What data for this category looks like
Tender-related data for furniture and wood products manufacturing is mostly sourced from order design systems, sheet material and wood batch ledgers, CNC cutting/drilling device logs, ERP/MES systems, and moisture content test records. Data update cadence adjusts based on orders, daily cycles, or material batches. Core documents include custom panel cutting drawings, cutting lists, edge banding and drilling programs, coating process specifications, hardware bill of materials, trial assembly and packaging records. Data fields include panel size parameters, sheet moisture content, hardware model and usage quantity, production process requirements, and more. There is no unified fixed format, and it is adjusted according to the customization of individual orders.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Data for this category is scattered and highly personalized. Multi-turn dialogue needs to gradually pull information such as cutting drawings and hardware lists from different sources, so chatId must be used to bind the session context of a single order to avoid mixing data across orders. Data update frequency is high, and panel parameters and bill of materials for each tender change each time. Prompts must load the latest variables parameter when called to pass customized business data. The number of panels is large and strongly linked to on-site installation dimensions. Multi-turn dialogue requires repeated verification of hole positions, dimensions and process requirements, so complete messages historical conversation records must be retained to ensure each round of prompts can build on previous verification results.
How to set configurations
The following configurations are formulated based on FastGPT v4.17.0:
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Generate a unique identifier for each order | Bind all conversations and data for a single customized order to avoid cross-order mixing |
messages | Retain the first 5 conversation entries | Carry over previously verified panel dimensions, hardware parameters and other information |
variables | Pass in the custom panel cutting drawings and hardware bill of materials data for the current order | Load customized business data required for this tender |
| Context retention duration | 300–600 seconds | Match the preparation cycle of a single tender document to avoid interference from expired data |
| Form rendering rules | Map fields according to furniture category | Ensure that cutting data and process requirements are displayed normally in the conversation interface |
Three common errors
- Failing to correctly pass
chatIdwhen calling the API, resulting in failed session association displayed in backend logs and lost conversation context. This occurs becausechatIdis not included as a required request parameter, or the parameter format does not meet platform requirements. - Not mapping form nodes according to furniture category fields, resulting in format errors for displayed panel dimensions and hardware lists in the conversation interface. This occurs because the form rendering rules are not adjusted for the personalized data structure of customized furniture.
- Deleting conversations via the guest login link triggers synchronous deletion in backend logs, making historical tender data untraceable. This occurs because session data persistence rules are not configured, and the default deletion logic cleans up log records.
How to confirm proper configuration
- Initiate a test conversation with the
chatIdparameter, and check whether the backend logs correctly associate the corresponding session identifier. - Submit test data including panel dimensions and hardware bill of materials, and confirm that the quotation parameters output by the conversation match the input data.
- Generate a guest test link, delete the conversation, and check whether the backend logs retain the session record.
- Initiate two or more test conversations, and confirm that the second conversation can correctly build on the panel verification results from the first round.
The values provided are common starting points and should be measured against the reader's own samples.