What this category of data looks like
Coal mining production policy and regulatory basis data is primarily sourced from public policy documents, industry safety regulations, and internal enterprise compliance documents issued by the National Energy Administration, the Ministry of Emergency Management, and local coal regulatory authorities. The documents include fields such as policy number, issuing entity, effective date, applicable mining area type, and full text of core clauses. They follow a hierarchical structure divided into articles, clauses, and sub-clauses. Supported formats include PDF, Word, and scanned documents. The length of a single document ranges from hundreds to thousands of characters. Synchronization and updates are completed within 1 to 3 working days after a new policy is released.
Constraints Imposed by These Characteristics on Model Access and Configuration
Inconsistent formats of multi-source policy documents require access and configuration workflows to support multiple formats including PDF, Word, and scanned documents. The general document parsing and OCR module must be enabled in FastGPT v4.17.0, otherwise valid text content cannot be extracted. Policy documents have clear effective and expiration dates. A time range filtering rule must be added to the retrieval configuration to avoid returning expired policies. The hierarchical clause structure of documents requires retaining the text hierarchical relationship during configuration, otherwise the model cannot accurately reference clause numbers. The large length of single policy documents requires configuring the context window to adapt to long text input, avoiding truncation of key compliance content.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | A single coal mining policy document can reach thousands of characters. A long context can cover retrieval and reference of multiple associated policies |
quoteMaxToken | 2000–3000 token | The full text of policy clauses must be retained to avoid truncation of core compliance content |
temperature | 0.1–0.3 | Policy Q&A requires strict accuracy, reducing the probability of random generation deviating from the original text |
maxTokens | 4000 token | Responses must clearly cover key points of policy interpretation, controlling output length to meet business requirements |
AIPROXY_API_ENDPOINT | Exclusive API address of the deployment node | Unified management of cross-model calls, adapting to model switching requirements for multiple scenarios |
AIPROXY_API_TOKEN | Authentication key corresponding to the proxy | Ensures identity legitimacy of API calls, preventing unauthorized access |
Three Common Configuration Errors
- Phenomenon: After adding a vector model via the OneAPI channel, large language model generation is still triggered for actual requests. Cause: The configuration link between the vector model and LLM is not differentiated, and the vector model is bound to the general LLM call channel.
- Phenomenon: After setting
maxContextto 6, the model cannot retain the context of the previous conversation. Cause: The unit ofmaxContextis mistakenly understood as conversation turns. The actual unit of this parameter is token, and a setting that is too small cannot carry conversation context. - Phenomenon: A 401 unauthorized error is returned during model test calls, and requests cannot be initiated normally. Cause:
AIPROXY_API_TOKENis configured incorrectly, or the proxy address is not correctly associated with the access permissions of the corresponding model.
How to Verify Successful Configuration
- Navigate to the model configuration page in FastGPT v4.17.0, review the channel binding relationship between the vector model and LLM, and confirm the vector model is not bound to the general LLM call link.
- Initiate a multi-turn conversation test, input questions associated with context, and verify that the model can correctly reference the content of the previous question.
- Initiate an API call test, check that the returned results include references to policy clauses, and that no truncation or deviation from the original text occurs.
- View call logs, confirm that the authentication request for
AIPROXY_API_TOKENreturns a 200 status code, with no unauthorized error reports.
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.