What Data for This Category Looks Like
The data for this category primarily comes from health supervision information systems, medical institution electronic registration systems, complaint and reporting platforms, and law enforcement inspection records. Commonly processed documents include supervision inspection forms, medical institution practice licenses, rectification notices, supervision opinions, and penalty case files. Data updates occur monthly. Most documents include fields such as institution qualification numbers, inspection items, rectification requirements, and penalty bases. No unified fixed unit exists across documents. Some documents are structured forms, while others are unstructured law enforcement record texts.
Constraints Imposed on Vector Models and Indexes
Monthly data updates must align with batch vectorization rhythms to avoid frequent interface rate limit triggers. Mixed structured forms and unstructured law enforcement records require differentiated text splitting logic to prevent critical compliance information across blocks from being split apart. Documents containing precise fields such as qualification numbers and penalty bases have high vector recall accuracy requirements, so adjust index matching weight logic. Multi-source data requires a unified preprocessing process to ensure consistent field extraction and vectorization, avoiding information misalignment during retrieval.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the length of mixed structured and unstructured documents, avoid single chunks that are too large to lose semantic meaning or too small to break associations |
| Overlap Length | 100–200 characters | Retain semantic connections between adjacent chunks, prevent cross-block inspection items and rectification requirements from being split apart |
| Similarity Threshold | Calibrate based on actual testing | Adapt to recall requirements for precise fields such as qualification numbers and penalty bases, balance recall rate and accuracy |
| Single Search Limit | Top 10 results | Control the volume of retrieval returns, adapt to the need to quickly locate core compliance basis in law enforcement scenarios |
maxToken | 4096 token | Adapt to input limits of mainstream embedding models, avoid length errors during vectorization |
defaultToken | Follow embedding model default values | Ensure consistency of basic vectorization, avoid semantic drift caused by custom parameters |
Three Common Configuration Mistakes
- Symptom: Text chunks are lost after setting a fixed chunk length. Cause: The configuration does not adapt to length differences between mixed structured forms and unstructured law enforcement records for this category. Single chunk parameters fail to cover semantic boundaries across different documents.
- Symptom: Rate limit errors trigger during vectorization. Cause: The concurrent thread count for batch vectorization remains unadjusted. The configuration does not match monthly batch update rhythms, leading to concurrent requests exceeding interface call quotas.
- Symptom: Knowledge base retrieval responses take too long. Cause: The number of results returned by a single search is not limited. Index matching logic is not optimized for precise fields such as qualification numbers and penalty bases. Returning too many irrelevant results increases processing overhead.
How to Confirm Correct Configuration
- Upload a single typical document, such as a supervision opinion, and verify that the number of vectorized chunks matches the number of semantic chunks in the original document.
- Simulate batch vectorization of monthly updated data, observe for rate limit errors, and adjust thread count until no errors occur.
- Enter a law enforcement scenario query, such as "practice qualification requirements for a medical institution", and verify that retrieval results include corresponding practice license and supervision inspection form content.
- Check index status, confirm that batch update tasks show as ready after completion, and ensure no tasks remain in an indexing state for extended periods.
The values provided are common starting points and should be evaluated against samples appropriate to the specific deployment context.
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.