What the Data for This Category Looks Like
Data for power grid enterprise policy and regulatory basis Q&A comes from industry policies released by national energy authorities, official policy documents from power grid enterprise headquarters and all levels, and industry standard specifications. Update schedules are adjusted irregularly alongside policy revisions and internal process optimizations, and must be completed synchronously after major policy releases. Most documents use a chapter-based body structure, with fields including document number, issuing authority, effective date, and clause details. Units primarily use formats such as clause items, chapter numbers, and dates. The length of individual documents varies widely.
Constraints Imposed on Vector Models and Indexes
The chapter-based structure and long length of power grid policy documents require vector chunking to match clause granularity. Splitting a single clause across multiple chunks reduces matching accuracy, so this must be avoided. The multi-field document structure requires indexes to specify core fields. This excludes non-policy auxiliary fields from vector calculations. The irregular update schedule requires indexes to support incremental updates. Full vector database rebuilds are not needed. The authoritative, binding nature of policy content requires strict control of vector matching thresholds. This ensures returned results align closely with queried policy bases, and prevents irrelevant content from interfering with decision-making.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the chapter and paragraph length of power grid policy documents, prevents single chunks from spanning core clauses |
| Overlap Length | 100–150 characters | Preserves contextual connections between adjacent clauses, prevents key policy bases from being truncated by chunking |
defaultToken | Calibrated based on actual testing | Adapts to the input length limit of local or called vector models, prevents chunks from exceeding model thresholds |
maxToken | 4096 token | Matches the maximum input length of mainstream Chinese vector models, balances contextual completeness and computational efficiency |
| Similarity Threshold | 0.75–0.85 | Filters accurately matched policy clauses, avoids irrelevant content compromising the accuracy of policy basis results |
| Single Search Limit | Top 10 results | Controls the number of returned results, ensures users can quickly locate core policy bases |
Three Common Misconfigurations
- Search results include non-policy auxiliary documents. This occurs when no configuration is set to index only core policy fields, and auxiliary data is included in the vector database.
- Multiple duplicate matches for the same policy clause are returned. This occurs when chunk overlap settings are improperly configured, or when deduplication is not enabled for multi-version chunks of the same clause.
- Vector queries return empty results or throw errors. This occurs when the embedding dimensions of different vector models are inconsistent, or when chunked documents do not meet the input requirements of the server-side model.
How to Verify Proper Configuration
- Upload a single typical policy clause, check that the chunked text length matches the preset chunk settings, with no obvious truncation of core clauses.
- Initiate a policy-related query, check that the similarity scores of returned results fall within the preset threshold range, with no abnormal values that are too low or too high.
- Test updating a single policy document, check that the vector database completes incremental updates automatically, and old versions of the content are no longer retrievable.
- After switching vector models, adjust the
defaultTokenandmaxTokenparameters, verify that query result accuracy meets expectations.
Reference only; verify against the target FastGPT release and deployment documentation.
The values listed above are common starting points, and should be evaluated against the user’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.