What this category of data looks like
Power generation equipment manufacturing policy and regulatory basis data primarily comes from industry policy documents issued by the National Energy Administration and the Ministry of Industry and Information Technology, as well as GB-series national standards related to power generation equipment. Data updates align with policy release timelines, with no fixed monthly frequency. Document structures include full policy text, clause annotations, and applicable equipment scope descriptions. Fields include policy number, issuing authority, effective date, and adapted equipment type. Units use industry standard units such as megawatts (MW) and megapascals (MPa).
What constraints these characteristics impose on the knowledge base retrieval and recall link
Policy sources are scattered and include cross-department documents. Retrieval links must support parallel recall across multiple datasets, with weight differentiation by issuing authority. Documents contain long full policy texts, structured standard tables, and clause summaries. This requires adapting chunking rules for different content formats, to prevent key information loss from long text truncation. The adapted equipment type field and industry-specific units require associative field matching during retrieval. Prioritize recalling policy entries matched to target equipment, while avoiding recall bias caused by unit terminology mismatches.
How to set configurations
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Aligns with semantic similarity requirements for policy texts, avoids recalling irrelevant policies or missing core clauses |
limit | Top 10 entries | Balances recall coverage and context length, adapts to the long text characteristics of policy documents |
searchMode | vector | Prioritizes semantic recall of policy clauses, combined with structured field filtering for adapted equipment types |
usingReRank | Enabled | Rearranges recall results by semantic relevance, optimizes sorting accuracy for long policy texts |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands industry terms related to policies, covers synonyms and unit expressions |
collectionIds | ID corresponding to the power generation equipment policy dataset | Limits retrieval scope to the exclusive policy knowledge base, prevents mixing data from other categories |
Three common mistakes
- Phenomenon: After importing policy tables from Feishu multi-dimensional documents or Excel formats, retrieval results cannot correctly match parameter values within the tables. Cause: The structured document parsing function of the dataset is not enabled, and chunking rules do not separately handle table fields.
- Phenomenon: Retrieved policy clauses include irrelevant content from non-power generation equipment fields. Cause: The
collectionIdsparameter is not used to limit retrieval scope to the exclusive power generation equipment policy dataset, resulting in mixing knowledge base data from other categories. - Phenomenon: Imported PDF format policy files fail to load normally in retrieval result previews. Cause: Pagination parameters for PDF text extraction are not configured, or the file is encrypted, preventing complete parsing of text content.
How to confirm the configuration is complete
- Upload a policy Excel spreadsheet from the power generation equipment field, initiate a retrieval, and confirm that parameter values within the table can be correctly recalled and matched to the query.
- Review the
collectionIdsparameter in the retrieval configuration, confirm that it only includes dataset IDs related to power generation equipment policies, with no other irrelevant datasets. - Initiate a query containing industry-specific units, such as "compliance policies for 300MW steam turbines", and confirm that recall results prioritize entries adapted to this equipment.
- After enabling
usingReRank, compare the order of retrieval results before and after reranking, and confirm that highly relevant entries are displayed first.
The values provided here are common starting points, and should be evaluated 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.