What this type of data entails
Document and material data for mining and mineral processing comes from sources including geological exploration reports, excavation work order sheets, safety inspection logs, mineral processing parameter records, and environmental protection acceptance documents. These sources are distributed across multiple separate business systems. Daily inspection records are updated daily. Monthly excavation plans are updated each month. Annual comprehensive reports are updated quarterly or annually. Most document structures include fixed headers, with fields such as project number, mining area, operation period, and core parameters. Professional fields use dedicated units: drilling depth is measured in meters, mineral processing recovery rate in percentage, and ventilation volume in cubic meters per hour.
Constraints on multi-turn dialogue and prompt engineering
Dispersed data sources require multi-turn dialogue to link fields across different systems. Prompts must clearly state field association rules to prevent generated document parameters from being disconnected. Some document content is lengthy. The multi-turn dialogue context window must be limited to a reasonable range to avoid parsing failures caused by content overflow. Professional fields use dedicated units. Prompts must explicitly require generated content to use the correct corresponding units, otherwise non-standard parameter issues will arise. Data is updated frequently. Session history must be filtered by project to prevent mixing historical records from different projects, which would negatively impact generation results.
Configuration settings
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum context window | 8000–12000 token | Matches the average content length of mining and mineral processing documents, prevents context overflow during multi-turn dialogue |
| Variable injection configuration | Bind professional fields via the variables parameter | Automatically imports business parameters such as mining area and ore grade, reduces manual input errors |
| Conversation message format | Organized according to the messages array structure | Complies with FastGPT's conversation parsing standards, ensures coherent multi-turn context |
| Session identifier association | Bind project number via chatId | Establishes a fixed unique session identifier, facilitates subsequent retrieval of historical conversations by project |
| Context filtering rules | Match historical records by chatId | Differentiates sessions for different projects, avoids cross-interference from historical records |
Three common implementation mistakes
- A 404 status code is returned when calling the conversation interface. The cause is incorrect configuration of the model access address or port, which prevents connection to the target model service.
- All records are returned when searching for historical sessions by project. The cause is failure to correctly bind the
chatIdparameter in the historical query logic, and no valid filtering operation was performed. - Residual think tag content appears in generated documents. The cause is that the code execution node in the workflow only takes effect in the debugging environment, and the content cleaning logic was not correctly executed during formal operation.
How to verify correct configuration
- Initiate a test conversation, input professional parameters for a mining project, and check if the generated document automatically includes the corresponding fields.
- Switch to a different
chatIdto initiate a conversation, then retrieve historical records to confirm that only session content for the current project is returned. - Review generated document content to confirm that no excess think tags remain.
- Call the interface to initiate a request, and check that the returned status code is 200, with no 404-level connection errors.
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.