Reference only; verify against the target FastGPT release and deployment documentation.
What Data for This Category Looks Like
Data related to document and material drafting for mining and mineral processing mainly comes from mining design drawings, mineral processing flow cards, safety production regulations, daily production reports, equipment operation logs, and similar sources. The update rhythm varies widely: daily production reports update daily, design documents are updated periodically over the project lifecycle, and safety regulations are updated irregularly alongside regulatory revisions. Most document structures include fields such as process number, equipment model, processing capacity, reagent ratio, and more. Units include tons, cubic meters, kilograms, hours, and similar. Some long documents include multi-page process descriptions and parameter tables.
What Constraints Do These Characteristics Impose on Model Integration and Configuration
Mining and mineral processing data contains a large number of industry-specific technical terms. Model integration must adapt to the semantic understanding of these terms to avoid professional errors in generated content. Long documents account for a high proportion of the dataset; some mineral processing flow documents exceed 1,000 words, requiring sufficient context configuration to prevent critical production data from being truncated. Frequently updated production report data requires regular triggering of the embedding process to ensure the timeliness of retrieved content. At the same time, document drafting must strictly match on-site operating parameters, with high requirements for model output accuracy. Overly divergent generation behavior must be restricted.
How to Set the Configuration
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
maxContext | 8000–12000 token | Mining and mineral processing flow documents and production reports are usually lengthy, requiring sufficient context to accurately associate on-site parameters |
maxTokens | 2000–3000 token | The output length of document drafting is usually around 1,000 words, matching the conventional length requirements of professional documents |
quoteMaxToken | 4000 token | Complete references to mineral processing flow parameters and equipment log content are required to avoid truncation of critical business data |
temperature | 0.3–0.5 | Document drafting requires rigor and accuracy, avoiding excessive divergence to ensure generated content complies with industry standards |
AIPROXY_API_ENDPOINT | Deployed proxy interface address | Adapt to the network environment for domestic model access, avoiding timeouts or access restrictions caused by direct calls |
AIPROXY_API_TOKEN | Assigned exclusive secret key | Ensure the security of model interface calls and prevent unauthorized access |
Three Common Configuration Mistakes
- Phenomenon: Knowledge base search takes more than 40 seconds, with insufficient returned result entries. Cause: Unreasonably set
maxContextleading to overly long context loading time, or unmatched embedding parameters for industry data leading to low recall efficiency. - Phenomenon: Inconsistent answer content between web debugging and integrated access. Cause: Ununified
temperatureormaxTokensconfiguration across different deployment environments, or failure to synchronize the latest embedding results of the data source during access. - Phenomenon: Model interface call returns a configuration error prompt. Cause: Incorrect entry of
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKEN, or failure to correctly associate the target model channel in the system.
How to Confirm Proper Configuration
- Review model call logs to confirm that the call return status codes for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare 200. - Import a long mining and mineral processing process document to test context loading, and confirm that the document content is not truncated.
- Adjust the
temperatureparameter and observe changes in the rigor of generated documents, confirming the expected control effect. - Compare configuration parameters between web debugging and formal access, confirming that all setting items are completely consistent.
The values provided here are common starting points and should be evaluated against the user'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.