What Data for This Category Includes
Daily documents for this category include order disassembly drawings, cutting lists, edge banding and drilling programs, coating processes, hardware parts lists, trial assembly and packaging records. Data sources cover order design systems, sheet and lumber batch data, CNC cutting and drilling equipment, ERP/MES systems, and moisture content test results. Data update frequency changes with orders, daily production batches, and material batches. Document fields include panel size parameters, material moisture content, hardware model and specification, coating process parameters, and other details. Some documents link to specific production batches and order numbers.
Constraints on Model Connection and Configuration
Data sources for this category are scattered and cover the entire production flow. When connecting models, it is necessary to accurately bind process documents from different sources to corresponding production batches and orders. This prevents cross-order data confusion. Documents include structured and semi-structured content such as CNC programs and cutting lists. Configuration must adapt to parsing logic for multi-format data sources. Data update frequency shifts with orders and production batches. Support for on-demand synchronization or scheduled update mechanisms is required. This stops outdated process parameters from being called. Customized orders lead to large panel volumes. The model context length must adapt to multi-document recall scenarios. Redundant information must be avoided to prevent interference with Q&A accuracy.
Setting Configuration Parameters
This content is written based on the configuration logic of FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale for This Setting |
|---|---|---|
maxContext | 8000–12000 token | Adapt to the recall volume of multi-panel process documents, avoid context overflow affecting Q&A logic |
maxTokens | 2000–3000 token | Control the output length of a single round of Q&A, ensure complete display of process parameters and requirements |
quoteMaxToken | 1500 characters | Limit the reference length of recalled documents, focus on core process requirements and parameters |
temperature | 0.2–0.4 | Reduce answer randomness, ensure the accuracy of answers about process parameters and production requirements |
AIPROXY_API_ENDPOINT | Local or intranet address where FastGPT is deployed | Adapt to the intranet deployment scenario of furniture manufacturing enterprises, ensure production data security |
AIPROXY_API_TOKEN | Enterprise-specific authentication token | Restrict interface access permissions, prevent unauthorized calls to production-related data |
Common Configuration Errors
- The
do_request_failederror occurs, indicating a failed POST request. The cause is incorrect configuration ofAIPROXY_API_ENDPOINTas an intranet-accessible address, or failed authentication verification forAIPROXY_API_TOKEN. - Q&A returns process parameters that do not match the actual production batch. The cause is failure to bind the recall data source according to order or production batch, leading to calls to outdated document data.
- Single-round Q&A output is too long, exceeding the display limit of on-site production terminals. The cause is unreasonable setting of the
maxTokensparameter, resulting in redundant returned content and excessive irrelevant information.
Verifying Successful Configuration
- Call the test interface to initiate a process parameter query. Check the matching degree between returned content and target data sources. Confirm recalled documents belong to the current query order or production batch.
- View interface call logs. Confirm request authentication information matches the configured
AIPROXY_API_TOKEN. No authentication failure records should appear. - Simulate query requests for different scenarios, including disassembly requirements and coating processes. Confirm returned content complies with process specifications for this category.
- Test query scenarios with large context lengths. Confirm the model does not throw abnormal errors caused by context overflow.
The values provided here are common starting points and should be measured against the reader's own samples.