What the Data for This Category Looks Like
The data for unit-based system retrieval originates from official internal system documents of central state-owned enterprises, government departments and their subordinate units. It covers multiple categories including personnel management, financial regulations, and business processes. The data update rhythm is triggered on demand by each individual unit. Synchronization occurs when new systems are released or existing systems are revised. There is no fixed batch cycle. Document structures follow a standardized format, with fixed fields including unit name, unit code, system document number, release date, effective date, and main body content. Each system is bound to a unique unit identifier. This identifier is used to distinguish exclusive system content across different subordinate units.
What Constraints These Characteristics Impose on Tool Calling and Plugins
Data is stored split by unit identifier and bound to exclusive fields. Tool calling must carry unit-level filter parameters. Otherwise, invalid cross-unit data will be returned. Documents include structured unit identifier fields. Plugin configurations must support retrieval by field. This avoids result confusion caused by full-text matching alone. The update rhythm has no fixed cycle. The data source synchronization for tool calling must support on-demand triggering. This ensures the timeliness of retrieved data. Subtle differences exist in system formats across different units. Plugins must support multi-format document parsing. This prevents parsing failures.
Configuration Settings
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Unit Filter Parameter | {"unit_code": "example value"}, calibrated based on actual testing | Data is stored by unit code, so the corresponding parameter must be passed to filter cross-unit results |
toolChoice | auto | Allows the model to independently decide whether to call tools to retrieve system data |
functionCall | Bind unit filter fields | Forces the model to carry unit parameters when calling tools, to avoid missing filter conditions |
| Data Source Synchronization Trigger Method | On-demand triggering + weekly full synchronization | System updates have no fixed cycle, this balances real-time performance and synchronization efficiency |
| Single Retrieval Result Limit | Top 10 entries | Individual system content is lengthy, too many results will exceed the context window |
| Timeout Setting | 600 seconds | Cross-unit batch retrieval requires longer response time, to avoid mid-run interruptions |
Three Common Misconfigurations
- Phenomenon: The bound system data source interface returns a 502 status code. Cause: No unit filter parameter is carried in the plugin request, triggering the target interface's cross-unit access verification.
- Phenomenon: Retrieval results include system documents from other subordinate units. Cause: No unit-level filter rule is configured, or the parameter transfer format does not meet interface requirements.
- Phenomenon: The number of results returned by tool calling is far lower than the set limit. Cause: Data source synchronization is incomplete, or the effective data volume returned by the interface is insufficient.
How to Verify Proper Configuration
- Enter a test question with a unit identifier in the FastGPT plugin debugging panel, and check whether the request parameters carry the correct unit code.
- Manually trigger a data source synchronization, and check whether the synchronization log shows successful update of system data for the corresponding unit.
- Send a test request without carrying unit parameters, confirm that the returned result is empty or prompts missing parameters, to verify that the filter rule is active.
- After calling the tool, check whether the returned context only contains system content from the specified unit, with no cross-unit data mixed in.
The values provided here are common starting points, and should be evaluated 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.