What data for this category looks like
Daily documents for basic chemical continuous production include operating procedures, process index cards, DCS trends, laboratory reports, interlock bypass records, special work permits, shift reports, and more. Data sources cover DCS/SIS systems, online analyzers, LIMS systems, equipment condition monitoring, energy metering devices, and raw material storage tanks. Data update frequencies are divided into three categories: real-time (such as DCS trends), per shift (such as shift reports and special work permits), and daily (such as laboratory reports). Document fields include professional chemical parameters such as reaction temperature, feed flow rate, and pressure values, using industry-standard units including ℃, m³/h, kPa.
What constraints these characteristics impose on the model access and configuration link
Multi-source and heterogeneous document types and data sources require model access configuration to support unified parsing and indexing of multi-format data. Real-time, per-shift, and daily update frequencies require the context window to dynamically adapt to data refresh frequencies, preventing expired data from interfering with review results. Professional chemical parameter fields and units require configuration to bind domain-specific context, ensuring the model accurately identifies process logic. The business characteristics of strong interlocks and hazardous media require strict control over the compliance of generated content, avoiding random outputs that deviate from established rules.
How to set the configuration
| Configuration Item | Recommended Setting Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Documents for basic chemical continuous production include multiple process index cards and DCS trend data, requiring coverage of at least one full shift's worth of business data |
quoteMaxToken | 1500–2000 token | Technical document cross-review needs to reference key process parameters and interlock logic; this length can cover the complete content of a single core work permit or interlock bypass record |
maxTokens | 2000–3000 token | Review results need to include multi-dimensional compliance check conclusions, avoiding truncation of key judgment content |
temperature | 0.1–0.3 | This business requires strict matching with established rules in process documents, reducing randomness in generated content |
AIPROXY_API_ENDPOINT | Locally deployed model service address | Basic chemical production data involves sensitive process information, so local deployment is required to prevent data leaks |
AIPROXY_API_TOKEN | Exclusive key generated according to the deployment environment | Restrict unauthorized access to locally deployed model services, ensuring data security |
Three Common Misconfiguration Issues
- Issue: After configuring
quoteMaxToken, the length of referenced document fragments does not meet expectations, with some long documents truncated or not fully referenced. Cause:quoteMaxTokenis mistakenly understood as the maximum length of the query, but it is actually the maximum token limit for a single referenced content piece. - Issue: In FastGPT v4.17.0, for short technical review requests, the model returns the full result at once before switching to streaming output. Cause: The update rhythm of basic chemical real-time data is not adapted, and the system loads excessive historical context by default, triggering pre-output.
- Issue: When locally loading a chemical domain model downloaded from a model platform, the service cannot properly respond to review requests. Cause: The model service address is not bound to the
AIPROXY_API_ENDPOINTconfiguration item, so the system cannot call the target model.
How to Confirm Proper Configuration
- Check the
AIPROXY_API_ENDPOINTconfiguration value, confirm it points to the locally deployed model service address, meeting data security requirements. - Submit a test review request, verify that the length of referenced document fragments matches the
quoteMaxTokensetting, with no excessive truncation or omissions. - Adjust the
temperatureparameter and compare output results, confirm that the randomness of generated content meets the business's compliance requirements. - Check system logs, confirm there are no context loading timeout errors caused by an excessively large
maxContextsetting.
The values provided here are common starting points, and should be evaluated against the user's own operational samples.