What this category of data looks like
Data for this category originates from BIM or detailed design documents, rebar and concrete batch ledgers, mold table and curing kiln operation records, laboratory test reports, and QR code traceability systems. Daily documents include component detailed drawings, rebar cutting lists, mold inspection records, pouring and vibration records, curing records, and demolding factory inspection reports. Data is updated per individual component, per batch of materials, and per curing kiln unit. Fields include component ID, rebar specification, concrete strength grade, curing duration, hoisting position identifier, and other relevant fields. Format is adjusted according to project design requirements.
What constraints these characteristics impose on the tool calling and plugins workflow
Data for this category is updated per individual item, per batch, and per curing kiln. This requires tool calls to precisely match the corresponding batch of components and material data, to avoid information confusion caused by cross-batch calls. Daily documents include multiple types of structured and unstructured files, so tools must adapt to format requirements for different scenarios such as detailed drawing parsing and cutting list verification. Data is linked to on-site hoisting positions, so tool calls must also match on-site installation parameters to prevent embedded part misalignment. Errors directly lead to component scrap and project delays, so an additional verification step must be added to tool calls to ensure data accuracy.
Configuration Settings
These configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapts to multi-tool linkage needs during precast component bidding, automatically selects matching tool types |
| Workflow/API function-call configuration | enabled | Supports calling specialized tools such as rebar bill verification and strength check to complete automatic verification of bidding data |
| Number of data entries per single tool call | Top 10 | The data volume of a single batch of precast components is moderate, avoiding context overload that affects model response |
| Context length limit | 800-1200 characters | Adapts to the average length of precast component bidding documents, avoiding context overflow |
| Tool call timeout threshold | 300 seconds | Covers the average time required for component data parsing and verification |
| Knowledge base recall count | Top 5 | Relevant information for precast component bidding documents is concentrated in historical data of a small number of batches |
| Result verification switch | Enabled | Blocks erroneous data that may lead to scrap such as embedded part misalignment and insufficient strength |
Three Common Mistakes
- Phenomenon: The component list returned by tool calls does not match the project detailed drawing. Cause: The trigger rule for filtering data by component batch is not configured, and an unfiltered global dataset is called.
- Phenomenon: The conversation interface does not return the referenced knowledge base ID. Cause: The knowledge base association log recording configuration after tool calls is not enabled, so the corresponding identifier is not generated.
- Phenomenon: The local MCP service cannot be called normally. Cause: The local service port mapping rule for tool calls is not configured, so a connection cannot be established.
How to Confirm Configuration is Complete
- Initiate a test conversation requesting precast component rebar bill verification, check whether the tool automatically triggers the corresponding call.
- View the conversation log, confirm that the returned result includes the referenced knowledge base information identifier.
- Simulate a timeout scenario, check whether the tool call triggers the corresponding processing logic according to the configured threshold.
- Call the local MCP service test interface, confirm that the tool can normally call locally deployed services.
The values provided here are common starting points and should be measured against the reader's own samples.