What this category of data looks like
Commercial property financial report data primarily comes from owned property operation ledgers, annual audit reports, lease contract archives, and public energy consumption reports. Data update cycles are divided into monthly, quarterly, and annual: operation ledgers and energy consumption reports are updated monthly, lease contracts are adjusted dynamically upon renewal, and annual audit reports are released per fiscal year. Document formats include multi-column structured Excel, mixed-text-image PDF, and detailed CSV. Structures cover fields such as basic property information, rent collection details, energy consumption data, and tenant performance records. Field units include square meters, yuan, kilowatt-hours, and more. Some fields are associated with tenant performance status and property area divisions.
What constraints these characteristics impose on the "document parsing and chunking" step
The complex column structures of multi-column structured Excel and detailed CSV require the parsing process to accurately identify the correspondence between column names and data rows, to avoid field loss caused by cross-column misalignment. Nested tables and charts in mixed-text-image PDF require distinguishing between text paragraphs and structured data blocks, to avoid mixing chart annotations with main text during chunking. High-frequency operation data updated monthly requires retaining contextual associations of monthly data during chunking, to avoid splitting identical types of data across months. Strongly associated fields between tenant performance records and rent details require retaining the correspondence between fields during chunking, to avoid losing association logic after splitting. Excel documents with multiple sheets need to be split by business module, and should not be fully merged by sheet by default.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_EXCEL_ENABLE | Enabled | Commercial property financial reports often contain structured Excel ledgers, so Excel parsing capability must be enabled |
PARSE_CSV_MAX_COLUMNS | 50 | Commercial property operation CSVs often include multiple columns of data such as tenant, rent, and energy consumption. 50 columns can cover conventional ledger fields |
CHUNK_SIZE | 800–1200 characters | Commercial property financial reports include long text analysis paragraphs and structured data. This range balances semantic completeness and chunking granularity |
PARSE_PDF_TABLE_MODE | Accurate table extraction | Commercial property PDF financial reports contain multiple nested tables. The accurate mode preserves table structure and field correspondence |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Parsing large annual audit reports requires a longer duration. 120 seconds covers the parsing needs of conventional large files |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Commercial property annual audit reports often include a large number of attachments. This upper limit meets conventional import requirements |
The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing the settings.
Three common mistakes
- Phenomenon: Parsing fails after uploading an Excel file, and the interface displays "unsupported file format". Cause: The
PARSE_EXCEL_ENABLEconfiguration item is not enabled, and the system disables Excel parsing capability by default. - Phenomenon: CSV data columns are misaligned in the parsed chunking results, and some fields are empty. Cause: The
PARSE_CSV_MAX_COLUMNSparameter is not adjusted. The default value is too low to cover the multi-column structure of commercial property financial report CSVs. - Phenomenon: Connection failure occurs after deploying the parsing service locally, and the interface displays "parsing service not responding". Cause: The port mapping of the local parsing service is not configured correctly, causing FastGPT to fail to access the deployed parsing container.
How to confirm the configuration is correct
- Upload a commercial property operation CSV file, check if the parsed fields match the original document column names, and adjust
PARSE_CSV_MAX_COLUMNSto a value that covers all columns. - Upload a PDF financial report with nested tables, check if the table structure in the parsed results is complete, and adjust
PARSE_PDF_TABLE_MODEto a mode suitable for the current document format. - Upload a large annual audit report, wait for the parsing to complete and check for timeout errors, and adjust
PARSE_FILE_TIMEOUT_SECONDSto a value that meets the parsing duration requirements. - Import an Excel ledger with multiple sheets, check if the chunking results are split by business module, and adjust the chunking rules to a granularity that retains field association relationships.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.