What the data for this category looks like
Data for coal chemical internal office and employee services mainly comes from enterprise internal production management systems, human resources systems, archives, and equipment operation and maintenance platforms. Production data such as equipment runtime and maintenance records are archived immediately upon completion of work. Personnel documents such as job manuals and attendance rules are updated quarterly. Internal notices are published as needed.
The data includes structured fields and unstructured text. Structured fields include equipment numbers, operating parameters, coal quality test values, and other items, with units such as hours, tons, and degrees Celsius. Unstructured text mostly consists of training minutes, work instructions, and similar content.
What constraints these characteristics impose on the "model access and configuration" link
Coal chemical production data has strong real-time performance and fixed field formats, and includes professional terms such as equipment operating parameters and coal quality test values. Model access must adapt to high-frequency call demands, and token consumption per single request must be limited to avoid timeouts.
Long unstructured texts such as training minutes and internal notices require adjustment of context window parameters to fully load content. Special industry terms need adaptation in model configuration to prevent incorrect explanations.
Personnel data involves employee privacy, so permission verification parameters must be configured to ensure compliance of data calls. When deploying models locally, extraction rules for coal chemical-specific fields need adaptation to avoid recognition bias in structured data.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the full loading requirements of long coal chemical documents such as equipment operation manuals and training minutes |
maxTokens | 4000 token | Limits the output length of a single generation to avoid exceeding the native supported limit of the model |
quoteMaxToken | 2000 token | Limits the total length of recalled knowledge base text to avoid context overload |
temperature | 0.3–0.5 | Reduces generation randomness to meet the accuracy requirements for professional terms and structured data extraction |
AIPROXY_API_ENDPOINT | Fill in according to the actual interface of the deployed model | Adapts to the access path of local or third-party models to ensure correct request routing |
AIPROXY_API_TOKEN | Fill in according to the key generated by the platform | Completes identity verification for model access and ensures compliance of call permissions |
Three common mistakes
- Text extraction of ID card information returns empty. The cause is that special rules for structured data extraction are not configured, so the model cannot recognize ID card field formats mixed with production terms in coal chemical scenarios.
- The locally deployed vector model keeps showing "indexing in progress". The cause is that the
maxContextparameter is not adjusted to adapt to the loading threshold of the local model, or the vector database connection configuration does not match the field length of coal chemical data. - An error is still reported after intercepting the large model response and replacing custom tags. The cause is that custom output format adaptation is not enabled in the model configuration, or the replaced tags do not fully match FastGPT’s parsing rules.
How to confirm the configuration is complete
- Initiate a test request containing coal chemical production terms, and check whether the term explanations returned by the model conform to industry specifications.
- Import a single long document such as an equipment operation manual, and check whether the context length loaded by the model meets expectations.
- Call the locally deployed vector model, and check whether the number of recalled knowledge base entries matches the configured recall rules.
- Submit test data containing privacy information, confirm that the permission verification parameters have taken effect, and no unauthorized call prompts appear.
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.