What the data for this category looks like
Data sources for internal office and employee services at power generation groups include internal OA systems, human resource management systems, production operation management systems, and compliance document libraries. Data is updated on three schedules: office data such as attendance records and work orders is updated daily; production operation data is updated per shift; employee handbooks and compliance policy documents are updated quarterly. The document structure includes structured metadata (employee ID, affiliated department, unit number, work order ID) and unstructured content (work order descriptions, production report text, policy document text). Professional fields include coal consumption, power generation, and similar metrics, with corresponding units of grams per kilowatt-hour and megawatt-hours.
What constraints do these characteristics impose on multi-turn dialogue and prompt engineering?
Professional fields and their associated units for power generation groups require prompt engineering to pre-set industry terminology alignment rules, to avoid generating content that does not comply with production specifications. Multi-turn dialogue must bind the context of a single employee session via chatId, associating historical work orders and personal habit profiles. Structured employee, unit, and work order data must be passed into the dialogue via variables to reduce duplicate data entry. Production and office data from different sources are stored in separate databases, so the corresponding knowledge base collection must be specified during dialogue to avoid returning irrelevant results across collections. The timeliness requirement for real-time production data means the context window should not be too long, to prevent outdated information from interfering with current decision-making.
How to configure settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId binding | Enable and bind employee session ID | Associate historical conversations and work order data for the same employee, retain personal habit profiles |
| Knowledge base collection specification | Select production data, employee handbook, work order system collections per scenario | Power generation group data is stored in separate databases for production, office, and human resources; precise calling of corresponding collections is required |
variables passing | Enable structured field passing | Professional fields such as employee ID and unit number are passed via variables to avoid duplicate data entry |
messages context organization | Automatically organize the most recent 8 rounds of dialogue | Filter redundant historical information to ensure timeliness and relevance of context |
| Maximum context length | 800–1200 characters | Limit the context scope to prevent expired shift production data from interfering with current dialogue |
| Dialogue timeout period | 600 seconds | Adapt to query waiting durations in production scenarios, avoid mid-session interruptions |
| Temperature coefficient | Calibrate based on actual testing | Balance professional terminology accuracy and dialogue flexibility; separate adjustments can be made for variable reference scenarios |
Three common configuration errors
- An error occurs after configuring the database connection plugin, prompting invalid connection parameters. The cause is failure to adapt to the dedicated authentication method and port number of the power generation group's intranet database, and failure to pass intranet whitelist verification.
- Unable to search the specified knowledge base collection, with irrelevant full-database results returned. The cause is failure to bind the target collection in the dialogue configuration, or inconsistency between the collection name and the actual stored identifier.
- The temperature setting button disappears in variable reference mode, making it impossible to adjust parameters separately. The cause is that in FastGPT v4.9.3 and earlier versions, variable reference mode reuses the global temperature configuration by default, with no independent setting entry available; this issue has been optimized in FastGPT v4.17.0.
How to confirm the configuration is successful
- Initiate a query that includes an employee ID, check whether historical work order data is automatically associated in the dialogue to verify that
chatIdbinding is effective. - Select the specified production data collection to initiate a query, confirm that the returned results only include documents within that collection to verify that collection specification is effective.
- Pass structured variables to initiate a dialogue, check whether the reply correctly uses the variable content to verify that
variablespassing is effective. - Initiate a dialogue that includes a production data query, confirm that the dialogue completes a response within 600 seconds to verify that the timeout period configuration is effective.
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.