Data Characteristics for This Use Case
Investment platform research data primarily originates from public listed company announcements, industry research reports, institutional research meeting transcripts, real-time trading market data, and macroeconomic indicators. Data update cycles include real-time (market data, temporary announcements), daily (industry research report updates), and periodic (quarterly/annual financial reports). Individual documents contain standardized fields such as disclosure time, security code, core financial indicators, and industry ratings. Financial indicator fields have clear units: for example, the price-to-earnings ratio uses "times" as its unit, and earnings per share uses "yuan" as its unit. Document length varies widely, from hundreds of words for market briefings to tens of thousands of words for in-depth research reports.
Constraints for Multi-turn Dialogue and Prompt Engineering
The multi-source and time-sensitive nature of investment research data requires multi-turn dialogue contexts to distinguish data release times, to avoid confusion between historical and real-time market data. The wide range of document lengths requires limiting the total character count of context retrieved per turn, to prevent exceeding the model's context window limits. Standardized financial fields have clear units. Prompt engineering must enforce that the model matches the corresponding units when outputting results, to avoid unit errors. Structured fields vary across different documents. Multi-turn dialogue must call the structured parsing rules corresponding to the user's question type, to ensure extracted fields align correctly. Real-time market data updates frequently. Dialogue processes must prioritize retrieving the latest datasets, to avoid returning outdated information.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | Covers context length requirements for both in-depth research reports and market briefings, avoids exceeding model window limits |
recallTopK | Top 6–10 entries | Balances comprehensiveness of investment research data with context load, prevents excessive low-relevance content from interfering with model inference |
similarityThreshold | 0.75–0.85 | Filters low-relevance non-professional retrieval results, meets the high professional standards required for the investment research domain |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Adapts to parsing time for large in-depth research reports and batch financial report files, prevents premature termination |
promptTemplate | Written using the fixed structure: "user question + retrieved investment research data + historical dialogue context" | Explicitly links retrieved investment research data to user questions, ensures model outputs are based on compliant data sources |
enableRerank | Enabled | Optimizes retrieval ranking for investment research data, elevates the display priority of highly relevant content |
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. Each scenario requires individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Issue: After uploading research reports or financial report files, the data processing page shows no content. Cause: No reasonable value is configured for
PARSE_FILE_TIMEOUT_SECONDS. Large documents fail to complete parsing before timeout, so data does not finish the ingestion process. - Issue: The model returns incorrect financial indicator units during multi-turn dialogue. For example, it labels the price-to-earnings ratio with "yuan" as its unit. Cause: The prompt does not enforce matching the standard units of corresponding fields, and does not clarify the field rules for investment research data.
- Issue: Detailed internal application call logs cannot be viewed in the conversation details. Cause: Full storage configuration for conversation logs is not enabled. Only basic interaction records are retained, and details of internal retrieval and prompt engineering calls are not logged.
How to Verify Proper Configuration
- Upload typical in-depth research reports and market briefings, check if the data processing page generates structured fields, and verify that parsing time stays within the preset timeout range.
- Launch a multi-turn conversational query about investment research, check that the model's output links to retrieved investment research data, and matches the unit rules for corresponding indicators.
- View the conversation details page, confirm that complete prompt engineering calls, retrieved data lists, and model inference processes are recorded.
- Adjust the similarity threshold and number of retrieved entries, verify changes in retrieval result relevance across different configurations, and confirm alignment with professional filtering requirements for investment research scenarios.
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.