category include batching lists, smelting logs, electrolyzer condition logs, laboratory reports, anode and cathode quality records, environmental protection ledgers, and more. Data sources include concentrate and auxiliary material analysis reports, DCS system data, cell voltage and current monitoring data, laboratory test results, and energy and environmental protection monitoring records. Updates occur in real time upon completion of each batch of raw materials, each furnace, or each tank operation. Some tank condition logs are updated at minute-level intervals. Most documents are structured tables and numeric records, with fields including mineral composition, current parameters, metal recovery rate, impurity content, and others. Units include percentage, ampere, ton, cubic meter, and more.
What constraints these characteristics impose on document parsing and chunking
This category has a high proportion of structured tables. Chunking must avoid splitting associated data across rows and columns to prevent loss of associated information such as batching ratios and tank condition parameters. High-frequency real-time updated documents require the parsing process to have low latency, so single-document parsing time must be controlled. Professional fields such as mineral impurity content and current density must retain complete context, avoiding chunk truncation of professional terms and unit combinations. Documents with mixed multi-source data must support automatic adaptation of parsing rules based on document type, ensuring consistent parsing of smelting logs and tank condition records in different formats.
Configuration settings
The following configuration items apply to FastGPT v4.17.0:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the structure of this category's documents, which mostly contain numeric values and short professional paragraphs, avoiding splitting associated content across fields |
chunkSplitter | Adaptive to document structure | This category includes multiple types of structured documents such as batching lists and electrolyzer condition logs. Adaptive chunking preserves the integrity of tables and paragraphs |
customPdfParse | Enable MinerU adaptation mode | Resolves table misalignment issues in generic parsing of PDF smelting reports and environmental protection ledgers for this category, improving parsing accuracy |
PARSE_FILE_TIMEOUT_SECONDS | 120–180 seconds | Some documents in this category contain historical data from multiple furnace runs, requiring sufficient time to complete full parsing and avoid timeout truncation |
chunkSettingMode | Manual classification configuration | Adjust chunking parameters separately for different document types such as batching lists and laboratory reports, adapting to content characteristics of different formats |
Three common mistakes
- Symptom: Parsing result is empty or formatted incorrectly after uploading a DOCX document. Cause: Custom parsing rules are not enabled for structured DOCX batching lists of this category, and generic parsing cannot recognize the embedded professional table format.
- Symptom: Electrolyzer condition tables in some PDF documents cannot be chunked correctly, with misaligned table rows and columns after parsing. Cause: MinerU adaptation mode is not enabled, and generic PDF parsing cannot adapt to the complex header and multi-page table structure of documents in this category.
- Symptom: Out-of-vRAM error or GPU unrecognized error occurs after deploying a custom PDF parsing service. Cause: GPU resources are not mapped to the Docker container, and the correct MinerU image version is not matched based on the graphics card model and CUDA version, resulting in failed resource invocation.
How to verify correct configuration
- Upload a typical batching list document of this category, and check that the parsed result tables fully retain rows, columns and numerical values, with no misalignment or truncation.
- Review parsing logs to confirm that the
PARSE_FILE_TIMEOUT_SECONDSparameter is active, with no timeout errors. - Test chunking effects separately for different document types such as smelting logs and environmental protection ledgers, confirming that chunk lengths fall within the preset
chunkSizerange. - Check the container status of the custom parsing service, confirming that GPU resources are correctly mapped, with no out-of-vRAM errors.
The values provided here are common starting points and should be measured against the reader's own samples.