What data for this category looks like
Data sources for multi-financial investment research include public regulatory disclosure documents, third-party industry statistical platforms, internal institutional investment research documents, and market transaction records. Update frequency varies by content type. Regulatory policy documents are released irregularly. Industry research reports are updated on a fixed schedule. Internal business documents are adjusted as needed. Document structures cover structured clauses, semi-structured research reports, and unstructured transaction logs. Fields include institutional code, regulatory document number, release date, risk level, and more. Units involve amount, cycle, risk rating level, and more.
Constraints on context and token processing
Long chapters of structured regulatory documents can cause single-document token counts to far exceed conventional thresholds. When splitting documents, retain the contextual association between document numbers and clauses. Otherwise, investment research logic cannot be accurately matched. Multi-module content in semi-structured research reports must be aggregated by theme. This avoids losing the association between core data and risk prompts after splitting. Unstructured transaction logs have no fixed format. Automatic slicing via fixed rules is not possible. Manual annotation of context boundaries is required, which increases token statistics and management costs. Investment research data linked across multiple fields requires simultaneous recall of multiple related fields. This can easily cause the total context token count to exceed model limits.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to context recall requirements for multi-field associated data in multi-financial investment research scenarios, and avoids exceeding single-round token limits of mainstream large models |
chunkSize | 1500–2000 characters | Matches conventional chapter lengths of regulatory documents and research reports, reducing token overflow risk for single-block content |
recallTopK | Top 3–5 entries | Prevents excessive recall of associated fields from causing total token overrun, while covering core investment research reference content |
similarityThreshold | 0.6–0.7 | Adjusts the threshold to filter irrelevant content and improve accuracy, targeting low-matching features of unstructured transaction logs |
tokenStatMode | Count by document chunk | Adapts to multi-module splitting requirements for semi-structured research reports, ensuring contextually associated content is counted uniformly |
overlapSize | 100–150 characters | Retains contextual overlap between adjacent document chunks, avoiding loss of key information such as regulatory document numbers and research report abstracts after splitting |
The parameter values provided on this page are conventional recommendations used to establish a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon:
413 Request Entity Too Largeerror is triggered, or the interface displays the prompt "Context token exceeds limit". Cause: No reasonablechunkSizeandmaxContextparameters are set for long-text regulatory documents, and unsplit ultra-long documents are uploaded directly. - Phenomenon: Core regulatory document numbers or research report abstracts are not included in recall results, and matching accuracy falls below expectations. Cause: The
overlapSizeparameter is not configured, and key contextually associated fields are lost when splitting document chunks. - Phenomenon: Counted token values deviate significantly from actual consumption values, or ultra-long tasks time out. Cause:
tokenStatModeis not used to count by document chunk. Instead, counting is done on the original entire document, without accounting for contextual overlap after splitting.
How to Verify Correct Configuration
- Upload the longest single regulatory document, check the number of automatically split document chunks and token count per chunk. Adjust
chunkSizeandoverlapSizeto align with business expectations. - Initiate an investment research query, check the number of recall results and total token consumption. Compare whether the settings for
recallTopKandmaxContextare appropriately matched. - View the token statistics panel, verify whether the value counted by document chunk matches the consumption value in actual call logs. Confirm that the
tokenStatModeconfiguration takes effect. - Adjust
similarityThresholdand run multiple tests to verify whether the relevance of recall results meets business 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.