What this category of data looks like
Data sources include EAM/CMMS systems, equipment sensor collection data, operator inspection records, maintenance work order history, spare parts inventory lists, and statutory inspection records for special equipment. Update frequencies cover shifts, daily/weekly/monthly cycles, and immediate updates during sudden equipment failures. Daily documentation includes equipment ledgers, inspection checklists, maintenance plans, work orders, failure analysis reports, and spare parts lists. Fields include equipment numbers, inspection parameters such as temperature and vibration values, maintenance cycles, and spare part models. Corresponding units include ℃, mm/s, hours, and other professional production parameter units.
What constraints do these characteristics impose on multi-turn dialogues and prompt engineering
Scattered and frequently updated data sources require multi-turn dialogues to bind session identifiers and historical message parameters. This retains context and avoids repeated retrieval of historical data. Diverse document types and concentrated professional terminology require prompts to limit retrieval scope to corresponding document categories. This ensures retrieval results match inspection and maintenance scenarios. Parameters carry professional units, so prompts must require output results to match the unit system of equipment documentation. This prevents parameter confusion. Real-time updated data requires limiting the context length of a single dialogue. This prevents loading excessive redundant information and causing processing timeouts.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 1000–1500 characters | Adapts to the single-chunk content length of equipment documentation, ensuring retrieval accuracy |
| Single Search Limit | Top 5 results | Covers commonly associated content across multiple document types such as equipment inspection checklists and maintenance work orders, avoiding redundant information overload |
| Maximum Context | 8000–12000 characters | Adapts to the combined length of equipment historical data, inspection records, and maintenance work orders, meeting information needs in production scenarios |
| Timeout Threshold | 600 seconds | Adapts to response waiting time in production scenarios, avoiding prolonged unresponsiveness |
| Variable Binding | Bind variables | Accurately limits the retrieval scope to the ledger and inspection records of specified equipment, filtering out document data from unrelated equipment |
| Session Tracking | Bind chatId | Retains context of maintenance progress and inspection results across multi-turn dialogues, avoiding repeated inquiries about basic equipment information |
| Historical Message Invocation | Invoke messages parameter | Retains historical interaction content across multi-turn dialogues, ensuring coherent context |
Three common mistakes
- Phenomenon: The dialogue cannot return image content from the knowledge base, only displaying URL links. Cause: Knowledge base image parsing configuration is not enabled, or image URLs lack accessible permission configurations.
- Phenomenon: Retrieval results deviate from the target equipment after multi-turn dialogues, with a large number of inspection records from unrelated equipment appearing. Cause: The equipment number parameter in
variablesis not bound, so the retrieval scope is not limited. - Phenomenon: Dialogue responses time out, or returned content is incomplete. Cause: Maximum context length is not restricted, causing the model to load excessive historical data beyond processing limits.
How to confirm configurations are correctly set
- Initiate an inspection question for a single piece of equipment. Verify that retrieval results only include the ledger and inspection records of that equipment.
- Initiate consecutive multi-turn dialogues. Verify that the session retains context information such as previous maintenance progress and spare parts requirements.
- Upload equipment documentation containing images. Initiate a question involving image descriptions. Verify that the model can associate image content and output relevant conclusions.
- View session logs. Confirm that
chatId,variables, andmessagesparameters are correctly passed in each dialogue request.
The values provided here are common starting points and should be evaluated against the reader's own samples.
This document is based on FastGPT v4.17.0 configuration.