What this category's data looks like
This category covers end-to-end documents for skincare, haircare, makeup, and special cosmetic manufacturers, including formula sheets, weighing and feeding records, emulsification or filling records, cleaning and sanitization records, semi-finished and finished product inspection reports, and label review documents. Data sources include registered and archived formulas, raw material safety materials, production equipment MES/ERP systems, laboratory test records, and packaging material version archives. Updates trigger per production batch or each line change. Each individual document includes clear fields such as raw material parameters, production parameters, batch numbers, archive numbers, and other relevant details.
What constraints do these characteristics impose on the vector model and indexing process
The multi-source nature, high-frequency updates, and strict compliance requirements of data in this category create multiple constraints for the vector model and indexing process. Multi-source data includes structured parameters and unstructured text, requiring adaptation to chunking rules of different lengths. High-frequency updates require incremental indexing to avoid time-consuming full reconstruction. Strict consistency requirements for archived data demand raising matching thresholds to filter non-compliant content. Differences in fields across documents require retaining metadata during chunking to support subsequent traceability.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the typical text length of formula sheets and inspection records, avoiding splitting critical parameters across separate chunks |
| Overlap Length | 100–200 characters | Preserve cross-chunk context connections, such as consecutive weighing steps and temperature change records |
| Similarity Threshold | 0.85–0.92 | Meet the strict consistency requirements of archived formulas, filtering non-compliant raw material and parameter content |
| Single Search Limit | Top 6 results | Cover the number of associated documents involved in a single production batch, avoiding redundant results interfering with audits |
defaultToken | 4096 | Adapt to the embedding processing length of individual documents, meeting embedding needs for long texts |
maxToken | 8192 | Meet the total length requirement for context stitching after recall, supporting complete display of audit basis |
Three Common Errors
- Symptom: Retrieved document chunks do not show the corresponding production batch number or archive number, with empty fields. Cause: Document metadata was not embedded during the chunking phase, making it impossible to associate specific production batches and compliance documents in subsequent traceability links.
- Symptom: Index building task returns a 504 timeout status code. Cause: Incremental indexing mode was not enabled, and full index reconstruction runs for each update, exceeding system processing time limits.
- Symptom: Matching results include unarchived raw material parameters. Cause: The similarity threshold was set below 0.85, failing to distinguish between archived and unarchived raw material content, leading to audit failure.
How to Confirm Proper Configuration
- Upload a single complete formula sheet, check that chunked text blocks retain core information such as raw material names, usage amounts, and production parameters.
- Initiate an archived formula matching query, verify that the similarity score of returned results falls within the preset 0.85–0.92 range.
- Upload new production records after a line change, verify that the system only performs incremental index updates with no full reconstruction log records.
- Check the FastGPT v4.17.0 system configuration logs, confirm that the
defaultTokenandmaxTokenconfigurations do not trigger embedding length limit errors.
The values provided are common starting points and should be measured against the reader's own samples.