What data for this category looks like
Data for this category comes from customer surface treatment specifications, bath laboratory test results, rectifier operation data, online water quality monitoring records, and hazardous waste management ledgers. Daily documents include bath analysis records, process parameter cards, fixture usage records, film thickness and adhesion reports, hazardous waste ledgers, and wastewater monitoring records. Data updates occur per bath cycle, per shift, or according to a fixed monitoring cycle. Fields cover process parameters, test values, and ledger record items. Units include categories such as temperature, concentration, thickness, and weight.
Constraints on multi-turn dialogue and prompt engineering
Data for this category has high update frequency and scattered sources. Multi-turn dialogue must limit the valid range of historical context. This avoids loading expired or irrelevant bath and water quality data. Field formats vary significantly across different documents. Prompts must explicitly specify reading specific fields from corresponding documents. For example, extract pH values from bath analysis records, and extract coating thickness values from film thickness reports. Hazardous waste and wastewater monitoring data must comply with compliance verification logic. Multi-turn dialogue must guide completion of missing ledger items. This ensures the completeness and accuracy of summarized data.
Configuration Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
messages | Retain the most recent 20 conversation messages | Data for this category updates frequently. Overly long history will introduce expired bath or water quality data, reducing summary accuracy |
variables | Preset two variables: bath type and monitoring cycle, with values set based on actual calibration | Adapt to different electroplating processes and monitoring frequency requirements, dynamically load corresponding ledger data |
chatId | Bind to production batch ID | Ensure ledger data for the same production batch is correctly associated and summarized, avoiding cross-batch data confusion |
| Maximum context window | 800–1200 characters | Balance data loading volume and dialogue response speed, adapting to the need for parallel reading of multiple documents for this category |
| Dynamic prompt loading | Link to process specification documents in the knowledge base | Quickly call the latest customer surface treatment requirements without modifying prompts each time |
| Dialogue timeout period | 600 seconds | Reading data for this category requires loading multiple ledger documents, reserve sufficient response time |
Three Common Configuration Mistakes
- New conversation version update notice: The interface displays version update prompt content at the top every time a new session is opened. This occurs when the version pop-up configuration for new conversation initialization is not turned off in system settings.
- Dynamic prompt loading failure: The prompt cannot read document content from the knowledge base when called. This occurs when the dynamic call logic for the
variablesparameter is not configured, and the prompt template is not bound to the knowledge base document library. - Tool call results not displayed: No return result appears in the dialog box after the first tool call. Content only displays after re-entering the conversation. This occurs when
chatIdis not correctly bound to the tool call context, resulting in unsaved session data.
How to Confirm Configuration is Complete
- Start a new conversation, check that the version update notice no longer appears, confirm the pop-up configuration is disabled.
- Enter queries for different electroplating processes, check that the prompt automatically calls the corresponding process specifications in the knowledge base, verify dynamic loading is active.
- Initiate a query containing bath data, check that the conversation context only retains the most recent 20 messages, verify the
messagesparameter configuration is correct. - Initiate a ledger query for cross-batch production, check that summarized data is correctly associated with batch information, verify the
chatIdbinding logic works.
The values provided are common starting points and should be measured against the reader's own samples.