What this category's data looks like
Data for this category comes from multiple business systems and equipment at carbonated drink, juice, tea, functional beverage, and packaged drinking water factories. Daily documents include recipe sheets, water treatment records, blending records, sterilization or aseptic filling records, packaging material inspection and finished product inspection documents. Data sources cover water source and raw material testing, formula systems, online sugar and acidity monitoring, filling equipment, visual inspection systems, and LIMS systems. Data is updated per batch, per shift, or via continuous collection. Document fields include raw material batch numbers, test values, equipment parameters, batch numbers, shelf life, and some records include professional units.
What constraints do these characteristics impose on document parsing and chunking
The multiple document types and high-frequency update characteristics of this category create clear constraints for document parsing and chunking. Recipe sheets and inspection records updated per batch require accurate binding to batch information. Associated data across batches must not be split during chunking. Continuously collected equipment logs have a large data volume. Single chunk length must be controlled to avoid exceeding model context limits. Document structures vary widely: some are structured table files, others are unstructured record text. Parsing rules must adapt to content extraction across different formats. Additionally, documents in this category often include professional parameters such as sugar content and acidity. The association logic between parameters and their corresponding batches must be retained during chunking to avoid information fragmentation.
Configuration Settings
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | This category’s documents include structured parameters and continuously collected equipment logs. This range balances context association and single-chunk information density, and supports complete extraction of professional parameters such as sugar content and acidity |
chunkSplitter | Chunk by paragraph + semantic meaning | Balances parsing for structured tables and unstructured records, retains association logic between fields, and avoids splitting critical parameters and batch information |
customPdfParse | Enable structured parsing | Adapts to PDF format documents in this category, can extract structured content such as feeding parameters and test values from tables, and avoids garbled text or information loss |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Batch inspection records and equipment log files in this category have large sizes. Sufficient time is required to complete parsing, adapting to large-volume continuously collected files |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Covers upload requirements for batch inspection records and log files corresponding to a single batch, preventing upload failures due to excessive file size |
chunkSettingMode | Custom mode | Configures differentiated chunking rules for multiple document types in this category, adapting to content in different formats such as recipe sheets and inspection records |
Three Common Misconfigurations
- Phenomenon: Parsing result is empty after uploading an image address, with a parameter error prompt returned. Cause: No trigger rule for image parsing is configured. FastGPT only triggers parsing for text file addresses by default, and does not adapt to parsing requirements for image addresses.
- Phenomenon: Parsing fails when attempting to upload audio or video files, with an unsupported file type prompt. Cause: FastGPT v4.17.0 does not integrate audio and video parsing capabilities by default, and only supports common document formats such as text, PDF, and images.
- Phenomenon: A parameter error is returned when calling the workflow API to upload images. Cause: Image parameters are not passed as required, and the field name and format for file upload are not correctly configured, causing the interface to fail to recognize the uploaded content.
How to Verify Configuration
- Upload a typical recipe sheet PDF for this category, check if the parsing result extracts feeding parameters and corresponding batch information, to verify if the
customPdfParseconfiguration takes effect. - Upload a device log file of approximately 100 MB, check if parsing time matches the
PARSE_FILE_TIMEOUT_SECONDSsetting, confirming no timeout error is triggered. - Call the chunk testing interface, pass in inspection record text for this category, check if the chunking result retains the context association between test values and batch numbers, to verify if the
chunkSizeandchunkSplitterconfigurations are reasonable. - Check the knowledge base storage configuration, confirm that the vectorized chunking results have been correctly written to the specified database, to verify the completeness of the chunking and storage process.
The values provided are common starting points and should be measured against the reader's own samples.