What the data for this category looks like
Usually, core data for this category comes from elderly care service information platforms, civil affairs statistics systems, fire safety inspection records, and institution operation ledgers. Daily documents consist of institution filing materials, operation subsidy applications, service quality inspection forms, safety hazard rectification ledgers, and elderly admission files. The data update cycle is monthly. Most document structures include fields such as institution identifier, number of beds, inspection results, and rectification requirements. There is no unified fixed format, and some documents are in structured table form.
What constraints do these characteristics impose on the vector model and index link
The monthly update cycle means indexes do not need high-frequency real-time synchronization. Scheduled incremental update tasks are a suitable configuration. The coexistence of structured tables and multi-type documents requires enabling multi-vector support. This setting separately processes text and table fields. Key fields such as institution identifiers and bed numbers need precise matching. Adjust the similarity threshold to avoid false recalls or missed detections. Supervision scenarios require strict safety and subsidy review. Ensure index-recalled documents fully cover associated business data, and avoid omitting key rectification or subsidy application information.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the paragraph length of elderly care service documents, avoid cutting key fields such as institution names and number of beds |
| Overlap Length | 100–200 characters | Retain cross-page associated information such as institution identifiers and rectification numbers across chunks |
| Similarity Threshold | 0.75–0.85 | Balance precise recall and missed detection risks in supervision scenarios, avoid mixing unrelated institution data into results |
| Single Search Limit | Top 8 entries | Cover multiple associated documents for the same institution such as filings, rectifications, and subsidy applications |
maxToken | 4096 token | Adapt to the long context requirements of party building Q&A, ensure complete recall of original policy documents and supervision materials |
defaultToken | Calibrated based on actual measurements | Adapt to request limits for custom embedding models, avoid index failures caused by exceeded call limits |
Three Common Mistakes
- The interface displays "No available index model detected". The cause is that the corresponding embedding model is not bound in the knowledge base settings, or the model call fails due to incorrect custom request address or API key configuration.
- Table data cannot enable multi-vector support. The cause is that the table multi-vector parsing switch is not enabled in the vector configuration, and vectors are not generated separately for structured table fields.
- The embedding model test directly returns an error. The cause is that the custom request address does not point to a compliant embedding service endpoint, or the API key has insufficient permissions to call the model.
How to Confirm the Configuration Is Complete
- Enter the vector model settings page of the knowledge base, confirm that the target embedding model has been selected and bound, and that the connection test returns a successful status.
- Upload a mixed document of elderly care institution filing materials and rectification ledgers, check whether key fields such as institution names and number of beds are correctly retained in the chunk preview.
- Launch a supervision Q&A test related to party building learning, check whether the returned results include associated policy documents and institution operation data.
- Enter the system status page of FastGPT v4.17.0, confirm that the available status of the index model is normal.
The values provided here 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.