What the data for this category looks like
Insurance investment research data sources include securities firm insurance industry research reports, insurance company actuarial reports, life and property insurance product terms, regulatory announcements from the banking and insurance regulatory authority, and statistical data from insurance industry associations. Update frequencies vary significantly by document type. Regulatory announcements update immediately when policies are released. Industry research reports update weekly or monthly based on market cycles. Product terms update when new products launch. Quarterly financial reports update per fiscal quarter. Document structures cover core assumptions, underwriting data, investment portfolio analysis, underwriting rules, coverage scope, effective dates, and other content. Fields include predetermined annualized yield, payout ratio, number of insured people, total investment asset scale, and others. Numeric fields use standard measurement methods.
What constraints these characteristics impose on vector models and indexing
Insurance investment research documents have complex structures. They include long-form research report analysis, as well as structured rate tables and term details. Separate vectorization processing is required for structured metadata and unstructured text to avoid semantic deviation from mixed encoding. Document update frequencies vary widely. High-frequency updated regulatory announcements and new product terms require incremental indexing to reduce the overhead of full reindexing. Some documents contain professional actuarial and investment-related terminology. Vector models must adapt to financial domain semantic understanding, otherwise term embedding deviation will occur. Single document lengths vary significantly, from hundreds of words of regulatory summaries to tens of thousands of words of research reports. Index sharding must support variable-length text blocks to avoid truncating key actuarial parameters. Structured fields such as effective dates and product types can be used to filter recall results. Indexes must support fast metadata retrieval.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Insurance investment research documents contain long sentences and professional terminology. 800-1200 characters preserves complete semantic units and avoids truncating actuarial assumptions or portfolio descriptions |
chunk_overlap | 100–150 characters | Cross-chunk professional terminology such as "predetermined annualized yield" requires contextual continuity to avoid semantic breaks |
embedding_batch_size | 8–16 | Reduces the risk of exceeding embedding rate limits, balances vectorization efficiency, and adapts to batch processing requirements for insurance documents |
retrieve_top_k | Top 10–15 results | Insurance investment research requires precise matching of actuarial parameters and regulatory terms. Too many recall results introduce irrelevant information, while too few miss critical content |
index_refresh_interval | Every hour | High-frequency updated regulatory announcements and new product terms require timely index synchronization to avoid delayed retrieval results |
metadata_filter_enable | Enabled | Insurance documents include underwriting rules, effective dates and other metadata. Enabling this allows filtering recall results by effective date and product type to improve retrieval accuracy |
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. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: The knowledge base remains in the "indexing" state for extended periods with no progress updates. Cause:
index_refresh_intervalis not set to a reasonable interval, orembedding_batch_sizeis set too large, leading to thread blocking and index task backlog. - Symptom: When
chunk_sizeis set to 3000 characters, some long documents have missing text blocks. Cause: Thechunk_overlapparameter is not adjusted synchronously. Truncated content at the end of long text blocks is not covered by subsequent blocks, resulting in missing key terms or data. - Symptom: Rate limit errors are returned during vectorization. Cause:
embedding_batch_sizeis set too high, exceeding the concurrent processing limit of the current vector model. The batch size was not adjusted based on actual testing.
How to Confirm Proper Configuration
- Upload a single typical insurance product term document, review the segment preview results to confirm that no core terms or rate information is truncated in each text block.
- Submit a batch document vectorization task, check system logs to confirm no rate limit errors occur, and that index progress updates as expected.
- Enter professional search terms such as "predetermined annualized yield" and "property insurance underwriting rules", verify the relevance and quantity of recall results, and adjust
retrieve_top_kto a range that meets business requirements. - Simulate a high-frequency update scenario such as uploading a new regulatory announcement, confirm that the index completes incremental updates within the set
index_refresh_interval, and that retrieval results include the latest content.
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.