What the Data for This Category Looks Like
Data for this category primarily comes from the national land and spatial planning unified map, project approval and supervision platform, land use status database, and ecological protection redline data. Daily processed documents include land pre-examination and site selection opinions, construction land planning permits, construction engineering planning permits, redline diagrams, and planning conditions. Data updates occur irregularly. Core fields include project site selection scope, land scale, planning control requirements, and redline coordinates. There is no unified fixed batch update cycle. Single-update data volume fluctuates based on the approval progress of individual projects.
Constraints Imposed on Vector Models and Indexes
Data sources for this category are scattered and have diverse formats. They include structured approval documents, coordinate-bearing redline diagrams, and unstructured planning text. This requires indexing systems to support compatibility with multi-source data. Updates occur without fixed cycles. Configurations must support incremental indexing and real-time synchronization to prevent indexing lag caused by changes to approval progress. Core fields include spatial coordinates and control rules. Vector models must adapt to semantic extraction of geospatial features. Because errors can lead to severe consequences, retrieval recall accuracy and completeness must be ensured. Key control clauses must not be omitted.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to paragraph lengths of planning documents for this category, avoid splitting that breaks semantic integrity of control rules |
| Overlap Length | 100–150 characters | Ensure cross-chunk control clauses are not split, improve retrieval coherence |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Match strict matching requirements for planning control rules, avoid incorrectly recalling irrelevant project data |
| Single Search Limit | Top 8 entries | Cover multiple pre-approval documents required for individual project approval, balance retrieval efficiency and recall scope |
maxToken | 4000 token | Adapt to large model context windows, fully carry retrieved planning clauses and project data |
defaultToken | 200 token | Used for vector extraction of short text fields, ensure retrieval accuracy of basic fields |
Three Common Configuration Errors
- After uploading data using the pushdata API in chunk mode, the interface permanently displays the "Indexing" status with no progress updates. The cause is that the chunk length is set too large. The single-batch data processing volume exceeds the system's default processing limit, causing indexing tasks to block.
- A large number of irrelevant real estate registration data entries appear in retrieval results. The cause is that no dedicated filtering rules are set for the spatial coordinate and planning control fields of this category. The recall scope is not limited to documents related to land pre-examination and planning permits.
- The final set of indexing tasks never completes. The cause is that no reasonable
maxTokenparameter is configured. Long text chunks exceed the large model context window, causing embedding generation to fail. Indexing tasks cannot be finalized.
How to Verify Successful Configuration
- Upload a single typical planning conditions document. Check if chunked text retains complete control clauses with no obvious semantic splitting.
- Initiate a retrieval targeting a project's site selection scope. Confirm that retrieval results include pre-approval documents and control rules for the project, with no irrelevant data mixed in.
- Test incremental upload of a single updated redline diagram. Confirm that indexing progress updates normally, with no long-term blocking.
- Call the embedding model API. Verify that embedding results for short text fields are generated normally, with no null values returned.
The values provided 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.