What this category’s data looks like
Investment platform research data primarily comes from public industry research reports, periodic reports of listed companies, and industry statistical datasets. Update cycles cover real-time industry comments, quarterly earnings announcements, and monthly industry updates. Individual document lengths vary widely, from short comments spanning a few pages to in-depth analysis reports of dozens of pages. Document structures include publishing entity, release time, covered targets, core analysis content, and supporting data tables. Fields include target codes, analysis dimensions, associated data, and more, with units such as amount and scale.
What constraints these characteristics impose on context and token handling
A high share of long individual documents can cause token usage to exceed per-round context token limits during single-document parsing, requiring finer-grained paragraph splitting. Multi-dimensional analysis content and supporting data require context recall to cover multiple types of associated information, which easily leads to total token overlimit. Data sources with different update cycles require dynamic adjustment of context recall scope based on update cycles, to avoid expired data occupying valid token quotas. Structured table data within documents consumes additional tokens when converted to text, so targeted parsing rule optimization is needed to reduce invalid token usage.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Adapts to the total length of split long research reports and multiple recalled contents, avoiding context overflow |
chunkSize | 1000–1500 characters | Balances per-segment token usage and recall accuracy, adapting to segmented parsing of long research reports |
similarityTopN | Top 8–12 results | Covers multi-dimensional associated research reports and earnings data required for investment research analysis, avoiding insufficient recall or token overlimit |
rerankTopN | Top 3–5 results | Filters highly relevant content, reducing total context token consumption |
UPLOAD_FILE_MAX_SIZE | 200 MB | Adapts to the file size of individual in-depth research reports, avoiding upload failures |
maxResponseTokens | 2000–4000 tokens | Matches the long response length required for investment research analysis, avoiding truncation of core conclusions |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing.
Three common issues and mistakes
- Uploading research report images in some formats triggers errors, while small-sized images can usually be uploaded normally. Cause: Converting images to text consumes additional token quotas. Complex image content will exceed the current context's token limit.
- Setting
maxResponseTokensto the preset upper limit, but the response length for some complex investment research questions is far lower than the set value. Cause: The total token usage of associated documents recalled by the context is too high, leaving insufficient quota to support the preset response length, so the system automatically truncates the response. - Unable to locate token-related configuration entries when accessing via WeChat Official Account. Cause: The lightweight deployment mode of WeChat Official Account does not expose the global token configuration entry. Preset quotas must be bound via custom context rules.
How to verify successful configuration
- Upload the longest individual in-depth research report, check the number of parsed segments and total token usage to confirm it does not exceed the preset
maxContextquota. - Initiate a test conversation containing multi-dimensional investment research questions, verify that the response length meets the preset
maxResponseTokensrequirement. - View the context recall log to confirm that the number and relevance of recalled documents match the configured
similarityTopNandrerankTopNparameters. - Upload supporting images of different sizes, check that token consumption after parsing meets expectations and no overlimit errors are triggered.
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.