What Data Looks Like for This Category
Typical entities in this category are factories producing baked goods including bread, pastries, biscuits, instant noodles, and frozen dough products. Daily documents include formulas and feeding lists, production records, baking or 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, oven and frying line equipment, laboratory data, and food traceability ledgers. Update frequency follows per batch, per shift, or daily schedules. Document fields include material name, usage amount, process parameters, test results, shelf life, and other items. Some documents require matching associated information such as allergen labels and production batches.
What Constraints Do These Characteristics Impose on the "Model Access and Configuration" Process
The high-frequency, multi-batch documents and frequent line change characteristics of this category require model access to adapt to short-cycle, high-frequency invocation scenarios. The existence of multiple types of heterogeneous documents requires configuration to support parsing logic for different document structures. Strict requirements for allergen and foreign matter control mandate that the model context cover full batch-related data to avoid truncation of key information. The daily or per-batch update rhythm requires configuration to support dynamic loading of the latest ledger data and adapt to format alignment of multi-source data. Additionally, the ready-to-eat nature of products amplifies the consequences of errors, requiring configuration to improve result accuracy and avoid missing key verification items.
How to Set Configuration Parameters
| Configuration Parameter | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers associated information of full-link documents for a single batch, avoiding truncation of key verification content such as allergens and production batches |
maxTokens | 2000–3000 token | Adapts to the output length requirement for single-batch review, ensuring complete output of verification results and supporting evidence |
quoteMaxToken | 1500–2000 characters | Limits the length of quoted documents, matching the typical length of single records for bakery and convenience food documents |
| Workflow/API tool selection | auto | Automatically selects whether to invoke tools to complete cross-review, adapting to multi-document associated review scenarios |
temperature | 0.1–0.3 | Reduces random errors, ensuring the accuracy of verification for key information such as allergens and shelf life |
AIPROXY_API_ENDPOINT | Fill in according to the actual deployed model address | Adapts to custom models deployed on internal factory Docker, matching internal network access rules |
Three Common Configuration Mistakes
- A connection timeout occurs when invoking a reranker model deployed via Docker. The cause is failing to set
AIPROXY_API_ENDPOINTto the container's local network address, instead incorrectly using a public domain name or external address. - After invoking an MCP tool, the model context is unexpectedly truncated. The cause is that the
maxContextconfiguration value is too low, failing to reserve context space for newly added content after tool invocation, leading to overwriting of key verification information. - The model response speed is too slow. The cause is that the
maxTokensconfiguration value exceeds the single-processing limit of the model, or the quoted length ofquoteMaxTokenis not limited, causing redundant historical data to participate in calculations.
How to Verify Correct Configuration
- Navigate to the model configuration page of FastGPT v4.17.0, verify that
AIPROXY_API_ENDPOINTmatches the deployed model address, and test the address connectivity via a local curl command. - Upload formula and production record documents for a single batch to trigger a cross-review task, and check whether the returned results include key verification items focused on by the business, such as allergens and production parameters.
- Invoke the MCP tool to complete one document association verification, and check whether the context fully retains key information from the prior review with no unexpected truncation.
- Adjust the
temperatureparameter and compare two review results, confirming that the consistency of the results after parameter adjustment meets business verification requirements.
The values provided are common starting points and should be validated against local deployment samples.