What the data for this category consists of
Daily documents for this category include territorial spatial master plans, detailed plans, special plans, planning implementation evaluation reports, public opinion summaries, and more. Data mainly comes from the territorial spatial basic information platform, natural resource survey and monitoring data, population and economic statistics, and remote sensing images, with an annual update frequency. Most documents contain fields such as spatial constraint indicators, regional layout descriptions, and public feedback ledgers. Some documents are associated with spatial layer data, and the overall content has both text descriptions and structured spatial data attributes.
Constraints on vector models and indexes
The annual update rhythm requires the indexing process to support batch offline data import, without needing to configure high-frequency real-time synchronization mechanisms. Multi-source heterogeneous data sources require vector models to handle both plain text descriptions and semantic encoding of structured spatial indicators, to avoid feature adaptation bias. Documents with spatially associated fields and wide length variations require chunking logic to adapt to text units of different lengths, preventing individual chunks from splitting the complete theme of spatial planning. Non-standardized wording in public opinion summary documents requires vector models to have general semantic understanding capabilities for various scenarios, to accurately recognize colloquial feedback content.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-theme paragraph length of territorial spatial planning documents, avoiding splitting complete descriptions related to spatial layout |
| Overlap Length | 100–150 characters | Retains spatially associated constraint indicator information across chunks, preventing semantic breaks |
| Similarity Threshold | 0.65–0.75 range | Filters low-relevance planning documents, matching the semantic matching accuracy requirements for territorial spatial planning content |
| Single Search Limit | Top 10 results | Covers the association scope of multi-source planning documents, avoiding missing constraint information across special plans |
defaultToken | 4096 | Adapts to the long-text context requirements of planning documents, ensuring complete semantic transmission |
| Maximum Context Length | 8000 tokens | Carries associated index content from multiple planning reports, meeting the need for multi-document reference when drafting documents |
Three common configuration mistakes
- Symptom: After configuring a custom vector model, requests still trigger large language model calls. Cause: The vector model association channel was not specified in the knowledge base configuration, only the large language model channel was configured.
- Symptom: The knowledge base remains in the indexing state for a long time after importing the dataset. Cause: The batch import logic for multi-source spatial data was not adapted, or the chunk length was set too large, causing single-chunk processing timeouts.
- Symptom: Semantic similarity return values fall outside the 0-1 range. Cause: The similarity threshold verification rule was not configured correctly, using non-normalized vector scoring logic.
How to confirm the configuration is correct
Reference only; verify against the target FastGPT release and deployment documentation.
- Upload a single territorial spatial master plan document, check the index generation log, and confirm that the vector model was called successfully with no errors.
- Enter query terms related to planning implementation, and verify that the returned document types match the business scenario.
- Adjust the similarity threshold range, observe changes in the relevance of search results, and confirm that the threshold configuration takes effect.
- Check the knowledge base update configuration, confirm that it matches the annual update rhythm, with no abnormal high-frequency synchronization settings.
The configuration values provided are common starting points, and should be evaluated against the reader's own sample data.
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.