What the data for this category looks like
Investment platforms have two core types of data. The first type is structured user behavior data, sourced from user position records, transaction flows, and risk assessment results. Updates occur in real time during trading hours, with daily post-market updates for position market value and returns. The second type is unstructured product documents, sourced from official product specifications, market research report summaries, and compliance notification materials. Document structures include fixed fields such as product name, risk level, investment term, and expected return range, with units including yuan, days, and percentage.
What constraints these characteristics impose on model access and configuration
High structured data proportion and real-time updates require the model access process to distinguish recall weights for different fields. This prevents generic content from overwriting users' exclusive transaction information. Long unstructured document length requires the parsing process to set reasonable timeout thresholds, to avoid parsing failures for lengthy documents. Marketing content must generate personalized recommendations based on users' real-time positions. This requires the context window to hold complete data from multi-turn interactions, while adapting to semantic understanding requirements for exclusive fields such as risk level and investment term.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | User conversations for investment platforms often include position details and information about multiple products, requiring sufficient context to retain complete transaction data |
RECALL_TOP_K | Top 8–12 entries | Investment scenarios need to cover relevant content across different product types and user historical transactions. Excessive entries increase interface latency |
RERANK_TOP_N | Top 3–5 entries | Marketing content requires precise matching of user current needs. Excessive entries slow down response speed |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Investment research reports and product specifications are typically lengthy, requiring sufficient parsing time to complete content splitting |
EMBEDDING_MODEL | text-embedding-v3 | This model has strong semantic understanding capabilities for structured financial fields, adapting to exclusive fields such as product risk level and investment term |
MAX_RETRY_TIMES | 2 times | Model call failures in investment scenarios can negatively impact user experience. Limited retries reduce the probability of errors |
The parameter values provided on this page are common recommendations that serve 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. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- The model returns empty content, or the frontend displays blank content. This occurs when fallback logic for empty results is not configured, and recall results with no matches are not filtered out.
- Message response delay exceeds 30 seconds. This occurs when the values of
RECALL_TOP_KandRERANK_TOP_Nare set too high at the same time, and cache optimization is not enabled. - The
503 No available channels for model text-embedding-v3 under current group defaulterror is returned. This occurs when no backup channels for this model are configured, or channel quotas are exhausted.
How to confirm the configuration is complete
- Initiate a test conversation that includes position details and product queries, check whether the returned content includes matching product fields and risk level information.
- View model call logs, confirm that the channel call status for
text-embedding-v3is normal, with no 503 errors. - Simulate an empty recall scenario, check whether the preset fallback response is triggered.
- Adjust the
RERANK_TOP_Nparameter, observe whether the interface response delay meets expected standards.
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.