What data looks like for this category
Data for this category originates primarily from real estate registration information systems, cadastral survey databases, housing transaction online signing systems, tax systems, and electronic certificate repositories. Daily processed documents include registration applications, ownership certificates, surveying and mapping results, tax payment vouchers, real estate ownership certificates, and real estate registers. Data is updated daily. Most documents have fixed fields such as real estate location, owner identity information, surveying and mapping area, and tax amount. Document formats exported from different systems have certain differences.
What constraints these characteristics impose on vector models and indexes
Heterogeneous data from multiple systems requires vector modeling to support varied field formats. The daily update frequency means index construction must support incremental synchronization, and full reconstruction processes must not be used. Non-standardized document structures require retaining business context during chunking, to avoid splitting that breaks core associated information such as ownership sources and surveying data. Data association must cover core links including real estate and surveying comparison and ownership verification. Index recall must accurately match key fields such as real estate location and rights status. Otherwise, it will reduce the accuracy of subsequent document drafting.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the length of core single-paragraph information in ownership certificates and surveying and mapping results in real estate registration documents, avoiding splitting that damages business associations |
| Overlap Length | 100–200 characters | Retains key context such as real estate location and owners across chunks, preventing loss of associated information during recall |
| Similarity Threshold | 0.75–0.85 | Matches the strongly associated nature of real estate fields, controlling false recall rates |
| Single Search Limit | Top 5 entries | Controls the volume of information in the verification link, adapting to the precise matching needs of real estate registration document drafting |
maxToken | 8000–12000 | Covers the vector recall context length of a complete single registration application material, meeting the information needs of document splicing |
defaultToken | Calibrated based on actual measurements | Adapts to the video memory limits of locally deployed models, avoiding exceeding hardware carrying capacity |
Three common mistakes
- An Embedding model cannot be selected in the interface. The cause is that the current version does not include interface adaptation configuration for this model. It must be added via the custom model access process of FastGPT v4.17.0.
- A vector model call failure prompt appears during local deployment. The cause is that the model's access token and interface address are not configured correctly, or there is a port mapping error in the forwarding link.
- The index generated when importing a knowledge base does not match the expected content. The cause is that the chunk length is set too large or too small, damaging the integrity of core business paragraphs such as real estate surveying and mapping data and ownership sources.
How to confirm the configuration is correct
- Check whether the
maxTokenconfiguration falls within the preset range, upload a single real estate registration application to the knowledge base, and verify the completeness of the chunked content. - Enter a test query containing a real estate location, and check whether the number of recall results matches the single search limit configuration.
- Start the local deployment test environment, call the vector model interface, and confirm that the interface returns normally without errors.
- Manually trigger an incremental synchronization task, and check whether the index has updated newly added registration material data.
Note: The configuration values listed above are common starting points. They should be evaluated against the reader's own specific samples and use cases.
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.