What the Data for This Category Looks Like
This category’s daily documents include territorial spatial master plans, detailed plans, special plans, planning implementation evaluation reports, and public opinion summaries. Data is primarily sourced from the territorial spatial basic information platform, natural resource survey and monitoring data, population and economic statistics, and remote sensing images. The update frequency is once per year. Most documents contain fields such as spatial coordinates, constraint indicators, and department coordination opinions, and have both text and spatial data characteristics.
Constraints on Vector Models and Indexes From These Data Characteristics
The data for this category has both text and spatial attributes, with a fixed annual update frequency. Vector indexes must support batch loading of multi-source heterogeneous data, and adapt to the annual full-update rhythm. The text volume of daily documents varies widely, which affects the rationality of the chunking strategy. The chunk length and context integrity must be balanced. Multi-source data comes from scattered sources, so a unified data format is required to avoid index exceptions caused by missing fields. Planning data has high accuracy requirements, and index recall results must strictly correspond to the original data to avoid matching deviations.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average text density of single documents in this category, balancing context integrity and chunk granularity |
| Overlap Length | 100–200 characters | Prevents chunk boundaries from truncating key spatial indicators or constraint clauses, ensuring semantic coherence |
maxToken | 8192 token | Adapts to the input length limit of mainstream open-source embedding models, matching the chunk semantic length of planning documents |
| Similarity Threshold | 0.75–0.85 | Filters low-match irrelevant planning content, avoiding redundant retrieval results |
| Single Search Upper Limit | Top 10 entries | Controls the number of retrieval results in internal office scenarios, adapting to employees' needs for quick reference |
defaultToken | 512 token | Used for context padding of default chunks, adapting to short-text public opinion summary data |
Three Common Mistakes
- Default indexes generated from manually inserted planning documents disappear after several hours. The cause is that manually inserted documents are not bound to a persistent index set. The system’s default temporary index cache period expires and the indexes are automatically cleaned up.
- After connecting qwen3-embedding-8b, the file status always shows "indexing". The cause is that the embedding model’s call waiting time was not adjusted in FastGPT v4.17.0. This model has a long inference time, exceeding the system’s default threshold and causing the task to hang.
- Retrieval results show mismatched spatial planning content. The cause is that the chunk length was set too small, truncating key paragraphs containing spatial coordinates, resulting in vector embeddings losing spatial semantic associations.
How to Confirm the Configuration Is Correct
- Upload a typical planning implementation evaluation report, check whether the chunked text fragments completely retain spatial coordinates and constraint indicators.
- Check the index list corresponding to manually inserted documents after 12 hours, confirm that the indexes have not been automatically cleaned up.
- Enter a query containing a specific spatial scope, call the retrieval interface to verify that the number and matching degree of recall results meet the preset threshold.
- Check the system logs, confirm that the
maxTokenvalue in the return parameters of the embedding model call matches the configured value, and there are no call timeout errors.
The values provided here 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.