What the data for this category looks like
Policy and regulatory basis data for advanced chemical materials comes from official policy documents released by the Ministry of Industry and Information Technology and the National Development and Reform Commission, as well as standard texts from the National Technical Committee for Standardization of Advanced Chemical Materials. Data updates occur irregularly alongside new policies and standards. Most documents are structured clause-based text, organized by hierarchical content levels, and include fields such as policy document number, issuing authority, effective date, applicable scope, and specific control requirements. Some attachments contain quantitative indicators such as production capacity and energy consumption, with units including ton, kilogram, standard coal, and natural date formats for policy effective dates.
What constraints these characteristics impose on model access and configuration
Most policy documents for advanced chemical materials are long, structured clause-based text with hierarchical content structures. This requires model access to support long context processing capabilities, to avoid truncating critical clauses. Policy updates do not follow a fixed cycle, and some new policies are released at high frequency. Data source synchronization configuration must support on-demand trigger updates. Policy documents include exclusive fields such as document number, effective date, and applicable field. Retrieval and Q&A configuration must associate these metadata fields, to ensure returned results are accurately bound to corresponding policies. Some policy attachments contain quantitative indicators such as production capacity and energy consumption. Model extraction must adapt to the recognition logic for industry-standard units, to avoid unit identification errors. In FastGPT v4.17.0, all model configurations are completed through the interface, with no need to modify local configuration files.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long text structure of advanced chemical materials policy documents, fully retaining contextual associations of clauses |
quoteMaxToken | 2000–3000 token | Ensures quoted original policy clause text is complete, avoiding truncation of critical control content |
temperature | 0.1–0.3 | Strictly matches original policy content, reducing randomness of generated content |
AIPROXY_API_ENDPOINT | Actual address of the deployed model gateway | Uniformly manages model call links, simplifying maintenance for multi-model switching |
AIPROXY_API_TOKEN | Access key assigned by the gateway | Implements permission control for model calls, ensuring interface security |
maxTokens | 1500–2000 token | Covers policy Q&A response requirements associated with multiple clauses, avoiding incomplete responses |
Three common configuration errors
- Model calls return
status code 404errors. The cause is incorrect filling of theAIPROXY_API_ENDPOINTparameter, or incorrect configuration of the gateway address, resulting in failure to locate the model service interface. - Policy files can be downloaded normally after upload, but the large language model prompts that no file has been uploaded. The cause is that the file processing logic does not adapt to long-text policy documents, and file metadata identifiers are lost during chunking, causing the system to fail to recognize the uploaded file.
- Model Q&A return results deviate significantly from original policy text. The cause is an excessively high
temperaturevalue, causing generated content to deviate from original requirements, which does not meet the rigor requirements of policy Q&A.
How to confirm successful configuration
- Verify the model gateway access address and key configuration, ensuring they match the parameters within FastGPT.
- Upload a single advanced chemical materials policy document, initiate a test Q&A targeting specific clauses within the document, and verify that returned results include corresponding clause content.
- Initiate cross-document policy association Q&A, verifying that the model can correctly match applicable scopes and clauses of different policies.
- Test Q&A for long-text policy documents, verifying that returned results do not show content truncation.
The values given 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.