Reference only; verify against the target FastGPT release and deployment documentation.
What data for this category looks like
This category's data mainly comes from real estate registration information systems, cadastral survey databases, housing transaction online signing systems, tax systems, and electronic certificate repositories, with a daily update frequency. Daily processed documents include registration applications, ownership certificates, survey results, tax payment vouchers, real property right certificates, and real estate registers, among others. Most documents contain structured fields such as subject of ownership, location, right type, and restriction status, as well as unstructured content like supporting scanned documents and form text. Data has strong correlation, and the accuracy of core fields directly impacts business compliance.
What constraints these characteristics bring to the vector model and indexing stage
Full daily updated data requires the indexing process to adapt to high-frequency incremental updates, avoiding excessive system resource usage from full reindexing. Multi-source heterogeneous data includes structured fields and unstructured form text, so differentiated chunking rules must be set for different content types. The accuracy of core business fields such as subject of ownership, right type, and restriction status directly affects the credibility of Q&A results, so a strong association between fields and vectors must be established during indexing. Real estate registration data involves administrative compliance, so the indexing process must retain data version traceability capabilities to avoid referencing expired or incorrect policy and registration information.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the information density per chunk of real estate registration documents, avoiding including too many irrelevant ownership-related content in a single chunk |
| Overlap Length | 100–200 characters | Ensures semantic coherence between adjacent chunks, covering key information such as cross-chunk right restrictions and location details |
| Similarity Threshold | 0.75–0.85 | Filters low-relevance policy and registration data to ensure the accuracy and compliance of Q&A results |
| Single Search Limit | Top 8 entries | Controls the number of returned results to avoid exceeding context carrying capacity, while covering core business information |
maxToken | 8000 tokens | Adapts to the processing needs of policy documents and long-text registers, aligning with the length characteristics of administrative text |
defaultToken | 200 tokens | Controls the output length of a single recalled content, ensuring readability and complete information for interface display |
Three common misconfigurations
- Symptom: A 60-second timeout error occurs when switching knowledge base indexes. Cause: No incremental indexing strategy is configured for high-frequency daily updated real estate registration data, and full indexing loads excessive historical data, leading to timeout.
- Symptom: A single registration-related file remains in the indexing state indefinitely. Cause: Chunk length is set too large, exceeding the single-task processing threshold of the system, causing the indexing process to get stuck and unable to complete.
- Symptom: A small amount of registration data remains in the training or rebuilding state for more than 24 hours. Cause: Incremental indexing mode is not enabled, and each rebuild trigger performs a full data scan, consuming large amounts of system resources.
How to confirm proper configuration
- A typical real estate registration application is manually uploaded, and the index completion status is checked for updates within a reasonable time frame.
- A Q&A test related to policy and regulatory basis is initiated, and the recalled results are verified to include relevant content from corresponding ownership certificates and registration rules.
- The chunk length parameter is adjusted, and the completeness of recalled content under different settings is compared to confirm alignment with business needs.
- System logs are reviewed to confirm that no frequent timeout errors or task stuck phenomena occur during the indexing process.
The values provided are common starting points and should be evaluated using sample data from the specific deployment.
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.