What Data This Category Includes
Core data for spraying and coating scenarios comes from coating lines in automotive, home appliance, construction machinery, metal furniture, and other industries. Daily documents include coating process cards, paint batch sheets, paint mixing records, film thickness and color difference reports, oven curves, defect repair sheets, and VOCs logs. Data sources include TDS/SDS documents from paint suppliers, data collected from spray booths and ovens, test results from colorimeters and film thickness gauges, environmental monitoring data, and MES systems. Updates occur per paint batch, per shift adjustment, and through continuous collection. Document fields include parameters with specified units: workpiece material, spray thickness (unit: μm), oven temperature curve (unit: ℃/minute), diluent ratio (volume ratio), and VOCs emission concentration (unit: mg/m³), among others.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
Data is updated frequently, and is split by batch and process stage. Multi-turn dialogue must real-time link to exclusive parameters for the current batch. This prevents calling expired data across batches. Document types are varied, and fields have fixed units. Prompts must clearly specify the unit and source of parameters. This ensures recalled process specifications match actual production. The most time-consuming process matching step requires gradual guidance for users to provide workpiece, paint, and equipment parameters. This avoids input overload that causes response delays. Compliance documents such as VOCs logs must be prioritized for recall. Prompts must set clear call priorities to ensure compliance requirements are met first.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to MES batch number + workpiece serial number | Links to full batch production data, prevents cross-batch session confusion |
messages | Retain the most recent 15–20 rounds of conversation context | Coating-related document data volume is large; excessive context increases response delay |
variables | Predefine three variables: coating workpiece material, paint model, oven temperature range | Quickly retrieves core parameters required to match process specifications |
| Maximum context length | 8000–12000 characters | Covers the longest single coating document and necessary information for multi-turn dialogue |
| Single recall limit | Top 6 matching results | Covers process documents for pre-treatment, spraying, and curing stages |
This page configuration is based on FastGPT v4.17.0.
Three Common Misconfiguration Issues
- Phenomenon: Coated batch documents linked to a conversation cannot be retrieved after 7 days, and an error "No matching documents" is returned. Cause:
chatIdis not bound to a persistently stored batch identifier, so linked files cannot be recovered after expiration. - Phenomenon: After inserting a multi-task AI conversation array in a batch execution node, the node has no valid output. Cause: Process stage parameters for coating technology are not configured in
variables, so the conversation node cannot obtain necessary context information. - Phenomenon: When multiple AI conversation nodes run in parallel, returned results contain duplicates or cross-interference. Cause: Independent
chatIdis not assigned to each parallel node, so conversation contexts interfere with each other.
How to Verify Proper Configuration
- Initiate a multi-turn conversation that includes workpiece material and paint model. Confirm returned process specifications link to predefined
variablesparameters and include correct unit information. - Review conversation logs to confirm each session is bound to a unique
chatIdidentifier that can be traced to the corresponding production batch. - Test batch insertion of a multi-task conversation array. Confirm each node’s output follows the preset division logic for coating process stages.
- Adjust the maximum context length threshold. Verify the balance between response speed and recall accuracy, and confirm the threshold meets actual production scene requirements.
The values provided here are common starting points, and should be evaluated against the reader's own samples.