What the Data for This Category Looks Like
Insurance research report data mainly comes from non-bank financial teams of securities firm research institutes, public reports from insurance industry associations, and internal research organized by insurance institutions. Update frequency varies by report type: regular industry research reports are updated weekly or monthly, while special topic reports are released alongside major industry events. Document structures uniformly include four sections: core conclusions, industry trends, breakdown of specific insurance lines, and risk warnings. Fields include values related to underwriting scale, payout performance, and channel proportion, with units including CNY 100 million and 10,000 pieces.
What Constraints These Characteristics Impose on Knowledge Base Retrieval and Recall
Dispersed sources of insurance research reports create notable format differences across individual documents. The retrieval link must support unified vectorization processing for multi-format content. Flexible update frequencies lead to frequent incremental data updates. The recall link must support fast incremental synchronization with publication time filtering. Documents are generally lengthy and include multi-dimensional specific insurance line breakdown fields. Retrieval must prioritize recalling specific sections that match user queries, to avoid invalid long text consuming tokens. There are many professional indicator fields for specific insurance lines. Recall results must retain associated information for these fields, to improve the professional accuracy of responses.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunk_max_tokens | 800–1200 characters | Insurance research report segments must retain complete analysis logic for specific insurance lines. Excessive length breaks semantic coherence, while insufficient length loses critical associated information |
recall_count | Top 10 results | Insurance research reports include multi-dimensional insurance line breakdown fields. A sufficient number of candidate results must be recalled to cover query needs across different scenarios |
similarity_score_threshold | 0.72–0.80 | Insurance research reports contain many professional terms. A threshold that is too low introduces irrelevant results, while a threshold that is too high misses precisely matched specific sections |
rerank_count | Top 5 results | Retain the most relevant entries after reranking recall results, to avoid context overflow caused by long text |
context_window_limit | 40000 tokens | Individual insurance research reports are lengthy. Sufficient context window space must be reserved to accommodate multiple recalled segments |
incremental_sync_interval | 3600 seconds | Matches the hourly/daily update rhythm of insurance research reports, to ensure the timeliness of knowledge base data |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: A
504 Gateway Timeouterror occurs after a retrieval request is triggered, or query latency exceeds the preset threshold. Cause: No total token limit is set for recalled text, causing the large language model to receive context beyond its processing capabilities. - Symptom: Semantic retrieval returns multiple matching results, but the large language model replies that no relevant answer is found. Cause: The sorting priority of recall results is incorrect, or recall results are not spliced into a coherent context by semantic relevance, preventing the model from extracting valid information.
- Symptom: The content generated by the large language model has obvious deviations from the original text in the knowledge base. Cause: The recall result similarity threshold is set too low, introducing irrelevant document fragments, or the reranking process loses critical professional expressions from the original text.
How to Verify Proper Configuration
- Check the knowledge base's incremental synchronization logs to confirm that the latest insurance research report data is automatically pulled at the set interval.
- Submit a test query and verify that the number of returned recall results matches the value set for the
recall_countconfiguration item. - Check the large language model's context input logs to confirm that the total token count of recalled text does not exceed the set
context_window_limit. - Compare the model's reply with the original knowledge base text to confirm that the core expressions in the reply match the professional fields in the recall results.
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.