What the data for this category looks like
The main entities in this category are bread and pastry, biscuit, instant noodle, and frozen pastry factories. Daily generated documents include formula and feeding lists, production records, baking/steaming curves, metal detection records, cleaning and disinfection records, and factory inspection reports. Data sources cover raw and auxiliary material qualification certificates, ERP/MES systems, weighing systems, production equipment, laboratories, and food traceability ledgers. Update frequency follows per batch, per shift, or daily schedules. Documents include fields such as allergen labels, process parameters, test results, and shelf life. Some documents like baking curves contain time-series numerical records. Data formats vary across different sources.
What constraints these characteristics impose on vector models and indexing
This category has high data update frequency, and single production batches are linked to multiple documents. Indexes must support incremental updates to adapt to frequent ledger synchronization needs. Documents contain sensitive key fields such as allergens and process parameters, so vector retrieval accuracy must be maintained to avoid quality issues from matching deviations. Frequent line changes create differences in document types and content, so chunking rules must adapt to mixed short and long text scenarios, including metal detection records and complete baking curves. Multi-source data format differences require unified field mapping and cleaning before indexing, otherwise vector generation anomalies or distorted retrieval results may occur.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average text length of formula sheets and production records for this category, avoids overly dispersed content in a single chunk |
| Overlap Length | 100–150 characters | Retains key information such as allergen labels and process parameters across chunks, prevents loss of context during retrieval |
| Similarity Threshold | 0.75–0.85 | Meets the retrieval accuracy requirements for key fields of this category, reduces false matching probability |
| Single Search Limit | Top 10 entries | Balances retrieval efficiency and result coverage, adapts to batch verification scenarios for multiple ledgers |
defaultToken | 1000 token | Meets the vector generation requirements for single short documents such as metal detection records |
maxToken | 8000 token | Covers the upper limit of vector generation for long texts such as complete baking curves |
Three Common Errors
- After uploading data using the chunk mode pushdata API, the interface continues to display "indexing in progress". This happens if the index synchronization cycle matching the data update frequency is not configured, or the incremental index trigger rule is not enabled.
- A model permission error occurs when connecting to the bce-embedding-v1 model. This happens if a valid key for the corresponding model is not configured on the FastGPT platform, or the key is not bound to the specified model permissions.
- When directly calling the embedding model without using one API, the error message "This token does not have permission to use the model" appears. This happens if a valid access token for the corresponding model is not bound, or the token has not been granted usage permission for the corresponding model.
How to Confirm Proper Configuration
- Upload a single formula document, and check if the FastGPT v4.17.0 index status completes updating within a reasonable time frame.
- Search for keywords containing allergen labels, and verify that the matching degree of returned results meets preset accuracy requirements.
- Call the pushdata API to upload test data, and confirm there are no permission, token, or model invocation related errors.
- View vector model call logs, and confirm that vector data for the corresponding document has been successfully generated and stored.
The values provided are common starting points and should be evaluated against the reader's own samples.