What Data Looks Like for This Category
Data for this category originates from health supervision information systems, medical institution electronic registration systems, complaint reporting platforms, and law enforcement inspection records. Daily documents include supervision inspection forms, medical institution practice licenses, rectification notices, supervision opinions, and penalty case files. Data updates occur on a monthly basis. Most documents are structured forms and semi-structured texts. Fields include institutional core information, inspection item lists, rectification requirements, penalty clauses, and more. Some documents contain handwritten supplementary non-standard content.
What Constraints These Characteristics Impose on Vector Models and Indexing
Monthly updated dataset scale changes dynamically with the coverage of the jurisdiction. It requires adaptable, elastically scalable index storage capabilities. Mixed semi-structured and unstructured document types demand chunking logic that balances accurate extraction of form fields and semantic integrity of text paragraphs. Multi-source data has inconsistent field formats. Unified mapping must be completed before index construction to avoid semantic deviation in vector embeddings. defaultToken must be set appropriately to adapt to the embedding limit for single documents. Time-sensitive law enforcement and rectification texts require index update frequency matching the data refresh rhythm. This ensures real-time retrieval results.
How to Set Configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the field and paragraph lengths of documents such as supervision inspection forms and rectification notices, avoiding semantic breaks |
| Overlap Length | 100–200 characters | Retains cross-chunk semantic connections, meeting context continuity needs for long texts |
| Similarity Threshold | 0.75–0.85 | Matches compliance retrieval accuracy for law enforcement scenarios, filtering low-relevance historical case files |
| Single Search Limit | Top 10 entries | Adapts to rapid review needs for internal office work, avoiding excessive results that interfere with judgment |
defaultToken | 4096 | Adapts to the total token length of most law enforcement documents, ensuring complete semantic embedding |
maxToken | 8192 | Reserves expansion space for index construction of large datasets after jurisdiction expansion |
Three Common Mistakes
- Phenomenon: After upgrading FastGPT from v4.9 to FastGPT v4.17.0, the original knowledge base cannot return vector retrieval results. The interface displays no search results. Cause: The new version adjusts index chunking rules and vector embedding logic. Failing to synchronize updates to the original knowledge base's chunking configuration causes a mismatch between the original chunks and the new model's embedding requirements.
- Phenomenon: After uploading a CSV-format supervision inspection form, the system prompts chunking failure. Logs show empty fields. Cause: The CSV file contains unfilled required fields such as license numbers and inspection dates. The system cannot extract valid semantic content during chunking, triggering an exception.
- Phenomenon: Retrieving penalty case files returns a large number of irrelevant medical institution registration information. The number of results exceeds expectations. Cause: The similarity threshold is set too low, failing to filter low-relevance structured data. This causes mixed recall results.
How to Confirm Configurations Are Correct
- Upload a single supervision inspection form to trigger index construction. Check the chunking log to confirm each chunk's length falls within the preset range.
- Input specific issues from past supervision inspections to run a retrieval. Verify that the returned results' similarity falls within the preset threshold interval.
- Import the monthly updated dataset. Check index storage usage to confirm it does not exceed the preset storage limit.
- Restart the FastGPT service, then run the retrieval again. Confirm the results match those before restarting, with no configuration loss.
The values provided are common starting points. They should be assessed against the target deployment’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.