What this category of data looks like
This category of data mainly comes from the medical insurance intelligent monitoring system, fund supervision rule base, medical insurance settlement data, drug and consumable traceability codes, and complaint clues. Daily processed documents include audit notices, inspection work drafts, suspicious point lists, interview records, handling decision documents, and rectification reports. Most fields include supervision object information, descriptions of suspicious violations, handling basis and results. The data update frequency is once per month, and the number of documents stored in a single batch fluctuates with the scale of special inspections.
What constraints do these characteristics impose on the "vector models and indexes" link
The data sources for this category are a mix of structured settlement data and unstructured supervision documents, and the update frequency is fixed monthly. This requires indexes to support incremental updates and batch storage. Most documents are compliance-related text, which requires accurate matching of policy clauses and violation facts. Therefore, vector models must adapt to long-text encoding to avoid truncation of key supervision basis. At the same time, audit data is associated with clear suspicious violations and handling results. When chunking, the integrity of associated fields must be retained to prevent loss of context information during retrieval. In addition, data errors will directly lead to fund losses, so the recall logic of the index must balance accuracy and recall rate to avoid missed detections or misjudgments.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the paragraph length of audit documents, retain complete association between policy clauses and violation facts |
| Overlap Length | 100–200 characters | Avoid truncating cross-paragraph policy basis during chunking, ensure complete context can be obtained during retrieval |
defaultToken | 1000 | Serve as the default context benchmark value during retrieval, adapt to most audit Q&A scenarios |
maxToken | 3000–4000 | Adapt to the context limit of FastGPT v4.17.0, accommodate multiple retrieved policy basis |
| Similarity Threshold | 0.75–0.85 | Balance misjudgment and missed detection risks in audit scenarios, ensure matched policy basis have sufficient relevance |
| Single Search Limit | Top 10 entries | Cover common multi-suspicious-point audit scenarios, avoid missing associated supervision rules |
| Vector Model | Alibaba multimodal-embedding-v1 | Support multi-modal data encoding, adapt to text and structured information such as traceability codes in medical insurance data |
Three Common Misconfigurations
- Index tasks stall for long periods, shown as the interface displaying the "Indexing" status for more than 24 hours. The cause is that incremental update configuration is not enabled, and full re-indexing is performed during monthly batch storage, leading to excessive time consumption.
- Retrieval results fail to obtain chunk content, shown as returned document fragments having no clear chunk metadata, or only displaying the full original document. The cause is that associated fields are not retained during chunking, or the chunk storage switch is not enabled in the index configuration.
- Multiple duplicate results are returned for the same document chunk, shown as duplicate policy basis fragments appearing in results returned by a single retrieval. The cause is that the chunk overlap length is set too large, or index deduplication rules are not configured, leading to multiple matches of the same content.
How to Confirm the Configuration is Correct
- Check the index management logs in FastGPT v4.17.0, confirm that the monthly incremental update task triggers normally, with no timeout or error records.
- Manually upload an audit notice, retrieve relevant policy clauses, confirm that the returned chunk content includes complete descriptions of violation basis and associated fields are intact.
- Test mixed multi-index retrieval, confirm that the returned results have no duplicate entries and the matching degree falls within the preset similarity threshold range.
- Enter the model management interface, verify that Alibaba multimodal-embedding-v1 has been correctly configured and completed test calls.
The values provided here are common starting points and should be evaluated against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.