What the data for this category looks like
This category includes typical entities such as carbonated drink, juice, tea, functional beverage, and packaged drinking water factories. Daily generated documents include formula sheets, water treatment records, blending records, sterilization or aseptic filling records, packaging material inspection reports, and finished product inspection reports. Data sources cover water source and raw material testing systems, formula management systems, online sugar and acidity monitoring equipment, filling equipment, visual inspection equipment, and LIMS systems. Updates occur per batch, per shift, or through continuous collection. Most document structures include fields such as batch number, testing values, and operating parameters. Some records include standardized unit labels, such as pH value, turbidity, filling liquid level height, and others.
What constraints do these characteristics impose on the "document parsing and chunking" link
High-frequency, continuously collected documents in this category generate large volumes of structured and semi-structured data with high update frequencies. Parsing and chunking must balance single-block information integrity and retrieval accuracy. Heterogeneous document formats from multiple sources — such as the table structure of formula sheets and time-series data of filling records — require adapted chunking logic. This prevents splitting associated data across batches. Precise values and their bound units in inspection records must retain field-unit associations during chunking. This avoids disconnected values and units during retrieval. Factories also upload large numbers of daily production record files in batches. The parsing and chunking process must support stability under high-concurrency scenarios to prevent timeouts or parsing failures.
How to set configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapt to the single-block information capacity of multi-field combinations in documents of this category, avoid splitting inspection-related data across batches, and ensure complete feeding or testing information is available during retrieval |
chunkSplitter | Mixed chunking by headings and paragraphs | Match the hierarchical structure of formula sheets and inspection reports, retain the binding relationship between section headings and body text, and prevent headings and corresponding content from separating during retrieval |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Adapt to large production record files uploaded in batches, avoid task failure due to insufficient parsing duration, and cover parsing requirements for multi-page inspection reports exported from LIMS |
customPdfParse | Enable table structured parsing | Extract complete field and value information for table data in water treatment records and filling records, retain the association between detection parameters and corresponding results |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Support large LIMS-exported files uploaded in batches per single batch, adapt to factories' daily large-volume document upload requirements |
chunkSettingMode | Manual configuration | Adjust chunking parameters for different document types such as formula sheets and inspection records to meet retrieval accuracy requirements for specific scenarios |
Three common mistakes
- Phenomenon: After uploading a docx-format formula sheet, RAG retrieval results include section headings, but some detection fields and their corresponding values lack associated relationships. Cause: The mixed chunking by headings and paragraphs configuration is not enabled, causing section headings and body text to split and misalign, so complete information cannot be matched during retrieval.
- Phenomenon: After uploading production record files in a server environment, the document parsing node returns a 404 error, but parsing completes normally in a local environment. Cause: Correct file storage path mapping is not configured during server deployment, so the parsing node cannot access uploaded file resources.
- Phenomenon: When deploying Qwen3-14B using Vllm 0.10, specified response fields cannot be extracted from parsing results of PDF-format inspection reports. Normal functionality resumes after switching to Qwen2.5-14B. Cause: The field output format after PDF parsing does not match the context adaptation logic of Qwen3-14B, and structured output parameters for
customPdfParseare not adjusted.
How to confirm the configuration is correct
- Upload a single formula sheet document, view the parsed chunk preview results, and confirm section headings and corresponding body text are not split or misaligned.
- Upload batch production record files, check the completion status of parsing tasks, and confirm no timeout or parsing failure prompts appear.
- Trigger RAG retrieval for inspection records, confirm retrieval results include complete field and value information, with no missing fields or disconnected units.
- View server file storage logs, confirm uploaded file paths match the access paths of the parsing node.
The values listed above are common starting points and should be evaluated against the reader's own samples.