What the data for this category looks like
Core data for beverage and bottled water production covers six document types: formula sheets, water treatment records, blending records, sterilization or aseptic filling records, packaging material inspection records, and finished product inspection records. Data sources include water source and raw material testing systems, formula management systems, online sugar and acidity monitoring equipment, filling line equipment, visual inspection equipment, and LIMS laboratory management systems. Data updates run per production batch and shift. Some equipment data is collected continuously. Core fields include production batch number, raw material input volume, water quality test values, filling liquid level parameters, sealing pass rate, label verification results, and more. Units include milligrams per liter, percentage, millimeters, and other category-specific measurement standards. This category does not include a raw milk acceptance step. It has production characteristics of rapid switching between multiple products, with higher production line data synchronization frequency.
What constraints these characteristics impose on tool calling and plugins
Parallel calls to multiple data sources require tool plugins to support format adaptation and cross-system aggregation. Single calls must not retrieve only one type of data. Mixed calls of continuously collected equipment data and batch-level documents require tool calls to support dual filtering rules based on time range and production batch number. This ensures retrieval results match the current production batch. Low-latency requirements from high-speed filling lines limit the maximum response time for tool calls. Excessive wait times are not acceptable. Production scenarios with rapid product switching require tool call configurations to quickly adapt to different product formulas and inspection standards. No new calling logic needs to be written. Rapid traceability requirements for recall-related quality issues require tool calls to accurately associate production data across links. Data matching deviations must be avoided.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapts to multi-type tool calling scenarios, no manual specification of call type required, reduces configuration complexity |
| Single tool call timeout | 30–60 seconds | Matches the delay window of line data collection, prevents timeout interruptions of batch production data query tasks |
| Workflow/API function-call configuration | enabled | Supports structured calls to structured data sources such as LIMS and device logs, improves the accuracy and readability of retrieval results |
| Tool call concurrency limit | 8 concurrent | Adapts to batch data requests from high-speed filling lines, avoids exceeding rate limiting thresholds of third-party interfaces |
| Tool call result cache duration | 1 shift (8 hours) | Matches the shift update rhythm of production lines, reduces overhead from repeated calls to the same batch data |
| Matching rule for triggering tool calls | Match by production batch number + sampling time | Accurately associates cross-source data from water treatment, filling, and inspection links, avoids mixing data from different batches |
Three Common Mistakes
- Phenomenon: Tool calls return a 429 status code, and the interface displays an interface rate limit error. Cause: The tool call concurrency limit is set too high, exceeding the rate limiting threshold of LIMS or production line device interfaces. The actual request load of the production line is not matched.
- Phenomenon: Fields returned by tool calls are empty, and no data for the target production batch is found. Cause: The call is not initiated using the production batch number + sampling time matching rule. Data from other batches is incorrectly retrieved.
- Phenomenon: Tool calls time out and interrupt, with no results returned. Cause: The single tool call timeout is set too short. It cannot complete cross-source data aggregation queries, such as requests that pull formula, water treatment, and filling data at the same time.
How to Confirm Correct Configuration
- In the application debugging panel of FastGPT v4.17.0, enter a query instruction for a specified production batch number. Verify that the returned formula, water treatment, and filling data fully match the production records for that batch number.
- Send 10 concurrent tool call requests. Confirm that no rate limit error is triggered, verifying the effectiveness of the concurrency limit configuration.
- Adjust the single tool call timeout to 10 seconds. Submit a cross-source data query that takes a long time. Confirm that a reasonable timeout handling logic is activated.
- Enable the
functionCallfunction. Submit a structured quality traceability query instruction. Confirm that the returned results conform to the preset field format and association rules.
The values listed here are common starting points. They should be evaluated against the samples relevant to the target deployment.