What Data for This Category Looks Like
This category’s data comes from public industry research reports, self-regulatory organization disclosure documents, product operation ledgers, and regulatory announcement information. Update cycles vary widely by content type. Research reports are updated per their published cycle. Regulatory documents are updated alongside policy changes. Product operation data is updated each trading day. Individual documents include core viewpoints, data summary tables, and compliance reminder content. Structured fields include report number, issuing institution, effective date, product code, operation data items, and others. Operation data items use universal financial measurement standards, with clear corresponding measurement units.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Dispersed data sources and inconsistent update cycles require multi-turn dialogue to support cross-source context association. Prompts must clearly define call priorities for different data sources to avoid retrieving outdated content. Documents contain structured tables and compliance reminders. Multi-turn dialogue must retain precise mapping of table contexts. Prompts must specify extraction rules for structured fields to ensure returned content matches preset field requirements. Unified measurement standards are required for operation data. Multi-turn dialogue must retain measurement unit information during context transfer. Prompts must explicitly require returned content to include corresponding measurement explanations to avoid data interpretation bias. Timing differences across cross-source data require adding a data source timeliness verification step to the dialogue workflow. This ensures all content called during each interaction is the latest available version.
Configuration Settings
| Configuration Item | Recommended Range | Rationale for This Setting |
|---|---|---|
recallTopK | 8–12 top results | Multi-financial data is dispersed and highly specialized. A sufficient number of candidate results must be retrieved to cover cross-source information and avoid missing key research reports or product data |
similarityThreshold | 0.72–0.78 | Investment research data has strong professional characteristics. A threshold that is too low will introduce irrelevant content. A threshold that is too high may miss associated information in specific sub-sectors. The threshold should be calibrated through testing to fit the scenario |
rerankTopN | 4–6 top results | Core associated content must be retained while compressing context length. This adapts to the long-form nature of investment research documents and avoids exceeding the model's context window |
maxContextLength | 8000–12000 characters | Investment research documents contain large numbers of tables and technical terms. Sufficient context length is needed to retain professional information across multi-turn interactions, adapting to the large model's long-text processing capabilities |
toolCallRetryTimes | 2 times | Tool calls such as chart generation may fail due to abnormal data formats. Setting a reasonable number of retries reduces the probability of interaction interruptions and avoids returning empty results after a single failure |
The parameter values provided on this page are common recommended 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 on your own samples before finalizing settings.
Three Common Mistakes
- When calling chart tools to generate investment research data visualizations, a blank canvas is returned. Cause: The prompt does not explicitly require binding the corresponding product code and measurement unit. The tool cannot extract valid data from the retrieved knowledge base content.
- After an AI dialogue node returns empty content or an error, the workflow directly returns empty content to the frontend. Cause: No fallback handling logic for dialogue exceptions is configured. No error capture and retry mechanisms are set up.
- Structured fields such as product code and release date are missing in multi-turn dialogue. Cause: The prompt does not explicitly specify the structured fields to extract. This causes the large model to fail to carry metadata as required.
How to Confirm Proper Configuration
- Initiate a multi-turn query involving cross-source data association. Verify that the returned content covers both research report viewpoints and product operation data to confirm normal context association.
- Trigger a tool call such as chart generation. Simulate an abnormal input scenario to confirm whether the fallback handling logic is triggered, verifying that the error capture mechanism is active.
- Adjust the context length parameter. Test content retention in long-text interaction scenarios to confirm the parameter value fits current scene requirements.
- Export dialogue content. Check whether preset structured fields are included to verify that the prompt's field extraction rules are active.
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.