What the data for this category looks like
This category’s data comes from formula and process libraries, raw material batch ledgers, reactor DCS systems, weighing systems, LIMS laboratory management systems, and warehouse systems of enterprises in coatings, resins, additives, and agrochemical intermediates. Daily documents include batch production instructions, feeding review sheets, reaction process records, cleaning records, inspection reports, and deviation and change orders. Document fields include batch numbers, feeding quantities, reaction temperatures, residue detection values, and other relevant metrics. Units include kilograms, degrees Celsius, ppm, and others. Data updates trigger once per production batch or each key process step.
What constraints these characteristics impose during model integration and configuration
Since this category switches production by batch and data updates alongside process steps, model integration must support multi-batch context isolation. This prevents information confusion across different production batches. Dispersed multi-source data requires configuration that supports multi-format document parsing. This adapts to differences in document structures exported by various systems. Confidentiality requirements for formulas and processes mandate sensitive information filtering rules. These rules prevent leakage of core technical data. Frequent high-volume call scenarios such as feeding reviews, reaction judgments, and cleaning confirmations require limiting token consumption per response. This ensures production schedules are not disrupted.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to multi-document context related to single-batch production, avoiding exceeding model window limits |
maxTokens | 2000–3000 token | Controls single-response length to meet text output requirements for scenarios such as feeding reviews and reaction judgments |
quoteMaxToken | 1500 token | Limits the token proportion of referenced documents, ensuring core content makes up a valid share of model responses |
| Workflow/API tool selection | auto | Automatically selects whether to call tools, adapting to scenarios where knowledge bases or system data must be called during multi-step audits |
temperature | 0.2–0.4 | Reduces randomness to ensure consistency and accuracy of quality audit results |
AIPROXY_API_TOKEN | Configured according to enterprise permissions | Binds access permissions for corresponding models, complying with confidentiality requirements for formula data |
AIPROXY_API_ENDPOINT | Filled according to deployment environment | Adapts to internal enterprise proxy access configurations, ensuring secure data transmission |
Three common mistakes
- Scenario: When using the knowledge base, the prompt "no matching content" appears, but the text-embedding-ada-002 model has been configured in the system. Cause: Documents corresponding to raw material batches or LIMS data are not correctly associated with the context of the corresponding batch, or the correct knowledge base source is not bound during model calls.
- Scenario: The r1 model is used when uploading documents, and the qwen14B model is used during queries, resulting in mismatches between query results and uploaded content. Cause: Embedding vector spaces differ across models, causing the semantic association between knowledge base recalled content and uploaded documents to fail.
- Scenario: When using the deepseek model for text extraction, ID card fields return empty values. Cause: The threshold settings for the model’s identification of document text formats are unreasonable, and do not adapt to document layout characteristics in fine chemical scenarios.
How to confirm successful configuration
- Users access the FastGPT v4.17.0 platform, upload documents such as single-batch feeding review sheets and reaction records, verify the matching degree between knowledge base recalled content and uploaded document fields, and adjust related configurations until recall results meet business requirements.
- Users initiate a model call for feeding review, verify that the response result includes required core fields such as feeding quantity and batch number, and adjust the
temperatureconfiguration until result stability meets requirements. - Users test the sensitive information filtering function, confirm that confidential data such as formulas and raw material batches is not leaked externally, and verify that the permission configuration of
AIPROXY_API_TOKENtakes effect. - Users simulate a multi-batch switching scenario, verify that the model context can correctly isolate production data across different batches to avoid cross-batch information interference.
The values provided are common starting points and should be measured against the reader's own samples.