What the data for this use case looks like
The data for power grid enterprise document and material drafting mainly comes from internal production operation documents, including transmission inspection reports, dispatch instructions, compliance submission documents, bidding documents, and the like. Data updates are performed according to project cycles or daily operation and maintenance rhythms. Single documents mix structured fields and unstructured text. Structured fields include equipment number, voltage level, operating unit, and the like, with units mostly kilovolts, megawatts, ten thousand kilowatt-hours, and the like. Unstructured text is mostly detailed descriptions from on-site inspections, with wide variation in content length.
Constraints imposed by these characteristics on multi-turn dialogue and prompt engineering
The fixed format of structured fields requires precise matching of fields and units during multi-turn dialogue, to avoid compliance issues such as incorrect voltage level units or mismatched equipment number digits. The mixed text structure requires distinguishing between structured field extraction and free text generation during context processing, to avoid confusing operation data and document formats. Periodically updated data requires prompts to associate with the latest operation node information, to ensure generated documents match current project progress. Long text content requires the multi-turn dialogue context window to adapt to long input and output, to avoid truncating core operation descriptions.
Reference only; verify against the target FastGPT release and deployment documentation.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Number of retained chat history entries | First 6 entries | The context of power grid document drafting usually revolves around 3-4 core nodes of a single project. 6 entries can cover complete dialogue logic without redundancy |
| Context message queue length | 8000-12000 characters | Adapts to the core content length of a single inspection report or dispatch document, and complies with the processing limit of the messages parameter |
| Variable binding rule | Bind exclusive fields via variables | Power grid documents must strictly correspond to fixed fields such as equipment ID and voltage level, to avoid generating incorrect content |
| Prompt verification switch | Enabled | Meets power grid compliance requirements, and constrains matching rules for fields and units |
| Dialogue ID binding | Bind chatId | Ensures that multi-turn dialogue context belongs to the same project, to avoid mixing cross-session data |
| Multi-turn trigger keywords | Match by operation types such as inspection and dispatch | Different power grid operation scenarios correspond to different document templates, improving generation accuracy |
Three common misconfigurations
- Phenomenon: Text extraction node returns empty fields. Cause: Failed to bind exclusive fields unique to power grid documents such as equipment ID and operating unit via
variables, so the extraction rule cannot match target content. - Phenomenon: Calling the multimodal image dialogue node reports an error, prompting format mismatch. Cause: Failed to configure input rules according to the annotation format of power grid equipment inspection images, so the model cannot recognize equipment numbers and fault descriptions in the images.
- Phenomenon: Dialogue has no response for a long time, and the backend shows service exception. Cause: Did not limit the number of retained chat history entries, causing the
messagescontext queue to be too long and exceeding the system processing threshold.
How to confirm the configuration is complete
- Input a real fragment of a power grid inspection document, check whether the text extraction node can accurately extract preset fields such as equipment ID and operation time.
- Launch a multi-turn dialogue, gradually supplement the demand parameters for document drafting, check whether the returned content includes preset fixed fields and compliant units.
- Launch more than 10 consecutive dialogues of the same type, check whether the system response time is stable, with no lag or unresponsive situations.
- Test JSON-formatted output requests, check whether the returned results meet the preset field structure requirements.
The values given 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.