What data for this category looks like
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. Daily processed documents include supervision inspection forms, medical institution practice licenses, rectification notices, supervision opinions, penalty case files, and more. Fields cover institutional qualifications, on-site inspection details, rectification requirements, penalty content, and other items. Data is updated monthly, exists in a mixed format of structured forms and unstructured documents, and the content complexity of individual data varies by document type.
What constraints these characteristics impose on the vector model and indexing workflow
Data for this category is multi-source mixed, inconsistent in format, and updated monthly. These traits create multiple constraints for the vector model and indexing process. Multi-source data requires compatibility with document formats exported from different systems. The indexing workflow must support bulk import of multiple file types. Monthly update frequency does not require high-frequency incremental indexing, but it must ensure the stability and consistency of full indexing. The mixed feature of structured and unstructured documents requires chunking logic to adapt to splitting rules for different content, to avoid destroying the integrity of key information. Exclusive fields for different documents (such as practice license numbers, rectification deadlines) require targeted configuration of field mapping rules, to ensure that vector indexes can accurately associate corresponding business information. At the same time, sensitive data related to institutional qualifications and penalties requires that the matching accuracy of the indexing link meets business compliance requirements.
How to configure settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the content length of mixed documents in this category, avoid splitting that destroys the information integrity of key documents such as medical institution practice licenses and rectification notices |
| Overlap Length | 50–80 characters | Ensure contextual coherence between chunks, avoid losing key cross-chunk information such as rectification requirements |
defaultToken | 1024 | Match the average token usage of individual data in this category, balance indexing efficiency and content coverage |
maxToken | 4096 | Adapt to the memory carrying capacity of GPU-free virtual machines, avoid memory overload during bulk import |
| Similarity Threshold | 0.75–0.85 | Filter low-match results, avoid incorrect association of institutional qualification and penalty information |
| Single Search Limit | Top 10 results | Control the number of returned results, adapt to the scenario needs of law enforcement personnel's consultation and response |
Reference only; verify against the target FastGPT release and deployment documentation.
Three common configuration errors
- Symptom: After importing Excel files and multi-dimensional documents from a Feishu folder, the indexing status always shows failure, and file parsing errors appear in logs. Cause: Parsing rules adapted to Feishu multi-dimensional documents and Excel formats are not configured, resulting in failure to correctly split unstructured content.
- Symptom: When deploying a PG database via Docker, indexing establishment times out, and host resource monitoring shows memory usage approaching the upper limit. Cause: The
maxTokenparameter value is not adjusted, and the single-batch import data volume exceeds the memory carrying capacity of GPU-free virtual machines. - Symptom: An error occurs after connecting to the oneapi embedding model, and indexing construction cannot be completed. Cause: The index model is not configured separately, and the default called interface exceeds rate limits or does not support embedding tasks.
How to verify a complete configuration
- Upload a single medical institution practice license document, check whether the chunked content retains complete license number and scope information.
- Perform a full indexing test, monitor host memory and CPU usage, confirm that continuous overload does not occur.
- Enter the test question "What is the practice scope of a certain medical institution", check whether the returned results associate the correct fields of the corresponding document.
- Check the indexing configuration page, confirm that the index model has been set separately, and the default oneapi interface link is not used.
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.