Data characteristics of this category
Data for joint-stock bank investment research knowledge bases comes primarily from internal self-developed reports, regulatory agency public documents, listed company periodic announcements, macroeconomic statistics, and peer research results. Update frequency varies by source. Regulatory documents and listed company announcements update with their disclosure timelines. Internal research reports update weekly or daily. Macroeconomic data updates monthly. Document types include structured financial report tables (CSV, Excel formats), semi-structured graphic-text research reports (PDF, Word formats), and unstructured analysis manuscripts. Fields mostly contain financial professional terms such as non-performing loan ratio, provision coverage ratio, and return on net assets. Units use standard financial industry units including percentage, 100 million yuan, and basis points (BP).
Constraints imposed on document parsing and chunking
Structured financial report files have strong field correlations. Parsing must retain the correspondence between cells and fields to avoid losing data associations after splitting. Frequently updated data sources require parsing processes to support batch and incremental processing, to reduce repeated computing overhead. Scenarios with mixed uploads of multiple document types require parsing engines to support parsing logic for multiple formats, while adapting to the structural features of different documents. Long-text research reports and content dense with professional terms require chunking logic to retain semantic integrity, avoiding splitting professional terms or long sentences into separate segments. Parsing large single files takes significant time, so longer timeout thresholds must be adapted to avoid parsing interruptions.
Configuration settings
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
CHUNK_SIZE | 800–1200 characters | Investment research documents contain many long sentences and professional terms. This range balances semantic integrity and vectorization computing efficiency |
PARSE_EXCEL_TABLE_MODE | Retain cell associations | Joint-stock bank investment research data mostly consists of structured financial report tables. Retaining the correspondence between fields and cells avoids data misalignment |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapt to the upload needs of single large research reports or batch financial report files |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large PDF or Excel files takes a long time. This threshold covers the parsing cycle of most conventional files |
ENABLE_INCREMENTAL_PARSE | Enabled | Investment research data has a high update frequency. Incremental parsing reduces resource consumption from repeatedly processing already uploaded files |
CSV_DELIMITER | Comma (,) | Most CSV files exported by bank investment research departments use standard commas as delimiters. Adapting this configuration improves parsing success rate |
The parameter values provided on this page are common recommended starting points 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 settings.
Three common configuration errors
- Parsing returns empty results with no table data after uploading a CSV file. The cause is incorrect configuration of the
CSV_DELIMITERparameter. Using tab characters or other non-standard delimiters causes the parsing engine to fail to recognize field separation rules. - A 504 timeout error is returned after uploading an Excel file. The cause is failure to adjust the
PARSE_FILE_TIMEOUT_SECONDSparameter. The default threshold cannot cover the parsing time of large financial report files, leading to parsing interruptions. - Vectorization speed is slow after uploading a PDF research report, and the number of results is far lower than expected. The cause is failure to set a reasonable
CHUNK_SIZE. An overly large chunk length increases the computing load per segment, while an overly small chunk size leads to a sharp increase in the number of segments, both of which slow down the vectorization process.
How to verify correct configuration
- Upload a standard CSV-format financial report data file, and check that the parsed field list matches the original file. Confirm that cell association relationships are not damaged.
- Upload a single-page PDF-format industry research report, check the parsing time and chunking results, and adjust
CHUNK_SIZEto a value range suitable for the current document type. - Upload batch Excel financial report files, test the incremental upload function, and confirm that only newly added files trigger the parsing process, while already uploaded files are not processed repeatedly.
- View the chunked document segments, confirm that professional terms are not split into different segments, and that the semantics are coherent and conform to the reading habits of investment research analysis.
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.