Data Profile for This Category
The typical entities in this category are carbonated beverage, juice, tea, functional beverage, and packaged drinking water manufacturing facilities. Daily documents include formula sheets, water treatment records, blending records, sterilization or aseptic filling records, packaging material inspection records, and finished product inspection records. Data sources cover water source and raw material testing, formula systems, online sugar and acidity monitoring equipment, filling equipment, visual inspection systems, and LIMS systems. Update frequencies include per batch, per shift, or continuous collection. Document fields include batch number, test values, feeding parameters, equipment operating parameters, and more. Units include mg/L, %, ml, batch identifiers, timestamps, and more.
Constraints for Document Parsing and Chunking
This category has a wide variety of document types, high update frequencies, and a large number of structured numerical fields. Chunking must retain the association between data from the same batch, and avoid splitting and mixing cross-batch data. Long documents collected continuously require longer parsing times, so chunk length must be controlled to match the complete data volume of a single batch ledger. Overly fragmented chunks prevent retrieval of complete business logic. Additionally, document layout variations caused by the category's high-speed filling processes require parsing to adapt to structured field alignment. General-purpose parsing may lose unit or field correspondence. Furthermore, multi-batch documents uploaded in batches must have no missing data fields after parsing, to ensure retrieval covers all complete data matching filter conditions.
Configuration Settings
This page follows the FastGPT v4.17.0 configuration specification.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the field count and data length of a single batch ledger, retains the integrity of same-batch business logic, and adapts to retrieval accuracy |
PARSE_FILE_TIMEOUT_SECONDS | 70000–90000 milliseconds | Covers the parsing time of continuously collected long documents, avoids platform timeout errors |
customPdfParse | Enabled | Adapts to structured PDFs such as formula sheets and filling records, retains original field layout and unit information, prevents misalignment from general parsing |
chunkSplitter | Split by batch number grouping | Ensures each chunk contains all relevant records for the same batch, avoids mixing cross-batch data |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Accommodates single-batch or bulk packaged multi-document uploads |
chunkSettingMode | Manual configuration | Flexibly adjusts chunking rules to adapt to different document structures for this category's multi-type ledger documents |
Three Common Configuration Errors
- Phenomenon: Fewer than expected search results appear after uploading Excel ledgers. For example, searching for data from a specified region fails to return all matching items. Cause: Associated fields of the same batch were split during chunking, preventing retrieval of all data matching the complete filter conditions.
- Phenomenon: Platform timeout errors trigger when parsing files that take longer than 60000 milliseconds. Cause:
PARSE_FILE_TIMEOUT_SECONDSwas not adjusted to a reasonable range, failing to cover the parsing time of long documents. - Phenomenon: Directly uploaded PDF documents cannot be parsed correctly, and API requests have no response. Cause:
customPdfParsewas not enabled, and general parsing caused loss of field and unit information for structured formula sheets or filling records.
How to Validate Configuration
- Upload a single-batch water treatment record PDF document, check that parsed chunks include complete batch numbers, detection times, and corresponding values.
- Upload an Excel ledger containing multiple batches, use filter conditions to search, and confirm all matching data is returned.
- Adjust
PARSE_FILE_TIMEOUT_SECONDSto 75000 milliseconds, upload a single long document, and confirm no timeout errors occur during parsing. - Review chunked text content, confirm each chunk contains complete relevant records for the same batch, with no mixed cross-batch data.
The values provided here are common starting points, and should be evaluated against applicable samples.