What This Category’s Data Looks Like
This category’s data covers six sources: animal quarantine information, supplier batches, production traceability, temperature collection, metal detection, and laboratory testing. Daily business documents include quarantine certificates, slaughter and cutting records, ingredient marinating records, thermal processing records, cold chain temperature records, and microbial test reports. Data updates are performed per production batch and team frequency, with cold chain temperature data collected continuously. Fields include unique traceability codes, batch numbers, quarantine identifiers, temperature values, test indicator results, and more. Field formats vary significantly across different documents.
What Constraints Do These Characteristics Impose on the "Tool Calling and Plugins" Link
Data in this category is multi-source and strongly linked to unique traceability codes. Tool calling must accurately bind this code to avoid mixing data from different batches. Cold chain temperature data collected continuously has a large volume, so tool calling must support bulk data retrieval and segmented processing to prevent exceeding interface limits per single call. Field formats vary significantly across different business documents. For example, quarantine certificates use text fields while temperature records use numeric fields. Tool calling must adapt to parsing multiple types of data. Timeliness requirements in production links mean tool calling must match real-time or near-real-time data responses to avoid delays that hinder rapid positioning of defect analysis. Cross-link batch association needs require tool calling to support joint queries across multiple data sources, ensuring the establishment of a complete traceability chain.
How to Set the Configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapts to the need for parallel multi-tool calls, covering scenarios such as batch query, data retrieval, and result verification |
| Single tool call timeout | 600 seconds | Covers the typical time required for bulk cold chain temperature data retrieval and laboratory test report parsing |
functionCall trigger condition | Triggered when traceability batch numbers or detection keywords are included | Accurately matches business queries for quality traceability and defect analysis |
| Bulk data return limit | Top 800 entries | Matches the typical data scale of a single production batch, avoiding interface call overload |
| Data parsing field mapping | Preset mapping rules based on business document type | Adapts to different field formats of quarantine certificates, temperature records, and test reports |
Three Common Misconfigurations
- Phenomenon: A 403 error is returned when calling multidimensional table tools or other business interfaces, or fields are returned empty. Cause: Exclusive authentication configuration for the corresponding business was not used, and a universal authentication key was misused, resulting in insufficient permissions.
- Phenomenon: After calling the bulk data retrieval tool, the number of returned results is far lower than expected. Cause: Batch filter conditions were not configured correctly, or the bulk data return limit was set too low, resulting in only partial data being retrieved.
- Phenomenon: After calling the MySQL tool to obtain production data, the large language model output does not reference original database text fragments. Cause: The original data context returned by the tool was not retained, preventing the large language model from acquiring and referencing query results.
How to Confirm the Configuration Is Correct
- Initiate a test query that includes a known traceability batch number, and verify whether tool calling is automatically triggered per the preset conditions.
- Check the tool call logs to confirm that the duration of a single call does not exceed the preset timeout limit.
- Verify whether the original data fragments returned by the tool are included in the large language model's response context.
- Validate that document data of different business types can be correctly parsed by the tool and matched to corresponding fields.
The values provided are common starting points and should be measured against the reader's own samples.