What the data for this category looks like
This category's data primarily comes from real estate registration information systems, cadastral survey databases, housing transaction online signing systems, tax systems, and electronic certificate repositories. The update frequency is daily. Documents processed daily include registration applications, ownership certificates, survey results, tax payment vouchers, real property right certificates, and real estate registers. Data fields cover real estate location, right type, right holder information, ownership restriction status, and more. Most individual documents combine structured and semi-structured content, with some electronic certificates being fixed-format scanned documents or structured files.
What constraints these characteristics impose on vector models and indexing
Full daily-updated business data requires indexes to support incremental updates, avoiding resource consumption and delays caused by full reconstruction. Data accessed across multiple systems has inconsistent field naming, so unified field mapping must be completed before index construction. Document types include structured forms, scanned certificate documents, and semi-structured register content, so vector indexing must support both text and images. The matching accuracy of core business fields such as ownership restriction status and survey area directly affects subsequent verification links, so recall and matching logic must be optimized for these specific fields.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the information density per chunk of real estate registration documents, avoiding scattered or overly long content within a single chunk |
| Overlap Length | 100–200 characters | Preserves core ownership information across chunks, preventing key content from being split |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Meets the accuracy requirements for matching core business fields such as ownership certificates and survey data |
| Single Search Limit | Top 10 results | Balances recall scope and verification efficiency, reducing redundant results |
defaultToken | 16384 | Carries long document context, meeting indexing requirements for long texts such as real estate registers |
| Reranking | Enabled | Sorts recall results by business relevance, prioritizing core ownership information |
Three common mistakes
- When deploying FastGPT v4.17.0 locally, no image indexing model option appears after creating a knowledge base and uploading documents. Cause: The corresponding feature switch is not enabled, or the deployment package does not include the model dependencies required for image indexing.
- Connection errors occur when connecting an Embedding model via OneAPI. Cause: The
defaultTokenparameter is not correctly configured to adapt to interface restrictions, or the interface key has insufficient permissions. - Core field matching errors appear after incremental index updates. Cause: Unified mapping is not performed for cross-system fields, resulting in a mismatch between the vector index and actual business fields.
How to confirm the configuration is correct
- Upload a test document containing scanned certificate documents, confirm that image content can be retrieved after the indexing task is completed.
- Enter ownership-related query terms, check whether the sorting and business relevance of recall results meet expectations.
- View index logs, confirm that incremental update tasks are automatically triggered at the daily frequency with no error records.
- Verify the
defaultTokenparameter configuration, confirm that no context truncation prompts appear after uploading long documents.
The values given 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.