What the data for this category looks like
The data for this category mainly comes from the 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, and public opinion summaries. Most documents have structures that include spatial layout descriptions, constraint indicator explanations, implementation progress records, and multi-party opinion texts. The update frequency is once per year.
What constraints these characteristics place on the vector models and indexes module
The annual update frequency of this category means vector indexes do not need frequent incremental updates, and batch rebuilding can be carried out to match the annual planning cycle. The multi-source and heterogeneous document types, including short-text public opinions and long-document planning reports, require a chunking strategy that balances the semantic integrity of short texts and the logical coherence of long documents. The official nature of the data sources requires that index recall results must accurately match the original policy and regulatory texts, to avoid semantic deviations affecting planning approval.
How to configure settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Size | 800–1200 characters | Adapts to the paragraph length of planning reports and evaluation reports, balancing semantic integrity and index density |
| Overlap Length | 100–150 characters | Prevents loss of contextual association after chunking long paragraphs, ensuring continuous semantics of policy provisions |
maxToken | 4096 token | Matches the context window limit of mainstream large models, to carry complete policy basis texts |
defaultToken | Calibrated based on actual measurements | Adapts to the average semantic complexity of documents in this category, balancing recall accuracy and retrieval speed |
| Similarity Threshold | 0.75–0.85 | Meets the rigor requirements for policy basis, filtering low-correlation non-official texts |
| Single Recall Limit | Top 8 entries | Controls the amount of retrieved information, adapting to the multi-basis reference needs of planning compilation |
Three common misconfigurations
- An error of the format
{"error":{"code":"Invalidoccurs during testing. The cause is that when accessing a multimodal Embedding model, the input format of the text and spatial association data of documents in this category is not correctly configured, causing the model to fail to recognize non-standard input content. - After rebuilding the index for the knowledge base, some policy provisions in planning reports are not correctly recalled. The cause is that a reasonable overlap length is not set during chunking, leading to truncation of the context of long paragraphs and loss of associated information for key policy provisions.
- After upgrading to a new version, old vector database data cannot be directly imported into the new index. The cause is that the vector dimension definitions of the old and new versions are inconsistent, and no dimension alignment processing is performed on the old data.
How to confirm the configuration is correct
- Upload a single typical planning report, check whether the chunked text fragments retain complete policy provisions and logical structure.
- Input policy-related test questions, verify whether the number and relevance of recall results meet the preset configuration requirements.
- Perform a vector database migration operation, confirm that old data can be normally retrieved after being imported into the new index, with no error messages.
- Call the API interface to initiate a retrieval request, confirm that the format and fields of the returned results meet expectations.
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.