What this category's data looks like
Joint-stock bank research data sources include internal industry research reports, regulatory agency public documents, real-time market data for Shanghai-Shenzhen and Hong Kong stocks, and peer business dynamic reports. Update frequencies vary. Real-time market data refreshes every second. Industry research reports update weekly or on special event triggers. Regulatory documents update irregularly alongside policy releases. Three document structure types exist. Structured market data includes standardized fields such as stock code, price, and trading volume. Semi-structured research reports include modules for abstracts, core viewpoints, and risk warnings. Unstructured regulatory documents consist primarily of clause-based long text.
What constraints these characteristics impose on context and token processing
Structured standardized formats require accurate distinction between field names, numerical values, and units during token counting. This prevents duplicate counting or missed valid information. Single semi-structured research reports can reach tens of thousands of characters. Their per-file token volume far exceeds general knowledge base scenarios. Pre-splitting is required to fit context windows. High-frequency updates of real-time market data require context recall to prioritize static research reports over dynamic market data. This prevents invalid dynamic data from consuming excessive token budgets. The dense clause structure of long regulatory documents requires preserving contextual relevance of core regulatory clauses during preprocessing. This avoids logical breaks after splitting.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–16000 token | Matches total token volume of a single core joint-stock bank research report plus multiple associated market data sets. Prevents truncation of research conclusions and risk warnings |
chunkSize | 1000–1500 characters | Preserves structured fields and contextual relevance when splitting long research reports. Prevents loss of critical information such as industry ratings and financial data |
topK | Top 6–8 entries | Aligns with token budget for multi-source data recall in research scenarios. Balances coverage of core information including industry dynamics, individual stock market data, and research analysis |
rerankTopN | Top 3–5 entries | Reduces token usage from redundant recall content. Focuses on highly relevant core research reports and regulatory interpretations |
UPLOAD_FILE_MAX_SIZE | 500 MB | Fits scenarios for batch uploading research reports and regulatory documents. Prevents token calculation timeouts caused by overly large single files |
tokenLimitPerRequest | 4000 token | Limits token cap per single request. Fits video memory and computing resource constraints for offline deployment servers |
The parameter values provided on this page are common 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 mistakes
- An error
get tiktoken dial tcpappears after deploying offline servers. The cause is failure to configure local token calculation dependencies, or failure to cover official source requests with proxy settings. - Setting
maxContextto 20000 token results in a model deployed via vllm only supporting 6656 token. The cause is a mismatch between the model's native context window limit and video memory utilization settings. Insufficient tokens are reserved for system prompts and context concatenation. - A call failure occurs when configuring custom token parameters to call external interfaces in a workflow. The cause is failure to correctly bind variable scopes. This prevents token parameters from being passed to target nodes.
How to confirm correct configuration
- Upload a single 12000-character industry research report and three real-time market data sets. Check context splicing logs returned by the system to confirm no content truncation occurs.
- Trigger an offline deployment token calculation task. Check logs for no
get tiktokenrelated errors to confirm local dependency configuration is correct. - Add a custom token variable node in a workflow. Call an external interface then check return results to confirm parameter transmission is correct.
- Adjust
chunkSizeto 1200 characters. Split long documents then check that recalled context fragments do not lose core financial fields and industry rating information.
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.