What professional services investment research data looks like
The data for professional services investment research mainly comes from public industry research reports, regulatory disclosure documents, periodic announcements of listed companies, institutional research meeting records, and specialized domain databases. Update cycles cover real-time regulatory documents, weekly industry tracking reports, and monthly and quarterly earnings report updates. Most documents feature hierarchical chapters, structured data tables, and citation annotation blocks. Fields include report numbers, issuing institutions, release dates, and core business indicators. Indicator units include percentages, multiples, currency units, and similar metrics.
How these characteristics create constraints for context and token handling
Heterogeneous multi-source data from professional services investment research causes format conflicts during context splicing, which adds extra token consumption. Frequently updated real-time regulatory and earnings report data requires context recall to match the update rhythm. Failing to do so will introduce outdated, invalid information. Hierarchical long documents and structured data tables quickly consume single-round token quotas, limiting the number of retrievable valid content items. Multi-dimensional business indicator fields require context association to match field priorities. If the token truncation rule is not properly adapted, information critical to core decision-making will be lost.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContextTokens | 8000–16000 tokens | Adapts to the multi-source long text splicing needs of professional services investment research, covering core fragments of multiple research reports and earnings reports |
recallTopK | 3–6 items | Investment research data has a high degree of structuring. A small number of precise recalls can cover the information required for decision-making, avoiding excessive token consumption |
chunkSize | 1000–1500 characters | Investment research documents contain a large number of structured tables and citation annotations. Segmentation must retain context association to avoid truncating core data |
chunkOverlap | 100–200 characters | Retains context association between segments, avoiding rigid truncation of long tables and cross-page data |
contextUpdateMode | Incremental update | Adapts to the high-frequency update characteristics of investment research data, only adding valid information within the session, avoiding repeated token consumption |
tokenTruncationRule | Core indicators first | Prioritize retaining core investment research fields such as revenue growth rate and PE ratio, avoiding loss of key decision-making information due to token exhaustion |
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 individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Errors
- Phenomenon: When orchestrating multiple AI large model dialogs in a workflow, in-session token consumption exceeds expected limits, or context cannot be shared across models. Cause: Independent session context isolation parameters are not configured, leading multiple model calls to share the same token quota and context window.
- Phenomenon: Logs display errors of the type
Reached the max retries p, accompanied by token limit exceeded prompts. Cause: Segmentation and context token limits are not adjusted for investment research long documents, causing single-call tokens to exceed model limits and trigger retry failures. - Phenomenon: Retrieved context does not include multi-round dialog history information, or historical information is incorrectly truncated. Cause: An incremental update context update strategy is not configured, only retaining context for a single round of the session, leading to loss of associated information from multi-round investment research dialogs.
How to Verify Correct Configuration
- Enter the knowledge base test page, upload 1-2 typical investment research documents, launch a test session with multiple rounds of follow-up questions, and verify that returned results include associated information from historical dialogs.
- View the session token consumption log, confirm that single-session token usage falls within the configured
maxContextTokensrange. - Trigger a multi-model orchestrated workflow, verify that the token quota for each model call is independent, with no token overflow across calls.
- Adjust the segmentation length parameter, upload an investment research document containing long tables, and confirm that segmentation results retain the complete structure and associated context of the tables.
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.