What data looks like for this category
Data for this category comes primarily from health supervision information systems, electronic medical institution registration systems, complaint reporting platforms, and law enforcement inspection records. Daily processed documents include supervision inspection forms, medical institution practice licenses, rectification notices, supervision opinions, and penalty case files. Data updates occur monthly. Most documents contain structured or semi-structured fields such as institution name, license number, rectification requirements, and penalty reasons. Some on-site inspection records are unstructured long text.
What constraints these characteristics impose on vector models and indexes
Monthly concentrated data batches require the index process to handle batches efficiently and avoid single-batch processing timeouts. Mixed document field types need adapted vectorization configurations for both structured qualification documents and unstructured inspection record text. Long on-site inspection records and penalty case files need appropriate chunking rules to prevent semantic fragmentation, ensuring accurate association during review. Differences in data formats across sources require the index process to support a unified vectorization workflow for multi-source data, plus validation of vectorization results to avoid incorrect associations in qualification verification and rectification tracking.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Aligns with semantic integrity of long health-related texts such as penalty case files and on-site inspection records, prevents single-chunk content breaks from reducing accuracy of qualification verification and rectification tracking |
| Overlap Length | 100–200 characters | Preserves cross-chunk context association, reduces semantic loss of key information such as rectification requirements and penalty reasons caused by chunking |
defaultToken | 1024 | Matches input token limit of the default FastGPT vector model, adapts to single-chunk length of most health-related documents |
maxToken | 1536 | Reserves redundant space to handle health supervision documents with many professional medical terms, avoids token limit exceeded due to dense terminology |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Distinguishes matching accuracy for different fields such as institution qualifications and rectification requirements, prevents incorrect association of qualification information across different institutions |
| Single Recall Limit | Top 10 entries | Aligns with single-round query needs of health supervision review, controls redundancy of returned results and improves review efficiency |
Three common errors
- Vectorization task errors prompt "input token exceeds model limit", with some long text chunks failing to generate vectors. Cause: Chunk length not adjusted per the
maxTokenconfiguration, causing single-chunk content to exceed the model's supported token limit. - After batch uploading multiple documents larger than 10MB, server resources are exhausted and restarted. Some knowledge bases show "not ready" with no automatic indexing action. Cause: No batch upload concurrency threshold set, indexing processes occupy excessive system resources, and automatic indexing scheduling is not restored after restart.
- Some chunk vectorization results are empty, and recover after retry. Cause: Locally deployed vector model loading instability, or special characters such as line breaks not handled correctly during chunking, causing vectorization request exceptions.
How to confirm proper configuration
- Upload a typical penalty case file document, view chunk details, and confirm each chunk's character count falls within the configured chunk length range.
- Initiate a qualification verification query, view the number of returned results and similarity matching degree, and confirm they align with the configured single recall limit and similarity threshold.
- Confirm the current FastGPT version is v4.17.0 to avoid version compatibility issues.
- Simulate batch upload of 3-5 monthly updated documents, confirm server resource usage is normal, and all knowledge bases complete indexing automatically.
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.