What the Data Looks Like for This Use Case
Data sources for large state-owned bank research reports include internal reports produced by the bank’s macroeconomic and financial market research departments, public industry research reports from cooperating third-party institutions, and public financial data attachments released by central banks and regulatory agencies. Update cadence falls into two categories: internal reports are released monthly, quarterly, and for ad-hoc special topics; externally collaborated reports are synced to the publishing schedule of the cooperating institutions. Most documents are in PDF format, with fixed header fields: report number, publishing department, release date, and applicable customer group scope. Core data indicators include units such as 100 million yuan and percentage. Document lengths range from a few pages to dozens of pages. Some historical documents are scanned copies.
How These Characteristics Create Constraints for Document Parsing and Chunking
The characteristics of large state-owned bank research reports create multiple constraints for document parsing and chunking. First, document lengths vary widely, and many reports contain embedded tables and charts. Splitting documents by a fixed character count can break table structures or disrupt data relevance. Second, research reports include fixed metadata fields and core indicators with attached units. Parsing must preserve the binding relationship between fields and units, to avoid separating indicators from their units after chunking. Third, some historical documents are scanned copies. The workflow must support high-precision OCR recognition to ensure accurate text extraction. Fourth, ad-hoc special topic research reports have sudden upload requirements. The parsing workflow needs adaptable timeout configurations to prevent batch task blocking.
How to Set the Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
parse_mode | Auto-detect (text + OCR) | Large state-owned bank research reports include both text-based PDFs and scanned documents. Auto-detect mode supports both formats |
chunk_size | 800–1200 characters | Research reports contain long paragraphs and tables. This range balances context integrity and chunk granularity, avoiding chunks that are too long or too fragmented |
chunk_overlap | 100–150 characters | Professional terminology and data indicators in research reports span paragraph boundaries. Overlap intervals preserve context coherence |
UPLOAD_FILE_MAX_SIZE | 200 MB | Individual large state-owned bank research reports can be dozens of pages long. 200 MB covers the size of most single files |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing long documents or scanned documents via OCR takes significant time. 600 seconds prevents premature timeout |
extract_table | Enabled | Research reports contain large amounts of structured table data. Enabling this setting preserves table structures and improves subsequent retrieval accuracy |
The parameter values provided on this page are common recommended 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 against your own samples before finalizing settings.
Three Common Configuration Errors
- Symptom: When uploading a single research report PDF larger than 200 MB, an "offset out of range" error appears at 90% of the upload progress. Cause: The
UPLOAD_FILE_MAX_SIZEparameter is not adjusted to a value suitable for large single files. The default parameter limits the maximum allowed file size. - Symptom: Research reports uploaded via the create file collection API have different chunking results than the same reports uploaded directly through the platform. Cause: The
parse_modeandchunk_sizeparameters are not unified. The default parsing configurations for API calls and platform uploads differ. - Symptom: When uploading historical research reports in scanned copy format, the parsing result contains large amounts of garbled text or missing fields. Cause: OCR parsing mode is not enabled, or the
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted to match the recognition time required for scanned documents.
How to Verify Correct Configuration
- Upload a typical large state-owned bank research report that includes a text-based PDF, a scanned PDF, and embedded tables. View the parsed chunk list and verify that metadata fields such as report number and release date are preserved.
- Call the file parsing API, pass in the same research report, and compare the chunking results returned by the API with those from the platform upload. Confirm that the configuration parameters are consistent across both methods.
- Upload a research report larger than 100 MB and check that the upload progress completes normally, with no timeout or offset errors.
- For research reports with embedded tables, review the chunking results to confirm that complete table structures and associated data are retained.
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.