What the data for this category looks like
The typical service entities for this category are real estate registration centers, natural resources authorities, and collaborative windows. Data sources include real estate registration information systems, cadastral survey databases, housing transaction online signing systems, tax systems, and electronic certificate repositories. The update frequency is daily. Commonly processed documents include registration applications, ownership certificates, survey results, tax payment vouchers, real estate ownership certificates, and registers. Fields include real estate location, right type, area, right holder information, restriction status, and more, with both structured fields and unstructured text.
What constraints do these characteristics impose on the vector models and indexes link
Format differences across multiple data sources require vector models to adapt to mixed structured fields and unstructured text. The daily update frequency requires indexes to support incremental synchronization to ensure data timeliness. Document types range from short text such as certificate numbers to long text such as survey reports, so chunking strategies need to balance context completeness and retrieval accuracy. Real estate registration ownership verification has strict requirements, so vector retrieval must be paired with precise filtering mechanisms to avoid disputes caused by incorrect association of ownership information.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the mixed length of real estate registration documents, balances context completeness for long-text survey reports and short-text certificates. Adjustable via the interface in FastGPT v4.17.0 |
| Overlap Length | 50–100 characters | Ensures cross-block ownership association information is not split, preventing loss of critical associated content during retrieval |
maxToken | 4096 token | Matches the input limit of most mainstream vector models, meets the average length requirements of real estate documents |
| Similarity Threshold | 0.75–0.85 | Meets the high-confidence requirements for real estate ownership verification, avoids incorrect association of information from different ownerships |
| Single Search Limit | Top 10 results | Aligns with the core verification needs of business processing, reduces verification burden from non-essential results |
defaultToken | 1500 token | Covers the typical context length of business process guidance, ensures completeness of guidance information |
Three common errors
- Symptom: The vector model configuration interface shows no available channels for m3e, making it impossible to select the corresponding embedding model. Cause: The deployment channel for m3e was not configured according to official documentation, or the locally deployed m3e service did not expose ports correctly.
- Symptom: The similarity scores returned by retrieval exceed 10000, which is outside the normal range. Cause: Normalization processing for the vector model was not enabled, resulting in unstandardized calibration of similarity calculations.
- Symptom: After uploading real estate registration documents to the knowledge base, the index status shows incomplete, with no retrieval results. Cause: The document format is not compatible with supported system types, or the vector database storage space is insufficient.
How to confirm the configuration is correct
- Upload a standard real estate ownership certificate sample, initiate a query containing the certificate information, and check if the returned results include matching ownership-related content.
- View the vector model's running logs to confirm that the
maxTokenanddefaultTokenconfiguration parameters have been loaded correctly. - Simulate a business process guidance query, and check if the similarity scores of the returned results fall within the preset threshold range.
- Manually trigger an incremental index task, and confirm that the day's updated cadastral data can be normally synchronized to the index library.
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.