What the data for this category looks like
Multi-financial research report data sources include public securities research report databases, non-bank financial research report sections of third-party financial data service platforms, and internal investment research archived documents of institutions. Update rhythm adjusts according to the research report release pace of the publishing entity, with no fixed cycle. Documents typically include structured chapters, embedded tables, and professional term paragraphs. Some content exists as PDF scan files, with redundant information such as institutional logos and page numbers in headers and footers. Fields include product type, report rating, core target code, outstanding scale, and others. Amount fields are marked with currency units. Time fields use standard date formats.
What constraints do these characteristics impose on the document parsing and chunking link?
Data sources include editable text and content converted from scanned documents. Parsing must support both native text extraction and OCR recognition to avoid missing content. A large number of embedded structured tables and professional terms are present. Chunking must retain contextual association of data within tables to avoid semantic fragmentation after splitting. Redundant information such as headers, footers and page numbers exists. The system must automatically identify and remove this content to prevent irrelevant material from mixing into chunk units. Research report release rhythm is not fixed. Parsing and chunking must adapt to low-latency requirements for batch processing to support real-time retrieval needs.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Multi-financial research reports contain a large number of professional long sentences and terms. This range retains semantic integrity and avoids contextual fragmentation caused by too short chunks |
chunk_overlap | 100–150 characters | Ensures semantic connection between adjacent chunks, supporting cross-chunk retrieval association and context completion |
PARSE_OCR_ENABLE | Enabled | Multi-financial research reports often include charts and tables in scan format. OCR is required to extract complete text content |
parse_remove_header_footer | Enabled | Research report headers and footers contain redundant information such as institutional logos and page numbers. Automatic removal improves the purity of chunked content |
UPLOAD_FILE_MAX_SIZE | 500 MB | A single multi-financial research report collection may include multiple documents. This setting adapts to batch upload file size limits |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Large research report collections take a long time to parse. This duration avoids timeout interruptions during the parsing process |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing the settings.
Three common configuration errors
- Phenomenon: When calling the API to obtain chunk index content in FastGPT 4.8.10, the returned fields are missing or the format does not meet expected requirements. Cause: Chunk index generation configuration is not enabled, or API interface parameters of the corresponding version are not used.
- Phenomenon: A 413 Request Entity Too Large error is triggered when parsing large research report collections. Cause: The UPLOADFILEMAX_SIZE parameter is not adjusted to a value suitable for the business scenario, exceeding the platform's default limit.
- Phenomenon: When parsing research reports in scan format, the OCR recognition results contain a large number of garbled characters or professional term recognition errors. Cause: The PARSEOCRENABLE parameter is not enabled, or the OCR recognition model adapted to financial professional terms is not configured.
How to confirm the configuration is correctly set
- Upload a typical multi-financial research report document, review the parsed text content, and confirm that redundant information has been automatically removed.
- Review the chunk list, confirm that the length of each chunk matches the preset range, and adjacent chunks contain reasonable overlapping content.
- Call the parsing API, verify that the returned result includes chunk index fields, and the format meets business requirements.
- Upload a research report collection within the maximum file range of the target business scenario, confirm that no abnormal error is triggered for the upload request.
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.