This configuration is based on FastGPT v4.17.0, and adapts to bid document and quotation preparation needs for steel rolling and finishing scenarios.
What the data for this category looks like
Business data for steel rolling and finishing comes from slab or billet batches, heating furnaces, mill automation control systems, online thickness and width measurement tools, surface inspection systems, and MES systems. Data updates in real time per coil or per piece. Daily documents include rolling plans, heating schedules, rolling specifications, plate shape and thickness records, surface quality reports, and quality certificates. Data fields include batch identifiers, process temperatures, reduction amounts, rolling speeds, geometric dimensions, surface defect grades, and more. Units include millimeters, degrees Celsius, meters per minute, and others.
Constraints on multi-turn dialogue and prompt engineering
Because data updates in real time per coil or per piece, the context window for multi-turn dialogue must be limited. This avoids loading outdated or redundant historical data. Business documents contain a large number of specialized technical terms. Prompts must clearly specify identifiable fields and units to ensure AI output complies with industry standards. Data is tied to batch dimensions, so multi-turn dialogue must support binding the current bid’s coil batch number. This allows accurate matching of process and quality data for the corresponding batch. Complex process calculations require long processing times. The dialogue timeout threshold must be adjusted to match business needs, to avoid interrupting the workflow.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum context length | 8000–12000 token | Matches the total character count of documents related to a single coil, avoids context overflow |
| Knowledge base recall count | Top 6 | Covers core documents such as rolling specifications and quality reports for a single batch |
| Prompt variable binding | Enable variables | Binds coil batch and material parameters involved in the bid to accurately match business data |
Dialogue context messages retention rounds | Last 3 rounds | Reduces redundant historical data, focuses on current quotation and document preparation needs |
Session identifier chatId binding | Bind current bid project ID | Differentiates dialogue contexts for different bid projects to avoid data confusion |
| Timeout threshold | 600 seconds | Adapts to retrieval and calculation processes for complex process data |
| Document chunk length | 1000–1500 characters | Matches single-section content length of rolling specifications, improves retrieval accuracy |
Three Common Misconfigurations
- Phenomenon: Incorrect labels for non-business fields appear in dialogue logs, such as marking device IP addresses as rolling parameters. Cause: The prompt does not limit the scope of labeled fields, leading to misidentification of unrelated data.
- Phenomenon: AI dialogue output is displayed in real time on the workflow page, interrupting subsequent text splicing steps. Cause: No delayed trigger rule for dialogue output is configured, and real-time generated results are returned directly.
- Phenomenon: Quality certificate data retrieved from the knowledge base cannot be correctly referenced in AI dialogue. Cause: The field format of quality certificates is not standardized in the HTTP request processing stage, preventing the AI from matching the business context.
How to Verify Configuration Completion
- Initiate a test dialogue that includes a specified coil batch number. Check whether the AI can associate rolling specifications and quality report data for that batch.
- Test multi-turn dialogue context transfer. Confirm that quotation parameters generated in the first round can be used as input for the second round of dialogue.
- Trigger the complete workflow. Check whether the AI output returns the specified result only after the text splicing component finishes processing.
- View the dialogue log. Confirm that labeled fields only include business-related process parameters and quality data.
The values provided are common starting points and should be measured against the reader's own samples.