What the data for this category looks like
Data sources for integrated services investment research knowledge bases cover multiple channels including brokerage research reports, industry databases, policy and regulatory documents, and periodic reports of listed companies. Update cycles vary significantly by data source type: research reports are updated in real time as published, policy documents are released alongside regulatory milestones, and announcement data aligns with corporate disclosure cycles. Document structures include long-form text, structured financial tables, and standardized rating fields. Core fields include publishing institution, release time, core viewpoints, and data indicators. Indicator units include standard financial statistical units such as hundred million yuan and ten thousand yuan.
What constraints these characteristics impose on the context and token workflow
Multi-source, heterogeneous document structures require context recall to balance semantic associations between structured and unstructured content. This avoids breaking the binding relationship between financial indicators and corresponding analysis due to segment splitting. Long-form text documents occupy large amounts of token quota, so the total length of context loaded in a single conversation round must be limited to prevent exceeding the model's native window limit. Frequently updated data sources require context recall to prioritize matching the latest published content, so a time filter parameter must be added to the recall logic. Uncompressed multi-field structured data will increase token consumption unnecessarily, so segmentation and recall strategies need targeted adjustments.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Most integrated services investment research documents are long-form text; this range balances semantic completeness and token consumption |
maxContext | 12000–16000 tokens | Adapts to the length of single investment research documents, ensuring core analysis content can be fully loaded into context |
similarityThreshold | 0.75–0.85 | Filters low-relevance non-professional content to avoid invalid token usage |
recallCount | Top 8–12 entries | Balances information density and token usage across multi-source data, covering core reference content |
tokenBudgetPerMessage | 2000–3000 tokens | Reserves sufficient tokens for generating investment research analysis content, preventing single-round conversation truncation |
contextWindowSize | Follow native model configuration | Matches the native context window limit of the large language model in use, preventing exceeding the model's carrying capacity |
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. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- The symptom is that after calling the search function in a conversation, the context does not associate with historical question content, causing search results to be irrelevant to the current session. The cause is that context recall binding is not enabled, or the recall logic does not carry the historical session's query field.
- The symptom is that after inputting a long investment research document, the system prompts a token limit exceeded error, returning the
413 Request Entity Too Largestatus code. The cause is that reasonablechunkSizeormaxContextparameters are not set, causing single-segment or total context token consumption to exceed the model's limit. - The symptom is that token consumption statistics do not match actual content, with excessive consumption occurring. The cause is that token compression configuration for structured data is not enabled, causing table, field label, and other content to be counted for tokens repeatedly.
How to confirm configurations are set correctly
- Upload a typical investment research document, check the parsed segment length to confirm it matches the
chunkSizesetting. - Initiate an investment research question that includes historical context, check if the recalled knowledge base entries include historically relevant content to confirm context binding is active.
- Test the token consumption of a single round of conversation, check the token statistics returned by the system to confirm it does not exceed the
tokenBudgetPerMessagesetting. - Adjust the
similarityThresholdparameter, test the number of recalled entries under different thresholds to confirm results meet expectations.
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.