What the data for this use case looks like
Data for internal office and employee services at state-owned capital operation companies comes from four main sources: internal OA systems, human resource management platforms, state-owned assets supervision reporting systems, and financial management modules.
Update frequencies vary by data type:
- Employee attendance and weekly report data: updated daily on workdays
- Salary and department budget data: updated monthly
- State-owned capital operation report data: updated quarterly
Most documents use structured fields, including employee ID, name, department, position, attendance days, approval status, budget amount and other fields. Units include days, yuan, person-times and others. The dataset also includes semi-structured employee service tickets and approval process documents.
What constraints these characteristics impose on tool calling and plugins
The primary use of structured fields requires that tool calls strictly match preset field names and types. Configuration of the toolChoice and functionCall parameters must be coordinated to ensure generated request content complies with specifications.
Data sources with different update frequencies require corresponding configuration of plugin trigger timing, to avoid frequent calls to interfaces of systems with low update frequencies. Compliance requirements for state-owned asset-related data mean that plugin calls must carry enterprise authentication information, and only fields within the authorized scope can be called.
Differences in the format of semi-structured ticket data require plugins to adapt to the field structures of different ticket types, to avoid request parameter mismatches.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | auto | Automatically select tools suitable for internal office scenarios, cover multi-system call requirements, and adapt to the tool calling logic of FastGPT v4.17.0 |
functionCall | Enable mandatory field validation | Ensure generated request parameters comply with the field specifications of internal systems |
| Request timeout | 25–35 seconds | Adapt to the response rhythm of internal systems at state-owned capital operation companies |
| Request retries | 2 times | Address temporary internal network fluctuations or short system freezes |
| Custom request headers | Carry enterprise authentication tokens | Meet permission verification requirements of internal systems |
| Per-call data items | First 100 items | Avoid exceeding the processing limit of the system with a single request data volume |
Three common errors
- Phenomenon: The plugin returns a 400 status code after sending a request, prompting a field format mismatch. Cause: Mandatory field validation via
functionCallis not enabled, and the AI generates request content that does not comply with the field types of internal systems. - Phenomenon: Non-essential AI conversation text is attached during tool calling, resulting in the internal system intercepting the request. Cause: The switch to return only tool call results is not configured, and irrelevant conversation content is additionally attached.
- Phenomenon: An error indicating model unavailability is returned when calling the bge-large-zh-1.5 embedding model. Cause: The embedding dimension of 1024 is not confirmed, and an interface of an embedding model with another dimension is mistakenly used.
How to confirm the configuration is correct
- Trigger a test internal office tool call, and check if the returned result only contains tool call data, with no additional AI conversation content.
- View plugin operation logs to confirm that the request format complies with the requirements of internal systems, with no format error prompts.
- Call the bge-large-zh-1.5 embedding model to verify that the returned vector dimension meets preset requirements.
- Simulate a high-frequency tool call to confirm that the retry mechanism works normally during temporary exceptions, with no consecutive failure records.
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.