What data looks like for this category
Data for this category 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, registration books, and more. Data fields cover right holder information, real estate location, area parameters, right type, restriction status, and others. Some documents are structured forms, while others are unstructured electronic materials or scanned copies.
What constraints these characteristics impose on vector models and indexing
Daily updated data requires indexes to support incremental synchronization, avoiding resource consumption from full rebuilds. Multi-source and heterogeneous document types, including both structured forms and unstructured electronic materials, require adaptation to different chunking rules to retain key ownership information. The need for precise matching of core fields requires vector models to have adequate semantic understanding of professional terminology. Additionally, since data directly relates to real estate right ownership, the accuracy of index recall directly impacts subsequent verification steps, so the false recall rate must be strictly controlled.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the length of structured fields and unstructured paragraphs in real estate registration documents, retaining complete ownership information units |
| Overlap Length | 100–200 characters | Prevents key information from breaking across chunks, ensuring semantic coherence |
| Similarity Threshold | 0.75–0.85 | Distinguishes similar expressions of ownership information, reduces false recall probability, and meets the rigor requirements of professional scenarios |
| Single Search Limit | Top 10 entries | Balances recall efficiency and result coverage, avoiding redundant data interference with ledger summary |
maxToken | 16000 characters | Adapts to the context requirements of long documents such as real property registration books, enabling complete information retrieval for report generation |
defaultToken | Calibrated based on actual measurements | Adapts to token consumption limits in different environments, preventing interruptions during indexing |
Three common mistakes
- Issue: After importing Excel files and multi-dimensional documents from Feishu folders, the index status always shows incomplete, with no parsing logs. Cause: No differentiated chunking rules configured for real estate registration structured forms and unstructured documents, leading to parsing timeouts for large files or chunking abnormalities for small files.
- Issue: When deploying with PG database Docker, index creation fails. The host machine shows normal resource usage of 8c16G without GPU. Cause: The
maxTokenvalue was not adjusted to adapt to low-compute environments, leading to memory overflow during vector generation. - Issue: Accessing the oneapi embedding model results in an error, and the index model cannot be configured separately. Cause: No independent index model interface specified in system settings, and a general conversation model was mistakenly called as the index model, leading to parameter mismatch.
How to confirm configurations are correct
- Upload a single real property right certificate document, check the parsed chunk results, and confirm that core fields such as right holder and location are fully retained.
- Initiate a ledger summary query, verify that the number of recall results matches the preset single search limit, with no redundant data.
- Simulate a daily incremental update scenario, perform an index synchronization operation, and confirm that no errors occur and data updates are completed.
- Call the independent index model interface, verify that the vector generation process works normally, with no parameter mismatch 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.