What data for this category looks like
This category’s data originates from special equipment safety supervision systems, inspection and testing institution systems, user ledgers, and operator certificate databases. The update frequency is once per month. Daily documents include use registration certificates, periodic inspection reports, maintenance records, hidden danger rectification notices, accident reports, and operator certificates. Each document contains fields such as equipment number, inspection cycle, maintenance unit, operator qualification number, and rectification status. Data is stored as a mix of structured ledgers and unstructured documents.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Data sources are scattered. Cross-system retrieval requires multi-turn dialogue to gradually align field differences across systems. The monthly update feature requires prompts to consistently call the latest monthly data source to avoid expired information. Equipment-related documents have inconsistent field naming. Multi-turn dialogue must gradually guide clarification of equipment type and corresponding fields. Hidden danger rectification tracking requires cross-referencing cancellation status across multiple documents. Multi-turn dialogue must sort out associated information for rectification, acceptance, and archiving by stage. Operator qualification verification requires combining the latest certificate database data. Prompts must limit calls to monthly updated certificate database content.
How to set up configurations
This configuration uses FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind a fixed session ID to a dedicated supervision ledger | Special equipment supervision requires long-term association with the same supervision object. A fixed chatId maintains context consistency across multi-turn dialogues. |
messages | Retain the first 6 dialogue context entries | Supervision scenarios require cross-checking historical operation records across multiple stages. 6 context entries cover the full process information of a single supervision task. |
variables | Pass two global variables: equipment number and supervision cycle | Quickly locate corresponding ledger data and reduce repeated inquiries during dialogue. |
| Maximum context | 8000–12000 token | Special equipment-related documents have relatively long lengths. Sufficient context is needed to accommodate multiple reports and ledger contents. |
| Single-match limit | Top 3 matching results | Supervision data requires precise matching. Excessive results will interfere with information extraction by the prompt. |
| Timeout threshold | 600 seconds | Cross-system retrieval of multiple ledger entries requires sufficient time to avoid dialogue interruption due to timeout. |
Three common configuration mistakes
- Phenomenon: The number of retained dialogue history entries is set to 6, but the model cannot associate context from the previous round of questions. Cause: The
messagesparameter is not correctly bound, or the context storage configuration does not cover the specified number of dialogue records. - Phenomenon: Key information extracted from ledger data displays as garbled text. Cause: A standardized output format is not specified in the prompt, and unified encoding processing is not applied to non-standard text returned across systems.
- Phenomenon: After embedding a chat window via iframe, idle sessions continue to occupy service resources. Cause: No session idle automatic release rule is configured, resulting in inactive sessions not being cleaned up in a timely manner.
How to confirm the configuration is correct
- Initiate a dialogue that includes an equipment number query, then initiate a follow-up query about the rectification status associated with that equipment. Verify that the model correctly associates the equipment information from the previous round.
- Import a special equipment maintenance record document, use a prompt to extract key fields, and verify that the output has no garbled text and complete fields.
- Initiate an idle dialogue, wait for the preset timeout threshold, and verify that the session is automatically released and service resources are not continuously occupied.
- Pass the equipment number from the
variablesparameter, and verify that the dialogue directly locates the corresponding ledger data without repeated inquiries about equipment information.
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.