What the data for this category looks like
Typical entities for this category are slaughterhouses, cooked meat and frozen prepared food enterprises. Daily ledger documents include quarantine certificates, slaughter and cutting records, ingredient marinating records, thermal processing records, cold chain temperature records, and microbial test reports. Data sources cover animal quarantine information, supplier batches, production traceability systems, temperature collection equipment, metal detection equipment, and laboratory test results. Data is updated per batch and shift. Cold chain temperature records are updated continuously in real time. Fields include batch number, quarantine ID, temperature value, test time, test result, and other items. Some fields must be matched and associated with other documents for the corresponding batch.
What constraints these characteristics impose on model access and configuration
Ledger data for this category must be associated by batch and shift, and includes sensitive structured information such as biological quarantine and temperature control. This requires model access configuration to ensure accurate cross-document association. Continuously updated temperature control data requires the model to support extraction and integration of time-series information, to avoid confusing records from different time periods. Mixed use of multiple types of documents requires configuration to adapt to parsing and field standardization for multi-format documents. Data is updated at a high frequency, so matching knowledge base synchronization rhythm must be configured to ensure the timeliness of summary reports. At the same time, data errors may cause product safety risks, so the randomness of model-generated content must be strictly limited to avoid batch association errors or missing information.
How to set the configuration
The configuration in this chapter is compatible with FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers the needs of cross-batch document association recall, avoids truncating key associated data such as cross-batch quarantine and temperature control records |
temperature | 0.1–0.3 | Reduces the randomness of generated content, ensures accuracy in key links such as batch association and quarantine information matching |
quoteMaxToken | 1500–2000 characters | Retains complete long-text details such as cold chain temperature records and microbial test reports, avoids truncating key information |
| Workflow/API tool selection | auto | Automatically adapts to parsing and association tasks for multiple types of ledger documents, no manual tool switching required |
AIPROXY_API_ENDPOINT | Fill in according to the local deployment environment | Adapts to the access path of self-built models, ensures connectivity for model calls |
AIPROXY_API_TOKEN | Fill in according to the key generated by the platform | Completes identity verification for model access, ensures call security |
Three Common Mistakes
- Knowledge base responses are truncated, with returned content incomplete. Cause:
maxContextorquoteMaxTokenvalues are too small, failing to retain complete batch-associated documents or long time-series temperature control records. - A 500 error is returned when importing ledger documents. Cause: The dedicated field structure of meat product ledgers is not adapted, and key identifiers such as batch numbers and quarantine IDs cannot be recognized during document parsing.
- No response is received after model access, and ledger summary content cannot be generated. Cause:
AIPROXY_API_TOKENis configured incorrectly, or the model pointed to byAIPROXY_API_ENDPOINThas not completed connectivity verification.
How to Confirm the Configuration Is Complete
- Upload a single quarantine certificate and the slaughter record for the corresponding batch, and verify that the model can correctly associate the batch number and quarantine ID.
- Enter a query containing a specific temperature control time period, and confirm that the returned result contains complete temperature collection data with no abnormal truncation.
- Call the model to generate a monthly production ledger summary report, and check that it automatically associates the test results and cold chain records of each batch.
- Verify the configuration of
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINT, and ensure that the model can be called normally to generate basic ledger content.
The values provided are common starting points and should be measured against the reader's own samples.