What the data for this category looks like
Daily documents for this category typically include filing materials, operation subsidy applications, service quality inspection forms, safety hazard rectification ledgers, and elderly admission files. Data is usually sourced from elderly care service information platforms, civil affairs statistics systems, fire inspection records, and institution operation ledgers. The update frequency is monthly. Most documents use structured formats, with fields such as institution filing number, bed count, inspection date, and rectification time limit. Units include sheets, days, and person-times, among others. Some documents have a hierarchical title structure.
Constraints Imposed on Knowledge Base Retrieval and Recall by These Characteristics
Cross-source monthly updated data requires the knowledge base synchronization cycle to match the data update frequency. This avoids data lag that reduces supervision accuracy. Differences in structured document fields and units require retrieval to support filtering by specific fields. Examples include filtering results by institution jurisdiction or inspection type. Damage to a document’s hierarchical title structure causes semantic association deviation, lowering recall accuracy. A large total volume of documents, with unrestricted recall, leads to context overflow and answer truncation. Supervision scenarios also require results to be associated with metadata. This ensures traceability of sources and supporting evidence when verifying documents.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Meets the rigor requirements of supervision documents, filters low-relevance redundant content |
limit | Top 6–8 entries | Prevents excessive recalled content from causing context overflow and stops AI answers from being truncated mid-way |
searchMode | Semantic Retrieval | Adapts to semantic association of cross-system documents, such as linking filing materials and rectification ledgers |
usingReRank | Enabled | Rearranges recalled results by relevance, ensuring high-priority core supervision documents are returned first |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands synonymous expressions related to supervision, improving cross-document associated recall capability |
collectionIds | Specified by supervision scope | Limits retrieval scope to datasets related to civil affairs and fire protection, eliminating interference from irrelevant data |
Three Common Misconfigurations
- Too many retrieval results cause AI answers to be truncated mid-way. The cause is failure to limit the value of the
limitparameter, with recalled document chunks exceeding the context window upper limit. - Retrieval results do not carry associated metadata, making direct verification of institution filing or inspection information impossible. The cause is failure to configure metadata binding logic, and failure to associate document chunks with fields such as institution ID and inspection date.
- After importing supervision documents in Markdown format, retrieval results show cross-chapter confusion. The cause is failure to retain title hierarchy during chunking, mixing content of different categories and destroying the document’s logical structure.
How to Verify Proper Configuration
- Initiate a retrieval targeting elderly care institution filing materials, check whether the number of returned results matches the preset
limitvalue range. - View the metadata fields of retrieval results, confirm whether they include associated information required for supervision scenarios such as institution filing number and inspection date.
- Upload a supervision document in Markdown format with hierarchical titles, verify that retrieval can match relevant content according to title hierarchy.
- Run a full dataset retrieval test, confirm that response time meets business requirements and no abnormal errors occur.
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.