What Data for This Category Looks Like
Data for this category comes from water source and raw material testing, formula systems, online sugar and acidity monitoring, filling equipment, visual inspection systems, and LIMS systems. Daily documents include formula sheets, water treatment records, blending records, sterilization or aseptic filling records, packaging material inspection reports, and finished product inspection reports. Fields cover raw material batch numbers, sugar content values, acidity values, filling liquid level parameters, sealing pass rates, product batch numbers, and more. Units include mg/L, %, ml, pieces, and others. Data update frequency follows each production batch, per-shift inspections, and continuous online collection. Single-batch data volume varies based on production scale.
Constraints on Model Connection and Configuration
The multi-source continuously collected data and batched document structure of this category require model connections to support multi-format input and batch association logic. High-frequency online collected data creates high-throughput text processing demands. Set limits on single-task context and token counts to avoid resource overload. Exclusive fields and unit differences across different documents require model configurations to support custom parsing rules. This ensures exception reports accurately match category production indicators. Continuously updated data also requires automatic synchronization mechanisms. This prevents the model from using outdated production data to generate process documents.
How to Configure Settings
This document is written for FastGPT v4.17.0. The following are configuration guidelines for this scenario:
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to batch input requirements for multi-batch production documents, avoids context overflow |
maxTokens | 2000–3000 token | Meets the generation length requirements for a single exception report or process document, covers complete field descriptions and anomaly analysis |
quoteMaxToken | 1000–1500 token | Limits the length of referenced production data, ensures the model focuses on core anomalies and process parameters |
temperature | 0.2–0.4 | Reduces generation randomness, ensures parameter accuracy and format consistency for process documents and exception reports |
| Workflow/API tool selection | auto | Automatically selects tools for structured data parsing, adapts to extraction requirements for multi-source production data |
AIPROXY_API_ENDPOINT | Fill in the model service address deployed on the intranet | Adapts to model call requirements for factory intranet environments, ensures secure data transmission |
AIPROXY_API_TOKEN | Match the authentication token for the deployed model | Completes identity verification for model access, prevents unauthorized access |
Three Common Misconfigurations
- Symptom: After configuring the model, API calls return incomplete results. Logs show context truncation prompts. Cause:
maxContextandmaxTokensparameters are not set reasonably, exceeding the context limit supported by the model. - Symptom: In an intranet environment, calls to the configured model interface are slow. Direct curl calls to the interface work normally. Cause: The intranet access path for
AIPROXY_API_ENDPOINTis not configured correctly, or outbound network ports for the FastGPT container are not open. - Symptom: Raw material parameter units are missing or do not match production documents in generated process documents. Cause: The model prompt does not explicitly require matching category-specific fields and units, leading to parsing results that do not comply with production specifications.
How to Verify Successful Configuration
- Upload a single water treatment record and exception report template, trigger model generation testing, verify that the generated content includes core parameters and units from the source document.
- Call the configured model interface in the intranet environment, confirm that the response is normal, and verify that
AIPROXY_API_ENDPOINTmeets intranet access requirements. - Adjust the
temperatureparameter to generate two test content sets, compare the consistency of the results, and confirm that the parameter settings meet strictness requirements. - View FastGPT access logs, confirm there are no identity verification-related errors, and verify that
AIPROXY_API_TOKENconfiguration is valid.
The values provided here are common starting points and should be measured against the reader's own samples.