What the data for this category looks like
Data for this category comes from engineering orders, sand, gravel, cement admixture inspection records, aggregate moisture content measurement data, mixing station control system logs, and project laboratory archives. Data updates follow the production flow. One production and transport order corresponds to each concrete truck. One inspection record corresponds to each raw material batch. Daily summaries cover test block strength reports and opening identification results. Core documents include mix proportion notices, production transport orders, raw material inspection reports, and test block strength records. Fields include raw material batch number, moisture content measurement value, batching dosage, pouring time, test block number, and strength indicators.
What constraints these characteristics impose on tool calling and plugins
Data updates happen frequently with fine granularity. Real-time sync calling is required for each truck’s data. Tool calling must support short-cycle triggers. Multi-source data is scattered across different systems and documents. Plugins must adapt to interface formats of different data sources. Batch data pulling requires filtering by batch number and time range. Test block strength reports require delayed verification. Tool calling must support associated queries for delayed data. A strong correlation between mix proportion and moisture content requires passing both current batch raw material parameters and on-site measured values during tool calling. This avoids cross-batch data confusion. Binding between production transport orders and pouring time must be precise to the minute. Tool calling context must be limited to the corresponding engineering order and vehicle batch.
How to set the configuration
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Single Tool Call Count | Top 10 entries | Matches the number of core inspection and production data entries per concrete truck |
| Historical Conversation Retention Rounds | 3 rounds | Balances context information volume and call response speed, adapts to associated queries for multiple batches of data |
| Context Window Size | 4000–6000 characters | Adapts to the total character count of single-batch documents such as mix proportions and inspection reports |
| Tool Call Trigger Condition | Triggered by raw material batch | Corresponds to the business logic of each batch of raw material inspection and batching adjustment |
| Model Response Timeout | 120 seconds | Covers the time limit for multi-source data pulling and calculation |
| Workflow/API tool selection | auto | Allows the model to independently choose whether to call tools, adapts to dynamically changing quality audit scenarios |
Three Common Mistakes
- Phenomenon: When calling multiple models, historical records are not retained as configured after multiple accesses with the same ID. Cause: Historical retention rounds are not configured separately for each model call, causing the round parameter to be overwritten globally.
- Phenomenon: After deploying the Ollama model locally, selecting a tool-call-supporting configuration still fails to trigger tool calls. Cause: Some early versions of Ollama do not fully implement the
functionCallprotocol. Confirm model version and protocol compatibility. - Phenomenon: When using a general account KEY to configure the aggregated BASE_URI, plugin calls return permission errors. Cause: The general KEY is not bound to the access permissions of the corresponding data source, making it unable to adapt to multi-source data pulling and verification.
How to Confirm the Configuration is Correct
- Initiate a quality audit request corresponding to a single batch of raw materials. Check whether tool calls are triggered as configured and return corresponding inspection data.
- View model call logs. Confirm that the
toolChoiceparameter takes effect, and that the model independently chooses to call tools or return results directly to meet scene expectations. - Simulate multiple accesses with the same ID. Verify that the historical conversation retention rounds match the configured values, and no cross-batch data mixing occurs.
- Verify the timeout configuration. Simulate delayed data pulling, and confirm that the call is not interrupted within the expected time.
The values provided are common starting points and should be measured against the reader's own samples.