What the data for this category looks like
The core data for this specific scenario comes from the civil affairs elderly care service information platform, civil affairs statistics system, fire safety inspection records, and institution operation ledgers. Daily processed documents include filing materials, operation subsidy applications, service quality inspection forms, safety hazard rectification ledgers, and elderly admission files. The data update frequency is monthly. Document fields include filing number, number of beds, inspection date, rectification time limit, and other items. Units include sheets, days, person-times, and others. Some documents contain both structured tables and unstructured text content.
What constraints do these characteristics impose on the vector models and indexes link
Monthly updated datasets have a relatively fixed scale, so high-frequency real-time indexing is not required. A regular full update strategy is appropriate. The mixed structure of multiple types of documents requires chunking logic to balance structured tables and non-continuous text, and avoid splitting associated data within tables. Data from multiple sources may have duplicate fields, so the indexing link must support deduplication verification. At the same time, errors in the supervision scenario are directly linked to public service compliance. The accuracy of vector recall must match the rigor of business review, and the matching threshold must not be overly relaxed.
How to configure the settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Covers core information of single-page documents such as filing materials and service quality inspection forms, and avoids information fragmentation within chunks |
| Overlap Length | 100–150 characters | Connects context across chunks for elderly admission files, safety hazard rectification records, and other content, and prevents key information from being broken |
| Similarity Threshold | 0.75–0.85 | Filters low-match redundant data, while retaining accurate matching results for elderly subsidy applications and institution filing materials |
| Single Search Limit | Top 10 entries | Adapts to the monthly updated dataset scale, and avoids returning too many irrelevant historical fire safety inspection records |
maxToken | 4000 token | Adapts to the context length of elderly care service supervision documents, and ensures recalled content can be fully included in the generation link |
defaultToken | Calibrated based on actual measurements | Adapts to locally deployed hardware resources, and balances the speed and accuracy of embedding vector generation |
Three common misconfigurations
- Phenomenon: An "embedding storage directory not found" error displays when starting FastGPT. Cause: No valid path for vector storage is specified, the disk space requirements of the elderly care service dataset are overlooked, and the storage location is not configured per official guidelines.
- Phenomenon: When searching for content related to elderly subsidy applications, the number of returned results is far lower than expected. Cause: The similarity threshold is set too high, filtering out some application materials that meet business matching requirements, and the threshold is not adjusted to match the rigor of the supervision scenario.
- Phenomenon: The disk space occupied by the knowledge base greatly exceeds the total size of the original files. Cause: Redundant split file chunks and duplicate embedding vectors are not cleaned up, and a regular cleanup strategy is not set for monthly updated datasets, leading to cumulative duplicate stored data.
How to confirm the configuration is correct
- View the file list in the vector storage directory, and confirm that embedding vector files matching the number of split file chunks have been generated.
- Input a test question for the supervision scenario, such as "Please verify the filing materials and actual bed data of a certain elderly care institution", and check that the matching degree of the returned results falls within the preset similarity threshold range.
- View the system monitoring panel of FastGPT v4.17.0, and confirm that the execution duration of the embedding vector generation task matches the calibrated value of the local hardware.
- Manually trigger a full index update, and confirm that the update process has no timeout errors, and the update duration matches the scale of the monthly dataset.
The values given 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.