What Data for This Category Looks Like
The core data for this category comes from raw material and auxiliary material qualification certificates, ERP/MES systems, weighing systems, ovens or frying lines, laboratories, and food traceability ledgers. Daily documents include formulas and feeding lists, production records, baking or steaming curves, metal detection records, cleaning and disinfection records, and factory inspection reports. Data is updated per batch, per shift, or daily. Fields include feeding volume, baking temperature, duration, allergen labels, and more. Most data exists as text paragraphs, tables, and time-series curves.
What Constraints These Characteristics Impose on the "Document Parsing and Chunking" Link
This category includes Word bid documents of 100,000 Chinese characters and Excel production records with 15,000 rows, which creates requirements for the semantic integrity of chunking and processing efficiency. Frequently updated multi-source data requires parsing logic to adapt to different document structures and avoid splitting key business units. Sensitive fields such as allergen labels and feeding parameters must retain contextual associations to prevent business verification errors caused by chunk breaks. Batch processing of large-volume documents requires sufficient parsing time, while also adapting to the input window limits of vector models.
How to Configure the Settings
The following configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the semantic integrity of texts such as baking formulas and production records, while matching the input window limits of most vector models |
chunkSplitter | Mixed paragraph and fixed-length splitting | Adapts to documents containing both text paragraphs (such as feeding instructions) and tabular data (such as 15,000-row Excel files), and avoids splitting cross-semantic units |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Reserves sufficient parsing time when processing 100,000-character Word files or 15,000-row Excel files, preventing mid-processing timeout interruptions |
UPLOAD_FILE_MAX_SIZE | 10240 MB | Supports uploading large-volume production record archives and bid documents, adapting to batch business scenarios |
chunkSettingMode | Custom mode | Adjusts the chunk overlap rate separately for key fields such as allergen labels and feeding parameters to retain contextual associations |
customPdfParse | Enabled | For documents such as baking or steaming curves, custom parsing rules retain the field associations of chart data, adapting to special document formats within this category |
Three Common Misconfigurations
- Phenomenon: When uploading files larger than 10 MB, vectorization exceptions occur after generating 1000+ chunks, and some recover after retries. Cause: The
chunkSizeand chunk overlap rate were not adjusted for the long-text documents of this category, causing some chunks to exceed the vector model input limits. - Phenomenon: After querying ERP/MES data from the DB node and returning a JSON array, it cannot be directly associated with document chunks. Cause:
customPdfParsewas not enabled or the corresponding data mapping rules were not configured, so structured database fields cannot be bound to document chunks. - Phenomenon: 15,000-row Excel production records are split into overly small chunks, making the verification logic for allergen materials unable to associate across chunks. Cause:
chunkSplitteronly uses fixed-length chunking, without combining paragraph or row-level splitting, which destroys the semantic association of continuous feeding batches in Excel.
How to Confirm Configurations Are Set Correctly
- Upload a preset 100,000-character Word bid document, and check that the length range of the chunk list matches the configured
chunkSizeinterval. - Import 15,000-row Excel production records, and verify that chunks retain the contextual association of continuous feeding batches, with no key business fields broken across chunks.
- Run a large file upload task, confirm that the parsing time does not exceed the configured
PARSE_FILE_TIMEOUT_SECONDS, and no timeout errors occur. - Test querying ERP data from the DB node, confirm that the returned structured data can be directly bound to the corresponding document chunks without additional code processing.
The values provided are common starting points and should be measured against the reader's own samples.