What data for this category looks like
Data for this category typically comes from enterprise ERP systems, bill of materials (BOM), process routes, equipment capacity ledgers, staff scheduling sheets, work-in-progress inventory records, and supplier arrival notices. Daily processed documents include sales orders, master production schedules, material requirements plans, weekly and daily production schedules, work orders, material shortage lists, and delivery change notices. Data updates are triggered daily, weekly, or temporarily based on order placement, exception occurrences, or cycle adjustments. Data fields include order numbers, process codes, available capacity, material codes, planned delivery dates, actual completion times, and other related fields.
Constraints on model access and configuration
Dispersed data sources with inconsistent formats require standardized processing logic for multi-source heterogeneous data during model access. Fluctuating data update frequencies may cause delays in model input data when updates are triggered by temporary exceptions. Real-time synchronization trigger mechanisms must be configured. A large number of document fields with close business correlations require model input context length to match the processing capability of long documents such as work orders and scheduling sheets. Field mapping for cross-departmental data interactions must be strictly aligned. Incorrect mapping will cause model analysis results to deviate from production planning logic.
Configuration settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Matches the context length requirements of long documents such as production work orders and scheduling sheets, to avoid truncation of core business fields |
maxTokens | 2000–3000 token | Adapts to the output length requirements of quality traceability analysis, covering multi-dimensional analysis results such as process abnormalities and material shortages |
temperature | 0.2–0.4 | Reduces output randomness to ensure that analysis results related to production plans comply with established business rules |
AIPROXY_API_ENDPOINT | http://localhost:11434/v1 | Adapts to local large model call requirements and matches data privacy requirements in production scenarios |
AIPROXY_API_TOKEN | Empty string | Most locally deployed large models do not require additional authentication, adapting to default configurations of local services such as ollama |
Three common misconfigurations
- A
Connection refusederror appears when debugging the workflow. The cause is an incorrectly configured local service address forAIPROXY_API_ENDPOINT, or the local large model service is not started. - Model output only contains Chinese content. The cause is failure to explicitly specify the output language in the prompt, or mismatch between the default language configuration of the local large model and requirements.
- The system prompt states that a large model interface must be configured to use the knowledge base. The cause is failure to correctly enable the offline knowledge base mode of FastGPT, or incomplete basic settings of the corresponding configuration items.
How to confirm successful configuration
- Check whether the
AIPROXY_API_ENDPOINTconfiguration matches the listening address of the local large model service, and test interface connectivity using the curl command. - Import a sample production work order data set, trigger model analysis, and verify whether the output content includes core business fields such as order numbers and process abnormalities.
- Adjust the
temperatureparameter value, observe changes in model output randomness, and confirm that the configuration takes effect. - Import local production scheduling documents to the knowledge base, and verify that basic content retrieval can be completed without calling an external large model.
The values listed here are common starting points and should be evaluated against local production samples.