What Data Looks Like for This Category
This category includes typical entities such as carbonated beverage, juice, tea, functional beverage, and packaged drinking water factories. Daily 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, formula systems, online sugar and acidity monitoring, filling equipment, visual inspection systems, and LIMS systems. Update frequency follows per batch, per shift, or continuous collection schedules. Documents contain structured numerical fields and text descriptions. Fields include sugar content, acidity, filling liquid level, sealing parameters, batch numbers, and other metrics. Some fields use industry-specific units.
Constraints Imposed by These Characteristics on Document Parsing and Chunking
Data collected continuously updates at a high frequency. Chunk length must not be excessive, as overly large chunks introduce redundant information and reduce retrieval efficiency. Daily documents include multiple file types, with both structured forms and sequential records. This requires support for multiple splitting rules. Fields involve industry-specific parameters, so parsing must retain field integrity to avoid breaking parameter associations during chunking. Single-batch documents may have large file sizes, so the system must support parsing large files and handle longer parsing durations.
How to Configure Settings
This page is compatible with FastGPT v4.17.0
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | This category’s documents include both structured forms and sequential records. This length retains parameter associations while avoiding information overload in single chunks |
chunkSplitter | Split by paragraph + fixed character count | Adapts to mixed-format documents, retaining logical connections within paragraphs while properly chunking long sequential records |
customPdfParse | Enabled | For structured PDF documents in this category (such as aseptic filling records), this setting accurately extracts header and parameter column content |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Large PDF documents like water treatment records or multi-shift sterilization records contain significant data, so sufficient parsing time must be reserved |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to large-volume documents generated by single-batch continuous collection, preventing upload interception |
Three Common Configuration Mistakes
- Parsing results display empty fields after uploading a formula sheet PDF. The cause is that
customPdfParseis not enabled, and no dedicated parsing rule is configured for structured PDFs. - Parsing results have chaotic splits and missing numerical values after uploading Excel-format packaging material inspection records. The cause is that
chunkSplitteris not set to split by table rows or columns, and direct character-based chunking breaks the data structure. - Parsing tasks time out when uploading large sterilization record PDFs. The cause is that
PARSE_FILE_TIMEOUT_SECONDSis set too low, failing to match the parsing duration requirements of large-volume documents.
How to Confirm the Configuration Is Correct
- Upload a single formula sheet PDF, and check if the parsing result includes core fields such as feeding parameters and batch numbers.
- Upload a single Excel-format packaging material inspection sheet, and confirm that post-parsing row-split results retain inspection items and corresponding values.
- Upload a 100 MB water treatment record PDF, and check that the parsing task does not time out and the number of chunks meets expectations.
- Upload a mixed document package containing PDFs and Excel files, and confirm that all document types are parsed and chunked according to the configured rules.
The values provided here are common starting points, and should be evaluated against the reader’s own samples.