What this category of data looks like
Data for investment research at large state-owned banks comes mainly from regular research reports produced by in-house macro research teams, public regulatory documents released by the central bank and banking and insurance regulatory authorities, operating data disclosed by peer institutions, and public indicators from macroeconomic databases. Update frequency varies widely by data type: regulatory documents are updated in real time as released, industry research reports are updated weekly or monthly, and operating data is updated quarterly. Most documents combine structured tables and paragraph text. Fields include publishing entity, release date, and core indicator values. Units include basis points, hundreds of millions of yuan, percentages, and more.
What constraints these characteristics impose on context and token workflows
Multiple heterogeneous data sources and differentiated update cycles cause large fluctuations in the length of recalled context content, which easily exceeds the token limit for a single call. A high proportion of structured documents means repeated headers and fixed fields consume additional token quota. Individual investment research documents are lengthy. After splitting, cross-paragraph context connections must be preserved, otherwise core logic will be lost. Time-sensitive regulatory data requires prioritizing recall of the latest content, which further compresses available token space.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Matches the context length requirements of split documents for large state-owned bank investment research, and avoids exceeding the large model's token limit |
chunkSize | 1500–2000 characters | Adapts to the paragraph structure of individual research reports, reduces context loss across segments, and controls token consumption per segment |
similarityTopK | Top 8–10 results | Balances the relevance of recalled content and token consumption, and avoids excessive low-relevance documents occupying quota |
rerankTopN | Top 3–5 results | Filters highly relevant content, reduces total context token count, and retains core investment research logic |
tokenLimitPerCall | 16000 token | Reserves sufficient space to handle the combined token consumption of user questions and recalled context |
PARSE_CHUNK_OVERLAP | 200 characters | Retains overlapping content between segments, and avoids losing critical cross-paragraph associated information after splitting |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are affected by material format, 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 misconfigurations
- Symptom: Token consumption after a call exceeds the preset value, and logs show abnormal context splicing length. Cause: The segment overlap parameter was not adjusted for repeated fields in structured documents, causing redundant content to be counted in the context multiple times.
- Symptom: Initial token generation time is excessively long, and timing metrics for online and offline models are inconsistent. Cause: The
tokenLimitPerCallparameter was not configured, causing the large model to process excess context beyond the quota and extend initial token generation time. Statistical logic for online and offline models was not unified either. - Symptom: Calls return an error prompt of "token overflow". Cause: The
chunkSizeandmaxContextparameters were not adjusted for the long document characteristics of large state-owned bank research reports, causing the total token count of recalled context to exceed the large model's supported limit.
How to confirm correct configuration
- Upload a typical large state-owned bank investment research report, review the parsed segment results, confirm that segment lengths match preset values and that sufficient overlapping content exists between segments.
- Launch a simulated investment research query, check the number and ranking of recalled context content, confirm that the number of recalled results and reranked results match the configured parameters.
- Check token consumption statistics in call logs, confirm that the total token count for a single call does not exceed the preset
tokenLimitPerCallvalue. - Compare timing metric interfaces for online and offline models, confirm that statistical dimensions have been uniformly aligned.
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.