What This Category’s Data Looks Like
Investment platform research data primarily comes from brokerage research reports, public financial reports of listed companies, public industry databases, and real-time market data application programming interfaces. Data update rhythms fall into three categories: real-time market data and newly published research reports use high-frequency updates; quarterly and annual financial reports use fixed-schedule updates; industry database content uses monthly or quarterly updates.
Document types include structured Excel/CSV financial report tables, semi-structured research report chapter text (with embedded tables), and unstructured industry analysis plain text. Fields include publishing institution, publish time, rating, core financial indicators and their corresponding units, and some data includes standardized industry classification tags.
Constraints on Document Parsing and Chunking
The multi-type and strongly correlated nature of investment research data creates multiple constraints for the parsing and chunking workflow. Structured financial report and position holding tables must retain business relationships between cells, to avoid losing data correspondence after splitting. Tables embedded in long research report text must be bound to their surrounding chapters, to prevent splitting that breaks complete logical flow. High-frequency updated market and research report data requires parsing to support batch incremental processing, to adapt to real-time investment research update needs. Financial indicators with units must be bound to their corresponding values, to avoid issues where units and data become separated during retrieval. The chapter structure of long research reports must be accurately identified, to avoid chunking that damages the integrity of core investment logic.
How to Set the Configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the chapter logic of long investment research texts, avoids splitting core investment viewpoints |
chunk_overlap | 100–150 characters | Connects context between adjacent chunks, prevents key logic from being broken by chunk gaps |
parse_excel_table_mode | Retain cell association + embed context | Excel files used by investment platforms mostly contain financial reports and position data, so the correspondence between data must be preserved |
max_parse_file_size | 500 MB | Adapts to scenarios where a single file contains multiple combined financial reports, meets batch import requirements |
enable_table_recall_priority | Enabled | Structured data has higher priority in investment research retrieval, aligns with user retrieval habits |
parse_timeout | 300 seconds | Adapts to parsing time for large financial report files, avoids task interruption due to timeout mid-process |
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 on your own samples before finalizing settings.
Three Common Mistakes
- Issue: After importing an Excel table into the knowledge base, search results do not retain the correspondence between cells. Cause: The cell association parsing configuration of
parse_excel_table_modeis not enabled, and only plain text content is extracted. - Issue: Tables display normally in knowledge base previews, but generative replies do not output table content. Cause: Structured metadata of tables is not retained during chunking, resulting in loss of table format identifiers after parsing.
- Issue: Documents chunked using a local vector model show retrieval anomalies when uploaded to a server using another vector model. Cause: Chunk granularity and vector encoding logic vary between different vector models, leading to deviations in semantic matching.
How to Verify Proper Configuration
- Upload a single Excel financial report file, check whether the parsed chunks retain the correspondence between cells, and adjust the
parse_excel_table_modeconfiguration based on actual business needs. - Trigger a retrieval request that includes tables, verify whether the generated reply can output the complete table structure, and confirm that chunking parameters do not damage table context.
- Import chunked data generated by different vector models, run retrieval tests, observe whether semantic matching results meet expectations, and adjust chunk granularity parameters based on matching effects.
- Batch upload multiple research report files, check the completion status of parsing tasks, and confirm that the
max_parse_file_sizeandparse_timeoutconfigurations adapt to batch import requirements.
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.