What the Data for This Category Looks Like
Business documents for this category originate from bakeries, pastry producers, cookie, instant noodle, and frozen pastry factories. Document types include formulas and feeding lists, production records, baking or steaming curves, metal detection logs, cleaning and disinfection records, and factory inspection reports. Data sources cover raw and auxiliary material qualification certificates, ERP/MES systems, weighing equipment, ovens and frying lines, laboratory systems, and food traceability ledgers. Updates occur per production batch, per shift, and on a daily aggregate basis. Document fields include allergen labels, feeding weights, baking temperatures and durations, cleaning batch numbers, and more. Some documents contain both standardized numerical values and unstructured text content.
Constraints Imposed by These Characteristics on Vector Models and Indexes
Multi-source, frequently updated business data for this category requires vector indexes to support incremental updates by production batch and product line. This avoids excessive time spent on full index reconstruction. Documents with standardized numerical values such as baking and steaming curves and feeding lists require vector models to maintain consistent encoding for both structured numerical values and unstructured text. This prevents excessive vector deviation between similar data sets. Frequent document category shifts caused by product line changes require index retrieval logic to adapt to exclusive fields for different product lines. High-risk fields including allergens and foreign matter control must be configured with higher retrieval priority to ensure accuracy during exception report drafting. The vector model's maxToken parameter must adapt to requirements for multi-document splicing.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxToken | Set to the selected embedding model/service maximum supported input tokens | maxToken is the input-token maximum supported by the selected embedding model/service; verify it in that provider configuration. See the v4.17.2 model-field reference. It is not retrieval/LLM context or a FastGPT-wide fixed cap. |
| Chunk Length | 800–1200 characters | Adapts to text length of formulas and production records in this category, avoids including cross-batch unrelated content in a single chunk |
| Overlap Length | 100–150 characters | Retains contextual association for production curves and feeding lists, prevents key numerical values from being truncated during chunking |
| Similarity Threshold | 0.75–0.85 | Matches retrieval accuracy for high-sensitivity fields such as allergen labels and baking parameters, balances recall rate and accuracy |
| Single Search Limit | Top 8 entries | Covers associated document count for a single production batch, avoids redundant results interfering with exception report drafting |
defaultToken | 200 token | Optimizes vector encoding efficiency for single-block text, adapts to average document length for this category |
Three Common Misconfigurations
- Phenomenon: Mixed retrieval takes over 10 seconds, with noticeable single-retrieval response delay. Cause: Incremental indexing is not configured for this category's frequently updated multi-source data, and too many expired historical documents are loaded during full retrieval.
- Phenomenon: The system remains stuck at the final index creation step with no progress updates for an extended period. Cause: Index groups are not split by production batch, excessive single-batch data volume causes index construction blocking, and the sharded index function is not enabled.
- Phenomenon: Some documents fail to vectorize, with empty fields or error prompts appearing in the knowledge base. Cause: Supported file types are not confirmed to include core category documents such as formula PDFs, production record Excel files, and baking curve CSV files, and unformatted raw data is uploaded directly.
How to Verify Correct Configuration
- Initiate a retrieval targeting allergen labels, verify that the sorting priority of returned results matches business risk levels.
- Upload production records and formula documents for a single batch, check that index creation progress proceeds smoothly without getting stuck at any stage.
- Initiate pure vector retrieval and hybrid retrieval separately, confirm that response duration meets current business usage requirements.
- Review the knowledge base's supported file type list, confirm that it includes core document formats such as PDF, Excel, and CSV for this category.
The values provided here are common starting points and should be evaluated against the reader's own samples.