What Data for This Category Looks Like
The data required for document and material drafting in refinery and chemical production mainly comes from production operation logs, process specifications, equipment ledgers, safety inspection records, and material balance reports. Data update cycles cover real-time (hourly generated operation parameters), daily (equipment status ledgers), and monthly (comprehensive reports). Documents mainly use structured fields, including identifiers such as equipment ID, process unit number, and material grade, paired with parameters with fixed units such as temperature, pressure, and flow. Unstructured inspection description text also exists, with lengths ranging from hundreds to thousands of characters.
Constraints Imposed by These Characteristics on Model Integration and Configuration
Refinery and chemical production data includes process parameters with fixed units, proprietary process terminology, and document content of varying lengths, as well as real-time updated operation logs. These characteristics require configuration to ensure context carries unit specification instructions, preventing the model from arbitrarily rewriting parameter formats. The context window size must be limited to adapt to high-frequency updated real-time data and avoid overload. The randomness of model generation must be adjusted to reduce incorrect generation of proprietary terminology. Processing requirements for long documents must be adapted to avoid content truncation. For local deployment requirements, the local model call path must be configured to ensure data security.
How to Set Configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length of refinery and chemical production process specifications, equipment ledgers, and real-time operation logs, avoiding context overflow |
maxTokens | 4000–6000 token | Covers the output requirements for long-document material balance reports and monthly safety summaries, avoiding truncation of critical data |
temperature | 0.1–0.3 | Reduces random rewriting of process parameters and proprietary terminology, matching the format accuracy requirements of refinery and chemical production |
AIPROXY_API_ENDPOINT | http://localhost:11434/v1 | Adapts to large model calls for local Ollama deployments, complying with data security requirements for refinery and chemical scenarios |
AIPROXY_API_TOKEN | Empty string | Most local Ollama deployments do not require additional authentication identifiers, matching local call configuration rules |
toolChoice | none | No tool calls are required for document drafting scenarios, avoiding unnecessary logic jumps |
Three Common Misconfigurations
- Increased concurrent requests lead to request timeouts, manifested as 504 status code returned by the interface. The cause is failure to adjust the
maxContextparameter based on real-time data update frequency, resulting in excessive resource usage from context loading. - Process parameters in model outputs have extra spaces or incorrect capitalization, manifested as "150℃" becoming "150 ℃" or "MPA" appearing in output text. The cause is failure to specify requirements to retain original document format in configuration, combined with an overly high
temperatureparameter leading to random rewriting. - Local Ollama model calls fail, manifested as a "model not found" error returned by the test interface. The cause is that the port configured in
AIPROXY_API_ENDPOINTdoes not match the actual listening port of Ollama, or the v1 path suffix was not added correctly.
How to Verify Successful Configuration
Reference only; verify against the target FastGPT release and deployment documentation.
- Initiate 3–5 concurrent document drafting requests, check that the interface response time has no significant fluctuations, and there are no persistent timeout errors.
- Enter the FastGPT model test page, enter the model name for the local Ollama deployment, initiate a test call, and confirm that normal text results are returned.
- View the system logs, confirm that the context token count during model calls does not exceed the configured
maxContextthreshold, and there are no context overflow errors.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.