What the data for this category looks like
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. Commonly processed documents include registration applications, ownership certificates, survey results, tax payment vouchers, real property right certificates, and real property registers.
There are significant structural differences across document types: registration applications are formatted fill-in text, ownership certificates are mostly structured scanned certificate copies, survey results include vector coordinates and graphic information, and tax payment vouchers are structured receipts. Fields include real estate location, right type, right holder information, survey area, tax amount, and more. There are no unified fixed units, so adaptation is required based on document type.
What constraints these characteristics impose on the document parsing and chunking workflow
The structural differences across document types require adaptable parsing logic. Formatted text must be split by paragraph. Scanned certificate copies must first undergo OCR before extracting structured fields. Vector survey data requires stripping graphic metadata to retain only text attributes.
Daily updated data sources require parsing processes with low latency to prevent backlogs of pending files. Certificate-type documents such as ownership certificates and tax payment vouchers have fixed field positions. Chunking must retain field relevance, avoiding damage to the integrity of key information like right subjects and location addresses.
It is also necessary to distinguish between structured entries in real property registers and formatted text in real property right certificates, to avoid mixing parsing logic across document types.
How to Configure Parameters
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the paragraph length of formatted applications, tax payment vouchers, and other documents in this category, preventing damage to the relevance of key fields after splitting |
chunkSplitter | Split by paragraph + heading | Adapts to documents with clear hierarchical structures such as survey results and real property registers, retaining logical coherence of content |
customPdfParse | Enable OCR mode | Processes scanned PDF documents such as ownership certificates and real property right certificates, extracting text and structured fields from them |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Covers parsing time for files with large amounts of vector data such as survey results, preventing parsing failures due to timeout |
chunkSettingMode | Manual configuration | Adjusts chunking parameters separately for different document types, adapting to the differentiated structures of multiple document types in this category |
Three Common Configuration Errors
- Issue: After a conditional judgment component processes a boolean parameter parsed from an HTTP component, the parameter becomes empty. Cause: No explicit type validation is performed on the original boolean value returned by the HTTP component. FastGPT’s conditional judgment component performs implicit conversion on non-standard boolean values by default, leading to incorrect judgment that the original boolean value is empty.
- Issue: An error "Cannot redefine property: toString at Object.defineProperty" occurs in the production environment. Cause: A custom parsing script incorrectly redefines built-in object prototypes, causing conflicts with the FastGPT runtime environment.
- Issue: The number of chunks after knowledge base document splitting exceeds 3000, resulting in missing results or timeouts during retrieval. Cause: Chunking parameters are not adjusted for the multiple document types in this category. The default chunking rules cause the chunk count to exceed the system default limit, and large files are not split for batch processing.
How to Verify Correct Configuration
A single scanned copy of an ownership certificate may be uploaded. The parsed text content is checked to confirm it includes complete key fields such as right holder information and real estate location address, verifying that the customPdfParse configuration takes effect. A survey result document may be imported. The number of chunks after splitting is checked to confirm it meets expectations, verifying that the chunkSize and chunkSplitter configurations adapt to the document structure. A daily updated batch file import process may be simulated. The parsing task time is checked to confirm it falls within the range set by PARSE_FILE_TIMEOUT_SECONDS, verifying that no timeout errors occur. Boolean-type parameters such as tax payment identifiers may be tested using the conditional judgment component, verifying that the parameters are not incorrectly converted to empty values.
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.