What this category of data looks like
Insurance marketing content data originates from official product terms, regulatory disclosure documents, agent training script libraries, marketing campaign materials, and claims reference cases. Update frequency follows business adjustments: product documents are synchronized when new insurance products launch, compliance content is updated when regulatory policies change, and short-term script materials are updated when marketing campaigns start. Most documents are structured text, with fields including insurance product name, eligible age, payment term, coverage scope, and exclusion clauses. Units include years, yuan, ten thousand yuan, and percentage. Some long documents exceed 10,000 characters per single piece.
What constraints these characteristics impose on knowledge base retrieval and recall
Insurance marketing content has strong professional attributes, with a high share of long documents. Retrieval must retain core semantic units, to avoid damaging key information like coverage responsibilities and underwriting rules through overly fragmented segmentation. Dynamically updated marketing scripts require incremental synchronization support, to ensure retrieval results match the latest campaigns. Structured data with multiple fields requires retrieval to support numerical range matching (such as eligible age ranges) and metadata filtering, to prevent unrelated insurance product content from being included. Compliance documents have high credibility requirements, so regulatory disclosure content must be prioritized for recall, to ensure marketing content aligns with regulatory requirements.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
segment_length | 800–1200 characters | Insurance marketing documents contain long paragraphs of coverage responsibility descriptions. This range balances semantic completeness and retrieval accuracy |
recall_count | 10–15 items | Insurance marketing needs to cover multi-dimensional information such as product selling points, underwriting rules, and exclusion clauses. Sufficient recall counts can meet scenario requirements |
similarity_threshold | 0.75–0.85 | Insurance content has high professionality. This threshold filters low-relevance non-target content while retaining core matching results |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing a single long document (such as a complete product manual) takes a long time. This duration prevents mid-parsing timeouts |
UPLOAD_FILE_MAX_SIZE | 100 MB | Most insurance marketing documents are multi-page PDF or Word files. This size limit covers the upload needs of most single documents |
rerank_return_count | Top 3 items | Prioritize displaying the most relevant core marketing information, which aligns with user needs for quickly obtaining key content |
The parameter values provided on this page are all conventional recommendations used as a starting point 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 mistakes
- Issue: A 413 Request Entity Too Large error is returned when uploading a single insurance product manual larger than 100 MB. Cause: The
UPLOAD_FILE_MAX_SIZEparameter was not adjusted, and the default value restricted large file uploads. - Issue: Retrieval results mix content from different insurance products, and cannot match the user-specified marketing scenario. Cause: Metadata filtering rules were not configured based on the
insurance_typefield of documents, and scenario-based recall screening was not enabled. - Issue: Semantic breaks appear when parsing long documents, and key coverage responsibilities are split across multiple segments. Cause: The
segment_lengthparameter was not adjusted, and the default segment length was too short, damaging the semantic integrity of long paragraphs.
How to confirm correct configuration
- Upload a typical insurance marketing document, view the parsed text segments, and confirm that long paragraphs are split reasonably without damaging core semantic units.
- Enter a query term for a specific marketing scenario, check whether the number of returned results matches the configured recall count, and core content is displayed first.
- Upload a test document exceeding the preset size, and confirm that no file size limit error is triggered.
- Enter a query term with a specific metadata tag, verify that retrieval results only include document content corresponding to the tag.
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.