What the data for this category looks like
Policy and regulatory basis data for the power generation equipment manufacturing sector comes from three main sources: compliance documents for the equipment manufacturing industry released by the National Energy Administration and the Ministry of Industry and Information Technology, standard specifications developed by industry associations, and industrial support policies issued by local governments. Data updates align with policy revisions and new national standard releases, with no fixed schedule. Document structure includes fields such as policy document number, issuing authority, effective date, applicable equipment type, and full clause text. Some long documents divide clauses by chapter. Units primarily follow the format of clause chapter numbers and effective dates, with no complex quantitative units used.
What constraints these characteristics impose on the vector model and indexing workflow
The hierarchical structure of policy documents requires retaining metadata such as document numbers and effective dates during chunking, to avoid damaging the complete compliance integrity of clauses. Long text clauses must adapt to the token limit of embedding models, to prevent truncation of critical content during chunking. Data updates occur infrequently but require timely synchronization. Indexing workflows must support incremental updates. Policy texts dense with professional terminology require embedding models with domain adaptation capabilities, to ensure accurate vector matching. Metadata fields such as effective dates must be used as index tags, to enable subsequent filtering of policy basis by time range.
Configuration settings
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-paragraph length of policy clauses, avoiding damage to the semantic integrity of complete compliance clauses |
| Overlap Length | 100–200 characters | Retains policy context across chunks, preventing truncation of cross-chapter associated compliance requirements during chunking |
defaultToken | 8192 | Matches the input token limit of Qwen3-Embedding-8B, adapting to the length of chunked policy text |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Filters non-core policy clauses, ensuring accuracy of question-and-answer matching and avoiding interference from irrelevant content |
| Single Search Limit | Top 6 results | Covers relevant policy documents in the same sector, avoiding omission of necessary compliance basis |
maxToken | 16384 | Adapts to the total length of context splicing after retrieval, meeting complete question-and-answer requirements for long policy texts |
Three common configuration errors
- Phenomenon: File status remains stuck on "Indexing" for an extended period with no completion. Cause: The configured
defaultTokendoes not match the input limit of the embedding model, causing chunked text to fail the vectorization process. - Phenomenon: Duplicate policy clause entries appear in retrieval results. Cause: The overlap length is set too large, leading to an overly high chunk overlap ratio, which generates a large volume of duplicate vector data during indexing.
- Phenomenon: Identically named embedding model configurations are automatically overwritten, making it impossible to use both local M3E and Qwen3-Embedding-8B simultaneously. Cause: Independent model aliases are not configured for different scenarios. The system uses the model name as the unique identifier by default, causing subsequent configurations to overwrite prior ones.
How to confirm proper configuration
- Access the model management page of FastGPT v4.17.0, view the list of configured embedding models, and confirm that target models such as Qwen3-Embedding-8B and locally deployed M3E exist and are not overwritten.
- Upload a single policy document, wait for indexing to complete, and view the chunk preview. Confirm that chunk length falls within the preset range and that metadata such as policy document numbers and effective dates are retained.
- Initiate a test question related to policies, review the number of retrieval results and their similarity scores, and confirm that matching results meet preset threshold requirements.
- Check system operation logs, confirm that there are no error messages such as embedding model call timeouts or chunking failures.
The values provided are common starting points, and should be evaluated against samples used in the target deployment.
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.