What the data for this category looks like
Data sources for this category include customer surface treatment specifications, bath chemistry test records, rectifier operation data, online water quality monitoring results, and hazardous waste management ledgers. Daily generated documents include bath analysis records, process parameter cards, fixture usage logs, film thickness and adhesion test reports, hazardous waste ledgers, and wastewater monitoring records. Data updates are performed per bath batch, shift, or fixed monitoring requirements. Document fields cover process parameters, test values, compliance records, and more. Units include concentration, temperature, current, film thickness units, and additional relevant units.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering workflow
Data sources are scattered and update frequencies are uneven. Multi-turn dialogue must trigger corresponding context calls based on different data types. Real-time data such as bath chemistry and water quality must call the latest records first. Bid quotations must link to customer specifications and compliance ledgers. Prompts must clearly distinguish the priority of process parameters, test results, and compliance requirements. Field units vary widely across different documents, so prompts must unify field parsing rules to avoid quotation deviations caused by unit confusion. Multi-turn dialogue must track requirements for the current bid stage, avoid repeatedly calling expired bath batch data, and ensure hazardous waste-related content complies with current compliance requirements.
How to configure the setup
This configuration scheme is based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Bind customer specification ID, bath parameters, compliance thresholds | Bid quotations must link to customized customer requirements and real-time process data |
messages | Retain the first 10 rounds of dialogue context | Avoid repeating explanations of process details and compliance requirements during multi-turn communication |
chatId | Bind the unique identifier of the current bid project | Ensure precise matching between dialogue context and the corresponding project’s data |
| File link variables | Bind uploaded bath analysis reports and wastewater monitoring records | Allow the AI to directly call test values from documents |
| Question optimization | Calibrate based on actual measurements | Adapt to the professional terminology habits of electroplating processes |
Three common misconfigurations
- Phenomenon: Attachments uploaded after mirror image packaging and deployment cannot be recognized, and no error prompt is displayed. Cause: The file storage path and
variablesbinding rules were not correctly configured during deployment, causing the AI to fail to read the local path mapping of attachments. - Phenomenon: Process parameter units are confused during multi-turn dialogue, leading to quotation calculation deviations. Cause: The prompt did not explicitly require the use of standard units from documents, and no unit verification was performed for parameters bound via
variables. - Phenomenon: The dialogue context loses previously mentioned customer surface treatment specification requirements. Cause: The current bid project identifier was not bound in
chatId, causing confusion between dialogue contexts of different projects.
How to verify a successful configuration
- Upload a bath analysis record, check whether the AI can extract the film thickness value and test date from it.
- Initiate a multi-turn dialogue, verify whether each round of reply retains the previously mentioned customer surface treatment specification requirements.
- View the
variablesbinding list, confirm that all required process and compliance data have been correctly associated. - Simulate the mirror deployment environment, upload attachments and verify whether the AI can normally read the document content.
The values provided here are common starting points and should be measured against the reader's own samples.