Data Characteristics for This Category
Data for this category comes from formula and process libraries, raw material batch records, reactor DCS data, weighing system logs, LIMS systems, and warehouse inventory data. Daily documents include batch production instructions, feeding verification slips, reaction process records, cleaning validation records, inspection reports, and deviation and change orders. Data updates trigger with each production batch and each key process step, covering multi-variant, small-batch batch synthesis workflows. Special emphasis is placed on ensuring document integrity during formula confidentiality and product change cleaning confirmation stages.
Constraints for Model Access and Configuration
Document format differences across multiple systems require the configuration phase to support unified parsing of structured and unstructured data. Data updated per batch and key steps requires limiting the total length of single-turn context to avoid redundant data interfering with review logic. Multi-variant production switching scenarios require configuration to isolate document data per batch, preventing mixing of formulas and cleaning records from different batches. Formula confidentiality requirements restrict knowledge base call scope to avoid leakage of sensitive process data.
Configuration Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Matches the full document set for a single production batch, avoiding truncation of critical verification information |
maxTokens | 2000–3000 token | Covers full requirements for outputting cross-review conclusions |
quoteMaxToken | 1500 token/entry | Ensures complete key content for each cited document, avoiding out-of-context quotes |
temperature | 0.1–0.3 | Reduces random output to ensure consistency of review conclusions |
AIPROXY_API_ENDPOINT | Fill in per enterprise internal deployment environment | Adapts access paths for enterprise private model proxies |
AIPROXY_API_TOKEN | Configure per enterprise permission rules | Ensures access security for model calls |
Three Common Configuration Errors
- Symptom: The large language model returns an empty response after being called, with logs showing context length exceeded. Cause: The
maxContextparameter was not adjusted based on the document volume for this category's single batch, leading to truncation of full documents during batch production, so the model cannot obtain sufficient review basis. - Symptom: Inconsistent answers are returned for the same question during cross-review of different batches. Cause: Knowledge base permissions isolated per batch were not configured, so document data from different batches was mixed during calls, leading to chaotic retrieval results.
- Symptom: Voice functions cannot call voice models normally after being enabled. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENwere not configured correctly, or platform-supported voice model access specifications were not followed.
How to Verify Successful Configuration
- Upload the full production document set for a single batch, test a call, and confirm that context citations cover all critical documents without truncation.
- Initiate multiple calls for the same verification scenario of the same batch, confirming consistency of output conclusions.
- Configure the knowledge base to isolate data by batch tags, verifying that review tasks for different batches can only call document data from their corresponding batch.
- Check the model proxy access logs, confirming that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations match the deployment environment.
The values provided are common starting points and should be measured against the reader's own samples.