What this type of data looks like
Securities investment research data comes from several sources. These include listed companies' periodic reports, temporary announcements, brokerage research reports, industry regulatory documents, and real-time market data. Update rhythms differ widely. Real-time market data updates every second. Announcement data goes live immediately upon release. Research report data updates weekly or monthly. Document structure has a high level of standardization. Research reports have fixed fields: title, core logic, investment rating, and target price. Announcements include issuing entity, document number, and body paragraphs. Market data includes standardized fields such as security code, transaction price, and trading volume. Units used include professional financial statistical units. These are currency, share count, percentage, and similar units.
Constraints on context and token handling
Securities investment research data often has long individual document lengths. The core content of a single research report can reach tens of thousands of characters. This quickly consumes context token quotas. Field differences across multiple data sources require precise matching. Retrieval must match dimensions such as security code and release time. This prevents irrelevant content from being included in the context. Real-time market data updates very frequently. The context window must support dynamic refreshing. Otherwise, data lag will occur. This reduces analysis accuracy. Documents dense with professional terminology may have semantic breaks if chunked improperly. This reduces the availability of the context. Investment research queries often require associating multiple documents. The reasonable allocation of context tokens directly affects response completeness and professionalism.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContextTokens | 8000–16000 token | Securities research reports have high token consumption for core content. This range covers key information from at least one complete research report, while adapting to native context limits of mainstream large language models |
chunkSize | 1500–2000 characters | Securities documents contain many long paragraphs and professional terms. Too small a chunk size breaks semantic coherence. Too large a chunk size increases per-block token usage and reduces retrieval accuracy |
recallCount | Top 3–5 entries | Securities investment research requires precise matching of data for target securities. Too many retrieved entries overload context tokens. Too few fail to cover necessary analysis basis |
imageTokenLimit | Within 1024×1024 pixels | High-resolution securities market charts and research report images use large amounts of tokens. Exceeding the limit triggers upload errors |
responseMaxTokens | 2000–4000 | Investment research responses need detailed logical analysis and data support. A low default upper limit causes premature truncation. This fails to meet professional analysis needs |
autoChunkEnable | Enabled | Automatically adapts to the long paragraph structure of securities documents. Reduces errors from manual chunking. Improves semantic completeness of the context |
The parameter values provided on this page are general recommendations used as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Each situation requires separate analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Phenomenon: Some uploaded images fail, while others upload successfully. Cause:
imageTokenLimitormaxUploadFileSizeis not configured. Some images have resolution or file size that exceeds system limits. Excessive token consumption triggers errors. - Phenomenon:
responseMaxTokensis set above 3000, but responses to some queries are truncated to 200 tokens. Cause: The token share used by context retrieval is too high. Insufficient available tokens remain to support the preset response upper limit. Or there is an implicit per-round token limit on the model. - Phenomenon: Semantic breaks appear in chunked context, leading to disjointed analysis logic. Cause: Chunking unit is incorrectly set to characters instead of tokens. Or chunk size exceeds the reasonable range for single-block model processing. This forces semantic splitting.
How to confirm proper configuration
- Upload a single brokerage research report of 5000 characters. Check the number of parsed chunks and per-block character count. Confirm alignment with the
chunkSizeconfiguration. - Upload securities market charts of varying resolutions. Verify upload success rate. Confirm the
imageTokenLimitconfiguration is active. - Submit a query containing content from multiple research reports for the same target security. Check the number of context retrieval entries. Confirm alignment with the
recallCountconfiguration. - Submit a complex investment research analysis request. Verify response length matches the
responseMaxTokenssetting. Confirm no premature truncation occurs.
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.