What this category of data looks like
This category of data primarily comes from the national land spatial planning unified map, project approval and supervision platform, land use status database, and ecological protection redline data, with irregular update frequencies. Daily processing covers documents including land use preliminary examination and site selection opinions, construction land planning permits, construction engineering planning permits, redline diagrams, planning conditions, and more. Fields related to these documents cover project site selection scope, land use scale, planning control requirements, prior approval conditions, and other related content. Some drawing documents require transcription into structured text before being added to the index. Individual document lengths vary significantly, ranging from a few hundred characters to tens of thousands of characters.
Constraints imposed on vector models and indexing
This category of data has scattered sources, including structured approval forms and unstructured text transcribed from drawings. Systems must support both data formats for indexing. Update frequencies are irregular, so indexing systems must support incremental synchronization to avoid excessive resource consumption from full reconstruction. Documents contain geospatial professional terminology. Vector models must accurately recognize specific expressions such as site selection scope and land use redlines. Document lengths vary significantly. Chunking processes must balance professional term integrity and semantic coherence to prevent key control requirements from being split apart.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Aligns with the semantic length of professional terms in this category, avoids splitting complete planning control requirements, and ensures accurate matching of relevant clauses during review |
| Chunk Overlap Length | 100–200 characters | Compensates for semantic breaks caused by chunking, ensures that cross-chunk redline control statements can be fully retrieved |
maxToken | 4096 token | Matches the input limit of mainstream vector services for FastGPT v4.17.0, ensures that redline diagram content transcribed from long texts can be fully vectorized |
defaultToken | 512 token | Meets the vectorization needs of short approval documents in this category, balancing accuracy and API call efficiency |
| Similarity Threshold | Calibrated via actual testing | Meets the strict requirements of this category’s administrative approval, avoids incorrectly recalling irrelevant land control content |
| Single Search Limit | Top 10 results | Controls the scale of returned results, adapts to the verification needs of refined review, and avoids information overload |
Three common errors
- Phenomenon: Vectorization service call frequency is too high, triggering API rate limit errors. Cause: Batch slicing processing is not configured, text slices are passed individually, and the service's batch processing capability is not utilized.
- Phenomenon: Retrieval results fail to match precise planning control clauses, with a large number of irrelevant recalls. Cause: A generic vector model is used that does not adapt to geospatial professional terminology, or the similarity threshold is set incorrectly.
- Phenomenon: The index does not synchronize new approval documents after incremental updates. Cause: The data source's update trigger mechanism is not bound, only manual full index execution is performed, which cannot adapt to the irregular document update rhythm of this category.
How to confirm correct configuration
- Upload a typical land use preliminary examination opinion document, perform a retrieval test, and verify whether the returned results include the project's planning control requirement fields.
- Simulate an incremental document update, check whether the index system automatically synchronizes newly uploaded approval materials without requiring manual full reconstruction.
- Call the vectorization service API, pass multiple text slices, and confirm whether the service supports batch processing to reduce call frequency.
- Adjust the similarity threshold, compare retrieval results across different thresholds, and confirm that they meet the strict requirements of this category’s approval review.
The values provided here 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.