What this category of data looks like
Document and material data for coal mine production mainly comes from internal systems including on-site operation records, safety check ledgers, shift daily reports, and monthly summaries. Data update cycles cover shifts, daily, and monthly. Documents fall into two categories: short form documents such as on-site rectification notices and shift handover records, and long documents such as special safety reports. Most documents have fixed fields including work area, equipment number, detection parameters, and responsible person. Some parameters have dedicated units, such as gas concentration, tunneling footage, and ventilation volume.
Constraints Imposed on Model Access and Configuration
High-frequency short text calls from on-site form documents require model access to be configured for fast response. Long document reports need configuration for large context windows to prevent critical content from being truncated. Accurate extraction of fixed professional fields requires the model to prioritize professional term matching capabilities, while limiting the generation of fictional parameters. Data with multiple update frequencies corresponds to different document processing priorities. Corresponding context refresh rules must be configured to avoid generating documents using expired data.
How to Set Configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–16000 token | Covers the full context of long documents such as monthly safety reports, avoiding truncation of critical content |
maxTokens | 2000–3000 characters | Adapts to the generation length of short documents such as on-site rectification notices and shift handover records, avoiding redundant output |
temperature | 0.2–0.4 | Reduces the probability of fictional parameters, ensuring the rigor of coal mine professional data |
toolChoice | auto | Automatically adapts to professional term calls required for document generation, no manual tool configuration needed |
AIPROXY_API_ENDPOINT | Fill in the dedicated address provided by the FastGPT v4.17.0 platform | Matches FastGPT’s proxy call rules to ensure normal routing of model requests |
AIPROXY_API_TOKEN | Bind the dedicated key for the coal mine production scenario | Isolates scenario permissions to avoid cross-scenario data leaks |
Three Common Configuration Mistakes
- An error occurs when saving an initialized model after creating a new document generation node: The initialized model lacks scenario-specific context parameters, so it cannot adapt to the long text processing requirements of coal mine documents.
- A bedrockruntime-related error appears after switching to the tool-call Claude 4 model: Regional routing rules for
AIPROXY_API_ENDPOINTare not configured correctly, leading to interception of cross-regional requests. - The text extraction component fails to parse professional fields normally: The
temperatureparameter is not adjusted to reduce randomness, causing the model to incorrectly split professional parameter units and values.
How to Verify Successful Configuration
- Upload a standard coal mine tunneling daily report, test generating corresponding safety rectification suggestions, and confirm the output includes correct work areas and parameter units.
- Call the model to generate a monthly safety summary, check that the output length matches the preset range with no content truncation.
- Switch between different professional document types, verify that
toolChoiceautomatically adapts to different generation logic with no fixed format errors. - View the API call log, confirm that the request parameters for
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINThave no format 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.