What the Data for This Category Looks Like
Expense list data comes from billing details issued by medical institutions, scanned reimbursement receipts or structured export files uploaded by policyholders, and expense accounting records automatically generated by insurance company claim systems. Update cadence: Each individual claim application corresponds to one list, created when the claim is submitted, updated only when expense information is supplemented or corrected, with no fixed batch update cycle. Document structure includes structured headers and line items. Some text transcribed from scanned documents has formatting errors and shifted item order. Fields include charge item name, unit price (unit: yuan), quantity (unit: times/unit), total cost, charging institution name, charging date, medical insurance overall plan payment amount, personal out-of-pocket amount, and some line items include brief charge descriptions.
What Constraints These Characteristics Impose on the Vector Model and Indexing Link
The large number of fine-grained charge line items and wide format differences require the vector model and indexing system to support long text segmentation and structured information fusion. Random, one-off data updates require the index to support on-demand incremental updates, avoiding resource consumption from full index rebuilding. Unstructured text transcribed from scanned documents has formatting errors, so text cleaning rules before embedding must adapt to common out-of-order issues in medical charge text. Structured numerical information such as amounts and dates in fields must be embedded together with text content, preventing charge item matching deviations caused by relying solely on text semantics. Additionally, the token count of a single list varies widely, so segmentation parameters need dynamic adjustment to balance context completeness and retrieval efficiency.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | aliyun-embedding-v3 (open-source adapted version) | Optimized for structured expense text in financial claim scenarios, context window adapted to semantic capture needs for long line item lists |
chunk_size | 800–1200 characters | A single charge line item is typically 50–200 characters. This segmentation range can cover complete line items and adjacent associated descriptions, avoiding semantic fragmentation |
chunk_overlap | 100–150 characters | Expense lists have associated charge notes across segments. Overlapping sections preserve contextual connections, improving retrieval completeness |
index_update_strategy | incremental | Expense lists are updated randomly per individual claim. Incremental indexing only generates vectors for newly submitted lists, reducing system resource usage |
retrieval_top_k | Calibrated based on actual testing | A single list has a limited number of valid line items. The number of recalled entries must be adjusted based on actual matching results to avoid interference from excessive irrelevant items |
similarity_threshold | Calibrated based on actual testing | Semantic differences between similar charge items must be distinguished. A threshold that is too low will introduce false matches, while a threshold that is too high will miss relevant items |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are affected by material form, data volume and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: Retrieval returns relevant segments of the expense list, but the large language model prompts that no matching content was found when generating a reply. Cause: Reranking functionality is not enabled, or the number of reranked returned entries is set too low, resulting in the final context passed to the large language model not containing critical charge line items.
- Phenomenon: A
413 Request Entity Too Largeerror appears in logs after an indexing task is triggered. Cause: TheUPLOAD_FILE_MAX_SIZEparameter has not been adjusted. The text transcribed from a scanned copy of a single expense list exceeds the maximum upload size allowed by the system. - Phenomenon: A large number of irrelevant charge items appear in retrieval results, for example, matching "western medicine" to "patent medicine". Cause: The
similarity_thresholdis set too low, or the embedding model has not been adapted for financial structured text, leading to deviations in semantic similarity calculations.
How to Confirm Proper Configuration
- Check the embedding model configuration item, confirm that the selected model name matches the scenario adaptation requirements, and verify that the model's parameter configuration has the financial text optimization option enabled.
- Upload a standard expense list sample, check the system's generated segmentation preview results, confirm that segments cover complete charge line items and are not overly split.
- Trigger an incremental indexing task, check the indexing logs, confirm that vectors are only generated for newly submitted expense lists, and no full index rebuilding is triggered.
- Enter a query term related to charge items, check the similarity distribution of retrieval results, adjust relevant parameters until matching results meet expectations.
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.