What the Data for This Category Looks Like
Typical data subjects for this category include medical insurance fund supervision departments, handling institutions, flight inspection teams, and designated medical and pharmaceutical institutions. Daily documents include audit notices, inspection work drafts, suspicious point lists, interview records, handling decision letters, and rectification reports. Data comes from medical insurance intelligent monitoring systems, fund supervision rule libraries, medical insurance settlement data, drug and consumable traceability codes, and complaint clues. Data updates occur once per month. Data fields include audit items, descriptions of suspected violations, settlement details, traceability code information, and more. Corresponding fields and units must be matched based on specific document types.
Constraints on Vector Models and Indexes
Low-frequency data updated monthly requires index construction to support batch incremental updates, without frequent full re-runs. Mixed document types include interview records transcribed from scanned documents, structured settlement data, and unstructured rule text. This requires vector models to adapt to semantic content across different formats. Single-document size varies significantly, from tens of characters in suspicious point lists to thousands of words in rectification reports. This requires chunking strategies with flexible adjustment options. Fields associated with traceability codes and settlement data must retain precise matching capabilities. This prevents loss of key associated information during vector recall. The party building learning Q&A scenario combined with audit business requires recall results to cover both supervision rules and suspicious point investigation logic.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the semantic integrity of long documents such as rectification reports and inspection work drafts, while avoiding vector embedding bias caused by overly long single chunks |
| Overlap Length | 100–200 characters | Covers key audit logic across chunks, preventing coherent violation descriptions in suspicious point lists and interview records from being split and lost |
| Similarity Threshold | Calibrated via actual testing | Must match the rigor of the fund supervision rule library, to avoid incorrectly recalling unrelated settlement data or complaint clues |
| Single Search Limit | Top 8 entries | Balances information completeness and processing efficiency for audit work, adapting to multi-dimensional suspicious point investigation needs for special inspections |
defaultToken | 4096 | Adapts to the context length requirements of party building learning Q&A, while accommodating associated analysis of audit rules and suspicious points |
maxToken | 8192 | Supports complete context recall for long documents, meeting full information reference needs for complex violation cases |
Three Common Misconfigurations
- Indexes disappear unexpectedly after manually inserting content into the knowledge base. The interface shows a default index has been generated, but index entries disappear from the list several hours later, with no clear error logs. Manually inserted content is not marked as a persistent index, triggering the system's automatic temporary index cleanup rule.
- Batch re-embedding tasks stop midway after switching embedding models. Some documents remain in the "indexing" status. The new model's
maxTokenlimit does not match the original document chunk length, exceeding the token range supported by the model. - Too few search recall results. After entering an audit suspicious point keyword, only fewer than 3 related results are returned. This cannot cover the investigation needs of special inspections. The similarity threshold is set too high, filtering out weakly matched content that complies with business rules.
How to Confirm Proper Configuration
- Upload a typical document such as an inspection work draft. View the chunked text fragments. Confirm that complete violation suspicious point descriptions are not overly split.
- Trigger a batch re-embedding task. Monitor the index status of all documents. Confirm that all entries eventually change to "completed". No lingering "indexing" entries should remain.
- Enter a known supervision rule keyword. Check the relevance of recall results. Adjust the similarity threshold to a range that meets business rigor requirements.
- Test context invocation for the party building learning Q&A scenario. Confirm that returned related content does not exceed the preset token range.
The values provided are common starting points and should be evaluated against applicable 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.