What Data Looks Like for This Category
Data for this category originates primarily from the national land spatial planning unified map, project approval and supervision platform, current land use database, and ecological protection red line data. Update frequency is irregular. Daily documents include land pre-examination and site selection opinions, construction land planning permits, construction engineering planning permits, red line drawings, and planning conditions. Core fields include project number, site selection scope, land use scale, planning control requirements, pre-coordination matters, and more. The length of individual documents varies significantly. Some red line drawings include vector attribute information, so parsing support for non-pure text formats is required.
Constraints Imposed on Vector Models and Indexes
Reference only; verify against the target FastGPT release and deployment documentation. Irregular update cycles require configuring an incremental index trigger mechanism to reduce time spent on full re-indexing. The wide range of document lengths requires flexible adjustment of chunking parameters to maintain semantic integrity across different document types. Core fields include spatial scope-related content. The vector model must accurately capture spatial semantic associations such as site selection and red lines, and reasonable index weights must be configured for spatial attributes.
How to Configure Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Aligns with the paragraph structure of land pre-examination documents, avoids including excessive cross-topic content in a single chunk |
| Overlap Length | 100–150 characters | Preserves cross-chunk context connections, ensures continuous information such as site selection scope and red line requirements is not split |
| Similarity Threshold | Calibrated via actual testing | Matches the semantic matching accuracy of planning control requirements, prevents mismatching different control rules |
| Single Search Limit | Top 8 entries | Balances retrieval efficiency and information completeness, covers associated rules for common approval scenarios |
maxToken | 4096 | Supports the long context needs of party building learning Q&A, fully accommodates associated content for planning conditions and policy requirements |
defaultToken | Follow the model’s adaptation interval | Standardizes the token benchmark for vector generation, ensures consistent vector dimensions across all documents |
Three Common Configuration Errors
- After importing a knowledge base, the dataset status stays "Indexing" for an extended period with no progress updates. Cause: No incremental index trigger rule is configured, or parsing fails due to incompatible multi-source data formats.
- After using an embedding model, the returned semantic similarity value exceeds 10000, falling outside the standard range. Cause: No correct similarity normalization configuration is applied, leaving the original similarity score unscaled.
- After batch importing documents, indexing progress stalls, taking far longer than expected. Cause: Chunking parameters are not adjusted based on document length differences, or batch indexing optimization is not enabled.
How to Verify Proper Configuration
- Import a single typical document, such as a construction land planning permit, and confirm that chunked text fragments fully retain core fields including project number and site selection scope.
- Submit test questions related to the business, review the similarity values of returned results, and adjust matching rules based on actual business scenarios.
- Batch import multiple documents of different types, and confirm that indexing progress proceeds normally without abnormal stalls.
- Test the incremental index function: after adding a single document, confirm that the indexing task triggers and completes successfully.
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.