What the Data for This Category Looks Like
Daily documents from baked goods and convenience food factories include recipe and ingredient lists, production records, baking/steaming curves, metal detection logs, cleaning and disinfection logs, factory inspection reports, and more. Data sources cover raw material qualification certificates, ERP/MES systems, weighing equipment, ovens/frying lines, laboratory testing systems, and food traceability ledgers. Updates follow production batches, shifts, or daily cycles. Each document includes fields such as allergen labels, ingredient weights, baking temperature and duration, and packaging dates. Units include degrees Celsius, seconds, kilograms, and others. Document formats exported from different systems have minor differences.
What Constraints These Characteristics Impose on Knowledge Base Retrieval and Recall
Documents for this category update frequently, and line change frequency is high. A single retrieval must cover production parameters and recipe information across multiple batches. This creates clear requirements for retrieval real-time performance. Retrieval needs extremely high precision for allergen and cleaning switch-related queries. It must avoid mistakenly recalling irrelevant recipes or cleaning logs. Multi-source heterogeneous data formats require the retrieval system to support documents with different structures. It must also ensure recalled results are strongly relevant to the current production scenario. This prevents redundant data from harming model response accuracy. Additionally, products are sold directly to consumers. Retrieval errors may cause food safety risks. This makes precision requirements for recalled results higher than general scenarios.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.72–0.85 | For high-risk retrieval scenarios such as allergen and foreign matter control, set a reasonable semantic matching threshold to avoid mistakenly recalling irrelevant recipes or production records |
limit | Top 6–10 entries | Match frequently changed production line scenarios, control the context length returned by a single retrieval, and avoid redundant information interfering with model generation |
usingReRank | Enabled | Improve sorting accuracy for semantically similar content such as recipes and production parameters, and distinguish differences in baking/steaming curves across batches |
datasetSearchUsingExtensionQuery | Enabled | Cover synonym scenarios for recipe and production terminology, such as "proofing time" and "fermentation duration", to expand retrieval recall scope |
collectionIds | Bind dedicated dataset IDs for baked goods and convenience foods | Isolate cross-category documents, avoid mixing data from other categories such as dairy products and cold-chain food to interfere with retrieval results |
Three Common Misconfigurations
- Symptom: The returned
similarityvalue is significantly higher than that of general scenarios. Some low-relevance results are misjudged as high matches. Cause: Threshold calibration was not performed for short-text recipes and production parameters of baked convenience foods. Direct reuse of general retrieval parameters leads to semantic matching deviations. - Symptom: No available interface is returned when calling the knowledge base retrieval function. Cause: Open call permissions were not configured for the target dataset. API call credentials that meet FastGPT v4.17.0 version requirements were not generated.
- Symptom: The model does not automatically trigger knowledge base retrieval when generating responses. Cause: The automatic retrieval trigger configuration item was not enabled, or the trigger logic was not bound to the conversation initialization stage. This leads to retrieval only executing when manually triggered.
How to Confirm Configurations Are Correctly Set
- Upload a batch of test allergen recipe documents and regular recipe documents. Run allergen-related queries. Check if recalled results only include content related to the target allergen.
- Call the API interface to send a retrieval request. Check if the number of returned results matches the value set in the
limitconfiguration. - Observe if retrieval is automatically triggered when a conversation initializes. Confirm that the automatic trigger logic works properly.
- Check the dataset binding relationship. Confirm that
collectionIdsonly includes dedicated document collections for the current category. No cross-category data is mixed in.
The values listed above are common starting points. They should be validated against the specific samples relevant to the target deployment.