What the Data for This Category Looks Like
Data sources for this category include cell banks and seed lots, process analytics systems, cleanroom monitoring equipment, MES, LIMS, and equipment integrity testing tools. Data update cadence falls into three categories: per batch, real-time, and per monitoring plan, which correspond to record requirements for different business links. Daily documents include cell bank or seed lot records, culture and purification records, aseptic filling records, environmental monitoring reports, media fill simulation results, sterility test reports, and batch release documents. Document fields include culture temperature, filling speed, colony count, and more. Some fields have fixed units: for example, ℃ for culture temperature, CFU/㎡ for colony count.
What Constraints Do These Characteristics Impose on the Tool Calling and Plugins Workflow
Multi-source heterogeneous data sources require tool calling to integrate with multiple different systems, and need to adapt to interface formats and permission rules of each system. Differentiated update frequencies require tool calling to support multiple triggers such as per-batch and real-time, to avoid data lag or redundant calls. Complex document structures and fields with fixed units require tool calling parameters to strictly match document field definitions, preventing call failures from parameter format errors. Additionally, high-risk data related to batch release requires tool calling to have a reliable result verification mechanism, to ensure the accuracy of audited data and avoid production risks from incorrect data.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Tool call trigger timing | Trigger before batch release, trigger when real-time monitoring detects anomalies | Match the per-batch and real-time data update cadence to ensure timeliness of audited data |
| Maximum number of documents returned per single call | Top 20 entries | Avoid context overload, adapt to the content volume of complex document structures |
| Timeout threshold | 600 seconds | Reserve sufficient time for data retrieval and verification when integrating with multiple systems |
| Workflow/API tool selection | auto | Adapt to multi-type tool calling needs, cover batch auditing and real-time monitoring scenarios |
| Workflow/API function-call configuration | Enable parameter verification | Match fixed units and value rules of document fields, prevent format errors |
| Concurrent call limit | Calibrated based on actual testing | Adapt to actual business concurrency needs, avoid interface overload |
Three Common Mistakes
- Symptom: Tool call returns 400 InternalError.Algo.InvalidParameter status code, prompting parameter range error. Cause: Failed to match fixed units and value ranges of specific fields in biologics documents, for example, failing to limit culture temperature parameters to 37±0.5℃.
- Symptom: Tool call returns empty results or missing core fields. Cause: Incorrect trigger timing configuration, failing to trigger the call after data update, resulting in failure to retrieve the latest cleanroom monitoring or process analysis data.
- Symptom: The large language model cannot call the specified tool, prompting that no available function was found. Cause: Failed to correctly configure the parameter verification rules for
functionCall, resulting in tool call being blocked and unable to complete the function call flow.
How to Confirm the Configuration Is Correct
- View the FastGPT v4.17.0 tool call logs to confirm that the trigger timing matches the data update cadence, with no abnormal blocking records.
- Simulate a tool call to verify that the returned results include the specified fields and fixed units of documents for this category, for example, the unit of colony count is CFU/㎡.
- Test the call flow for different trigger timings to confirm that tool calls can be performed normally in both pre-batch release and real-time monitoring scenarios.
- Verify that the configurations for
toolChoiceandfunctionCalltake effect, and that tool calls return correct audited data according to preset rules.
The values provided here are common starting points and should be evaluated against the reader's own samples.