What Data Looks Like for This Category
Daily documents for this category include territorial spatial master plans, detailed plans, special plans, planning implementation evaluation reports, and public opinion summaries. Data sources cover territorial spatial basic information platforms, natural resource survey and monitoring data, population and economic statistics data, and remote sensing images. Data is updated annually. Most documents contain spatial coordinate fields, planning indicator values, and regional scope descriptions. Individual document lengths vary widely, with some special plan documents reaching tens of thousands of characters.
Constraints Imposed by These Characteristics on Vector Models and Indexing
Documents have wide length variations, and include structured fields such as spatial coordinates and planning indicators alongside long text passages. Vector embedding processes must balance text semantic relevance and spatial description correlation. Annual updated data does not require frequent incremental indexing, but must support full reconstruction and migration. Multi-source data have significant format differences, so embedding dimensions need to be unified and aligned. Party building learning Q&A needs to connect planning policies and party building requirements, so the index must support cross-document thematic associated retrieval to avoid fragmented search results.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum chunk length | 800–1200 characters | Planning documents have wide length variations; this range preserves paragraph semantic integrity and meets the context requirements of party building Q&A |
| Maximum paragraph depth | 3 | Planning document chapter levels are mostly within 3 levels; a 3-level depth can fully extract chapter logic |
| maxToken | 1024 | Adapts to the context window limits of mainstream large models and matches the input length requirements of party building Q&A |
| Index dimension | 128 | Matches the semantic characteristics of documents in this category; reference FastGPT v4.17.0 adaptation parameters |
| Full reconstruction trigger condition | After annual update | Aligns with the annual update business rhythm of data to ensure the index matches the latest data |
Three Common Misconfigurations
- Phenomenon: Test failure when accessing multimodal Embedding models, with Invalid-prefixed error messages returned. Cause: Adaptation parameters for the model are not configured, or the model input format does not match the spatial fields of the documents.
- Phenomenon: Abnormally reduced number of search results after full index reconstruction. Cause: Vector generation for all documents was not regenerated after adjusting chunk parameters, or index dimension configuration errors caused some vectors to not be stored correctly.
- Phenomenon: Paragraph logic breaks after knowledge base chunking. Cause: Maximum paragraph depth is set too low, failing to fully extract the chapter hierarchy of planning documents, leading to damaged semantic association.
How to Confirm the Configuration Is Correct
- Run a chunking test for a single planning document, check if the chunked paragraphs retain complete chapter logic.
- Call the vector retrieval interface to verify that the number of returned results matches the configured single search limit.
- Perform a full index reconstruction operation, confirm that the index update progress is normal and there are no error logs.
- Compare vector database data before and after upgrade, confirm that migrated vectors can be normally retrieved and called.
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.