Data characteristics of this category
Data for this category originates from multiple business documents of skincare, haircare, makeup, and special cosmetics manufacturing enterprises. These documents include formula process sheets, weighing and feeding records, emulsification and filling records, cleaning and sanitization records, semi-finished and finished product inspection reports, and label review documents.
Data sources cover registered and filed formulas, raw material safety materials, production equipment, ERP/MES systems, laboratory data, and packaging material version materials. Updates occur per production batch or each line change. Each individual document contains fields such as raw material lists, process parameters, inspection results, and batch association information.
Constraints on vector models and indexing
The multi-source heterogeneous data structure, high-frequency update frequency, and strict compliance association requirements of this category impose multiple constraints on the vector model and indexing process.
Data sources in varying formats such as registered formulas, production records, and inspection reports require indexes to support semantic association across cross-format fields. The high-frequency updates per batch or line change require indexes to adapt to incremental synchronization logic, avoiding performance losses from full reconstruction.
Each document includes tightly bound fields like raw material lists, process parameters, and batch numbers. When the vector model splits text chunks, it must retain associations between fields to prevent chunking from breaking full batch link information.
Additionally, quality issue traceability requires coverage of the complete compliance chain. Index recall results must link data across filing, production, and inspection links.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the single-paragraph semantic length of process parameters and inspection records in cosmetics manufacturing documents, avoiding chunking that destroys batch association information |
| Overlap Length | 150–200 characters | Retains key association information such as batch numbers and raw material names between adjacent chunks, preventing context breaks during recall |
| Similarity Threshold | 0.72–0.85 | Adapts to the semantic similarity distribution of texts such as formulas and inspection reports, filtering low-relevance interfering results |
| Single Search Limit | Top 8 entries | Covers the number of core associated documents for a single batch production, avoiding recall of excessive redundant data |
maxToken | 4096 token | Adapts to the model context limit of FastGPT v4.17.0, carrying complete batch-associated chunk content |
defaultToken | Set from representative sample documents | defaultToken is the default token count for direct text chunking; tune it on representative source documents. See the v4.17.2 model-field reference. It is not a vector dimension, embedding-model input cap, or retrieval/LLM context. |
Three common configuration errors
- When retrieving formula filing information for a specified batch, the filing formula field in returned results is empty. Cause: The batch number field linking production records and filing data was not configured in the index, leading to cross-document association failure.
- When running batch line change data updates, the system triggers a timeout error. Cause: Full index synchronization logic was used, without adapting to the high-frequency update rhythm per batch or line change, resulting in excessive resource usage.
- When retrieving microbial non-conformity issues, recall results include many irrelevant packaging material version documents. Cause: The similarity threshold was set too low, failing to filter content with insufficient semantic matching.
How to verify correct configuration
- Import the full production documents for a single batch, run a batch number retrieval, and confirm returned results cover full-link associated data including formulas, feeding, inspection, and packaging materials.
- Simulate an incremental update operation after a single line change, verify the index only synchronizes new data and does not initiate a full reconstruction process.
- Adjust the similarity threshold, compare the number of recall results across different configurations, and confirm the matching range aligns with business requirements.
- Check the FastGPT v4.17.0 system logs to confirm vector model and index call parameters match the configured settings.
The values listed here are common starting points and should be evaluated against the reader's own samples.