Service Inquiry and Response for Territorial Spatial Planning Formulation and Implementation Evaluation: Vector Models and Indexes

What data looks like for this category

The data for this category mainly comes from four sources: territorial spatial basic information platform, natural resource survey and monitoring data, population and economic statistics, and remote sensing images. Commonly processed documents include territorial spatial overall plans, detailed plans, special plans, planning implementation evaluation reports, public opinion summaries, and similar materials. The data update cycle is annual. Most documents contain fields such as spatial coordinates, constraint indicators, and regional scope. Some documents include multi-source spatial data attachments. The overall data volume increases as the service area expands.

What constraints do these characteristics impose on the vector model and index link

The annual data update cycle means index rebuilding does not need to be performed frequently, but batch processing capabilities for annual full updates must be reserved. Fields such as spatial coordinates and constraint indicators require vector models to support hybrid encoding of spatial semantics and text semantics, to avoid spatial association deviations caused by relying only on text semantics. In scenarios with mixed document types, the index must support setting retrieval filtering rules by document category. At the same time, short-text public opinions and long-text planning reports must adapt to a unified chunking logic, to prevent chunk length imbalance from affecting retrieval recall performance.

How to set configurations

Three common configuration mistakes

• Phenomenon: After replacing the vector model, the existing knowledge base cannot be reused directly, and the index must be regenerated. Cause: The vector embeddings of existing documents were not recalculated for the new vector model, and only the model configuration was updated without triggering the full re-encoding process.
• Phenomenon: The sorting of knowledge base retrieval results does not meet expectations, and results are not displayed in priority order of semantic similarity. Cause: Retrieval result re-ranking rules were not configured correctly, or the weighted sorting logic based on spatial semantics was not enabled.
• Phenomenon: Semantic retrieval returns high scores but the retrieval results do not match business requirements. Cause: The vector model was not adapted to the semantic characteristics of spatial planning data, resulting in score calculations that do not associate with spatial constraint indicators.

How to confirm configurations are set correctly

Reference only; verify against the target FastGPT release and deployment documentation.

• Upload a single planning report and public opinion summary, use the platform's chunk preview function to verify that the text length after chunking falls within the preset chunk length range.
• Enter a query statement containing spatial scope and constraint indicators, check that the sorting logic of retrieval results matches business expectations, and adjust the similarity threshold and re-ranking rules for verification.
• After replacing the vector model, trigger a full index rebuild, verify that vector embedding updates can be completed without re-importing original documents.
• Upload a single long document, check the content integrity of retrieval results, confirm that the maxToken parameter configuration does not cause chunk encoding to be truncated.

Note: The values listed above are common starting points and should be evaluated against samples relevant to the specific deployment context.

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.