What the data for this category looks like
Data for internal office and employee services at power grid enterprises primarily comes from internal OA systems, operation and maintenance work order platforms, equipment inventory databases, and employee attendance systems. Data update schedules cover three categories: real-time (such as operation and maintenance alerts), daily updates (such as attendance reports), and weekly updates (such as team performance). Individual documents typically include fields like work order number, equipment ID, processing node, responsible team, and operation duration. Units include kilowatt, hour, number of work orders, and others. Some long documents contain multi-turn operation and maintenance disposal records.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The multi-source heterogeneous nature of internal data from power grid enterprises requires multi-turn dialogue to bind exclusive session context via chatId, to avoid mixing data from different work orders and equipment. The high-frequency updates of real-time operation and maintenance data require prompts to limit the time range of context recall, preventing the loading of expired or redundant information. The specificity of professional fields and units requires prompts to preset field mapping rules, ensuring accurate parsing of content such as equipment ID and operation duration. Long operation and maintenance record documents occupy more context quota, so the number of context recall entries per single dialogue turn must be restricted.
How to set the configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Session context binding | Enable chatId binding | Ensure coherent dialogue context within the same session, avoid mixing cross-work order data |
| Context recall count | Top 3-5 entries | Limit the number of documents per single call, adapt to the length of power grid operation and maintenance records |
| Timeout threshold | 600 seconds | Adapt to the loading duration of large equipment inventory and multi-turn work orders |
| Variable mapping configuration | Bind the variables field, preset professional units | Accurately parse professional fields with units such as equipment ID and operation duration |
| Initial prompt configuration | Preset internal office process templates | Standardize the execution logic for employee inquiries |
Three Common Mistakes
- A new conversation takes 3 seconds or longer to start, while subsequent conversations run at normal speed. This occurs because the first call loads large datasets such as equipment inventory and operation and maintenance work order libraries, and the knowledge base is not pre-warmed.
- The preset process cannot be reused after restarting a conversation. This occurs because
chatIdis not bound, session context is not persistently saved, and each conversation is reinitialized. - A dialogue returns a 504 status code or no response. This occurs because the timeout threshold is not adjusted, failing to adapt to the loading duration of large equipment documents.
How to Confirm the Configuration Is Complete
- A new conversation is initiated, a query related to an operation and maintenance work order is entered, and subsequent questions in the session are checked for adherence to the same context logic.
- Whether
chatIdbinding is enabled in the configuration items is checked, confirming that questions within the same session will not lose previous context information. - The first call is simulated, the startup duration is checked against the preset threshold, and the timeout configuration is confirmed to adapt to the current dataset.
- A query containing professional units is entered, and the returned result is checked for accurate parsing of fields such as equipment ID and operation duration.
- Verification is performed against the configuration logic of FastGPT v4.17.0, confirming that all configuration items comply with the rules of the current version.
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.