What data for this category looks like
Data comes primarily from official product manuals, rate adjustment announcements, insurance purchase guidelines, archived past customer support Q&As, and product compliance information published by financial regulators. Update frequency aligns with product iterations. Full batch updates run when new products launch. Incremental updates handle daily rate and rule changes. Documents include structured fields and unstructured text. Structured fields cover product ID, annualized yield, minimum investment amount, risk level, and target customer group. Units include percentage, yuan, year, and others. Unstructured content includes clause details, claims condition explanations, and similar material.
What constraints do these characteristics impose on vector models and indexing
Mixed structured and unstructured text requires vector models to handle both numerical parameters and natural language descriptions. This avoids encoding bias in numerical semantics. Combined full and incremental updates require indexes to support incremental synchronization. This reduces resource use from full index rebuilding. Wide variation in document length requires chunking strategies that work for both short parameter entries and long clause text. This avoids excessive truncation of critical information. Specific fields and units require indexes to retain metadata tags. This supports later recall filtering by dimensions such as product risk level and yield range.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Chunk Length | 800–1200 characters | Product consultation documents include short parameter entries and long clause text. This range balances semantic integrity for both content types, avoiding excessive truncation of critical parameters |
Chunk Overlap | 50–100 characters | Preserves contextual links between chunks, preventing loss of logical connection between split product parameters and explanatory text |
Vector Model | Select a model tailored for the financial professional domain | Semantic encoding of financial numerical and regulatory text has high precision requirements. A domain-optimized model must be used |
Number of Recall Results | Top 8–12 results | Product consultation requires coverage of multi-dimensional parameters and clause details. Too many results increase inference latency. Too few may miss critical information |
Similarity Threshold | Calibrated based on actual testing | Adjust according to the recall accuracy requirements of the business scenario, to avoid incorrectly recalling irrelevant products or missing target content |
Incremental Index Toggle | Enabled | Meets the incremental update needs of product consultation, reducing index rebuilding time during daily rule adjustments |
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 errors
- Issue: The knowledge base remains in the "Indexing" state for a long time after dataset import. Cause: The incremental index toggle is not enabled, or the vector model configuration is incorrect, causing index construction to time out and exceed the threshold set by
PARSE_FILE_TIMEOUT_SECONDS. - Issue: The similarity values in recall results are abnormally high, such as exceeding 10000. Cause: The normalization parameters for similarity calculation are not configured correctly, resulting in unstandardized vector distance calculations, with raw distance values returned directly.
- Issue: The knowledge base cannot be indexed normally, and the prompt "m3e no available channel" is displayed. Cause: The deployment channel for the m3e model was not configured according to official documentation, and access permissions for the corresponding channel were not added correctly.
How to confirm the configuration is complete
- Check the dataset index status, confirm that construction completes within a reasonable time frame, with no long-term stuck in the "Indexing" state.
- Launch a simulated consultation test, verify that recall results include target product parameters and clause content, and there are no abnormal similarity value deviations.
- Check the vector model configuration items, confirm that the selected model matches the currently deployed service, with no prompts for unconfigured model channels.
- Trigger an incremental update for a single product information entry, verify that the index completes the update automatically, without requiring a full rebuild of the knowledge base.
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.