What this category of data looks like
Data for this category originates from the national land and spatial planning unified map, project approval and supervision platform, land use status database, and ecological protection redline data. Daily documents include land pre-examination and site selection opinions, construction land planning permits, construction engineering planning permits, redline diagrams, and planning conditions. Data updates follow no fixed schedule, and are synchronized with project approval progress or planning adjustments. Most documents contain fields such as project site selection scope, land use scale, and planning control requirements. Some drawing-based files require structured information extraction before vectorization can be performed.
Constraints imposed by these characteristics on the vector model and index workflow
Dispersed data sources and diverse data types, including structured text and drawing files, require the index workflow to support unified vector adaptation for multi-source data. No fixed update cycle means incremental indexing is needed to avoid resource consumption from full reconstruction. Documents contain strongly correlated fields such as project site selection and redline conflicts, which demand high retrieval recall accuracy and require alignment with semantic relevance for business scenarios. Some pre-approval data must be retrieved across platforms, so the index must support fast retrieval and calls associated associated with external data sources.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to semantic integrity of long text such as project scope and control requirements in documents of this category, avoid information fragmentation within chunks |
| Overlap Length | 100–200 characters | Retain overlapping content between adjacent chunks, reduce loss of cross-chunk semantic connections |
maxToken | 4096 token | Match context window limits of mainstream large models, ensure retrieval results can be fully passed to the model |
| Similarity Threshold | 0.75–0.85 | Filter low-relevance retrieval results, focus on documents strongly related to project site selection and planning control |
| Single Search Limit | Top 8 entries | Balance retrieval efficiency and recall coverage, avoid excessive redundant results affecting business judgment |
defaultToken | 2048 token | Adapt to context length requirements of common business inquiries in this category, ensure information integrity for basic question answering |
Three Common Misconfigurations
- Index construction gets stuck during final steps. This occurs when the incremental index breakpoint resume function is not configured, or access permissions for cross-platform data sources are not properly set.
- Mixed retrieval takes more than 10 seconds. This occurs when non-essential retrieval branches are not disabled, or the single search limit is set too high, leading to too many results participating in re-ranking.
- Empty results or missing fields appear after vectorization. This occurs when unprocessed drawing binary files are imported, and structured text information from the files has not been extracted first.
How to Verify Configurations Are Correct
- Import one typical land pre-examination and site selection opinion document, and check if chunked text retains core fields such as project site selection scope and land use scale in full.
- Run one business inquiry test, and confirm retrieval result return time meets expectations. Adjust configurations as needed for mixed retrieval to validate processing time.
- Check data source association status on the index configuration page, and confirm access permissions for sources including the national land and spatial planning unified map and project approval and supervision platform are normal.
- View the log panel in FastGPT v4.17.0, and confirm there are no error messages for index construction failures or retrieval timeouts.
The values listed above are common starting points, and should be evaluated 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.