What data for this category looks like
Data sources include registered process documents, raw material batch ledgers, DCS control systems, LIMS laboratory information management systems, chromatography data systems, and quality management systems. Updates trigger every production batch and every process step. Daily documents cover batch production records, material balance sheets, reaction purification records, cleaning validation records, impurity profile reports, and deviation and change documents. Fields include process parameters, batch identifiers, yield data, impurity content indicators, solvent residue levels, and change approval status. Units include kilograms, hours, ppm, and other standard units.
What Constraints Do These Characteristics Impose on the Model Access and Configuration Link
Dispersed data sources trigger updates per production batch. Fixed association rules for multi-source data are required during model access to avoid cross-batch data confusion. Daily documents have a fixed structure but highly professional fields. Adapt structured field extraction logic during configuration to reduce unstructured parsing errors. Professional content such as impurity profiles and process parameters requires accurate recognition. Adjust model generation rigor to meet this need. Tendering documents must link compliant registration files. Bind compliant document retrieval paths to the tool call link to ensure content meets industry compliance requirements.
How to Configure the Settings
In FastGPT v4.17.0, the following configuration items can be configured in the advanced settings tab of the model access page:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Matches the total length requirement of single-batch production documents and tendering documents |
maxTokens | 2000–3000 token | Controls the generation length of a single tendering document or quotation sheet, to avoid exceeding output limits |
quoteMaxToken | 1500 token | Limits the length of referenced compliant documents, to ensure referenced content is accurate and does not occupy excessive context |
| Workflow/API tool selection | auto | Automatically triggers the compliant document retrieval tool, adapting to the need to link registration files for tendering documents |
temperature | 0.2–0.4 | Reduces generation randomness, ensuring accuracy of professional fields such as process parameters and impurity content |
AIPROXY_API_ENDPOINT | Filled according to the deployment instance | Matches the local or private deployed model service address, to ensure production data is not leaked |
AIPROXY_API_TOKEN | Key generated according to permissions | Ensures identity legitimacy for model calls, preventing unauthorized access |
Three Common Misconfigurations
- Phenomenon: The default model used during tool calls does not match the configured professional model, and content that meets pharmaceutical compliance requirements cannot be generated. Cause: The bound professional model is not specified in the conversation configuration, or the
AIPROXY_API_TOKENconfiguration is incorrect, resulting in insufficient model permissions. - Phenomenon: Incorrect impurity content values or process parameters appear in the generated quotation sheet or tendering document. Cause: The
temperatureparameter is set too high, causing the model to generate hallucinations that deviate from actual production data. - Phenomenon: The model fails to load documents related to the current batch, returning empty content or data from unrelated batches. Cause: The
maxContextparameter is set too small to accommodate the total length of single-batch production documents and tendering documents.
How to Confirm Correct Configuration
- Call the test interface, input a single-batch production document and tendering document template, and check whether the generated content includes correct process parameters and impurity content indicators.
- Trigger a tool call, confirm that the system automatically retrieves and references compliant registered process documents, with no unrelated content mixed in.
- View the model call log, confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly, with no connection errors. - Adjust the
temperatureparameter to 0.3, generate the same tendering document multiple times, check the consistency of professional fields, and confirm there are no obvious hallucinations.
The values provided here are common starting points and should be measured against the reader's own samples.