Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
The internal office data for urban investment platforms mainly comes from proprietary OA systems, project management ledgers, employee attendance systems, and financial reimbursement modules. There are two types of data update rhythms: structured form data is synchronized and updated daily, while unstructured documents such as project plans and internal notifications take effect after being manually uploaded by administrators. The document structure includes two parts: fixed fields and free text. Fixed fields include employee ID, department code, project number, and reimbursement order number, with units including yuan, days, person-times, etc. Free text mostly consists of project progress descriptions and internal communication minutes.
What constraints do these characteristics impose on the multi-turn dialogue and prompt engineering links
The characteristics of internal office data for urban investment platforms impose multiple constraints on the multi-turn dialogue and prompt engineering links. Structured form fields are numerous and have fixed coding rules, so prompts must clearly specify field matching logic to avoid result deviations caused by fuzzy recognition. The length of unstructured documents varies widely, so multi-turn conversations must limit the total context length to prevent exceeding the model's processing limits. There are differences in data update rhythms, so multi-turn conversations must support dynamic calls to the latest synchronized data without relying on static caching. Fields and business logic vary significantly across different business scenarios, so prompts must adapt to the calling rules of specialized scenarios such as reimbursement, project management, and attendance query.
How to configure settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to the unique identifier of the current employee session, persist until the session ends | Ensure that multi-turn dialogue history messages are associated with the current request, meeting the context continuity requirements of employee services for urban investment platforms |
messages | Carry the first 15 historical conversation messages, automatically truncate excess content | Multi-turn interactions for internal office work of urban investment platforms usually do not exceed 15 turns, limiting the quantity to reduce model computing load and avoid context overflow |
variables | Bind permission variables such as employee ID and department code, valid for 8 hours | Internal data of urban investment platforms must be associated with employee permissions, global variables can be maintained across multi-turn sessions without repeated input of identity information |
| Maximum context length | 8000–12000 characters | Adapt to the length range of internal documents of urban investment platforms, avoid model processing failures caused by long text |
| Session timeout period | 300 seconds | Match the conventional interaction rhythm of internal office work for urban investment platforms, prevent invalid sessions from occupying resources |
Three common errors to avoid
- Issue:
Cannot read properties of null (reading 'q')error occurs when calling the interface. Cause: ThechatIdparameter is not passed correctly, resulting in empty session context, and the model cannot read the query field of the conversation history. - Issue: The problem classification results in the workflow have large deviations. Cause: The prompt is not adapted to the specialized business terms and field rules of internal office work for urban investment platforms, and cannot accurately identify the business scenario of the user's question.
- Issue: Global variables cannot be maintained across contexts during multi-turn conversations. Cause: The variable binding rules are not configured correctly, resulting in variable reset after session switching.
How to confirm the configuration is complete
- Send a single-turn test request, check whether the returned result is associated with the current employee's department and permission information to confirm that the variable binding takes effect.
- Conduct continuous multi-turn conversations, check whether historical messages are correctly carried to confirm that the historical message configuration meets expectations.
- View the session log to confirm that
chatIdremains consistent across multiple requests in the same session with no abnormal changes. - Trigger the timeout scenario, check whether the session automatically terminates after the set time to confirm that the timeout configuration takes effect.
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.