What the data for this category looks like
Business data for batch fine chemical synthesis comes from multiple sources: formula and process libraries, raw material batches, reactor DCS systems, weighing systems, LIMS, and warehouse systems. Daily documents include batch production instructions, feeding review slips, reaction process records, cleaning records, inspection reports, deviation and change orders. Each field maps to parameters like feeding quality, reaction temperature, reaction duration, and inspection results. Units include kilograms, liters, degrees Celsius, hours, and others. The system updates data per production batch or each key step. Updates happen in real time when batches switch or processes adjust. The data involves confidential formula content.
What constraints these characteristics impose on the "model access and configuration" link
This category’s data updates per batch and key step, and includes sensitive formula and process information. Model access configurations must support associating context by batch. Configurations must also limit sensitive data leakage scope. The multi-variety, multi-step production mode needs flexible tool call logic switching. This adapts to feeding and quotation rules for different batches. Multiple data sources require unified interface adaptation rules. This avoids model input errors from cross-system data format differences. Real-time updated data needs appropriate context length configuration. This ensures the model accesses the latest production and inventory information.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Matches the full document length of a single batch of fine chemical synthesis processes, covering context information for feeding, reaction, inspection, and other links |
maxTokens | 2000–3000 token | Ensures generated bid documents and quotations are complete, including details such as process parameters, raw material cost calculations, delivery cycles, and more |
quoteMaxToken | 1500 token | Limits the length of referenced business documents, avoids introducing irrelevant historical batch data, and focuses on the requirements of the current bid project |
| Workflow/API tool selection | auto | Automatically triggers tool calls such as feeding review, cost accounting, process verification, etc., adapting to the multi-step bid preparation logic |
temperature | 0.3–0.5 | Balances the rigor and flexibility of quotations, avoids content that does not comply with process specifications or has cost accounting errors |
AIPROXY_API_ENDPOINT | Fill in the intranet proxy address according to the deployment environment | Connects to the enterprise's internal model proxy service, adapts to the security requirements of intranet deployment, and prevents sensitive data from being leaked |
AIPROXY_API_TOKEN | Fill in the key assigned according to actual conditions | Ensures secure identity verification for model access, complies with compliance requirements for formula confidentiality |
Three common mistakes
- Symptom: The platform shows no available model list, only
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENhave been configured. Cause: FastGPT v4.17.0 deployment lacks model proxy whitelist binding, or the proxy service and FastGPT deployment have no network connectivity. - Symptom: Saved model mapping configurations do not take effect, and preset business tools do not trigger during calls. Cause: The
toolChoiceparameter is not set to auto, or the system has not bound the corresponding tool to the fine chemical process document library. - Symptom: Model calls return connection errors or format exceptions, and issues occur when using Bailian models or Ollama images. Cause: The filled
AIPROXY_API_ENDPOINTaddress lacks the correct path suffix, orAIPROXY_API_TOKENpermissions do not cover the corresponding model’s call scope.
How to confirm the configuration is complete
- Enter the FastGPT v4.17.0 model management page. Check if the configured proxy model displays normally. Click the test button to verify return of normal basic response content.
- Upload a single batch’s feeding review slip and process document. Call the model to generate a bid document fragment. Check that the fragment includes correct process parameters and raw material cost information.
- Set
toolChoiceto auto. Trigger a test request with multi-step logic. Confirm the model automatically calls the corresponding tool verification module. - Check system operation logs. Confirm model call request headers carry the correct
AIPROXY_API_TOKEN. Confirm no identity verification failure error records exist.
The values given are common starting points and should be measured against the reader's own samples.