What the data for this category looks like
Data for construction machinery-related documents mainly comes from internal enterprise equipment ledger systems, maintenance record databases, contract template libraries, and quality inspection report systems. Some large state-owned enterprises also add fields such as equipment installation location and factory serial number. Data update rhythm falls into three categories: equipment ledgers are updated quarterly, maintenance records are uploaded in real time with on-site operations, and contract templates and quality inspection reports are updated quarterly.
The document structure includes structured fields and unstructured body text. Structured fields include equipment number (with alphabetic prefix), rated lifting capacity (unit: tons), maintenance cycle (unit: days), and contract amount (unit: yuan). Unstructured body text covers equipment fault descriptions, maintenance plans, and other content. The length of a single document ranges from hundreds to tens of thousands of characters.
What constraints these characteristics impose on tool calling and plugin configuration
These characteristics impose three constraints on tool calling and plugin configuration: First, structured fields have fixed prefixes and units. Tool calling must accurately match field formats and units to avoid parameter parsing errors. Second, data sources are scattered across multiple systems. Plugins must support calling multiple data source interfaces at the same time to obtain complete data required for document generation. Third, document lengths vary widely. Tool calling must adapt to context inputs of different lengths to avoid generation failures caused by context overflow.
In addition, construction machinery documents often include compliance content related to special equipment. Tool calling must link preset compliance verification plugins to ensure generated content complies with industry specifications.
How to Configure
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
functionCall | Enabled | Supports accurate calling of tools such as equipment parameter query and template generation, adapting to the structured data requirements of construction machinery documents |
toolChoice | auto | Automatically matches tools corresponding to the current document type, adapting to generation requirements for different categories of construction machinery such as cranes and tunneling equipment |
| API timeout setting | 30–60 seconds | Covers request response durations for multiple data sources (ledgers, maintenance systems) to avoid generation process interruptions caused by timeouts |
| Single batch data retrieval limit | First 20 items | Adapts to the single data volume of construction machinery ledgers, avoiding interface overload |
| Single document processing limit | 8000–12000 characters | Adapts to the longest construction machinery procurement contracts and quality inspection reports |
| Data source association configuration | Bind equipment ledger, maintenance record database | Matches structured fields and unstructured content required for construction machinery documents |
Three Common Mistakes
- Symptom: Equipment fields returned by database plugin calls are empty or garbled. Cause: The character set of the database connection is not configured as utf8mb4, causing Chinese fields in construction machinery ledgers to fail normal parsing.
- Symptom: Parameter units such as rated lifting weight are incorrect in tool-call-generated documents. Cause: No unit verification rules are specified in the
functionCallconfiguration, leading to deviations in parameter conversion. - Symptom: HTTP module calls to equipment interfaces fail, but Postman can access normally. Cause: No correct request headers or authentication tokens are configured in the FastGPT plugin, or the timeout setting is lower than the actual response duration.
How to Confirm Proper Configuration
- Run a generation test for a crane equipment maintenance report, check whether the returned content includes correct fields such as equipment number and rated lifting capacity.
- View plugin operation logs, confirm that the character set of the database connection is utf8mb4, and there are no garbled code-related errors.
- Call the
functionCallfunction, verify that it can accurately retrieve ledger data for specified equipment, and the returned results match the content stored in the system. - Test HTTP module calls to equipment interfaces, confirm that the returned results match the content obtained via Postman.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.