What the Data for This Category Looks Like
Core data for this category comes from health supervision information systems, medical institution electronic registration systems, complaint reporting platforms, and law enforcement inspection records. The update frequency is monthly. Daily processed documents fall into two categories: structured qualification documents such as medical institution practice licenses, and unstructured law enforcement documents such as supervision checklists, rectification notices, and penalty case files. Data fields include institution name, license scope, inspection items, and rectification requirements, covering full process records of medical institution practice compliance and public health inspection.
What Constraints These Characteristics Impose on the Knowledge Base Retrieval and Recall Link
Decentralized storage of multi-source data requires the knowledge base to support dataset partitioning by department or region. This prevents irrelevant data from interfering with retrieval results. The monthly update frequency means real-time synchronization does not need to be configured. Only periodic incremental updates are required to cover the latest data. Document types include structured qualification files and unstructured law enforcement case files. This requires the retrieval tool to support text splitting and matching for different formats. Core business links rely on qualification verification and rectification tracking. Retrieval must accurately locate content related to institution qualifications, on-site inspections, and rectification requirements. At the same time, recall results must be complete. Missing key law enforcement information could lead to compliance risks.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches key information in medical institution qualifications and law enforcement records, filters low-relevance non-compliant content |
limit | Top 10 | Controls the number of recall results, avoids exceeding the context window limit while covering core law enforcement and rectification information |
searchMode | Vector retrieval + keyword hybrid | Balances keyword matching for structured qualification files and semantic recall for unstructured case files, adapting to multiple document types in this category |
usingReRank | Enabled | Law enforcement case files have strong content relevance; re-ranking can improve the ranking priority of highly relevant results and optimize retrieval experience |
datasetSearchUsingExtensionQuery | Enabled | The wording of declaration materials may differ from knowledge base terminology; extended queries can cover more relevant content |
collectionIds | Bind corresponding health supervision dataset ID | Isolates law enforcement data from different regions or departments, ensuring the retrieval scope only covers the target business scenario |
Three Common Misconfigurations
- Phenomenon: Garbled characters appear in Chinese file names uploaded via API. Cause: The character encoding parameter is not correctly specified in the API request header, leading to encoding conflicts during file name parsing.
- Phenomenon: Knowledge base search response takes too long. Cause: The configured
limitvalue is too high, and re-ranking optimization is not enabled, causing the retrieval and sorting process to occupy excessive system resources. - Phenomenon: AI-generated answers are interrupted mid-process. Cause: The number of recalled chunks exceeds the context window limit, forcing the generation process to be truncated and preventing a complete answer from being generated.
How to Confirm the Configuration Is Correct
- Upload a batch of test files including medical institution practice licenses and penalty case files. Perform targeted retrieval and check whether the returned results include the core content of the corresponding documents.
- View the retrieval log to confirm that the results filtered by the
similaritythreshold have no obvious low-similarity irrelevant content, meeting business expectations. - Call the API to upload a test file with a Chinese file name. Confirm that the file name is displayed normally after upload with no garbled characters.
- Adjust the value of the
limitparameter. Verify that the number of recall results can be adjusted as needed, and that the generation process will not be interrupted.
The values provided are common starting points and should be measured 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.