What data looks like for this category
The core data for this category comes from order design systems, sheet material and wood batch ledgers, CNC cutting and drilling equipment logs, ERP/MES systems, and moisture content testing equipment. Data updates are performed per order, daily, or per material batch. Daily produced documents include order breakdown drawings, cutting lists, edge banding and drilling programs, coating process specifications, hardware bill of materials, trial assembly and packaging records. Most data fields include panel dimensions, material batch numbers, equipment operating parameters, moisture content values, and installation matching relationships; there are no uniform fixed units as they vary by document type.
What constraints these characteristics impose on multi-turn dialogue and prompts
Data for this category is updated per order, daily, or per material batch. Multi-turn dialogue must bind corresponding order or batch data via chatId to avoid mixing cross-order data. There are many daily document types with large structural differences, so prompts must first guide users to clarify the document type before extracting corresponding fields. The number of panels is large and installation association relationships are complex, so multi-turn dialogue must gradually ask for missing parameters—for example, first confirm the order number before retrieving the corresponding cutting list. Data sources are scattered, so prompts must explicitly specify the data source range to retrieve, avoiding calls to irrelevant non-target document content. The custom order-driven nature also requires the dialogue flow to adapt to personalized order breakdown data, rather than using the general logic of fixed BOM.
How to set configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId binding rule | Bind the unique identifier of the current order or material batch | Matches the category's characteristic of managing data per order or batch, preventing cross-order data calls in multi-turn dialogue |
variables parameter configuration | Pass custom variables such as order number, material batch number, document type, etc. | Helps prompts accurately locate target data, and adapts to FastGPT v4.17.0's custom variable binding logic |
| Single search limit | Top 8 entries | This category has a large number of panels and documents, avoiding too many irrelevant results that affect dialogue efficiency |
| Maximum context length | 8000–12000 characters | Accommodates multi-turn dialogue history and multiple document fragments, adapting to complex quality traceability logic |
| Question optimization switch | Enabled | Performs structured conversion of users' colloquial quality questions, adapting to the retrieval logic of professional documents |
| Temperature setting | 0.6–0.8, calibrated via actual testing | Balances answer accuracy and flexibility, avoiding overly divergent traceability conclusions |
Three common misconfigurations
- Issue: Unable to retrieve documents from a specified collection when calling the knowledge base during dialogue. Cause: No collection identifier parameter was passed in
variables, causing the system to retrieve the full knowledge base by default and fail to match the category's collection logic divided by order. - Issue: The temperature setting control disappears and cannot be adjusted after configuring variable references. Cause: Some interface logic does not retain advanced parameter entry points, and temperature adjustment permissions were locked without pre-configuring
variables. - Issue: The running data in the dialogue log is empty after calling the interface workflow. Cause: No
chatIdwas used to bind the dialogue context, causing the traceability data returned by the workflow to not be associated with the current session and unable to be displayed in the log.
How to confirm proper configuration
- Initiate a test dialogue, enter the order number and specific quality issue, and check if the returned document fragments are only associated with data from the current order.
- After configuring variable references, check if the temperature setting control is retained in the dialogue interface, or confirm that the temperature parameter has been passed to the model via debug logs.
- Call the interface to trigger the workflow, check if the dialogue log contains detailed data from the workflow run, and confirm that
chatIdhas correctly bound the context. - Copy the Markdown content generated by the dialogue, check if it has correct carriage return and line break formatting, and confirm that the front-end rendering logic has not lost formatting parameters.
The values provided are common starting points and should be measured against the reader's own samples.