Q&A for General Mechanical Equipment Assembly Process Specifications and Technical Standards: Tool Calling and Plugins

Core data for general mechanical equipment assembly comes from business systems and on-site collection links of pump, valve, compressor, reducer

What the data for this category looks like

Core data for general mechanical equipment assembly comes from business systems and on-site collection links of pump, valve, compressor, reducer, machine tool and non-standard equipment manufacturers. Daily documents include assembly process cards, BOM lists, torque records, clearance adjustment records, commissioning reports, factory inspection sheets and packing lists. Data sources cover PLM, ERP, parts inspection systems, assembly tool collection terminals, test benches, MES and customer technical agreements. Data updates are synchronized with each device or order, and include specific assembly indicators such as torque values, clearance parameters, and coaxiality tolerances.

Data for this category is scattered across multiple systems and document types. Each device’s assembly data is unique. This requires tool calling to support dynamically pulling exclusive data by order or device number. Multi-source data has differing formats and fields. Plugins must adapt to parsing rules for different documents to avoid mixing irrelevant data. High-frequency order-based updates require tool calling configurations to support dynamic parameter binding. Fixed datasets cannot be used. In addition, assembly data accuracy directly affects final device performance. Tool calling return results must be strictly filtered to only retain content related to the current question and answer. This prevents incorrect data from interfering with judgment.

How to set configurations

Configuration ItemRecommended SettingRationale
Application call trigger methodDynamically triggered by order numberMatches exclusive assembly data for each device and order
Tool call timeout period30–60 secondsAdapts to the average time required to pull data from multiple systems
Plugin data filtering rulesOnly return assembly documents associated with the current orderAvoids irrelevant data interfering with question and answer accuracy
Workflow/API tool selectionautoLets the large language model independently select the tool suitable for the current question and answer
Workflow/API function-call configurationEnabledForces structured output of tool call parameters to ensure calling accuracy
Number of returns per single tool callTop 3 entriesControls context length to avoid exceeding the model’s processing limit
Plugin request body size limitNo more than 1000 KBAdapts to the request upper limit of most HTTP plugins

Three common mistakes

  • Phenomenon: After the HTTP plugin is imported, the assembly process card for the corresponding order cannot be pulled, and a 404 status code is returned. Cause: The plugin request parameters are not configured to bind the order number, causing the interface to fail to match exclusive data.
  • Phenomenon: The original data of the application call is directly displayed on the conversation interface and not used as context reference. Cause: The direct output switch of the application call is not turned off, and the tool results are not synchronized to the large language model context.
  • Phenomenon: The workflow has no response after being called via API, and the log shows a call timeout. Cause: The tool call timeout period is not adjusted, and the default threshold causes multi-system pull requests to be interrupted.

How to confirm the configuration is correct

  • Manually trigger tool calling and check whether the returned plugin data only includes assembly documents for the current test order.
  • Check the application call output log to confirm that only structured data is returned and is not directly displayed on the conversation interface.
  • Call the FastGPT v4.17.0 API to trigger the workflow, and check whether the detailed records of tool calling include complete parameters and return results.
  • Simulate assembly process-related questions and confirm that the large language model can generate qualified answers based on the torque and clearance data returned by the plugin.

The values provided here are common starting points and should be evaluated against the reader's own samples.