What This Category’s Data Looks Like
Data sources for this category include animal quarantine information, supplier batches, production traceability, temperature collection, metal detection, and laboratory test results. Updates occur with each production batch and each shift. Cold chain temperature data must be collected and recorded continuously. Daily documents include quarantine certificates, slaughter and cutting records, ingredient marinating records, thermal processing records, cold chain temperature records, and microbial test reports. Fields covered include batch number, quarantine identifier, temperature parameters, test indicators, and corresponding production link information. Some documents require association between batch and quarantine traceability information.
Constraints on Model Access and Configuration
The batch-based and real-time data characteristics of this category require accurate association of batch and traceability information during model access and configuration. This prevents confusion of quarantine and temperature control data across different production batches. Continuous collection of large volumes of cold chain temperature data requires limiting the effective data range of the context window. This prevents redundant information from interfering with quotation and document generation. Documents include highly compliance-focused fields such as quarantine identifiers and microbial test results. Configuration must ensure the model can accurately identify and associate corresponding business fields. This avoids generating tender content that does not meet production specifications. Special requirements for raw/cooked separation and temperature control require explicit restriction of the data source scope for model calls during configuration. This ensures generated content matches the compliance requirements of the current production link.
How to Configure Settings
The following configuration is based on FastGPT v4.17.0
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to context needs for multi-batch traceability data and multi-document splicing, avoids truncating critical quarantine and batch information |
maxTokens | 4000 token | Controls the length of tender documents and quotation content generated per round, aligns with standard length requirements for enterprise tender documents |
quoteMaxToken | 1500 token | Limits the token proportion of referenced production records, quarantine certificates and other documents, ensures generated content focuses on core quotations and compliance explanations |
temperature | 0.2–0.4 | Reduces generation randomness, ensures accuracy of fields such as batch numbers, temperatures, and quarantine identifiers in tender documents, meets compliance requirements |
AIPROXY_API_ENDPOINT | Fill in according to the official endpoint of the accessed model | Adapts to call paths for different models, ensures the model can normally receive configured requests |
AIPROXY_API_TOKEN | Fill in according to the key assigned by the platform | Completes identity verification for model access, ensures legitimacy of call permissions |
Three Common Configuration Mistakes
- Phenomenon: A model with internet access enabled is configured, but fails to call the latest cold chain temperature or quarantine update data when generating tender documents. Cause: Context association rules are not configured correctly. Only static knowledge base data is bound, and real-time data source call permissions are not enabled.
- Phenomenon: Model calls return empty fields, such as batch numbers or quarantine identifiers not being correctly extracted. Cause: The model’s context range is not limited, and irrelevant data from different production batches is mixed in. This prevents the model from accurately identifying target fields.
- Phenomenon: Quotation content generated by the model does not match the actual temperature parameters in production records. Cause: The
temperatureparameter is set too high, causing generated content to deviate from actual production data, or themaxContextconfiguration is too small, truncating critical temperature control records.
How to Verify Proper Configuration
- Upload a single batch’s quarantine certificate and production records, trigger the model to generate a tender document fragment, and verify that the extracted batch number and quarantine identifier match the uploaded documents.
- Test calls to cold chain temperature data with different update times, confirm that generated content can associate temperature control records from the corresponding time period.
- Check the model call log, confirm that the request carries the correct
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENparameters, with no identity verification errors. - After adjusting the
temperatureparameter, compare the consistency of generated quotation content, confirm that the parameter adjustment takes effect.
The values provided are common starting points and should be measured against the reader's own samples.