What this category of data looks like
The policy and regulatory basis data for coal-electric integration comes primarily from publicly available normative documents and industry standard materials issued by the National Energy Administration, the State-owned Assets Supervision and Administration Commission of the State Council, and provincial-level energy authorities. Data updates follow policy release timelines, with no fixed cycle. Updates are synchronized immediately after new policies are issued. Individual documents typically include fields such as document number, issuing authority, effective date, applicable scenarios, and core clauses, organized by article, paragraph, and subparagraph levels. Some documents also include supporting interpretation materials.
What constraints do these characteristics impose on the model access and configuration link
Policy documents are mostly long texts with clear hierarchical structures. The model needs to accurately identify clause levels and associated content, which places clear requirements on context window handling. Structured fields such as policy document numbers and effective dates require precise matching, so the vector model needs to meet requirements for structured information encoding. Non-fixed update data sources mean the configuration needs to support dynamic switching of knowledge bases, and regular validation of data source validity is needed. Chunking of long documents needs to adapt to clause hierarchies to avoid breaking context associations, while ensuring that recalled policy clauses accurately correspond to the scenarios covered by user queries.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapt to context associations in coal-electric integration policy long documents, avoid truncation of key clauses |
maxTokens | 2000–3000 token | Meet the output length requirements for policy interpretation responses, avoid incomplete answers |
quoteMaxToken | 1500–2000 token | Ensure recalled policy original text fragments sufficiently cover scenario details from user queries |
temperature | 0.1–0.3 | Maintain rigor in policy responses, avoid generating content that deviates from the basis |
AIPROXY_API_ENDPOINT | Official access address corresponding to the deployed model | Ensure normal operation of the model invocation link |
AIPROXY_API_TOKEN | Valid key obtained from the model service provider | Complete identity verification for model access |
Three common configuration errors
- Phenomenon: Calling a multimodal model to process images in policy documents returns format errors, with a prompt indicating unsupported image format. Cause: The image was not converted to PNG or JPG format as required, and the image URL or base64 encoded content was not correctly included in the request.
- Phenomenon: After configuring the
maxContextparameter, recalled policy clauses still show context fragmentation. Cause: Chunking was not adapted to the clause hierarchy of policy documents, and long documents were directly truncated at fixed lengths, leading to loss of clause association relationships. - Phenomenon: The model access function fails to restart normally after changing the FastGPT service password. Cause: Relevant service processes dependent on model invocation were not restarted at the same time; only restarting the main service resulted in configuration changes not being synchronized and applied.
How to confirm successful configuration
- Initiate a test query containing core terminology from coal-electric policies, verify whether the returned results include accurate citations of policy clauses.
- View model configuration items in the system settings of FastGPT v4.17.0, confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENdo not have null values or incorrect characters. - Adjust the value of the
temperatureparameter, observe whether the rigor of the response changes as expected. - Upload a test coal-electric policy document, verify that the recalled fragments match the document content.
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.