What the Data for This Use Case Looks Like
Data sources for power grid enterprise inquiry and response include 95598 hotline work orders, offline service center consultation records, electricity installation application archives, power outage notices and announcements. Data update schedule: routine inquiries are synchronized in real time; work orders are archived and updated upon completion; electricity usage archives are updated in real time when user business changes occur. The document structure of a single data entry includes fields such as inquiry content, user ID, electricity usage address, request type, processing progress, and reply script template. The user ID uses a 10-digit numeric format, the electricity usage address includes administrative division and street number information, and the request type is a fixed enumeration value.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
The precise positioning fields such as the 10-digit user ID and electricity usage address included in the data require that these fields must be extracted and verified during multi-turn dialogue, otherwise the corresponding business data cannot be matched. The real-time updated business data feature requires that expired information be filtered from the conversation context to avoid interference with current inquiries. The fixed enumeration request type field requires that prompt engineering limit conversations to only revolve around preset business types, preventing scope drift. The feature where a single data entry is associated with multiple process links requires that multi-turn dialogue confirm step-by-step according to process nodes, avoiding information overload that affects response accuracy.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Question Optimization | Enabled, calibrated based on actual testing | Matches the step-by-step, multi-link request characteristics of power grid inquiries |
| Maximum Context Window | Retain the first 3 rounds of conversation content | Avoids redundant historical information interfering with current business matching |
chatId | Bind to the user's 10-digit electricity account number | Ensures correct association of business data across multi-turn conversations for the same user |
messages | Filter historical conversations older than 24 hours | Adapts to real-time updated power grid business data |
variables | Inject preset electricity usage address and request type fields | Reduces repetitive input operations for users |
| Single Search Limit | Recall the top 5 matching documents | Focuses on the most relevant power grid business knowledge base content |
Three Common Misconfigurations
- Symptom: The workflow stops at the AI dialogue stage with no subsequent output, and the status code returns
400. Cause:chatIdis not correctly bound to the user's electricity account number, causing the context to fail to match the corresponding business data and interrupting the workflow. - Symptom: The inquiry response returned by the dialogue does not match the user's actual electricity usage address. Cause: The user's electricity usage address field is not injected into
variables, causing the model to match incorrect business archives when calling the knowledge base. - Symptom: Conversation logs are not automatically generated or cleared prematurely. Cause: Conversation log persistence configuration is not enabled, or an incorrect log retention duration threshold is configured.
How to Verify Successful Configuration
- Manually input a test inquiry containing an electricity usage address and request type, and confirm whether the corresponding fields are automatically extracted and injected into the conversation context.
- Initiate two consecutive questions, and check whether
chatIdis consistently bound and whether key information from the previous round is retained in the conversation context. - Trigger workflow execution, and confirm that there are no interruptions across the question optimization, knowledge base search, and AI dialogue stages.
- Review the conversation log configuration, and confirm that the log retention duration meets business requirements, with no automatic early deletion.
The values provided here 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.