What the data for this category looks like
Data for this category typically comes from order design systems, sheet material and wood batch ledgers, CNC cutting and drilling equipment logs, ERP/MES systems, and on-site production testing equipment. Update cycles include per-order generation, daily synchronization, and updates per material batch. Daily documents include order breakdown drawings, cutting lists, edge banding and drilling programs, coating process requirements, hardware accessory lists, trial assembly and packaging records. Fields include panel dimensions, accessory models, production process numbers, and other relevant details.
What constraints these characteristics impose on multi-turn dialogue and prompts
This category has strong customization attributes, with a large number of panels per order. Multi-turn dialogue must continuously associate order-specific dimensions and process parameters to avoid losing context. Daily document types are diverse, so prompts must clearly define extraction rules for different documents such as order breakdown drawings and cutting lists, to ensure accurate information matching. Data update cycles are frequent, so multi-turn dialogue must call the latest sheet material batch and equipment log data to avoid using expired information. Panel dimensions are directly tied to on-site installation, so each turn of dialogue must verify dimensional consistency across previous steps to prevent cumulative errors.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
messages | First 8–12 conversation records | This category has many panels per order, so sufficient process verification context must be retained to avoid losing key parameters, and this aligns with the conversation storage logic of FastGPT v4.17.0 |
variables | Bind order-specific panel dimensions and hardware list variables | Custom order parameters vary widely, so variables must be used to pass real-time updated production data to ensure information consistency across multi-turn dialogue |
| Maximum context length | 8000–12000 token | Daily documents include multiple types of content such as order breakdown drawings and cutting lists, so sufficient length is required to carry complete production information |
| Single search limit | First 5–8 associated documents | This category has a large number of production documents, so search scope must be limited to avoid the system loading excessive non-essential data |
| Trigger condition | Trigger dialogue by process node | The production process is split into steps such as order breakdown, cutting, and edge banding, so verification of corresponding data must be done per node to avoid invalid calls |
chatId | Bind unique order identifier | Different order conversation contexts must be distinguished to avoid mixing cross-order parameters and ensure dedicated multi-turn dialogue logic |
| Result verification threshold | Calibrate based on actual measurements | Process standards vary across factories, so verification rules must be adjusted based on on-site production data |
Three common misconfigurations
- Symptom: A text content extraction node is embedded in the workflow, and the number of chat records is configured, but multi-turn dialogue cannot be implemented. Cause: The
chatIdparameter is not bound, so the system cannot associate the conversation context of the corresponding order, and only retains single-turn input information. - Symptom: A multimodal model is called to process image documents such as order breakdown drawings, and a prompt format error is returned. Cause: The supported document parsing rules are not clearly specified in the prompt, or image content is not converted into structured format.
- Symptom: The online dialogue link continuously triggers knowledge base searches without a response. Cause: The single search limit is not set, so the system loads excessive non-essential production data and exceeds processing load.
How to confirm the configuration is complete
- Initiate a first-round conversation by entering basic order information, and check whether the system retains the exclusive variable data for that order.
- After triggering multi-turn dialogue, verify whether the system can associate the previous round’s verification results and continue advancing the process audit workflow.
- Call the multimodal model to process the order breakdown drawing, and confirm that the returned extraction results conform to the preset document parsing rules.
- Test the single search function, and confirm that only the specified number of associated documents are returned.
- Test the workflow node trigger logic, and confirm that the dialogue process is only started at the specified process nodes.
The values provided here are common starting points, and should be evaluated against the reader’s own samples.