Data Characteristics for This Category
Insurance intelligent due diligence report data primarily comes from insurance product filing clauses, application notice templates, underwriting rule manuals, historical claims case documents, and compliance documents published by regulatory authorities. Data follows three update schedules:
- Product clauses are updated irregularly to align with regulatory filing adjustments
- Underwriting rules are updated quarterly or semi-annually to match industry policy changes
- Claims cases are generated in real time as business operations progress
Most documents are long-form text, and also include structured fields such as product unique identifiers, insured age ranges, deductibles, payout ratios, and more. Field units include yuan, integer ranges, and other standard units. Some unstructured text contains professional compliance language and business jargon.
Constraints on Vector Models and Indexes
The mixed structure of insurance due diligence data requires vector models and indexes to support joint retrieval of structured and unstructured text, avoiding missing key business rules. Real-time updated claims cases and irregularly adjusted product clauses require indexes to support incremental updates, reducing resource consumption from full index reconstruction. Long text and dense professional terminology require preserving context semantic connections during segmentation, preventing key compliance clauses from being truncated. Additionally, the business has high retrieval accuracy requirements. Vector models must accurately match professional semantic units such as pre-existing condition exclusion scopes and underwriting thresholds, avoiding misjudgments.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Insurance due diligence documents contain long clauses and structured fields. This segmentation length preserves key semantic units such as underwriting rules and exclusion scopes, avoiding semantic breaks across segments |
recall_top_k | Top 20 results | Insurance due diligence requires coverage of multi-dimensional compliance rules. Recall a sufficient number of candidate documents to ensure retrieval coverage |
similarity_threshold | 0.75–0.85 | Insurance text has precise semantics. Low-match irrelevant documents must be filtered to avoid misjudgments related to underwriting |
rerank_top_k | Top 5 results | Due diligence reports need to focus on high-match core rules, reducing subsequent manual screening costs |
vector_db_type | Zilliz or PGVector | Supports incremental indexing and high-concurrency queries, adapting to data volume changes as insurance business grows |
incremental_index_enable | Enabled | Insurance claims cases and product clauses are updated in real time or irregularly. Incremental indexing reduces time and resource consumption from full reconstruction |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material form, 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 Configuration Mistakes
- Scenario: Retrieval results are empty after migrating vector storage from PGSQL to Zilliz. Cause: Cross-database vector dimension alignment parameters were not configured, leading to mismatched vector embedding dimensions between old and new indexes.
- Scenario: A 500 error is returned when calling an external index model during deployment of version 4.9.6. Cause: The API address and secret key of the external model were not configured in environment variables, or access permissions for the corresponding port were not enabled.
- Scenario: Using a free open-source vector model to build an index results in low recall rates for underwriting rules in due diligence reports. Cause: Segmentation parameters were not adjusted for insurance due diligence-specific professional terms such as pre-existing conditions and deductibles, or the model was not fine-tuned for adaptation.
How to Verify Proper Configuration
- Upload a single complete insurance clause document, verify that generated text blocks retain complete underwriting rules, exclusion scopes and other key content, confirming segmentation parameter values meet business requirements.
- Submit a standard due diligence report retrieval request, review the vector database query logs, confirm the number of recalled documents matches the configured recall count.
- Compare results from the private reranking model with manually screened core rules, confirm the number of reranked returned results covers required key documents.
- Submit updated insurance product clauses, wait for index updates to complete, then initiate a retrieval request to confirm the incremental indexing function operates correctly.
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.